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Abubakar H, Al-Turjman F, Ameen ZS, Mubarak AS, Altrjman C. A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images. Heliyon 2024; 10:e26939. [PMID: 38463848 PMCID: PMC10920381 DOI: 10.1016/j.heliyon.2024.e26939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/12/2024] Open
Abstract
COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces more transmissible new different strains. It is therefore of great significance to diagnose COVID-19 early to curb its spread and reduce the death rate. Owing to the COVID-19 pandemic, traditional diagnostic methods such as reverse-transcription polymerase chain reaction (RT-PCR) are ineffective for diagnosis. Medical imaging is among the most effective techniques of respiratory disorders detection through machine learning and deep learning. However, conventional machine learning methods depend on extracted and engineered features, whereby the optimum features influence the classifier's performance. In this study, Histogram of Oriented Gradient (HOG) and eight deep learning models were utilized for feature extraction while K-Nearest Neighbour (KNN) and Support Vector Machines (SVM) were used for classification. A combined feature of HOG and deep learning feature was proposed to improve the performance of the classifiers. VGG-16 + HOG achieved 99.4 overall accuracy with SVM. This indicates that our proposed concatenated feature can enhance the SVM classifier's performance in COVID-19 detection.
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Affiliation(s)
- Hassana Abubakar
- Biomedical Engineering Department, Faculty of Engineering, Near East University, Mersin 10, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
| | - Zubaida S. Ameen
- Operational Research Center in Healthcare, Near East University, Mersin 10, Turkey
| | - Auwalu S. Mubarak
- Operational Research Center in Healthcare, Near East University, Mersin 10, Turkey
| | - Chadi Altrjman
- Waterloo University, 200 University Avenue West. Waterloo, ON, Canada
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2
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Gurumoorthy KB, Rajasekaran AS, Kalirajan K, Gopinath S, Al-Turjman F, Kolhar M, Altrjman C. Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning. Sensors (Basel) 2023; 23:4924. [PMID: 37430838 DOI: 10.3390/s23104924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 05/02/2023] [Accepted: 05/08/2023] [Indexed: 07/12/2023]
Abstract
Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals. Therefore, considering the significance of continuous data gathering and transmission sequence for WS, this article introduces a Concerted Sensor Data Transmission Scheme (CSDTS). This scheme endorses aggregation and transmission that aims at generating continuous data sequences. The aggregation is performed considering the overlapping and non-overlapping intervals from the WS sensing process. Such concerted data aggregation generates fewer chances of missing data. In the transmission process, allocated first-come-first-serve-based sequential communication is pursued. In the transmission scheme, a pre-verification of continuous or discrete (missing) transmission sequences is performed using classification tree learning. In the learning process, the accumulation and transmission interval synchronization and sensor data density are matched for preventing pre-transmission losses. The discrete classified sequences are thwarted from the communication sequence and are transmitted post the alternate WS data accumulation. This transmission type prevents sensor data loss and reduces prolonged wait times.
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Affiliation(s)
- Kambatty Bojan Gurumoorthy
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India
| | - Arun Sekar Rajasekaran
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India
| | - Kaliraj Kalirajan
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India
| | - Samydurai Gopinath
- Department of Electronics and Communication Engineering, Karpagam Institute of Technology, Coimbatore 641105, Tamilndu, India
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
| | - Manjur Kolhar
- Department Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Al Kharj 11990, Saudi Arabia
| | - Chadi Altrjman
- Chemical Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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3
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Deebak BD, Al-Turjman F. EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats. Sensors (Basel) 2023; 23:2995. [PMID: 36991706 PMCID: PMC10051552 DOI: 10.3390/s23062995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/11/2022] [Accepted: 12/30/2022] [Indexed: 06/19/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient's body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy (~98.3%) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease.
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Affiliation(s)
- B. D. Deebak
- Department of Computer Engineering, Gachon University, Gyeonggido, Seongnam 13120, Republic of Korea
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Deptartment, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
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4
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Ibrahim AU, Kibarer AG, Al-Turjman F. Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images. Data Intelligence 2023. [DOI: 10.1162/dint_a_00198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Abstract
Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both Microbiologists and Radiologists and can lead to miss-diagnosis. These challenges can be solved by employing Computer-Aided Detection (CAD)via AI-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle repository. For classification of tuberculosis using microscopic slide images, the model achieved 90.56% accuracy, 97.78% sensitivity and 83.33% specificity for 70: 30 splits. For classification of tuberculosis using X-ray images, the model achieved 93.89% accuracy, 96.67% sensitivity and 91.11% specificity for 70:30 splits. Our result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.
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Affiliation(s)
| | - Ayse Gunnay Kibarer
- Department of Biomedical Engineering, Near East University, Nicosia, Mersin 10, Turkey
| | - Fadi Al-Turjman
- Department of Artificial Intelligence, Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
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5
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Irkham I, Ibrahim AU, Pwavodi PC, Al-Turjman F, Hartati YW. Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review. Sensors (Basel) 2023; 23:2240. [PMID: 36850837 PMCID: PMC9964617 DOI: 10.3390/s23042240] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/12/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare settings, food control and monitoring, and environmental monitoring. In the past, conventional biosensors that have employed bioreceptors, such as enzymes, antibodies, Nucleic Acid (NA), etc., and used different transduction methods such as optical, thermal, electrochemical, electrical and magnetic detection, have been developed. Yet, with all the progresses made so far, these biosensors are clouded with many challenges, such as interference with undesirable compound, low sensitivity, specificity, selectivity, and longer processing time. In order to address these challenges, there is high need for developing novel, fast, highly sensitive biosensors with high accuracy and specificity. Scientists explore these gaps by incorporating nanoparticles (NPs) and nanocomposites (NCs) to enhance the desired properties. Graphene nanostructures have emerged as one of the ideal materials for biosensing technology due to their excellent dispersity, ease of functionalization, physiochemical properties, optical properties, good electrical conductivity, etc. The Integration of the Internet of Medical Things (IoMT) in the development of biosensors has the potential to improve diagnosis and treatment of diseases through early diagnosis and on time monitoring. The outcome of this comprehensive review will be useful to understand the significant role of graphene-based electrochemical biosensor integrated with Artificial Intelligence AI and IoMT for clinical diagnostics. The review is further extended to cover open research issues and future aspects of biosensing technology for diagnosis and management of clinical diseases and performance evaluation based on Linear Range (LR) and Limit of Detection (LOD) within the ranges of Micromolar µM (10-6), Nanomolar nM (10-9), Picomolar pM (10-12), femtomolar fM (10-15), and attomolar aM (10-18).
