1
|
Yan L, Webber JL, Mehbodniya A, Moorthy B, Sivamani S, Nazir S, Shabaz M. Distributed optimization of heterogeneous UAV cluster PID controller based on machine learning. Computers and Electrical Engineering 2022. [DOI: 10.1016/j.compeleceng.2022.108059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
2
|
Tandon A, Guha SK, Rashid J, Kim J, Gahlan M, Shabaz M, Anjum N. Graph based CNN Algorithm to Detect Spammer Activity Over Social Media. IETE Journal of Research 2022. [DOI: 10.1080/03772063.2022.2061610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Aditya Tandon
- Krishna Engineering College, Ghaziabad, Up, Ghaziabad, India
| | - Shouvik Kumar Guha
- The West Bengal National University of Juridical Sciences, Kolkata, India
| | - Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Cheonan 31080, Korea
| | - Jungeun Kim
- Department of Computer Science and Engineering, Kongju National University, Cheonan 31080, Korea
| | - Mamta Gahlan
- Maharaja Surajmal Institute of Technology, Delhi, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India
| | - Nasreen Anjum
- Department of Computing and Engineering, University of Gloucestershire, Gloucestershir, UK
| |
Collapse
|
3
|
Zheng W, Mehbodniya A, Neware R, Wawale SG, Ganthia BP, Shabaz M. Modular unmanned aerial vehicle platform design: Multi-objective evolutionary system method. Computers and Electrical Engineering 2022; 99:107838. [DOI: 10.1016/j.compeleceng.2022.107838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
4
|
Murugesan G, Ahmed TI, Shabaz M, Bhola J, Omarov B, Swaminathan R, Sammy F, Sumi SA. Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder. Comput Intell Neurosci 2022; 2022:8555489. [PMID: 35401736 PMCID: PMC8989570 DOI: 10.1155/2022/8555489] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/01/2022] [Accepted: 03/08/2022] [Indexed: 02/07/2023]
Abstract
Major depressive disorder (MDD) is a mood state that is not usually associated with vision problems. Recent research has found that the inhibitory neurotransmitter GABA levels in the occipital brain have dropped. Aim. The aim of the research is to evaluate mental workload by single channel electroencephalogram (EEG) approach through visual-motor activity and comparison of parameter among depressive disorder patient and in control group. Method. Two tests of a visual-motor task similar to reflect drawings were performed in this study to compare the visual information processing of patients with depression to that of a placebo group. The current study looks into the accuracy of monitoring cognitive burden with single-channel portable EEG equipment. Results. The alteration of frontal brain movement in reaction to fluctuations in cognitive burden stages generated through various vasomotor function was examined. By applying a computerised oculomotor activity analogous to reflector image diagram, we found that the complexity of the path to be drawn was more important than the real time required accomplishing the job in determining perceived difficulty in depressive disorder patients. The overall perceived difficulty of the exercise is positively linked with EEG activity measured from the motor cortex region at the start of every experiment test. The average rating for task completion for depression patients and in control group observed and no statistical significance association reported between rating scale and time spent on each trial (p=1.43) for control group while the normalised perceived difficulty rating had 0.512, 0.623, and 0.821 correlations with the length of the pathway, the integer of inclination in the pathway, and the time spent to complete every experiment test, respectively (p < 0.0001) among depression patients. The findings imply that alterations in comparative cognitive burden levels during an oculomotor activity considerably modify frontal EEG spectrum. Conclusion. Patients with depression perceived the optical illusion in the arrays as weaker, resulting in a little bigger disparity than individuals who were not diagnosed with depression. This discovery provided light on the prospect of adopting a user-friendly mobile EEG technology to assess mental workload in everyday life.
Collapse
Affiliation(s)
- G. Murugesan
- Department of Computer Science and Engineering, St. Joseph's College of Engineering, Chennai 600119, India
| | - Tousief Irshad Ahmed
- Department of Clinical Biochemistry, Sher-i-Kashmir Institute of Medical Sciences, Soura, Srinagar, J&K, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India
| | - Jyoti Bhola
- Electronics & Communication Engineering Department, National Institute of Technology, Hamirpur, India
| | - Batyrkhan Omarov
- Al-Farabi Kazakh National University, Almaty, Kazakhstan
- International University of Tourism and Hospitality, Turkistan, Kazakhstan
- Suleiman Demirel University, Kaskelen, Kazakhstan
| | - R. Swaminathan
- Saveetha School of Engineering, Chennai, Tamil Nadu, India
| | - F. Sammy
- Department of Information Technology, Dambi Dollo University, Dembi Dolo, Welega, Ethiopia
| | - Sharmin Akter Sumi
- Department of Anatomy, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| |
Collapse
|
5
|
Mehbodniya A, Webber JL, Neware R, Arslan F, Pamba RV, Shabaz M. Modified Lamport Merkle Digital Signature blockchain framework for authentication of internet of things healthcare data. Expert Systems 2022. [DOI: 10.1111/exsy.12978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering Kuwait College of Science and Technology (KCST) Doha Kuwait
| | - Julian L. Webber
- Graduate School of Engineering Science Osaka University Osaka Japan
| | - Rahul Neware
- Department of Computing, Mathematics and Physics Høgskulen på Vestlandet Bergen Norway
| | - Farrukh Arslan
- School of Electrical and Computer Engineering Purdue University USA
| | | | | |
Collapse
|
6
|
kaur S, kaur G, Shabaz M, Nazir S. A Secure Two-Factor Authentication Framework in Cloud Computing. Security and Communication Networks 2022; 2022:1-9. [DOI: 10.1155/2022/7540891] [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] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Cloud computing technology has brought tremendous evaluation in the arena of IT (information technology). This technology paves the path of starting business with lowest investment by availing infrastructure as a service (IAAS), platform as a service (PAAS) and software as a service (SAAS) pay per uses model. Cloud computing services can be quickly and easily provisioned and discharged with minimum efforts and service provider (SP) relationship. Cloud computing characteristics such as on demand self-service, broad network access, resource pooling, and rapid elasticity lead the demand of computing. Despite these features, this platform is free to security issues and attacks specifically in terms of communication because of unsecure authentication and privacy. However, strong user authentication procedure impedes illegal access to the SP which is the principal requirement for securing cloud computing ecosystem. In this regard, we attempt to propose possible counter measures for the cloud ecosystem. Hence, this paper presented a novel one way hash and nonce-based two-factor secure authentication scheme with traditional user IDs, password, and OTP verification procedure that resist brute force attack, session and account hijacking attack, MITM attacks, and replay attacks.
