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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Anpan, Sakshi Gourshettiwar, Palash Daigavhane, Mansvi Kishorrao Gadkari, Ayush Pravin Faculty of Engineering and Technology Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Faculty of Engineering and Technology Wardha442001 India

ISBN: (纸本)9798331528713

Mental health dynamics have also been affected with significant changes by the digital age with the changes in the way people interact, work, or get information. What has emerged over the last decade, while information technologies such as social media, mobile applications, and telemedicine in the broad sense contain the potential for increasing awareness, availability, and support for individuals with mental health problems, new risks also appear. Screens are addictive, and increased use of social media can result in cyberbullying and social comparison, spreading of fake news which causes anxiety, depression, anxiety and other mental illnesses. On the other hand, as found in the digital contexts, there are support networks, opportunities to plan early intervention, along with mental health literacy. The current study discusses the twofold nature of the effects of digital technology on mental health and how both positive and negative outcomes should be used as the guide for the use of technology in the improvement of mental health. It raises awareness of digital literacy that is necessary to teach people healthy virtual behaviors and discussion of mental-health issues to contribute to the creation of a positive online culture. Given that the process advances daily, it also becomes necessary to assess the influence of digital environment on mental health and work on shielding from potential dangers while enhancing advantages © 2024 IEEE.

关键词： Electronic health record

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Renewable Integration for Green Urban Mobility: A Case Study of Public Transport Depot Electrification 14

Renewable Integration for Green Urban Mobility: A Case Study...

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14th International Conference on Power, Energy and Electrical engineering, CPEEE 2024

作者： Colombo, Cristian Giovanni Saldarini, Alessandro Longo, Michela Ota, Ryosuke Politecnico di Milano Department of Energy Milan Italy Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Faculty of Systems Design Japan

ISBN: (纸本)9781643685199

This paper analyses an electrification strategy for a depot for a public transport company, through an optimized model. The case study analyses both the transition of a depot able to host 10 buses towards e-buses and proposes the integration of a micro-grid, powered by PV panels to power the e-buses in a sustainable way. The study addresses key challenges such as spatial constraints, passenger demand, and financial considerations, through a Genetic Algorithm (GA). The analysis encompasses two scenarios: a mixed fleet of electric and diesel buses and an exclusive focus on e-buses. The study highlights the importance of considering both electric and diesel buses in fleet optimization, and combined with renewables emphasize the advantages of operational flexibility and sustainability. Thanks to the implementation of a micro-grid capable of providing 1123.87 kWh/day to power the e-buses as much as possible. © 2024 The Authors.

关键词： Electric buses

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A Weighted Optimal Scheduling Scheme for Congestion control in Cloud Data Center Networks

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IEEE Transactions on Services Computing 2023年第4期16卷 1-9页

作者： Li Y. Jian S. Hsieh S. Chung W. Zomaya A.Y. Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Systems The University of Sydney Australia

Cloud services are experiencing a remarkable increase in the number of users and the resource required over the past few years. Thus, it has become a great challenge for the internet vendors to make a robust framework to serve the customers with low cost and delay. Congestion control is one of the essential topics of routing algorithms in cloud data center networks. In this paper, we propose a weighted optimal scheduling scheme WSPR for congestion control in cloud data center networks which prevents the congestion in advance with the global view so that it can make good use of vacant network resources. We choose BCube as our network model and modify the network topology to fit software-defined networks so as to have a full view of the topology. First, we design the SP graph which contains all shortest paths between a source server and a destination server. Second, we propose WSPR to allocate the most appropriate path to each flow for congestion control. We implement a system to simulate a data center, and evaluate our proposed scheme WSPR by comparing WSPR with other classical methods. The experimental results demonstrate that our proposed scheme WSPR has the best performance in terms of the maximum delay, average delay, and throughput among all compared methods. IEEE

关键词： Cloud computing Cloud data center networks congestion control Data centers Network topology Optical switches Routing Servers software-defined networks Topology

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

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computers, Materials & Continua 2023年第12期77卷 2899-2914页

作者： Vinay Kukreja Asha Abraham K.Kalaiselvi K.Deepa Thilak Shanmugasundaram Hariharan Shih-Yu Chen Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Networking and Communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and *** data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw *** fusion is the process of integrating multiple data *** filtering means examining a dataset to exclude,rearrange,or apportion data according to the *** sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional *** advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional ***,many open issues still exist as machine learning algorithms are used for data *** the literature,nine issues have been identified irrespective of any *** decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these *** helps to get the weights for each corresponding issue and rank issues based on these calculated *** most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted *** least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

关键词： Signal level fusion feature level fusion decision level fusion fuzzy hierarchical process machine learning

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Optimal Deep Belief Network Enabled Malware Detection and Classification Model

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Intelligent Automation & Soft Computing 2023年第3期35卷 3349-3364页