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Affiliation(s)
- Irkham Irkham
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia
| | - Abdullahi Umar Ibrahim
- Department of Biomedical Engineering, Near East University, Mersin 10, Nicosia 99010, Turkey
| | - Pwadubashiyi Coston Pwavodi
- Department of Bioengineering/Biomedical Engineering, Faculty of Engineering, Cyprus International University, Haspolat, North Cyprus via Mersin 10, Nicosia 99010, Turkey
| | - Fadi Al-Turjman
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Kyrenia 99320, Turkey
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Nicosia 99010, Turkey
| | - Yeni Wahyuni Hartati
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia
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6
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Mubarak AS, Ameen ZS, Al-Turjman F. Effect of Gaussian filtered images on Mask RCNN in detection and segmentation of potholes in smart cities. Math Biosci Eng 2023; 20:283-295. [PMID: 36650766 DOI: 10.3934/mbe.2023013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Accidents have contributed a lot to the loss of lives of motorists and serious damage to vehicles around the globe. Potholes are the major cause of these accidents. It is very important to build a model that will help in recognizing these potholes on vehicles. Several object detection models based on deep learning and computer vision were developed to detect these potholes. It is very important to develop a lightweight model with high accuracy and detection speed. In this study, we employed a Mask RCNN model with ResNet-50 and MobileNetv1 as the backbone to improve detection, and also compared the performance of the proposed Mask RCNN based on original training images and the images that were filtered using a Gaussian smoothing filter. It was observed that the ResNet trained on Gaussian filtered images outperformed all the employed models.
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Affiliation(s)
- Auwalu Saleh Mubarak
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Electrical Engineering Dept., Kano University of Science and Technology, Wudil, Kano, Nigeria
| | - Zubaida Said Ameen
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Biochemistry Dept., Maitama Sule University, Kano, Nigeria
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Kyrenia, Mersin 10, Turkey
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7
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Irkham I, Ibrahim AU, Nwekwo CW, Al-Turjman F, Hartati YW. Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review. Sensors (Basel) 2022; 23:426. [PMID: 36617023 PMCID: PMC9824404 DOI: 10.3390/s23010426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/14/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.
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Affiliation(s)
- Irkham Irkham
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia
| | | | - Chidi Wilson Nwekwo
- Department of Biomedical Engineering, Near East University, Mersin 99138, Turkey
| | - Fadi Al-Turjman
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 99138, Turkey
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 99138, Turkey
| | - Yeni Wahyuni Hartati
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia
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8
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Yedurkar DP, Metkar SP, Al-Turjman F, Stephan T, Kolhar M, Altrjman C. A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm. Sensors (Basel) 2022; 22:9302. [PMID: 36502005 PMCID: PMC9737714 DOI: 10.3390/s22239302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/07/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients. Next, the EEG signal was pre-processed utilizing a multiresolution-based adaptive filtering (MRAF) method. Then, the maximal frequency point at which the power spectral density (PSD) of each EEG segment was greater than the average spectral power of the corresponding frequency band was computed. This point was further optimized to extract a point termed as critical spectral verge (CSV) to extract the exact high frequency oscillations representing the actual seizure activity present in the EEG signal. Next, a support vector machine (SVM) classifier was used for channel-wise classification of the seizure and non-seizure regions using CSV as a feature. This process of classification using the CSV feature extracted from the MRAF output is referred to as the MCSV approach. As a final step, cloud-based services were employed to analyze the EEG information from the subject's smart phone. An exhaustive analysis was undertaken to assess the performance of the MCSV approach for two datasets. The presented approach showed an improved performance with a 93.83% average sensitivity, a 97.94% average specificity, a 97.38% average accuracy with the SVM classifier, and a 95.89% average detection rate as compared with other state-of-the-art studies such as deep learning. The methods presented in the literature were unable to precisely localize the origination of the seizure activity in the brain region and reported a low seizure detection rate. This work introduced an optimized CSV feature which was effectively used for multichannel seizure classification and localization of seizure origination. The proposed MCSV approach will help diagnose epileptic behavior from multichannel EEG signals which will be extremely useful for neuro-experts to analyze seizure details from different regions of the brain.
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Affiliation(s)
| | - Shilpa P. Metkar
- Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune 411005, India
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
| | - Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore 560054, India
| | - Manjur Kolhar
- Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
| | - Chadi Altrjman
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
- Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Sakthivel RK, Nagasubramanian G, Sankayya M, Al-Turjman F. Multilingual News Feed Analysis Using Intelligent Linguistic Particle Filtering Techniques. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3569899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Analyzing real-time news feeds and their impacts in the real world is a complex task in the social networking arena. Particularly, countries with a multilingual environment have various patterns and perceptions of news reports considering the diversity of the people. Multilingual and multimodal news analysis is an emerging trend for evaluating news source neutralities. Therefore, in this work, four new deep news particle filtering techniques were developed, including generic news analysis, sequential importance re-sampling (SIR)-based news particle filtering analysis, reinforcement learning (RL)-based multimodal news analysis, and deep Convolution neural network (DCNN)-based multi-news filtering approach, for news classification. Results indicate that these techniques, which primarily employ particle filtering with multilevel sampling strategies, produce 15% to 20% better performance than conventional news analysis techniques.
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Affiliation(s)
| | | | | | - Fadi Al-Turjman
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
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10
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Stephan T, Al-Turjman F, Ravishankar M, Stephan P. Machine learning analysis on the impacts of COVID-19 on India's renewable energy transitions and air quality. Environ Sci Pollut Res Int 2022; 29:79443-79465. [PMID: 35715677 PMCID: PMC9205654 DOI: 10.1007/s11356-022-20997-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/17/2022] [Indexed: 05/12/2023]
Abstract
India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country's immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India's renewable energy transition and air quality. India's economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India's climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government's must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.
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Affiliation(s)
- Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka India 560054
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
| | - Monica Ravishankar
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka India 560054
| | - Punitha Stephan
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu India 641114
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11
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Umar Ibrahim A, Al-Turjman F, Ozsoz M, Serte S. Computer aided detection of tuberculosis using two classifiers. BIOMED ENG-BIOMED TE 2022; 67:513-524. [PMID: 36165698 DOI: 10.1515/bmt-2021-0310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 09/13/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The main objective of this study is to addressed these challenges by employing Computer Aided Detection (CAD) using Artificial Intelligence-driven models which learn features based on convolution and result in an output with high accuracy. METHOD In this paper, we described automated discrimination of X-ray and microscopic slide images of tuberculosis into positive and negative cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East university hospital and Kaggle repository. RESULTS For classification of tuberculosis and healthy microscopic slide using AlexNet+Softmax, the model achieved accuracy of 98.14%. For classification of tuberculosis and healthy microscopic slide using AlexNet+SVM, the model achieved 98.73% accuracy. For classification of tuberculosis and healthy chest X-ray images using AlexNet+Softmax, the model achieved accuracy of 98.19%. For classification of tuberculosis and healthy chest X-ray images using AlexNet+SVM, the model achieved 98.38% accuracy. CONCLUSION The result obtained has shown to outperformed several studies in the current literature. Future studies will attempt to integrate Internet of Medical Things (IoMT) for the design of IoMT/AI-enabled platform for detection of Tuberculosis from both X-ray and Microscopic slide images.