Collapse
|
7
|
Tharewal S, Ashfaque MW, Banu SS, Uma P, Hassen SM, Shabaz M, Jain DK. Intrusion Detection System for Industrial Internet of Things Based on Deep Reinforcement Learning. Wireless Communications and Mobile Computing 2022; 2022:1-8. [DOI: 10.1155/2022/9023719] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The Industrial Internet of Things has grown significantly in recent years. While implementing industrial digitalization, automation, and intelligence introduced a slew of cyber risks, the complex and varied industrial Internet of Things environment provided a new attack surface for network attackers. As a result, conventional intrusion detection technology cannot satisfy the network threat discovery requirements in today’s Industrial Internet of Things environment. In this research, the authors have used reinforcement learning rather than supervised and unsupervised learning, because it could very well improve the decision-making ability of the learning process by integrating abstract thinking of complete understanding, using deep knowledge to perform simple and nonlinear transformations of large-scale original input data into higher-level abstract expressions, and using learning algorithm or learning based on feedback signals, in the lack of guiding knowledge, which is based on the trial-and-error learning model, from the interaction with the environment to find the best good solution. In this respect, this article presents a near-end strategy optimization method for the Industrial Internet of Things intrusion detection system based on a deep reinforcement learning algorithm. This method combines deep learning’s observation capability with reinforcement learning’s decision-making capability to enable efficient detection of different kinds of cyberassaults on the Industrial Internet of Things. In this manuscript, the DRL-IDS intrusion detection system is built on a feature selection method based on LightGBM, which efficiently selects the most attractive feature set from industrial Internet of Things data; when paired with deep learning algorithms, it effectively detects intrusions. To begin, the application is based on GBM’s feature selection algorithm, which extracts the most compelling feature set from Industrial Internet of Things data; then, in conjunction with the deep learning algorithm, the hidden layer of the multilayer perception network is used as the shared network structure for the value network and strategic network in the PPO2 algorithm; and finally, the intrusion detection model is constructed using the PPO2 algorithm and ReLU (R). Numerous tests conducted on a publicly available data set of the Industrial Internet of Things demonstrate that the suggested intrusion detection system detects 99 percent of different kinds of network assaults on the Industrial Internet of Things. Additionally, the accuracy rate is 0.9%. The accuracy, precision, recall rate, F1 score, and other performance indicators are superior to those of the existing intrusion detection system, which is based on deep learning models such as LSTM, CNN, and RNN, as well as deep reinforcement learning models such as DDQN and DQN.
Collapse
|
8
|
Wang H, Sharma A, Shabaz M. Research on digital media animation control technology based on recurrent neural network using speech technology. Int J Syst Assur Eng Manag. [DOI: 10.1007/s13198-021-01540-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
9
|
Phasinam K, Mondal T, Novaliendry D, Yang C, Dutta C, Shabaz M, Khan R. Analyzing the Performance of Machine Learning Techniques in Disease Prediction. J FOOD QUALITY 2022; 2022:1-9. [DOI: 10.1155/2022/7529472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways. This study is concerned with the diagnosis and estimation of heart disease. Heart disease is one of the most dangerous illnesses for humans, leading to death all over the world. Many different groups of researchers have used knowledge exploration methods in diverse fields to forecast heart disease and have shown acceptable degrees of precision. There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. Classification algorithms such as Naive Bayes, ID3, C4.5, and SVM are being investigated. The UCI machinery heart disease data set is used in experimental studies.
Collapse
|
10
|
Liu W, Shabaz M, Garg U. Moving Target Depth Information Extraction Based on Nonlinear Strategy Network. J Inter Net 2022. [DOI: 10.1142/s0219265921480066] [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] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
To improve the effect of depth information extraction of moving targets in the network, a nonlinear strategy-oriented method is proposed. With the advancement of science and technology, especially in wireless networks, a large amount of data is provided to people every hour of every day. Hence, it can increase the demand for data analysis tools. Nonlinear system modeling by using rough set theory to extract valuable information from large amounts of information, and then through the analytic hierarchy process (ahp) to determine the effect of input factors, then use particle swarm optimization algorithm (PSO) to find the accurate function, and USES the adaptive and population catastrophe and vaccine algorithm to make it to the local optimum, to achieve the aim of the complex. The experimental results show that, compared with M2 and M1 for 30 groups of samples, the model obtained by using M2 has a better fitting effect on the actual curve. The error of M2 is within ±3%, and the error of M1 is within ±6%, and the error is relatively large. The accuracy of the proposed method is higher than that of the neural network method, which proves that the nonlinear strategy is effective in the actual target depth information extraction.
