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Jithendra T, Sharief Basha S. A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm. Diagnostics (Basel) 2023; 13:diagnostics13091641. [PMID: 37175032 PMCID: PMC10178244 DOI: 10.3390/diagnostics13091641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/02/2023] [Accepted: 03/08/2023] [Indexed: 05/15/2023] Open
Abstract
This research is aimed to escalate Adaptive Neuro-Fuzzy Inference System (ANFIS) functioning in order to ensure the veracity of existing time-series modeling. The COVID-19 pandemic has been a global threat for the past three years. Therefore, advanced forecasting of confirmed infection cases is extremely essential to alleviate the crisis brought out by COVID-19. An adaptive neuro-fuzzy inference system-reptile search algorithm (ANFIS-RSA) is developed to effectively anticipate COVID-19 cases. The proposed model integrates a machine-learning model (ANFIS) with a nature-inspired Reptile Search Algorithm (RSA). The RSA technique is used to modulate the parameters in order to improve the ANFIS modeling. Since the performance of the ANFIS model is dependent on optimizing parameters, the statistics of infected cases in China and India were employed through data obtained from WHO reports. To ensure the accuracy of our estimations, corresponding error indicators such as RMSE, RMSRE, MAE, and MAPE were evaluated using the coefficient of determination (R2). The recommended approach employed on the China dataset was compared with other upgraded ANFIS methods to identify the best error metrics, resulting in an R2 value of 0.9775. ANFIS-CEBAS and Flower Pollination Algorithm and Salp Swarm Algorithm (FPASSA-ANFIS) attained values of 0.9645 and 0.9763, respectively. Furthermore, the ANFIS-RSA technique was used on the India dataset to examine its efficiency and acquired the best R2 value (0.98). Consequently, the suggested technique was found to be more beneficial for high-precision forecasting of COVID-19 on time-series data.
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Affiliation(s)
- Thandra Jithendra
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore 632014, India
| | - Shaik Sharief Basha
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore 632014, India
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2
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Zhong B, Gao H, Ding L, Wang Y. A Blockchain-Based Life-Cycle Environmental Management Framework for Hospitals in the COVID-19 Context. ENGINEERING (BEIJING, CHINA) 2023; 20:208-221. [PMID: 36245898 PMCID: PMC9540700 DOI: 10.1016/j.eng.2022.06.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 05/13/2022] [Accepted: 06/16/2022] [Indexed: 06/16/2023]
Abstract
During the coronavirus disease 2019 (COVID-19) emergency, many hospitals were built or renovated around the world to meet the challenges posed by the rising number of infected cases. Environmental management in the hospital life cycle is vital in preventing nosocomial infection and includes many infection control procedures. In certain urgent situations, a hospital must be completed quickly, and work process approval and supervision must therefore be accelerated. Thus, many works cannot be checked in detail. This results in a lack of work liability control and increases the difficulty of ensuring the fulfillment of key infection prevention measures. This study investigates how blockchain technology can transform the work quality inspection workflow to assist in nosocomial infection control under a fast delivery requirement. A blockchain-based life-cycle environmental management framework is proposed to track the fulfillment of crucial infection control measures in the design, construction, and operation stages of hospitals. The proposed framework allows for work quality checking after the work is completed, when some work cannot be checked on time. Illustrative use cases are selected to demonstrate the capabilities of the developed solution. This study provides new insights into applying blockchain technology to address the challenge of environmental management brought by rapid delivery requirements.
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Affiliation(s)
- Botao Zhong
- National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Han Gao
- National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- Department of Civil and Building Systems, Technische Universität Berlin, Berlin 13156, Germany
| | - Lieyun Ding
- National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yuhang Wang
- National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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3
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Saleem K, Saleem M, Ahmad RZ, Javed AR, Alazab M, Gadekallu TR, Suleman A. Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch. IEEE SENSORS JOURNAL 2023; 23:898-905. [PMID: 36913222 PMCID: PMC9983688 DOI: 10.1109/jsen.2022.3156819] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/28/2022] [Indexed: 05/09/2023]
Abstract
Ambient intelligence plays a crucial role in healthcare situations. It provides a certain way to deal with emergencies to provide the essential resources such as nearest hospitals and emergency stations promptly to avoid deaths. Since the outbreak of Covid-19, several artificial intelligence techniques have been used. However, situation awareness is a key aspect to handling any pandemic situation. The situation-awareness approach gives patients a routine life where they are continuously monitored by caregivers through wearable sensors and alert the practitioners in case of any patient emergency. Therefore, in this paper, we propose a situation-aware mechanism to detect Covid-19 systems early and alert the user to be self-aware regarding the situation to take precautions if the situation seems unlikely to be normal. We provide Belief-Desire-Intention intelligent reasoning mechanism for the system to analyze the situation after acquiring the data from the wearable sensors and alert the user according to their environment. We use the case study for further demonstration of our proposed framework. We model the proposed system by temporal logic and map the system illustration into a simulation tool called NetLogo to determine the results of the proposed system.