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Affiliation(s)
| | - Fadi Al-Turjman
- Department of Artificial Intelligence, Research Center for AI and IoT, Near East University, Nicosia, Turkey
| | - Mehmet Ozsoz
- Department of Biomedical Engineering, Near East University, Nicosia, Turkey
| | - Sertan Serte
- Department of Electrical and Electronics Engineering, Near East University, Nicosia, Turkey
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12
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Punitha S, Al-Turjman F, Stephan T. A novel e-healthcare diagnosing system for COVID-19 via whale optimization algorithm. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2125079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- S. Punitha
- Department of Computer Science and Engineering, Graphics Era Deemed to be University, Dehradun, India
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department of AI and Robotics Institute, Near East University, Nicosia, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Kyrenia, Turkey
| | - Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, India
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13
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Karmore S, Bodhe R, Al-Turjman F, Kumar RL, Pillai SK. IoT-Based Humanoid Software for Identification and Diagnosis of Covid-19 Suspects. IEEE Sens J 2022; 22:17490-17496. [PMID: 36346089 PMCID: PMC9564039 DOI: 10.1109/jsen.2020.3030905] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/19/2020] [Accepted: 09/30/2020] [Indexed: 05/24/2023]
Abstract
COVID-19 pandemic has a catastrophic consequence globally since its first case was detected in December 2019, with an aggressive spread. Currently an exponential growth is expected. If not diagnosed at the proper time, COVID-19 may lead to death of the infected individuals. Thus, continuous screening, early diagnosis and prompt actions are crucial to control the spread and reduce the mortality. In this paper we focus on developing a Medical Diagnosis Humanoid (MDH) which is a cost effective, safety critical mobile robotic system that provides a complete diagnostic test to check whether an individual is infected by Covid-19 or not. This paper highlights the development of a system based on Artificial Intelligence for Medical Science, where humanoids can navigate through desired destinations, diagnose an individual for Covid-19 through various parameters and make a survey of a locality for the same. The humanoid uses the concept of real time data sensing and processing through machine learning produced by various sensors used in the context.
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Affiliation(s)
- Swapnili Karmore
- Department of Computer ScienceS. B. Jain Institute of Technology, Management, and ResearchNagpur441501India
| | - Rushikesh Bodhe
- Department of Information TechnologyS. B. Jain Institute of Technology, Management, and ResearchNagpur441501India
| | - Fadi Al-Turjman
- Research Center for AI and IoTNear East University99138IstanbulTurkey
| | - R. Lakshmana Kumar
- Department of Computer ApplicationsHindusthan College of Engineering and TechnologyCoimbatore641032India
| | - Sofia K. Pillai
- Centre of Excellence in AIMLGalgotias UniversityNoida226001India
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14
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Karim F, Shah MA, Khattak HA, Ameer Z, Shoaib U, Rauf HT, Al-Turjman F. Towards an effective model for lung disease classification. Appl Soft Comput 2022; 124:109077. [PMID: 35662915 PMCID: PMC9153181 DOI: 10.1016/j.asoc.2022.109077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/27/2022] [Accepted: 05/24/2022] [Indexed: 02/06/2023]
Abstract
Machine Learning and computer vision have been the frontiers of the war against the COVID-19 Pandemic. Radiology has vastly improved the diagnosis of diseases, especially lung diseases, through the early assessment of key disease factors. Chest X-rays have thus become among the commonly used radiological tests to detect and diagnose many lung diseases. However, the discovery of lung disease through X-rays is a significantly challenging task depending on the availability of skilled radiologists. There has been a recent increase in attention to the design of Convolution Neural Networks (CNN) models for lung disease classification. A considerable amount of training dataset is required for CNN to work, but the problem is that it cannot handle translation and rotation correctly as input. The recently proposed Capsule Networks (referred to as CapsNets) are new automated learning architecture that aims to overcome the shortcomings in CNN. CapsNets are vital for rotation and complex translation. They require much less training information, which applies to the processing of data sets from medical images, including radiological images of the chest X-rays. In this research, the adoption and integration of CapsNets into the problem of chest X-ray classification have been explored. The aim is to design a deep model using CapsNet that increases the accuracy of the classification problem involved. We have used convolution blocks that take input images and generate convolution layers used as input to capsule block. There are 12 capsule layers operated, and the output of each capsule is used as an input to the next convolution block. The process is repeated for all blocks. The experimental results show that the proposed architecture yields better results when compared with the existing CNN techniques by achieving a better area under the curve (AUC) average. Furthermore, DNet checks the best performance in the ChestXray-14 data set on traditional CNN, and it is validated that DNet performs better with a higher level of total depth.
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15
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Sekeroglu B, Ever YK, Dimililer K, Al-Turjman F. Comparative Evaluation and Comprehensive Analysis of Machine Learning
Models for Regression Problems. Data Intelligence 2022. [DOI: 10.1162/dint_a_00155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Abstract
Artificial intelligence and machine learning applications are of significant importance almost in every field of human life to solve problems or support human experts. However, the determination of the machine learning model to achieve a superior result for a particular problem within the wide real-life application areas is still a challenging task for researchers. The success of a model could be affected by several factors such as dataset characteristics, training strategy and model responses. Therefore, a comprehensive analysis is required to determine model ability and the efficiency of the considered strategies. This study implemented ten benchmark machine learning models on seventeen varied datasets. Experiments are performed using four different training strategies 60:40, 70:30, and 80:20 hold-out and five-fold cross-validation techniques. We used three evaluation metrics to evaluate the experimental results: mean squared error, mean absolute error, and coefficient of determination (R2 score). The considered models are analyzed, and each model's advantages, disadvantages, and data dependencies are indicated. As a result of performed excess number of experiments, the deep Long-Short Term Memory (LSTM) neural network outperformed other considered models, namely, decision tree, linear regression, support vector regression with a linear and radial basis function kernels, random forest, gradient boosting, extreme gradient boosting, shallow neural network, and deep neural network. It has also been shown that cross-validation has a tremendous impact on the results of the experiments and should be considered for the model evaluation in regression studies where data mining or selection is not performed.
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Affiliation(s)
- Boran Sekeroglu
- Information Systems Engineering Department, Near East University, Nicosia, Cyprus, Mersin 10, Turkey
- Research Centre for AI and IoT, Near East University, Nicosia, Cyprus, Mersin 10, Turkey
| | - Yoney Kirsal Ever
- Software Engineering Department, Near East University, Nicosia, Cyprus, Mersin 10, Turkey
- Research Centre for AI and IoT, Near East University, Nicosia, Cyprus, Mersin 10, Turkey
| | - Kamil Dimililer
- Electrical and Electronic Engineering Department, Near East University, Nicosia, Cyprus, Mersin 10, Turkey
- Research Centre for AI and IoT, Near East University, Nicosia, Cyprus, Mersin 10, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Near East University, Nicosia, Cyprus, Mersin 10, Turkey
- Research Centre for AI and IoT, Near East University, Nicosia, Cyprus, Mersin 10, Turkey
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16
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Jawahar M, Anbarasi LJ, Jayachandran P, Ramachandran M, Al-Turjman F. Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images. International Journal of E-Health and Medical Communications 2022. [DOI: 10.4018/ijehmc.20220701.oa2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.