Collapse
Affiliation(s)
- Wei Liu
- People’s Public Security University of China, School of National Security, Beijing 100038, P. R. China
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology (MIET), Jammu, India
| | - Urvashi Garg
- Department of Computer Science Engineering, Chandigarh University, Mohali, India
| |
Collapse
|
11
|
Abstract
To study nonlinear distortion image recognition technology. Through the study of neural networks, an image recognition model based on BP neural network is proposed: An improved algorithm for driving quantity factor. According to the established neural network model, 10 commonly used images of Arabic numeral characters are recognized. The effectiveness of the model is verified by experiments with the extracted feature parameters of the target image. The results show that 38 of the 40 distorted images with noise can be correctly identified and 2 of them can be incorrectly identified by the single-stage recognition network, and the recognition rate reaches 95%; the recognition rate of cascade network reaches 100%. Therefore, the BP network which drives the number term can accelerate the training time of the network and improve the recognition efficiency of the system.
Collapse
Affiliation(s)
- Wensheng Yan
- School of Information Technology Engineering, Taizhou Vocational and Technical College, Zhejiang 318000, P. R. China
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology (MIET), Jammu, India
| | - Manik Rakhra
- School of Computer Science Engineering, Lovely Professional University, India
| |
Collapse
|
12
|
Abstract
Agriculture is critical to human life. Agriculture provides a means of subsistence for a sizable portion of the world’s population. Additionally, it provides a large number of work opportunities for inhabitants. Many farmers prefer traditional farming approaches, which result in low yields. Agriculture and related industries are vital to the economy’s long-term growth and development. The primary issues in agricultural production include decision-making, crop selection, and supporting systems for crop yield enhancement. Agriculture forecasting is influenced by natural variables such as temperature, soil fertility, water volume, water quality, season, and crop prices. Growing advancements in agricultural automation have resulted in a flood of tools and apps for rapid knowledge acquisition. Mobile devices are rapidly being used by everyone, including farmers. This paper presents a framework for smart crop tracking and monitoring. Sensors, Internet of Things cameras, mobile applications, and big data analytics are all covered. The hardware consists of an Arduino Uno, a variety of sensors, and a Wi-Fi module. This strategy would result in the most effective use of energy and the smallest amount of agricultural waste possible.
Collapse
|
13
|
Huang X, Sharma A, Shabaz M. Biomechanical research for running motion based on dynamic analysis of human multi-rigid body model. Int J Syst Assur Eng Manag 2022. [DOI: 10.1007/s13198-021-01563-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
14
|
Ramalingam P, Mehbodniya A, Webber JL, Shabaz M, Gopalakrishnan L. Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning. Comput Intell Neurosci 2022; 2022:4886586. [PMID: 35047035 PMCID: PMC8763529 DOI: 10.1155/2022/4886586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/10/2021] [Accepted: 12/17/2021] [Indexed: 02/07/2023]
Abstract
Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.
Collapse
Affiliation(s)
- Parameshwaran Ramalingam
- Department of ECE, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641048, Tamilnadu, India
| | - Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City, Kuwait
| | - Julian L. Webber
- Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Mohammad Shabaz
- Arba Minch University, Arba Minch, Ethiopia
- Department of Computer Science Engineering, Chandigarh University, Ajitgarh, Punjab, India
| | | |
Collapse
|
15
|
G M, Ahmed TI, Bhola J, Shabaz M, Singla J, Rakhra M, More S, Samori IA. Fuzzy Logic-Based Systems for the Diagnosis of Chronic Kidney Disease. Biomed Res Int 2022; 2022:2653665. [PMID: 35360514 PMCID: PMC8964165 DOI: 10.1155/2022/2653665] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/02/2022] [Accepted: 03/08/2022] [Indexed: 02/07/2023]
Abstract
Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual's health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient's stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient's kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.
Collapse
Affiliation(s)
- Murugesan G
- Department of Computer Science and Engineering, St. Joseph's College of Engineering, Chennai 600119, India
| | - Tousief Irshad Ahmed
- Department of Clinical Biochemistry, Sher-i-Kashmir Institute of Medical Sciences, Soura, Srinagar, J&K, India
| | - Jyoti Bhola
- Electronics & Communication Engineering Department, National Institute of Technology, Hamirpur, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India
| | - Jimmy Singla
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 14411, India
| | - Manik Rakhra
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 14411, India
| | - Sujeet More
- Department of Information Technology, Trinity College of Engineering and Research, Pune, India
| | | |
Collapse
|
16
|
Ajaz F, Naseem M, Sharma S, Shabaz M, Dhiman G. COVID-19: Challenges and its Technological Solutions using IoT. Curr Med Imaging 2022; 18:113-123. [PMID: 33588738 DOI: 10.2174/1573405617666210215143503] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [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: 10/24/2020] [Revised: 12/13/2020] [Accepted: 01/06/2021] [Indexed: 02/07/2023]
Abstract
COVID-19 is a global pandemic that has affected many countries in a short span of time. People worldwide are susceptible to this deadly disease. To control the prevailing havoc of coronavirus, researchers are adopting techniques like plasma therapy, proning, medicines, etc. To stop the rapid spread of COVID-19, contact tracing is one of the important ways to check the infected people. This paper explains the various challenges people and health practitioners are facing due to COVID-19. In this paper, various ways with which the impact of COVID-19 can be controlled using IoT technology have been discussed. A six-layer architecture of IoT solutions for containing the deadly COVID-19 has been proposed. In addition to this, the role of machine learning techniques for diagnosing COVID-19 has been discussed in this paper, and a quick explanation of the unmanned aerial vehicle (UAVs) applications for contact tracing has also been specified. From the study conducted, it is evident that IoT solutions can be used in various ways for restricting the impact of COVID-19. Furthermore, IoT can be used in the healthcare sector to assure people's safety and good health with minimal costs.