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Affiliation(s)
- Kiran Saleem
- School of SoftwareDalian University of Technology Dalian 116024 China
| | - Misbah Saleem
- Institute of Diet and Nutritional Science, University of Lahore Lahore 54590 Pakistan
| | | | | | - Mamoun Alazab
- College of EngineeringIT and Environment, Charles Darwin University Darwin NT 0815 Australia
| | | | - Ahmad Suleman
- Center of Excellence in Solid State PhysicsUniversity of Punjab Lahore 05422 Pakistan
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4
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Agrawal S, Jain SK, Sharma S, Khatri A. COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:432. [PMID: 36612755 PMCID: PMC9819913 DOI: 10.3390/ijerph20010432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward the pandemic on different social media platforms. This resulted in a huge information flow on social media and attracted many research studies aimed at extracting useful information to understand the sentiments. This paper analyses data imported from the Twitter API for the healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues and healthcare service providers. The main objective of this research is to analyze machine learning models for classifying the sentiments of people and analyzing the direction of polarity by considering the views of the majority of people. The inferences drawn from this analysis may be useful for concerned authorities as they work to make appropriate policy decisions and strategic decisions. Various machine learning models were developed to extract the actual emotions, and results show that the support vector machine model outperforms with an average accuracy of 82.67% compared with the logistic regression, random forest, multinomial naïve Bayes and long short-term memory models, which present 78%, 77%, 68.67% and 75% accuracy, respectively.
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Affiliation(s)
- Shweta Agrawal
- Institute of Advanced Computing, SAGE University, Indore 452010, India
| | - Sanjiv Kumar Jain
- Electrical Engineering Department, Medi-Caps University, Indore 453331, India
| | - Shruti Sharma
- Department of Computer Science and Engineering, Indore Institute of Science &Technology, Indore 453332, India
| | - Ajay Khatri
- Bellurbis Technologies Private Limited, Indore 452001, India
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5
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Khan F, Siddiqui MA, Imtiaz S, Shaikh SA, Chen CL, Wu CM. Determinants of mental and financial health during COVID-19: Evidence from data of a developing country. Front Public Health 2022; 10:888741. [PMID: 36117608 PMCID: PMC9471958 DOI: 10.3389/fpubh.2022.888741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 07/18/2022] [Indexed: 01/21/2023] Open
Abstract
Mental and emotional issues are the top-level concerns of public health worldwide. These issues surged during Coronavirus (COVID-19) pandemic due to varied medical, social, and personal reasons. The social determinants highlighted in the literature mainly focus on household solutions rather than on increasing the financial wellbeing of individuals, especially for the most vulnerable groups where the psychological distress coming from the social inequalities cannot be entirely treated. Hence, this study attempts to familiarize the financial capability (the financial literacy, attitude, skills and behavior required for effective financial management) construct into public health domain in the times of COVID-19 as a determinant of psychological distress, and also explores the role of gender in it. The study uses Ordinary Least Square (OLS) regression analysis and employs mental distress questions and Organization for Economic Cooperation and Development (OECD) 2018 financial capability toolkit to collect data from a large sample of households from all over Pakistan. It is inferred that the higher the financial capability, the lower the financial and mental distress during COVID-19. Additionally, females are less financially knowledgeable, depict poor financial behaviors, and face more psychological issues than their counterparts. Age and education are also linked to mental stress during COVID-19. Finally, gender plays a moderating role in financial behavior, and financial and mental stress of households. As evident, COVID-19 is not going away soon hence the findings are relevant for policymakers to proactively plan for the pandemic's upcoming waves and help people be better financially equipped to fight against this or any upcoming crisis, and achieve better mental and physical health.