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17
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Dimililer K, Teimourian H, Al-Turjman F. Radio galaxies classification system using machine learning techniques in the IoT Era. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2080277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Kamil Dimililer
- Electrical and Electronic Engineering, International Research Centre for AI and IoT, Applied Artificial Intelligence Research Centre (AAIRC), Near East University, Nicosia, North Cyprus, Turkey
| | - Hanifa Teimourian
- Electrical and Electronic Engineering, International Research Centre for AI and IoT, Near East University, Nicosia, North Cyprus, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering, International Research Centre for AI and IoT, AI and Robotics Institute, Near East University, Nicosia, North Cyprus, Turkey
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18
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Narendra M, Valarmathi ML, Anbarasi LJ, Sarobin MVR, Al-Turjman F. High embedding capacity in 3D model using intelligent Fuzzy based clustering. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07404-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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19
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Bacanin N, Zivkovic M, Al-Turjman F, Venkatachalam K, Trojovský P, Strumberger I, Bezdan T. Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application. Sci Rep 2022; 12:6302. [PMID: 35440609 PMCID: PMC9016213 DOI: 10.1038/s41598-022-09744-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 03/16/2022] [Indexed: 02/04/2023] Open
Abstract
Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model. Several regularization methods have been introduced to prevent the overfitting problem. In the research presented in this manuscript, the overfitting challenge was tackled by selecting a proper value for the regularization parameter dropout by utilizing a swarm intelligence approach. Notwithstanding that the swarm algorithms have already been successfully applied to this domain, according to the available literature survey, their potential is still not fully investigated. Finding the optimal value of dropout is a challenging and time-consuming task if it is performed manually. Therefore, this research proposes an automated framework based on the hybridized sine cosine algorithm for tackling this major deep learning issue. The first experiment was conducted over four benchmark datasets: MNIST, CIFAR10, Semeion, and UPS, while the second experiment was performed on the brain tumor magnetic resonance imaging classification task. The obtained experimental results are compared to those generated by several similar approaches. The overall experimental results indicate that the proposed method outperforms other state-of-the-art methods included in the comparative analysis in terms of classification error and accuracy.
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Affiliation(s)
- Nebojsa Bacanin
- Singidunum University, Danijelova 32, 11000, Belgrade, Serbia.
| | | | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoT, AI and Robotics Institute, Near East University, Mersin 10, Turkey
| | - K Venkatachalam
- Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003, Hradec Králové, Czech Republic
| | - Pavel Trojovský
- Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003, Hradec Králové, Czech Republic.,Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003, Hradec Králové, Czech Republic
| | | | - Timea Bezdan
- Singidunum University, Danijelova 32, 11000, Belgrade, Serbia
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20
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Al-Turjman F. AI-assisted Solutions for COVID-19 and Biomedical Applications in Smart-Cities. Mobile Netw Appl 2022. [PMCID: PMC8904064 DOI: 10.1007/s11036-022-01954-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Fadi Al-Turjman
- Faculty of Engineering, Near East University, Nicosia, Mersin 10 Turkey
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21
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Fazal R, Shah MA, Khattak HA, Rauf HT, Al-Turjman F. Achieving data privacy for decision support systems in times of massive data sharing. Cluster Comput 2022; 25:3037-3049. [PMID: 35035271 PMCID: PMC8743442 DOI: 10.1007/s10586-021-03514-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient's data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients' data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.
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Affiliation(s)
- Rabeeha Fazal
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Munam Ali Shah
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Hasan Ali Khattak
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), H12, Islamabad, Pakistan
| | - Hafiz Tayyab Rauf
- Department of Computer Science, Faculty of Engineering and Informatics, University of BRADFORD, Bradford, UK
| | - Fadi Al-Turjman
- Artificial Intelligence Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Istanbul, Turkey
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22
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Ibrahim AU, Al-Turjman F, Sa’id Z, Ozsoz M. Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview. Multimed Tools Appl 2022; 81:35143-35171. [PMID: 32837247 PMCID: PMC7276962 DOI: 10.1007/s11042-020-09010-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/16/2020] [Accepted: 05/01/2020] [Indexed: 05/02/2023]
Abstract
Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10-18 M), Femtomolar (10-15 M) and Picomolar (10-12 M) in comparison to conventional biosensors with sensitivity of nanomolar 10-9 M and micromolar 10-3 M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.
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Affiliation(s)
| | - Fadi Al-Turjman
- Department of Artificial Intelligence, Near East University, Nicosia, 10 Mersin, Turkey
| | - Zubaida Sa’id
- Department of Biomedical Engineering, Near East University, Nicosia, 10 Mersin, Turkey
| | - Mehmet Ozsoz
- Department of Biomedical Engineering, Near East University, Nicosia, 10 Mersin, Turkey
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23
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Goyal B, Dogra A, Khoond R, Al-Turjman F. An Efficient Medical Assistive Diagnostic Algorithm for Visualisation of Structural and Tissue Details in CT and MRI Fusion. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09958-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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24
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Al-Turjman F. AI-powered cloud for COVID-19 and other infectious disease diagnosis. Pers Ubiquitous Comput 2021; 27:661-664. [PMID: 34413717 PMCID: PMC8363856 DOI: 10.1007/s00779-021-01625-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
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25
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Umar Ibrahim A, Pwavodi PC, Ozsoz M, Al-Turjman F, Galaya T, Agbo JJ. Crispr biosensing and Ai driven tools for detection and prediction of Covid-19. J EXP THEOR ARTIF IN 2021. [DOI: 10.1080/0952813x.2021.1952652] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
| | - Pwadubashiyi Coston Pwavodi
- Department of Biomedical Engineering, Near East University, Nicosia, Turkey
- Department of Artificial Intelligence, Near East University, Nicosia, Turkey
| | - Mehmet Ozsoz
- Department of Biomedical Engineering, Near East University, Nicosia, Turkey
- Department of Medical Biology and Genetics, Near East University, Nicosia, Turkey
| | - Fadi Al-Turjman
- Department of Artificial Intelligence, Research Center for AI and IoT, Near East University, Mersin, Turkey
| | - Tirah Galaya
- Department of Medical Biology and Genetics, Near East University, Nicosia, Turkey
| | - Joy Johnson Agbo
- Department of Nursing, Cyprus International University, Nicosia, Turkey
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26
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Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021; 13:153-175. [PMID: 33886097 PMCID: PMC8060789 DOI: 10.1007/s12539-021-00431-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 04/03/2021] [Accepted: 04/09/2021] [Indexed: 12/23/2022]
Abstract
The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.