Collapse
Affiliation(s)
- Farhana Ajaz
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Mohd Naseem
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Sparsh Sharma
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India
| | - Mohammad Shabaz
- Department of Computer Science Engineering, Lovely Professional University, Phagwara, India
| | - Gaurav Dhiman
- Department of Computer Science, Government Bikram College of Commerce, Patiala, India
| |
Collapse
|
17
|
Bhola J, Shabaz M, Dhiman G, Vimal S, Subbulakshmi P, Soni SK. Performance Evaluation of Multilayer Clustering Network Using Distributed Energy Efficient Clustering with Enhanced Threshold Protocol. Wirel Pers Commun 2022; 126:2175-2189. [PMID: 34456513 PMCID: PMC8380017 DOI: 10.1007/s11277-021-08780-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [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] [Accepted: 08/04/2021] [Indexed: 02/07/2023]
Abstract
In this research, pure deterministic system has been established by a new Distributed Energy Efficient Clustering Protocol with Enhanced Threshold (DEECET) by clustering sensor nodes to originate the wireless sensor network. The DEECET is very dynamic, highly distributive, self-confessed and much energy efficient as compared to most of the other existing protocols. The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols. An enhanced result has been obtained for equitable energy dissipation for systematized networks using DEECET.
Collapse
Affiliation(s)
- Jyoti Bhola
- grid.419487.70000 0000 9191 860XDepartment of ECE, NIT, Hamirpur, India
| | - Mohammad Shabaz
- grid.428245.d0000 0004 1765 3753Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India
| | - Gaurav Dhiman
- grid.412580.a0000 0001 2151 1270Department of Computer Science, Government Bikram College of Commerce, Punjabi University, Patiala-147001, Punjab India
| | - S. Vimal
- grid.252262.30000 0001 0613 6919Department of Computer Science and Engineering, Ramco Institute of Technology, Tamil Nadu, India
| | - P. Subbulakshmi
- grid.412813.d0000 0001 0687 4946School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil
Nadu India
| | - Sunil Kumar Soni
- grid.412580.a0000 0001 2151 1270Punjabi University, Patiala, Punjab India
| |
Collapse
|
18
|
Mehbodniya A, Webber JL, Shabaz M, Mohafez H, Yadav K. Machine Learning Technique to Detect Sybil Attack on IoT Based Sensor Network. IETE Journal of Research 2021. [DOI: 10.1080/03772063.2021.2000509] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha Area, 7th Ring Road, Kuwait
| | - Julian L. Webber
- Graduate School of Engineering Science, Osaka University, 1–3 Machikaneyamacho, Toyonaka, Osaka, Japan
| | - Mohammad Shabaz
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
| | - Hamidreza Mohafez
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Jalan Universiti, 50603 Kuala Lumpur, Malaysia
| | - Kusum Yadav
- College of Computer Science and Engineering, University of Ha’il, Ha’il, Kingdom of Saudi Arabia
| |
Collapse
|
19
|
Dhiman G, Kaur G, Haq MA, Shabaz M, Meng D. Requirements for the Optimal Design for the Metasystematic Sustainability of Digital Double-Form Systems. Mathematical Problems in Engineering 2021; 2021:1-10. [DOI: 10.1155/2021/2423750] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The United Nations defined tenable progress as a development that responds to the demands of the current without adjusting the capacity of further generations to fulfil their own requirements; this is a fundamental idea in this article. This study recognizes three aspects, financial, social, and environmental sustainability, although its emphasis is on the latter. An electronic copy is sometimes characterized a physical thing, a real counterpart, and the data, which indicates the presence of a connector and block for effective and efficient data transmission. This article offers a systematic literature review on the sustainability of designed technology-based systems. This article also introduces the major requirements which can be helpful in designing optimal design for sustainability of a digital double-form system. Many articles on DT have also been chosen since they referenced the studied SLRs and were deemed to be significant for the objectives of this study. Selected and analysed for papers revealed so many flaws and challenges: the boons of are not clear; DTs throughout the result the wheel of life of the DTs is not adequately surveyed; DTs can contribute to cost reduction or to support decision-making is unclear; Internet practice should be improved and better integrated Moreover, it has not been feasible from our study to locate a publication which solely discusses DTs in relation with situational sustainability.
Collapse
|
20
|
Kumar MN, Jagota V, Shabaz M, Gupta P. Retrospection of the Optimization Model for Designing the Power Train of a Formula Student Race Car. Scientific Programming 2021; 2021:1-9. [DOI: 10.1155/2021/9465702] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This article describes the power train design specifics in Formula student race vehicles used in the famed SAE India championship. To facilitate the physical validation of the design of the power train system of a formula student race car category vehicle engine of 610 cc displacement bike engine (KTM 390 model), a detailed design has been proposed with an approach of easing manufacturing and assembly along with full-scale prototype manufacturing. Many procedures must be followed while selecting a power train, such as engine displacement, fuel type, cooling type, throttle actuation, and creating the gear system to obtain the needed power and torque under various loading situations. Keeping the rules in mind, a well-suited engine was selected for the race track and transmission train was selected which gives the maximum performance. Based on the requirement, a power train was designed with all considerations we need to follow. Aside from torque and power, we designed an air intake with fuel efficiency in mind. Wireless sensors and cloud computing were used to monitor transmission characteristics such as transmission temperature management and vibration. The current study describes the design of an air intake manifold with a maximum restrictor diameter of 20 mm.
Collapse
|
21
|
Godara J, Batra I, Aron R, Shabaz M. Ensemble Classification Approach for Sarcasm Detection. Behav Neurol 2021; 2021:9731519. [PMID: 34853618 PMCID: PMC8629652 DOI: 10.1155/2021/9731519] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/25/2021] [Accepted: 11/01/2021] [Indexed: 02/07/2023] Open
Abstract
Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K-means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.
