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IEEE Transactions on Technology and Society 2022年第3期3卷 189-198页

作者： Patil, Rajkumar V. Ambritta, Nancy P. Mahalle, Parikshit N. Dey, Nilanjan Vishwakarma Institute of Information Technology Department of Information Technology Pune411048 India Glareal Software Solutions PTE. Ltd. Singapore200809 Singapore Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune411048 India Jis University Department of Computer Science and Engineering Kolkata700109 India

Advancements in cyber-physical systems (CPSs) makes CPSs essential entities in society today, and have made them prominent across all fields. For example, the healthcare industry has evolved technologically and has effectively incorporated CPS into many application areas. While these advancements, involving medical CPSs (MCPS), have been revised according to Society 4.0, there are still issues that need to be considered while we transition from an information-based society (Society 4.0) to a human-centric society (Society 5.0). In this article, we propose an MCPS framework for Society 5.0 in the context of some state-of-the-art technologies. The proposed system focuses on enhancing security aspects of Society 5.0 through introduction of a security layer equipped with protocols, such as open authorization (OAuth), user-managed access (UMA), and self-sovereign identity (SSI). The added security layer is designed to incorporate country-specific security laws. Challenges and design issues in developing a trusted and secure service for MCPS are also discussed using vulnerability assessment and attack modeling. Finally, we discuss ethical aspects that need to be addressed in the transition from Society 4.0 to Society 5.0. © 2020 IEEE.

关键词： Cyber Physical System

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Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

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computers, Materials & Continua 2022年第9期72卷 5233-5250页

作者： Nawaf Alhebaishi Abdulrhman M.Alshareef Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Sejong UniversitySeoulKorea School of Software Soongsil UniversitySeoul06978Korea Artificial Intelligence Education Hankuk University of Foreign StudiesDongdaemun-guSeoul02450Korea

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end *** computing is considered a method of forwarding the processing and communication resources in the cloud towards the *** of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved *** this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing *** SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource *** goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy *** problem is formulated based on the availability of VMs,task characteristics,and queue *** integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data *** experimental validation,a comprehensive set of simulations were performed using the CloudSim *** experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

关键词： Resource scheduling edge computing soft computing fitness function virtual machines

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

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computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

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Rainfall Forecasting Using Machine Learning Algorithms for Localized Events

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computers, Materials & Continua 2022年第6期71卷 6333-6350页

作者： Ganapathy Pattukandan Ganapathy Kathiravan Srinivasan Debajit Datta Chuan-Yu Chang Om Purohit Vladislav Zaalishvili Olga Burdzieva Centre for Disaster Mitigation and Management Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Service Systems Technology Center Industrial Technology Research InstituteHsinchuTaiwan School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Geophysical Institute of Vladikavkaz Scientific Centre Russian Academy of Sciences(RAS)VladikavkazRussian Federation

A substantial amount of the Indian economy depends solely on ***,on the other hand,plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing,if the amount is inordinate or scant,it can ruin the entire hard work of the *** this work,the rainfall dataset of the Vellore region,of Tamil Nadu,India,in the years 2021 and 2022 is forecasted using several machine learning *** engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the *** removal of autocorrelation,the data could be used for performing operations on the time-series data,which otherwise could only be performed on any other regular regression *** work uses forecasting techniques like the AutoRegessive Integrated Moving Average(ARIMA)and exponential smoothening,and then the time-series data is further worked on using Long Short Term Memory(LSTM).Later,regression techniques are used by manipulating the *** work is benchmarked with several evaluation metrics on a test dataset,where XGBoost Regression technique outperformed the *** uniqueness of this work is that it forecasts the daily rainfall for the year 2021 and 2022 in Vellore *** work can be extended in the future to predict rainfall over a bigger region based on previously recorded time-series data,which can help the farmers and common people to plan accordingly and take precautionary measures.