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Affiliation(s)
- Jawad Rasheed
- Department of Computer Engineering, Istanbul Aydin University, Istanbul, 34295, Turkey.
| | - Akhtar Jamil
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Alaa Ali Hameed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - Ahmad Rasheed
- Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
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27
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Reghukumar A, Anbarasi LJ, Prassanna J, Manikandan R, Al-Turjman F. Vision Based Segmentation and Classification of Cracks Using Deep Neural Networks. INT J UNCERTAIN FUZZ 2021. [DOI: 10.1142/s0218488521400080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Deep learning artificial intelligence (AI) is a booming area in the research field. It allows the development of end-to-end models to predict outcomes based on input data without the need for manual extraction of features. This paper aims for evaluating the automatic crack detection process that is used in identifying the cracks in building structures such as bridges, foundations or other large structures using images. A hybrid approach involving image processing and deep learning algorithms is proposed to detect automatic cracks in structures. As cracks are detected in the images they are segmented using a segmentation process. The proposed deep learning models include a hybrid architecture combining Mask R-CNN with single layer CNN, 3-layer CNN, and8-layer CNN. These models utilizes depth wise convolution with varying dilation rates for efficiently extracting diversified features from the crack images. Further, performance evaluation shows that Mask R-CNN with a single layer CNN achieves an accuracy of 97.5% on a normal dataset and 97.8% on a segmented dataset. The Mask R-CNN with 2-layer convolution resulted in an accuracy of 98.32% on a normal dataset and 98.39% on a segmented dataset. The Mask R-CNN with 8-layers convolution achieves an accuracy of 98.4% on a normal dataset and 98.75% on a segmented dataset. The proposed Mask R-CNN have proved its feasibility in detecting cracks in huge building and structures.
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Affiliation(s)
- Arathi Reghukumar
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600037, India
| | - L. Jani Anbarasi
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600037, India
| | - J. Prassanna
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600037, India
| | - R. Manikandan
- School of Computing, SASTRA Deemed University, Thanjavur, 613401, India
| | - Fadi Al-Turjman
- Artificial Intelligence Department, Research Centre AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
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28
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Mastoi QUA, Memon MS, Lakhan A, Mohammed MA, Qabulio M, Al-Turjman F, Abdulkareem KH. Machine learning-data mining integrated approach for premature ventricular contraction prediction. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05820-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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29
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Zivkovic M, Bacanin N, Venkatachalam K, Nayyar A, Djordjevic A, Strumberger I, Al-Turjman F. COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain Cities Soc 2021; 66:102669. [PMID: 33520607 PMCID: PMC7836389 DOI: 10.1016/j.scs.2020.102669] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 11/25/2020] [Accepted: 12/14/2020] [Indexed: 05/10/2023]
Abstract
The main objective of this paper is to further improve the current time-series prediction (forecasting) algorithms based on hybrids between machine learning and nature-inspired algorithms. After the recent COVID-19 outbreak, almost all countries were forced to impose strict measures and regulations in order to control the virus spread. Predicting the number of new cases is crucial when evaluating which measures should be implemented. The improved forecasting approach was then used to predict the number of the COVID-19 cases. The proposed prediction model represents a hybridized approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics. The enhanced beetle antennae search is utilized to determine the parameters of the adaptive neuro-fuzzy inference system and to improve the overall performance of the prediction model. First, an enhanced beetle antennae search algorithm has been implemented that overcomes deficiencies of its original version. The enhanced algorithm was tested and validated against a wider set of benchmark functions and proved that it substantially outperforms original implementation. Afterwards, the proposed hybrid method for COVID-19 cases prediction was then evaluated using the World Health Organization's official data on the COVID-19 outbreak in China. The proposed method has been compared against several existing state-of-the-art approaches that were tested on the same datasets. The proposed CESBAS-ANFIS achieved R 2 score of 0.9763, which is relatively high when compared to the R 2 value of 0.9645, achieved by FPASSA-ANFIS. To further evaluate the robustness of the proposed method, it has also been validated against two different datasets of weekly influenza confirmed cases in China and the USA. Simulation results and the comparative analysis show that the proposed hybrid method managed to outscore other sophisticated approaches that were tested on the same datasets and proved to be a useful tool for time-series prediction.
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Affiliation(s)
| | - Nebojsa Bacanin
- Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
| | - K Venkatachalam
- School of Computer Science and Engineering, VIT Bhopal University, Bhopal, India
| | - Anand Nayyar
- Graduate School, Duy Tan University, Da Nang 550000, Viet Nam
- Faculty of Information Technology, Duy Tan University, Da Nang, Viet Nam
| | | | | | - Fadi Al-Turjman
- Research Centre for AI and IoT, Department of Artificial Intelligence Engineering, Near East University, 99138 Nicosia, Mersin 10, Turkey
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30
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Ullah F, Al-Turjman F. A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05800-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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31
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Kumar R, Al-Turjman F, Anand L, Kumar A, Magesh S, Vengatesan K, Sitharthan R, Rajesh M. Genomic sequence analysis of lung infections using artificial intelligence technique. Interdiscip Sci 2021; 13:192-200. [PMID: 33558984 DOI: 10.1007/s12539-020-00414-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 02/04/2023]
Abstract
Attributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services, various developments including Support Vector Machine (SVM), and profound learning. For example, Convolutional Neural systems (CNN) have prevalently engaged in a significant job of various classificational investigation in lung malignant growth, and different infections. In this paper, Parallel based SVM (P-SVM) and IoT has been utilized to examine the ideal order of lung infections caused by genomic sequence. The proposed method develops a new methodology to locate the ideal characterization of lung sicknesses and determine its growth in its early stages, to control the growth and prevent lung sickness. Further, in the investigation, the P-SVM calculation has been created for arranging high-dimensional distinctive lung ailment datasets. The data used in the assessment has been fetched from real-time data through cloud and IoT. The acquired outcome demonstrates that the developed P-SVM calculation has 83% higher accuracy and 88% precision in characterization with ideal informational collections when contrasted with other learning methods.