Collapse
Affiliation(s)
- Jyoti Godara
- Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
| | - Isha Batra
- Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
| | - Rajni Aron
- SVKM's Narsee Monjee Institute of Management Studies (NMIMS), Mumbai, India
| | - Mohammad Shabaz
- Department of Computer Science Engineering, Chandigarh University, Punjab, India
- Arba Minch University, Ethiopia
| |
Collapse
|
22
|
Hu Y, Sharma A, Dhiman G, Shabaz M, Abdul Halin A. The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm. Journal of Sensors 2021; 2021:1-12. [DOI: 10.1155/2021/7548329] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This study draws attention towards the application of identification nanoparticle (NPs) sensor based on back propagation (BP) neural network optimized by genetic algorithm (GA) in the early diagnosis of cancer cells. In this study, the traditional and optimized BP neural networks are compared in terms of error between the actual value and the predictive value, and they are further applied to the NP sensor for early diagnosis of cancer cells. The results show that the root mean square (RMS) and mean absolute error (MAE) of the optimized BP neural network are comparatively much smaller than the traditional ones. The particle size of silicon-coated fluorescent NPs is about 105 nm, and the relative fluorescence intensity of silicon-coated fluorescent NPs decreases slightly, maintaining the accuracy value above 80%. In the fluorescence imaging, it is found that there is obvious green fluorescence on the surface of the cancer cells, and the cancer cells still emit bright green fluorescence under the dark-field conditions. In this study, a phenolic resin polymer CMK-2 with a large surface area is successfully combined with Au. NPs with good dielectric property and bioaffinity are selectively bonded to the modified electrode through a sulfur-gold bond to prepare NP sensor. The sensor shows good stability, selectivity, and anti-interference property, providing a new method for the detection of early cancer cells.
Collapse
|
23
|
Zhan X, Mu ZH, Kumar R, Shabaz M. Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning. Nonlinear Engineering 2021. [DOI: 10.1515/nleng-2021-0028] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
The speed sensor fusion of urban rail transit train speed ranging based on deep learning builds a user-friendly structure but it in-turn increases the risk of traffic that significantly challenges its safety and transportation efficacy. In order to improve the operation safety and transportation efficiency of urban rail transit trains, a train speed ranging system based on embedded multi-sensor information is proposed in this article. The status information of the train is acquired by the axle speed sensor and the Doppler radar speed sensor; however, the query transponder collects the status information of the train, and is used in the embedded system. Various other modules like adaptive correction, idling/sliding detection and compensation of speed transition/sliding are used in the proposed methodology to reduce the vehicle speed positioning errors due to factors such as wheel wear, idling, sliding, and environment. The results show that the running time of the train is 1000s, the output period of the axle speed sensor is 0.005s and the accelerometer output period is 0.01s. The output cycle of doppler radar is observed to be 0.1s, the output cycle of the transponder is 1s and the fusion period of the main filter is observed as 1s. The train speed ranging system of the embedded multi-sensor information fusion system proposed in this article can effectively improve the accuracy of the train speed positioning.
Collapse
Affiliation(s)
- Xuemei Zhan
- Zheng Zhou Railway Vocational & Technical College , Henan Zhengzhou , , China
| | - Zhong Hua Mu
- Zheng Zhou Railway Vocational & Technical College , Henan Zhengzhou , , China
| | - Rajeev Kumar
- Chitkara University Institute of Engineering and Technology , Chitkara University , Punjab , India
| | | |
Collapse
|
24
|
Gera T, Singh J, Mehbodniya A, Webber JL, Shabaz M, Thakur D, Cui J. Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware. Security and Communication Networks 2021; 2021:1-22. [DOI: 10.1155/2021/7035233] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance.
Collapse
|
25
|
|
26
|
Liu Y, Shabaz M. Design and research of computer network micro-course management system based on JSP technology. Int J Syst Assur Eng Manag 2022; 13:203-11. [DOI: 10.1007/s13198-021-01368-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
27
|
Yang M, Kumar P, Bhola J, Shabaz M. Development of image recognition software based on artificial intelligence algorithm for the efficient sorting of apple fruit. Int J Syst Assur Eng Manag 2021. [DOI: 10.1007/s13198-021-01415-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
28
|
|
29
|
Li C, Niu H, Shabaz M, Kajal K. Design and implementation of intelligent monitoring system for platform security gate based on wireless communication technology using ML. Int J Syst Assur Eng Manag 2021. [DOI: 10.1007/s13198-021-01402-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
30
|
Almarzouki HZ, Alsulami H, Rizwan A, Basingab MS, Bukhari H, Shabaz M. An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors. J Healthc Eng 2021; 2021:1233166. [PMID: 34745488 PMCID: PMC8566034 DOI: 10.1155/2021/1233166] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/09/2021] [Accepted: 10/12/2021] [Indexed: 02/07/2023]
Abstract
In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study's aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3-10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3-10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.
Collapse
Affiliation(s)
- Hatim Z. Almarzouki
- Department of Radiology, Faculty of Medicine, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Hemaid Alsulami
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Ali Rizwan
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohammed S. Basingab
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Hatim Bukhari
- Department of Industrial and Systems Engineering, College of Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Mohammad Shabaz
- Arba Minch University, Arba Minch, Ethiopia
- Department of Computer Science Engineering, Chandigarh University, Punjab, Ajitgarh, India
| |
Collapse
|
31
|
Chen Z, Cong B, Hua Z, Cengiz K, Shabaz M. Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation. Journal of Intelligent Systems 2021. [DOI: 10.1515/jisys-2021-0096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Abstract
In synthetic aperture radar (SAR) image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution (OEA) algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex.