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Affiliation(s)
- Falak Khan
- FAST School of Management, Islamabad, Pakistan,National University of Computer and Emerging Sciences, Islamabad, Pakistan,*Correspondence: Falak Khan
| | - Muhammad A. Siddiqui
- FAST School of Management, Islamabad, Pakistan,National University of Computer and Emerging Sciences, Islamabad, Pakistan
| | - Salma Imtiaz
- Department of Software Engineering, International Islamic University, Islamabad, Pakistan
| | - Shoaib A. Shaikh
- Electrical Engineering Department, Sukkur IBA University, Sukkur, Pakistan
| | - Chin-Ling Chen
- School of Information Engineering, Changchun Sci-Tech University, Changchun, China,Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan,School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen, China,Chin-Ling Chen
| | - Chih-Ming Wu
- School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen, China
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6
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A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6354579. [PMID: 35990145 PMCID: PMC9391128 DOI: 10.1155/2022/6354579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/04/2022] [Accepted: 07/13/2022] [Indexed: 11/17/2022]
Abstract
Coronavirus (COVID-19) is a highly severe infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). The polymerase chain reaction (PCR) test is essential to confirm the COVID-19 infection, but it has certain limitations, including paucity of reagents, is computationally time-consuming, and requires expert clinicians. Clinicians suggest that the PCR test is not a reliable automated COVID-19 patient detection system. This study proposed a machine learning-based approach to evaluate the PCR role in COVID-19 detection. We collect real data containing 603 COVID-19 samples from the Pakistan Institute of Medical Sciences (PIMS) Hospital in Islamabad, Pakistan, during the third COVID-19 wave. The experiments are separated into two sets. The first set comprises 24 features, including PCR test results, whereas the second comprises 24 features without PCR test. The findings demonstrate that the decision tree achieves the best detection rate for positive and negative COVID-19 patients in both scenarios. The findings reveal that PCR does not contribute to detecting COVID-19 patients. The findings also aid in the early detection of COVID-19, mainly when PCR test results are insufficient for diagnosing COVID-19 and help developing countries with a paucity of PCR tests and specialist facilities.
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7
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Amanat A, Rizwan M, Maple C, Zikria YB, Almadhor AS, Kim SW. Blockchain and cloud computing-based secure electronic healthcare records storage and sharing. Front Public Health 2022; 10:938707. [PMID: 35928494 PMCID: PMC9343689 DOI: 10.3389/fpubh.2022.938707] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Healthcare information is essential for both service providers and patients. Further secure sharing and maintenance of Electronic Healthcare Records (EHR) are imperative. EHR systems in healthcare have traditionally relied on a centralized system (e.g., cloud) to exchange health data across healthcare stakeholders, which may expose private and sensitive patient information. EHR has struggled to meet the demands of several stakeholders and systems in terms of safety, isolation, and other regulatory constraints. Blockchain is a distributed, decentralized ledger technology that can provide secured, validated, and immutable data sharing facilities. Blockchain creates a distributed ledger system using techniques of cryptography (hashes) that are consistent and permit actions to be carried out in a distributed manner without needing a centralized authority. Data exploitation is difficult and evident in a blockchain network due to its immutability. We propose an architecture based on blockchain technology that authenticates the user identity using a Proof of Stake (POS) cryptography consensus mechanism and Secure Hash Algorithm (SHA256) to secure EHR sharing among different electronic healthcare systems. An Elliptic Curve Digital Signature Algorithm (ECDSA) is used to verify EHR sensors to assemble and transmit data to cloud infrastructure. Results indicate that the proposed solution performs exceptionally well when compared with existing solutions, which include Proof-Of-Work (POW), Secure Hash Algorithm (SHA-1), and Message Digest (MD5) in terms of power consumption, authenticity, and security of healthcare records.
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Affiliation(s)
- Amna Amanat
- Department of Computer Science, Kinnaird College for Women, Lahore, Pakistan
| | - Muhammad Rizwan
- Department of Computer Science, Kinnaird College for Women, Lahore, Pakistan
- Secure Cyber Systems Research Group, WMG, University of Warwick, Coventry, United Kingdom
| | - Carsten Maple
- Secure Cyber Systems Research Group, WMG, University of Warwick, Coventry, United Kingdom
| | - Yousaf Bin Zikria
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, South Korea
- *Correspondence: Yousaf Bin Zikria
| | - Ahmad S. Almadhor
- College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia
| | - Sung Won Kim
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, South Korea
- Sung Won Kim
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8
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Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey. SENSORS 2022; 22:s22124394. [PMID: 35746176 PMCID: PMC9229631 DOI: 10.3390/s22124394] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/04/2022] [Accepted: 06/06/2022] [Indexed: 02/04/2023]
Abstract
The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.
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9
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Shakeel T, Habib S, Boulila W, Koubaa A, Javed AR, Rizwan M, Gadekallu TR, Sufiyan M. A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects. COMPLEX INTELL SYST 2022; 9:1027-1058. [PMID: 35668731 PMCID: PMC9151356 DOI: 10.1007/s40747-022-00767-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/15/2022] [Indexed: 12/23/2022]
Abstract
Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.