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Affiliation(s)
- R Kumar
- Department of Electronics and Instrumentation Engineering, National Institute of Technology, Chumkedima, Dimapur, Nagaland, 797103, India
| | - Fadi Al-Turjman
- Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - L Anand
- School Computing Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India
| | - Abhishek Kumar
- School of Computer science and IT, JAIN (Deemed to be University), Banglore, Karnataka, India
| | - S Magesh
- Maruthi Technocrat E Services, Chennai, India
| | - K Vengatesan
- Department of Computer Science, Sanjivani College of Engineering, Kopargaon, India
| | - R Sitharthan
- Department of Electrical Engineering, School of Electrical Engineering, Vellore Institute of Technology and Science, Vellore, 632014, India.
| | - M Rajesh
- Department of Computer Science, Sanjivani College of Engineering, Kopargaon, India
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Rakesh Kumar S, Muthuramalingam S, Al-Turjman F. Multimodal News Feed Evaluation System with Deep Reinforcement Learning Approaches. ACM T ASIAN LOW-RESO 2021. [DOI: 10.1145/3414523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Multilingual and multimodal data analysis is the emerging news feed evaluation system. News feed analysis and evaluations are interrelated processes, which are useful in understanding the news factors. The news feed evaluation system can be implemented for single or multilingual language models. Classification techniques used on multilingual news analysis require deep layered learning techniques rather than conventional approaches. In this proposed work, a hierarchical structure of deep learning algorithms is implemented for making an effective complex news evaluation system. Deep learning techniques such as the Deep Cooperative Multilingual Reinforcement Learning Model, the Multidimensional Genetic Algorithm, and the Multilingual Generative Adversarial Network are developed to evaluate a vast number of news feeds. The proposed tech-niques collaborate in a pipeline order to build a deep news feed evaluation system. The implementation details project that the newly proposed system performs 5% to 12% better than the other news evaluation systems.
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33
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Ibrahim AU, Ozsoz M, Serte S, Al-Turjman F, Yakoi PS. Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19. Cognit Comput 2021:1-13. [PMID: 33425044 PMCID: PMC7781428 DOI: 10.1007/s12559-020-09787-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 10/21/2020] [Indexed: 12/15/2022]
Abstract
The outbreak of the novel corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. Currently, the outbreak has affected more than 200 countries with more than 37 million confirmed cases and more than 1 million death tolls as of 10 October 2020. Reverse-transcription polymerase chain reaction (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. As the number of cases continue to grow, there is a high need for developing a rapid screening method that is accurate, fast, and cheap. Chest X-ray (CXR) scan images can be considered as an alternative or a confirmatory approach as they are fast to obtain and easily accessible. Though the literature reports a number of approaches to classify CXR images and detect the COVID-19 infections, the majority of these approaches can only recognize two classes (e.g., COVID-19 vs. normal). However, there is a need for well-developed models that can classify a wider range of CXR images belonging to the COVID-19 class itself such as the bacterial pneumonia, the non-COVID-19 viral pneumonia, and the normal CXR scans. The current work proposes the use of a deep learning approach based on pretrained AlexNet model for the classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and normal CXR scans obtained from different public databases. The model was trained to perform two-way classification (i.e., COVID-19 vs. normal, bacterial pneumonia vs. normal, non-COVID-19 viral pneumonia vs. normal, and COVID-19 vs. bacterial pneumonia), three-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. normal), and four-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. non-COVID-19 viral pneumonia vs. normal). For non-COVID-19 viral pneumonia and normal (healthy) CXR images, the proposed model achieved 94.43% accuracy, 98.19% sensitivity, and 95.78% specificity. For bacterial pneumonia and normal CXR images, the model achieved 91.43% accuracy, 91.94% sensitivity, and 100% specificity. For COVID-19 pneumonia and normal CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity, and 100% specificity. For classification CXR images of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity, and 99.89% specificity. For the three-way classification, the model achieved 94.00% accuracy, 91.30% sensitivity, and 84.78%. Finally, for the four-way classification, the model achieved an accuracy of 93.42%, sensitivity of 89.18%, and specificity of 98.92%.
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Affiliation(s)
| | - Mehmet Ozsoz
- Department of Biomedical Engineering, Near East University, Nicosia, Mersin 10, Turkey
| | - Sertan Serte
- Department of Electrical Engineering, Near East University, Nicosia, Mersin 10, Turkey
| | - Fadi Al-Turjman
- Department of Artificial Intelligence, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
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Almezhghwi K, Serte S, Al-Turjman F. Convolutional neural networks for the classification of chest X-rays in the IoT era. Multimed Tools Appl 2021; 80:29051-29065. [PMID: 34155434 PMCID: PMC8210525 DOI: 10.1007/s11042-021-10907-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 03/19/2021] [Accepted: 04/01/2021] [Indexed: 05/08/2023]
Abstract
Chest X-ray medical imaging technology allows the diagnosis of many lung diseases. It is known that this technology is frequently used in hospitals, and it is the most accurate way of detecting most thorax diseases. Radiologists examine these images to identify lung diseases; however, this process can require some time. In contrast, an automated artificial intelligence system could help radiologists detect lung diseases more accurately and faster. Therefore, we propose two artificial intelligence approaches for processing and identifying chest X-ray images to detect chest diseases from such images. We introduce two novel deep learning methods for fast and automated classification of chest X-ray images. First, we propose the use of support vector machines based on the AlexNet model. Second, we develop support vector machines based on the VGGNet16 method. Combined deep networks with a robust classifier have shown that the proposed methods outperform AlexNet and VGG16 deep learning approaches for the chest X-ray image classification tasks. The proposed AlexNet and VGGNet based SVM provide average area under the curve values of 98% and 97%, respectively, for twelve chest X-ray diseases.
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Affiliation(s)
- Khaled Almezhghwi
- Electrical and Electronic Engineering, College Of Electronic Technology, Tripoli, Libya
| | - Sertan Serte
- Electrical and Electronic Engineering, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Department, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey
- Research Center for AI and IoT, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey
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35
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Al-Turjman F, Hamouda W, Mumtaz S. Call for Special Issue Papers: Programming Models and Algorithms for Big Data. Big Data 2020; 8:542-543. [PMID: 33347369 DOI: 10.1089/big.2020.29039.cfp2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Affiliation(s)
| | | | - Shahid Mumtaz
- Instituto de Telecomunicações Aveiro, Aveiro, Portugal
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36
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Deebak BD, Al-Turjman F, Nayyar A. Chaotic-map based authenticated security framework with privacy preservation for remote point-of-care. Multimed Tools Appl 2020; 80:17103-17128. [PMID: 33204211 PMCID: PMC7659916 DOI: 10.1007/s11042-020-10134-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 10/13/2020] [Accepted: 10/23/2020] [Indexed: 06/11/2023]
Abstract
The challenge of COVID-19 has become more prevalent across the world. It is highly demanding an intelligent strategy to outline the precaution measures until the clinical trials find a successful vaccine. With technological advancement, Wireless Multimedia Sensor Networks (WMSNs) has extended its significant role in the development of remote medical point-of-care (RM-PoC). WMSN is generally located on a communication device to sense the vital signaling information that may periodically be transmitted to remote intelligent pouch This modern remote system finds a suitable professional system to inspect the environment condition remotely in order to facilitate the intelligent process. In the past, the RM-PoC has gained more attention for the exploitation of real-time monitoring, treatment follow-up, and action report generation. Even though it has additional advantages in comparison with conventional systems, issues such as security and privacy are seriously considered to protect the modern system information over insecure public networks. Therefore, this study presents a novel Single User Sign-In (SUSI) Mechanism that makes certain of privacy preservation to ensure better protection of multimedia data. It can be achieved over the negotiation of a shared session-key to perform encryption or decryption of sensitive data during the authentication phase. To comply with key agreement properties such as appropriate mutual authentication and secure session key-agreement, a proposed system design is incorporated into the chaotic-map. The above assumption claims that it can not only achieve better security efficiencies but also can moderate the computation, communication, and storage cost of some intelligent systems as compared to elliptic-curve cryptography or RSA. Importantly, in order to offer untraceability and user anonymity, the RM-PoC acquires dynamic identities from proposed SUSI. Moreover, the security efficiencies of proposed SUSI are demonstrated using informal and formal analysis of the real-or-random (RoR) model. Lastly, a simulation study using NS3 is extensively conducted to analyze the communication metrics such as transmission delay, throughput rate, and packet delivery ratio that demonstrates the significance of the proposed SUSI scheme.