Collapse
Affiliation(s)
- Zhuoran Chen
- School of Computer and Information Science, Boda College, Jilin Normal University , Siping City , China
| | - Biao Cong
- Institute of Computer Science, Jilin Normal University , Siping City , China
| | - Zhenxing Hua
- Institute of Computer Science, Jilin Normal University , Siping City , China
| | - Korhan Cengiz
- Department of Electrical – Electronics Engineering, Trakya University , Edirne , Turkey
| | | |
Collapse
|
32
|
Chaudhury S, Shelke N, Sau K, Prasanalakshmi B, Shabaz M. A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization. Comput Math Methods Med 2021; 2021:4019358. [PMID: 34721657 PMCID: PMC8550839 DOI: 10.1155/2021/4019358] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/01/2021] [Accepted: 10/01/2021] [Indexed: 02/07/2023]
Abstract
Breast cancer is the most common invasive cancer in women and the second main cause of cancer death in females, which can be classified benign or malignant. Research and prevention on breast cancer have attracted more concern of researchers in recent years. On the other hand, the development of data mining methods provides an effective way to extract more useful information from complex databases, and some prediction, classification, and clustering can be made according to the extracted information. The generic notion of knowledge distillation is that a network of higher capacity acts as a teacher and a network of lower capacity acts as a student. There are different pipelines of knowledge distillation known. However, previous work on knowledge distillation using label smoothing regularization produces experiments and results that break this general notion and prove that knowledge distillation also works when a student model distils a teacher model, i.e., reverse knowledge distillation. Not only this, but it is also proved that a poorly trained teacher model trains a student model to reach equivalent results. Building on the ideas from those works, we propose a novel bilateral knowledge distillation regime that enables multiple interactions between teacher and student models, i.e., teaching and distilling each other, eventually improving each other's performance and evaluating our results on BACH histopathology image dataset on breast cancer. The pretrained ResNeXt29 and MobileNetV2 models which are already tested on ImageNet dataset are used for "transfer learning" in our dataset, and we obtain a final accuracy of more than 96% using this novel approach of bilateral KD.
Collapse
Affiliation(s)
| | - Nilesh Shelke
- Priyadarshini Indira Gandhi College of Engineering, Nagpur, India
| | - Kartik Sau
- University of Engineering and Management, Kolkata, India
| | - B. Prasanalakshmi
- Department of Computer Science, King Khalid University, Abha, Saudi Arabia
| | | |
Collapse
|
33
|
Zhang X, Rane KP, Kakaravada I, Shabaz M. Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology. Nonlinear Engineering 2021. [DOI: 10.1515/nleng-2021-0019] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.
Collapse
Affiliation(s)
- Xiaoran Zhang
- Zhengzhou Vocational University of Information and Technology , Zhengzhou , China
| | | | - Ismail Kakaravada
- Prasad V Potluri Siddhartha Institute of Technology , Kanuru , Vijayawada , India
| | - Mohammad Shabaz
- Institute of Engineering and Technology , Chitkara University , Punjab , India
| |
Collapse
|
34
|
Sharma C, Bagga A, Sobti R, Shabaz M, Amin R. A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications. Comput Math Methods Med 2021; 2021:8081276. [PMID: 34594397 PMCID: PMC8478543 DOI: 10.1155/2021/8081276] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/25/2021] [Accepted: 08/28/2021] [Indexed: 02/07/2023]
Abstract
The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder's intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this paper, a new method of watermarking is proposed on both standard and medical images. The paper addresses the use of digital rights management in medical field applications such as embedding the watermark in medical images related to neurodegenerative disorders, lung disorders, and heart issues. The various quality parameters are used to figure out the evaluation of the developed method. In addition, the testing of the watermarking scheme is done by applying various signal processing attacks.
Collapse
Affiliation(s)
- Chirag Sharma
- Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
| | - Amandeep Bagga
- Department of Computer Application, Lovely Professional University, Punjab, India
| | - Rajeev Sobti
- Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
| | - Mohammad Shabaz
- Arba Minch University, Ethiopia
- Department of Computer Science and Engineering, Chandigarh University, India
| | - Rashid Amin
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
| |
Collapse
|
35
|
Mehbodniya A, Alam I, Pande S, Neware R, Rane KP, Shabaz M, Madhavan MV, Chakraborty C. Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques. Security and Communication Networks 2021; 2021:1-8. [DOI: 10.1155/2021/9293877] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.
Collapse
|
36
|
Thakur D, Singh J, Dhiman G, Shabaz M, Gera T, Wang L. Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA. Complexity 2021; 2021:1-28. [DOI: 10.1155/2021/4551067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.
Collapse
|
37
|
Lohani TK, Ayana MT, Mohammed AK, Shabaz M, Dhiman G, Jagota V. A comprehensive approach of hydrological issues related to ground water using GIS in the Hindu holy city of Gaya, India. WJE 2021. [DOI: 10.1108/wje-04-2021-0223] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Purpose
Gaya, the holy city of Hindus, Buddhists and Jains, is facing an acute shortage of potable water. Although the city is blessed with some static and dynamic water bodies all around the region, they do not fulfill the requirement of millions of public either inhabitants of the area or tourists or pilgrims flocking every day. Countless crowds, congested roads, swarming pedestrians, innumerable vehicles moving throughout the day and night have made the city into a non-livable one. The present status of surface water is a mere nightmare to the requirements of the people. Due to which, massive ground water pumping mostly illegally has added a grid in addition to the other socio-economic issues.
Design/methodology/approach
To focus on such problem, the ground water of the region was studied thoroughly by calculating the depth of water level, discharge, pre-and post-monsoon water table and specifically the storativity in ten different locations. Some data were acquired, others were assessed, and few are calculated to provide an overall view of the ground water scenario.