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Affiliation(s)
- Tanzeela Shakeel
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
| | - Shaista Habib
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
| | - Wadii Boulila
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, 12435 Saudi Arabia
| | - Anis Koubaa
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, 12435 Saudi Arabia
| | - Abdul Rehman Javed
- Department of Cyber Security, PAF Complex, E-9, Air University, Islamabad, Pakistan
| | - Muhammad Rizwan
- Department of Computer Science, Kinnaird College for Women, Lahore, Pakistan
| | - Thippa Reddy Gadekallu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Mahmood Sufiyan
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
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10
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Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1359019. [PMID: 35027940 PMCID: PMC8752232 DOI: 10.1155/2022/1359019] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/01/2021] [Indexed: 01/03/2023]
Abstract
Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies' major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.
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11
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Jalil Z, Abbasi A, Javed AR, Badruddin Khan M, Abul Hasanat MH, Malik KM, Saudagar AKJ. COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques. Front Public Health 2022; 9:812735. [PMID: 35096755 PMCID: PMC8795663 DOI: 10.3389/fpubh.2021.812735] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/15/2021] [Indexed: 12/22/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.
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Affiliation(s)
- Zunera Jalil
- Department of Cyber Security, Air University, Islamabad, Pakistan
| | - Ahmed Abbasi
- Department of Cyber Security, Air University, Islamabad, Pakistan
| | | | - Muhammad Badruddin Khan
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Mozaherul Hoque Abul Hasanat
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Khalid Mahmood Malik
- Department of Computer Science and Engineering, Oakland University Rochester, Rochester, MI, United States
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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12
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Ronald Doni A, Sasi Praba T, Murugan S. Weather and population based forecasting of novel COVID-19 using deep learning approaches. INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022; 13. [PMCID: PMC8396801 DOI: 10.1007/s13198-021-01272-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The spread of novel corona virus across the globe has a significant impact on various stake holders and posting a major challenge to the research community. Government has taken several measures for maintaining social distance and containment of disease, but still it is not a sufficient for the developing countries like India where the level of understanding the issue is deprived and hence it is a major challenge to the Health Care professionals. Therefore, it is mandatory that a prediction of the number of possible cases enables the preparedness of the Government and the Hospitals in resolving the issues and to take measures in controlling the spread of the disease Series. Deep learning model has been built by considering the features of weather and COVID-19 data (recovered, infected and deceased) for predicting the number of cases expected in India. The model is built on Concurrent Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional RNN (BRNN), Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) based on the daily weather and COVID-19 data collected from Indian subcontinent. The results revealed that the algorithm BRNN yields a better prediction model when compared with the other models.
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Affiliation(s)
- A. Ronald Doni
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu India
| | - T. Sasi Praba
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu India
| | - S. Murugan
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu India
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13
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Mehmood M, Rizwan M, Gregus ml M, Abbas S. Machine Learning Assisted Cervical Cancer Detection. Front Public Health 2021; 9:788376. [PMID: 35004588 PMCID: PMC8733205 DOI: 10.3389/fpubh.2021.788376] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 11/16/2021] [Indexed: 12/31/2022] Open
Abstract
Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named CervDetect that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. CervDetect uses Pearson correlation between input variables as well as with the output variable to pre-process the data. CervDetect uses the random forest (RF) feature selection technique to select significant features. Finally, CervDetect uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that CervDetect accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100%.
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Affiliation(s)
- Mavra Mehmood
- Department of Computer Science, Kinnaird College for Women, Lahore, Pakistan
| | - Muhammad Rizwan
- Department of Computer Science, Kinnaird College for Women, Lahore, Pakistan
| | - Michal Gregus ml
- Information Systems Department, Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia
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Pandya S, Sur A, Solke N. COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks. Front Public Health 2021; 9:797808. [PMID: 34917585 PMCID: PMC8669395 DOI: 10.3389/fpubh.2021.797808] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/02/2021] [Indexed: 12/24/2022] Open
Abstract
The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach-The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings-The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.
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Affiliation(s)
- Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
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Dash S, Chakraborty C, Giri SK, Pani SK. Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics. Pattern Recognit Lett 2021; 151:69-75. [PMID: 34413555 PMCID: PMC8364174 DOI: 10.1016/j.patrec.2021.07.027] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 07/11/2021] [Accepted: 07/28/2021] [Indexed: 11/29/2022]
Abstract
Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure.
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Affiliation(s)
- Sujata Dash
- Maharaja Sriram Chandra BhanjaDeo University (Erstwhile North Orissa University) Takatpur, Baripada, India
| | | | - Sourav K Giri
- Maharaja Sriram Chandra BhanjaDeo University (Erstwhile North Orissa University) Takatpur, Baripada, India
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