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Affiliation(s)
- B. D. Deebak
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014 India
| | - Fadi Al-Turjman
- Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - Anand Nayyar
- Graduate School, Duy Tan University, Da Nang, 550000 Viet Nam
- Faculty of Information Technology, Duy Tan University, Da Nang, 550000 Viet Nam
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37
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Srivastava V, Srivastava S, Chaudhary G, Al-Turjman F. A systematic approach for COVID-19 predictions and parameter estimation. Pers Ubiquitous Comput 2020; 27:675-687. [PMID: 33173450 PMCID: PMC7644415 DOI: 10.1007/s00779-020-01462-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/12/2020] [Indexed: 05/10/2023]
Abstract
The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19.
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Affiliation(s)
- Vishal Srivastava
- Netaji Subhas Institute of Technology, Sector 3, Dwarka, New Delhi India
| | - Smriti Srivastava
- Netaji Subhas Institute of Technology, Sector 3, Dwarka, New Delhi India
| | - Gopal Chaudhary
- Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, Delhi India
| | - Fadi Al-Turjman
- Artificial Intelligence Dept., Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
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38
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Rahman MA, Zaman N, Asyhari AT, Al-Turjman F, Alam Bhuiyan MZ, Zolkipli MF. Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices. Sustain Cities Soc 2020; 62:102372. [PMID: 32834935 PMCID: PMC7333601 DOI: 10.1016/j.scs.2020.102372] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 05/18/2023]
Abstract
The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three different measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the first measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The findings signify the promises of dynamic clustering for lockdown coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for significantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.
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Affiliation(s)
- Md Arafatur Rahman
- Faculty of Computing, University Malaysia Pahang, Gambang 26300, Malaysia
- IBM CoE, Malaysia
| | | | - A Taufiq Asyhari
- School of Computing and Digital Technology, Birmingham City University, Millennium Point, Birmingham, B4 7XG, UK
| | - Fadi Al-Turjman
- Research Center for AI and IoT, Near East University, 99138 Nicosia, Turkey
| | - Md Zakirul Alam Bhuiyan
- Department of Computer and Information Sciences, Fordham University, New York, NY 10458, USA
| | - M F Zolkipli
- Faculty of Computing, University Malaysia Pahang, Gambang 26300, Malaysia
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Al-Turjman F, Hamouda W, Mumtaz S. Call for Special Issue Papers: Programming Models and Algorithms for Big Data. Big Data 2020; 8:454-455. [PMID: 32845727 DOI: 10.1089/big.2020.29039.cfp] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Affiliation(s)
| | | | - Shahid Mumtaz
- Instituto de Telecomunicações Aveiro, Aveiro, Portugal
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40
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Abstract
Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.
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Affiliation(s)
- Adedoyin Ahmed Hussain
- Department of Computer EngineeringNear East University99138NicosiaMersin 10Turkey
- Research Centre for AI and IoTDepartment of Artificial Intelligence EngineeringNear East University99138NicosiaMersin 10Turkey
| | - Ouns Bouachir
- Department of Computer EngineeringZayed UniversityDubaiUnited Arab Emirates
- College of Technological InnovationZayed UniversityDubaiUnited Arab Emirates
| | - Fadi Al-Turjman
- Research Centre for AI and IoTDepartment of Artificial Intelligence EngineeringNear East University99138NicosiaMersin 10Turkey
| | - Moayad Aloqaily
- College of EngineeringAl Ain UniversityAl AinUnited Arab Emirates
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41
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Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR. CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection. IEEE Access 2020; 8:91916-91923. [PMID: 34192100 PMCID: PMC8043420 DOI: 10.1109/access.2020.2994762] [Citation(s) in RCA: 238] [Impact Index Per Article: 59.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 05/11/2020] [Indexed: 05/08/2023]
Abstract
Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.
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Affiliation(s)
- Abdul Waheed
- Maharaja Agrasen Institute of TechnologyNew Delhi110086India
| | - Muskan Goyal
- Maharaja Agrasen Institute of TechnologyNew Delhi110086India
| | - Deepak Gupta
- Maharaja Agrasen Institute of TechnologyNew Delhi110086India
| | - Ashish Khanna
- Maharaja Agrasen Institute of TechnologyNew Delhi110086India
| | - Fadi Al-Turjman
- Artificial Intelligence DepartmentResearch Center for AI and IoTNear East University99138MersinTurkey
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42
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Zhang Q, Li Y, Al-Turjman F, Zhou X, Yang X. Transient ischemic attack analysis through non-contact approaches. Hum Cent Comput Inf Sci 2020. [DOI: 10.1186/s13673-020-00223-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Abstract
The transient ischemic attack (TIA) is a kind of sudden disease, which has the characteristics of short duration and high frequency. Since most patients can return to normal after the onset of the disease, it is often neglected. Medical research has proved that patients are prone to stroke in a relatively short time after the transient ischemic attacks. Therefore, it is extremely important to effectively monitor transient ischemic attack, especially for elderly people living alone. At present, video monitoring and wearing sensors are generally used to monitor transient ischemic attacks, but these methods have certain disadvantages. In order to more conveniently and accurately monitor transient ischemic attack in the indoor environment and improve risk management of stroke, this paper uses a microwave sensing platform working in C-Band (4.0 GHz–8.0 GHz) to monitor in a non-contact way. The platform first collects data, then preprocesses the data, and finally uses principal component analysis to reduce the dimension of the data. Two machine learning algorithms support vector machine (SVM) and random forest (RF) are used to establish prediction models respectively. The experimental results show that the accuracy of SVM and RF approaches are 97.3% and 98.7%, respectively; indicating that the scheme described in this paper is feasible and reliable.