Findings
After a long and tedious field study, it was finally established from that static water level ranges from 2.45 to 26.59 m, below ground level (bgl), discharge varies from 3.21 m3/day to 109.32 m3/day. Post pumping drawdown falls between 0.93 m and 16.59 m, whereas the specific capacity lies in between 0.96 and 7.78 m3/hr/m. Transmissivity, which is a key objective to assess ground water potential ranges from 109.8 to 168.86 m2/day.
Originality/value
This research work is original.
Collapse
|
38
|
Chopra S, Dhiman G, Sharma A, Shabaz M, Shukla P, Arora M. Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences. Comput Intell Neurosci 2021; 2021:6455592. [PMID: 34527042 PMCID: PMC8437605 DOI: 10.1155/2021/6455592] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/17/2021] [Indexed: 02/07/2023]
Abstract
Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.
Collapse
Affiliation(s)
| | - Gaurav Dhiman
- Government Bikram College of Commerce, Patiala, Punjab, India
| | - Ashutosh Sharma
- Institute of Computer Technology and Information, Security Southern Federal University, Taganrog, Russia
| | - Mohammad Shabaz
- Arba Minch University, Arba Minch, Ethiopia
- Institute of Engineering and Technology, Chitkara University, Punjab, Chandigarh, India
| | | | - Mohit Arora
- Lovely Professional University, Phagwara, Punjab, India
| |
Collapse
|
39
|
Mohanasundaram S, Ramirez-Asis E, Quispe-Talla A, Bhatt MW, Shabaz M. Experimental replacement of hops by mango in beer: production and comparison of total phenolics, flavonoids, minerals, carbohydrates, proteins and toxic substances. Int J Syst Assur Eng Manag 2021. [DOI: 10.1007/s13198-021-01308-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
40
|
Du Y, Gao P, Yang J, Shi F, Shabaz M, Chelladurai SJS. Experimental Analysis of Mechanical Properties and Durability of Cement-Based Composite with Carbon Nanotube. Advances in Materials Science and Engineering 2021; 2021:1-12. [DOI: 10.1155/2021/8777613] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In order to study the mechanical properties and durability of cement-based composite with carbon nanotube, the test and analysis experiments are designed. Raw materials and related pharmaceutical instruments are prepared, to obtain cement-based composite with carbon nanotube samples by catalytic pyrolysis according to different proportions. The prepared sample is taken as the experimental object, and different bearing capacities are applied on different positions of the sample, to observe the change of the sample, and then, the experimental results of the mechanical properties of composite materials are obtained. The durability test results are obtained by combining the impermeability and frost resistance of the test object. The average compressive strength is 84.09 MPa, the average flexural strength is 16.9 MPa, and the crack resistance index is 22.5. In addition, the structure and diffusion coefficient of the sample also change in different degrees after the solution immersion and freeze-thaw treatment. Through longitudinal comparison, the more the carbon nanotubes are added into cement-based composite, the better its mechanical properties and durability are.
Collapse
|
41
|
Lokhande MP, Patil DD, Patil LV, Shabaz M, Chakraborty C. Machine-to-Machine Communication for Device Identification and Classification in Secure Telerobotics Surgery. Security and Communication Networks 2021; 2021:1-16. [DOI: 10.1155/2021/5287514] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The capacity of machine objects to communicate autonomously is seen as the future of the Internet of Things (IoT), but machine-to-machine communication (M2M) is also gaining traction. In everyday life, security, transportation, industry, and healthcare all employ this paradigm. Smart devices have the ability to detect, handle, store, and analyze data, resulting in major network issues such as security and reliability. There are numerous vulnerabilities linked with IoT devices, according to security experts. Prior to performing any activities, it is necessary to identify and classify the device. Device identification and classification in M2M for secure telerobotic surgery are presented in this study. Telerobotics is an important aspect of the telemedicine industry. The major purpose is to provide remote medical care, which eliminates the requirement for both doctors and patients to be in the same location. This paper aims to propose a security and energy-efficient protocol for telerobotic surgeries, which is the primary concern at present. For secure telerobotic surgery, the author presents an Efficient Device type Detection and Classification (EDDC) protocol for device identification and classification in M2M communication. The periodic trust score is calculated using three factors from each sensor node. It demonstrates that the EDDC protocol is more effective and secure in detecting and categorizing rogue devices.
Collapse
|
42
|
Dou C, Zheng L, Shabaz M. Corrigendum to “Evaluation of Urban Environmental and Economic Coordination Based on Discrete Mathematical Model”. Mathematical Problems in Engineering 2021; 2021:1-1. [DOI: 10.1155/2021/9872387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
43
|
Li H, Shabaz M, Castillejo-Melgarejo R. Implementation of python data in online translation crawler website design. Int J Syst Assur Eng Manag 2021. [DOI: 10.1007/s13198-021-01215-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
44
|
Thakur T, Batra I, Luthra M, Vimal S, Dhiman G, Malik A, Shabaz M. Gene Expression-Assisted Cancer Prediction Techniques. J Healthc Eng 2021; 2021:4242646. [PMID: 34545300 PMCID: PMC8449724 DOI: 10.1155/2021/4242646] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 08/13/2021] [Indexed: 02/07/2023]
Abstract
Cancer is one of the deadliest diseases and with its growing number, its detection and treatment become essential. Researchers have developed various methods based on gene expression. Gene expression is a process that is used to convert deoxyribose nucleic acid (DNA) to ribose nucleic acid (RNA) and then RNA to protein. This protein serves so many purposes, such as creating cells, drugs for cancer, and even hybrid species. As genes carry genetic information from one generation to another, some gene deformity is also transferred to the next generation. Therefore, the deformity needs to be detected. There are many techniques available in the literature to predict cancerous and noncancerous genes from gene expression data. This is an important development from the point of diagnostics and giving a prognosis for the condition. This paper will present a review of some of those techniques from the literature; details about the various datasets on which these techniques are implemented and the advantages and disadvantages.