关键词： Time-series ARIMA LSTM regression rainfall

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Price Estimation of Used Cars Using Machine Learning Algorithms 3rd

Price Estimation of Used Cars Using Machine Learning Algorit...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Santhi, K. Chellatamilan, T. Kavitha, A. Raahil, Armaan Padmavathy, N. Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031289743

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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A Mathematical Model for the Selection of software Requirements Elicitation Techniques 6th

A Mathematical Model for the Selection of Software Requireme...

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6th International Conference on information systems and Management science, ISMS 2023

作者： Saxena, Virat Raj Yadav, Rajeev Parveen, Azra Sadiq, Mohd. Department of Computer Science and Engineering Mangalayatan University Uttar Pradesh Aligarh202001 India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

ISBN: (纸本)9783031664090

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： software testing

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A Framework for Hyperspectral Image Classification 2

A Framework for Hyperspectral Image Classification

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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Phaneendra Kumar, B.L.N. Vaddi, Radhesyam Manoharan, Prabukumar K L Deemed to Be University Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Velagapudi Ramakrishna Siddhartha Engineering College Department of Inforamtion Technology Vijayawada India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350325799

The high volume and replication of data in hyperspectral images results in improper classification. This paper addresses a non-linear spectral feature extraction using Local Linear Embeddings in which graph-based structures are utilized. The representative features are then trained for smoothed features using a spatial filter. The extracted spatial features are then trained for classification using a nonlinear version of Support Vector Machine. The proposed model is tested on two datasets: Indian Pines & Pavia. Proposed approach is tested with well-known dimensionality reduction techniques. The reported Overall Accuracy is 92.33% & 98.21% on Indian Pines & Pavia respectively and outperformed well. The algorithm is used for hyperspectral change detection. © 2023 IEEE.

关键词： Support vector machines

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An Efficient Federated Transfer Learning Approach for Multi-UAV systems

An Efficient Federated Transfer Learning Approach for Multi-...

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National Conference on Communications (NCC)

作者： Vedang Bhupesh Shenvi Nadkarni Sandeep Joshi L. Rajya Lakshmi Department of Computer Science and Information Systems Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Rajasthan India

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

Recent advances in multi-unmanned aerial vehicle (UAV) based federated learning do not take into consideration the massive computational requirements of modern deep learning models on mobile UAV s. Additionally, there has been significant progress that shows that the information transmitted between the federated agent and the central hub can be attacked to undermine the privacy of the data. We propose a novel multi-UAV-based federated transfer learning system that drastically reduces the computational burden overall, shifts it from UAV s to the ground fusion center, and reduces the bandwidth requirements while enhancing its secure nature. The proposed system makes multi-UAV learning significantly fast, reliable, power efficient, and practically feasible. Furthermore, we provide simulation and experimental results to demonstrate the effectiveness of the proposed system.

关键词： Deep learning Image segmentation Data privacy Federated learning Power supplies Transfer learning Object detection Autonomous aerial vehicles Data models Reliability

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Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN

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computers, Materials & Continua 2021年第6期67卷 3505-3521页

作者： Maryam Shaq Humaira Ashraf Ata Ullah Mehedi Masud Muhammad Azeem N.Z.Jhanjhi Mamoona Humayun Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Systems College of Computer and Information ScienceJouf UniversityAl-JoufSaudi Arabia

The Internet of Things(IoT)is gaining attention because of its broad applicability,especially by integrating smart devices for massive communication during sensing ***-assisted Wireless Sensor Networks(WSN)are suitable for various applications like industrial monitoring,agriculture,and *** this regard,routing is challenging to nd an efcient path using smart devices for transmitting the packets towards big data repositories while ensuring efcient energy *** paper presents the Robust Cluster Based Routing Protocol(RCBRP)to identify the routing paths where less energy is consumed to enhances the network *** scheme is presented in six phases to explore ow and *** propose the two algorithms:(i)energy-efcient clustering and routing algorithm and (ii)distance and energy consumption calculation *** scheme consumes less energy and balances the load by clustering the smart *** work is validated through extensive simulation using *** elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption,the number of packets received at BS and the number of active and dead *** the future,we shall consider edge computing to analyze the performance of robust clustering.

关键词： Energy efciency routing load balancing cluster selection

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