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43
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Jamal N, Xianqiao C, Al-Turjman F, Ullah F. A Deep Learning–based Approach for Emotions Classification in Big Corpus of Imbalanced Tweets. ACM T ASIAN LOW-RESO 2020. [DOI: 10.1145/3410570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Emotions detection in natural languages is very effective in analyzing the user's mood about a concerned product, news, topic, and so on. However, it is really a challenging task to extract important features from a burst of raw social text, as emotions are subjective with limited fuzzy boundaries. These subjective features can be conveyed in various perceptions and terminologies. In this article, we proposed an IoT-based framework for emotions classification of tweets using a hybrid approach of Term Frequency Inverse Document Frequency (TFIDF) and deep learning model. First, the raw tweets are filtered using the tokenization method for capturing useful features without noisy information. Second, the TFIDF statistical technique is applied to estimate the importance of features locally as well as globally. Third, the Adaptive Synthetic (ADASYN) class balancing technique is applied to solve the imbalance class issue among different classes of emotions. Finally, a deep learning model is designed to predict the emotions with dynamic epoch curves. The proposed methodology is analyzed on two different Twitter emotions datasets. The dynamic epoch curves are shown to show the behavior of test and train data points. It is proved that this methodology outperformed the popular state-of-the-art methods.
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Affiliation(s)
- Nasir Jamal
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Chen Xianqiao
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Fadi Al-Turjman
- Artificial Intelligence Dept., Research Center for AI and IoT, Near East University, Nicosia, Mersin, Turkey
| | - Farhan Ullah
- School of Software, Northwestern Polytechnical University, Xi'an Shaanxi, P.R. China
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44
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Zilani TA, Al-Turjman F, Khan MB, Zhao N, Yang X. Monitoring Movements of Ataxia Patient by Using UWB Technology. Sensors (Basel) 2020; 20:E931. [PMID: 32050576 PMCID: PMC7039007 DOI: 10.3390/s20030931] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 02/07/2020] [Accepted: 02/07/2020] [Indexed: 01/19/2023]
Abstract
Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.
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Affiliation(s)
- Tanjila Akter Zilani
- School of Electronic Engineering, Xidian University, Xi’an 710071, China; (T.A.Z.); (M.B.K.); (N.Z.)
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Near East University, 99138 Nicosia, Mersin 10, Turkey;
- Research Centre for AI and IoT, Near East University, 99138 Nicosia, Mersin 10, Turkey
| | - Muhammad Bilal Khan
- School of Electronic Engineering, Xidian University, Xi’an 710071, China; (T.A.Z.); (M.B.K.); (N.Z.)
| | - Nan Zhao
- School of Electronic Engineering, Xidian University, Xi’an 710071, China; (T.A.Z.); (M.B.K.); (N.Z.)
| | - Xiaodong Yang
- School of Electronic Engineering, Xidian University, Xi’an 710071, China; (T.A.Z.); (M.B.K.); (N.Z.)
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45
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Kolhar M, Al-Turjman F, Alameen A, Abualhaj MM. A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak. IEEE Access 2020; 8:163608-163617. [PMID: 34812355 PMCID: PMC8545303 DOI: 10.1109/access.2020.3021983] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 09/01/2020] [Indexed: 05/03/2023]
Abstract
In this article, we have built a prototype of a decentralized IoT based biometric face detection framework for cities that are under lockdown during COVID-19 outbreaks. To impose restrictions on public movements, we have utilized face detection using three-layered edge computing architecture. We have built a deep learning framework of multi-task cascading to recognize the face. For the face detection proposal we have compared with the state of the art methods on various benchmarking dataset such as FDDB and WIDER FACE. Furthermore, we have also conducted various experiments on latency and face detection load on three-layer and cloud computing architectures. It shows that our proposal has an edge over cloud computing architecture.
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Affiliation(s)
- Manjur Kolhar
- Department of Computer SciencePrince Sattam Bin Abdulaziz University Wadi Ad-Dawasir 11990 Saudi Arabia
| | - Fadi Al-Turjman
- Research Center for AI and IoTArtificial Intelligence DepartmentNear East University 99138 Mersin Turkey
| | - Abdalla Alameen
- Department of Computer SciencePrince Sattam Bin Abdulaziz University Wadi Ad-Dawasir 11990 Saudi Arabia
| | - Mosleh M Abualhaj
- Department of Networks and Information SecurityAl-Ahliyya Amman University Amman 19328 Jordan
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46
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B D D, Al-Turjman F, Mostarda L. A Hash-Based RFID Authentication Mechanism for Context-Aware Management in IoT-Based Multimedia Systems. Sensors (Basel) 2019; 19:s19183821. [PMID: 31487847 PMCID: PMC6766990 DOI: 10.3390/s19183821] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/02/2019] [Accepted: 08/09/2019] [Indexed: 12/02/2022]
Abstract
With the technological advances in the areas of Machine-To-Machine (M2M) and Device-To-Device (D2D) communication, various smart computing devices now integrate a set of multimedia sensors such as accelerometers, barometers, cameras, fingerprint sensors, gestures, iris scanners, etc., to infer the environmental status. These devices are generally identified using radio-frequency identification (RFID) to transfer the collected data to other local or remote objects over a geographical location. To enable automatic data collection and transition, a valid RFID embedded object is highly recommended. It is used to authorize the devices at various communication phases. In smart application devices, RFID-based authentication is enabled to provide short-range operation. On the other hand, it does not require the communication device to be in line-of-sight to gain server access like bar-code systems. However, in existing authentication schemes, an adversary may capture private user data to create a forgery problem. Also, another issue is the high computation cost. Thus, several studies have addressed the usage of context-aware authentication schemes for multimedia device management systems. The security objective is to determine the user authenticity in order to withhold the eavesdropping and tracing. Lately, RFID has played a significant for the context-aware sensor management systems (CASMS) as it can reduce the complexity of the sensor systems, it can be available in access control, sensor monitoring, real time inventory and security-aware management systems. Lately, this technology has opened up its wings for CASMS, where the challenging issues are tag-anonymity, mutual authentication and untraceability. Thus, this paper proposes a secure hash-based RFID mechanism for CASMS. This proposed protocol is based on the hash operation with the synchronized secret session-key to withstand any attacks, such as desynchronization, replay and man-in-the-middle. Importantly, the security and performance analysis proves that the proposed hash-based protocol achieves better security and performance efficiencies than other related schemes. From the simulation results, it is observed that the proposed scheme is secure, robust and less expensive while achieving better communication metrics such as packet delivery ratio, end-to-end delay and throughput rate.
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Affiliation(s)
- Deebak B D
- Schoool of Computer Science and Engineering, Vellore Institute of Technology, Vellore-632014, India.
| | - Fadi Al-Turjman
- Computer Engineering Department, Antalya Bilim University, 07190-Antalya, Turkey
| | - Leonardo Mostarda
- Computer Science Department, Camerino University, 62032-Camerino, Italy
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Hameed KW, Noras JM, Radwan A, Al-Turjman F, Rodriguez J, Abd-Alhameed RA. Optimal Array size for Multiuser MIMO. 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC) 2018. [DOI: 10.1109/iwcmc.2018.8450285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Singh G, Abu-Elkheir M, Al-Turjman F, Taha AEM. Towards prolonged lifetime for large-scale Information-Centric Sensor Networks. 2014 27th Biennial Symposium on Communications (QBSC) 2014. [DOI: 10.1109/qbsc.2014.6841190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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