Collapse
Affiliation(s)
- Tanima Thakur
- School of Computer Science and Engineering, Lovely Professional University, Jalandhar, India
| | - Isha Batra
- School of Computer Science and Engineering, Lovely Professional University, Jalandhar, India
| | | | - Shanmuganathan Vimal
- Department of CSE, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India
| | - Gaurav Dhiman
- Department of Computer Science, Government Bikram College of Commerce, Patiala, India
| | - Arun Malik
- School of Computer Science and Engineering, Lovely Professional University, Jalandhar, India
| | - Mohammad Shabaz
- Arba Minch University, Arba Minch, Ethiopia
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| |
Collapse
|
45
|
Khan R, Shabaz M, Hussain S, Ahmad F, Mishra P. Early flood detection and rescue using bioinformatic devices, internet of things (IOT) and Android application. WJE 2021. [DOI: 10.1108/wje-05-2021-0269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Purpose
The impact of natural disasters on human life, the environment and the flora and fauna can be contained to large extent by intelligent human intervention. This study introduces the human capabilities which can be extended considerably with technology. Internet of things have always provided opportunities for predicting and managing manmade/natural disasters. The extreme reason for causing soil erosions, landslides, cloud bursts, floods, etc., are due to excessive rainfall. However, the flood is one of the most happening natural disasters, following Bihar to be the most affected region due to floods. Lots of lives and properties were lost and damaged.
Design/methodology/approach
This implemented researchers to introduce an advanced solution for such calamities. Expectations were developed that it would signalize authority as early as possible so that advanced measures are taken before the effect. The lack of sensing or alarming technology in India pushed researchers to develop a model using the Android app that basically detected the upcoming flood and other calamities.
Findings
Most importantly the entire model was programmed with IoT and its techniques so that the response is quicker and more accurate.
Originality/value
This research study is original.
Collapse
|
46
|
Sharma S, Rattan P, Sharma A, Shabaz M. Voice activity detection using optimal window overlapping especially over health-care infrastructure. WJE 2021. [DOI: 10.1108/wje-02-2021-0112] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Purpose
This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector machine. The algorithm for clustering K-mean used to solve speech behaviour detection issues was later applied, the application, therefore, did not permit the identification of voice detection. This is critical in demands for speech recognition.
Design/methodology/approach
Here, the authors find a voice activity detection detector based on a report provided by a K-mean algorithm that permits sliding window detection of voice and noise. However, first, it needs an initial detection pause. The machine initialized by the algorithm will work on health-care infrastructure and provides a platform for health-care professionals to detect the clear voice of patients.
Findings
Timely usage discussion on many histories of NOISEX-92 var reveals the average non-speech and the average signal-to-noise ratios hit concentrations which are higher than modern voice activity detection.
Originality/value
Research work is original.
Collapse
|
47
|
Abstract
In the present scenario, image fusion is utilized at a large level for various applications. But, the techniques and algorithms are cumbersome and time-consuming. So, aiming at the problems of low efficiency, long running time, missing image detail information, and poor image fusion, the image fusion algorithm at pixel level based on edge detection is proposed. The improved ROEWA (Ratio of Exponentially Weighted Averages) operator is used to detect the edge of the image. The variable precision fitting algorithm and edge curvature change are used to extract the feature line of the image edge and edge angle point of the feature to improve the stability of image fusion. According to the information and characteristics of the high-frequency region and low-frequency region, different image fusion rules are set. To cope with the high-frequency area, the local energy weighted fusion approach based on edge information is utilized. The low-frequency region is processed by merging the region energy with the weighting factor, and the fusion results of the high findings demonstrate that the image fusion technique presented in this work increases the resolution by 1.23 and 1.01, respectively, when compared to the two standard approaches. When compared to the two standard approaches, the experimental results show that the proposed algorithm can effectively reduce the lack of image information. The sharpness and information entropy of the fused image are higher than the experimental comparison method, and the running time is shorter and has better robustness.
Collapse
Affiliation(s)
- Jiming Chen
- School of Computer and Information Science, Hunan Institute of Technology, Hengyang 421002, China
| | - Liping Chen
- School of Computer and Information Science, Hunan Institute of Technology, Hengyang 421002, China
| | - Mohammad Shabaz
- Arba Minch University, Arba Minch, Ethiopia
- Department of Computer Science Engineering, Chitkara University, Chandigarh, India
| |
Collapse
|
48
|
Sanober S, Alam I, Pande S, Arslan F, Rane KP, Singh BK, Khamparia A, Shabaz M, Shanmuganathan V. An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication. Wireless Communications and Mobile Computing 2021; 2021:1-14. [DOI: 10.1155/2021/6079582] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.
Collapse
|
49
|
Sun Y, Li H, Shabaz M, Sharma A. Research on building truss design based on particle swarm intelligence optimization algorithm. Int J Syst Assur Eng Manag 2022; 13:38-48. [DOI: 10.1007/s13198-021-01192-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
50
|
Tang S, Shabaz M. A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism. J Healthc Eng 2021; 2021:3688881. [PMID: 34239707 PMCID: PMC8241525 DOI: 10.1155/2021/3688881] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/05/2021] [Accepted: 06/14/2021] [Indexed: 02/07/2023]
Abstract
Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique.
Collapse
Affiliation(s)
- Shoujun Tang
- Guangdong Polytechnic Institute, The Open University of Guangdong, Guangzhou 510091, China
| | | |
Collapse
|