作者： P.Pandi Chandran N.Hema Rajini M.Jeyakarthic Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of CSE Alagappa Chettiar Government College of Engineering and TechnologyKaraikudi630003India Department of Computer and Information Science Annamalai UniversityChidambaram608002India

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily *** Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading *** recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify *** this motivation,this study focuses on the design of mayﬂy optimization with a deep belief network for PDF malware detection and classiﬁcation(MFODBN-MDC)*** major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the *** proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature *** addition,Adamax optimizer with the DBN model is used for PDF malware detection and classiﬁ*** design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-ﬁcation demonstrates the novelty of the *** demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various *** comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

关键词： PDF malware data classiﬁcation security deep learning feature selection metaheuristics

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A novel network intrusion detection method based on metaheuristic optimisation algorithms

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7575-7592页

作者： Ghanbarzadeh, Reza Hosseinalipour, Ali Ghaffari, Ali Faculty of Science and Engineering Southern Cross University Gold Coast Australia Department of Computer Engineering Heris Branch Islamic Azad University Heris Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran

The growing use of the Internet with its vulnerabilities has necessitated the adoption of Intrusion Detection systems (IDS) to assure security. IDSs are protective systems that detect outsider infiltrations, unauthorised accesses and malfunctions occurring in computer networks. Intrusions can be detected and reported to the network administrator by IDSs using various pieces of information such as port scanning and irregular traffic detection. Intrusion detection is a classification problem, and identifying effective features is an essential aspect of classification methods. Standard methods used for classification are neural networks, fuzzy logic, data mining techniques and metaheuristics. One of the novel metaheuristic algorithms introduced to address optimisation problems is the Horse herd Optimisation Algorithm (HOA). This paper introduces a new approach on the basis of HOA for network intrusion detection. The new method uses horse behaviours in the herd to select effective features to detect intrusions and interactions between features. For the purpose of the new approach, HOA is first updated into a discrete algorithm using the floor function. The binarised algorithm is then converted into a quantum-inspired optimiser by integrating the concepts of quantum computing with HOA to improve the social behaviours of the horses in the herd. In quantum computing, Q-bit and Q-gate aid in striking a greater balance between the exploration and exploitation processes. The resulting algorithm is then converted into a multi-objective algorithm, where the objectives can be chosen from a set of optimal solutions. The new algorithm, MQBHOA, is then used for intrusion detection in computer networks, which is a multi-objective optimisation problem. For the classification, the K-Nearest Neighbour (KNN) classifier is applied. To evaluate the new algorithm’s performance, two data sets, NSL-KDD (Network Security Laboratory—Knowledge Discovery and Data Mining) and CSE-CIC-IDS2018, are

关键词： Feature Selection

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IoT Based Smart Agriculture Management System 3

IoT Based Smart Agriculture Management System

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Hasan, Anamul Diya, Nadia Subrin Sultana, Sadia United International University Department of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh Sher-e-Bangla Agricultural University Faculty of Agriculture Dhaka Bangladesh

ISBN: (纸本)9798350388282

In Bangladesh and the Indian subcontinent, agriculture is the backbone of any country. It is crucial for a nation's overall economic development. Among the many social advantages of agriculture is that it meets people's requirements for food, shelter, and employment. Most of our farmers use traditional farming practices. Agriculture-related projects on a large scale are now occurring in our country. Despite using advanced technology, they do not manage and maintain it smartly. If we handle farm management smartly, we might be able to reduce a lot of waste. Every facet of the normal person's life has been altered by IoT technology, which imbuing everything with smart, intelligent capabilities. We demonstrated an 'IoT based smart agricultural management system'. The technology, among other things, can reduce waste of water, power, and expensive fertilizer. The device will automatically keep the soil's fertility and moisture levels at their ideal levels. so that we can acquire the best yield feasible and exact crop growth. This technique will enable successful farming and save a lot of money and effort with manual upkeep for any project. © 2024 IEEE.

关键词： Farms

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer systems science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Access Management in IoT: Implementing a Secure Access control System 15

Access Management in IoT: Implementing a Secure Access Contr...

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15th International Conference on Communications, COMM 2024

作者： Neculai, Razvan-Sebastian Bogdan, Ioana-Corina Alexandru, Marian Robu, Dan-Nicolae Transilvania University of Brasov & Eviden Faculty of Electrical Engineering and Computer Science Department of Cybersecurity Braşov Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Braşov Romania

ISBN: (纸本)9798350355956

This paper aims to implement and use the Spring Security framework to secure and authenticate the connection between a web application and the ESP32 device. In the context of a web application, by analyzing the integration of the Spring Security framework, it investigates the ways in which it manages the complex process of authentication and authorization, thus providing a secure and protected connection to an IoT device. Additionally, this study investigates the integration and use of the RTOS operating system to manage physical access in an IoT environment, in particular by implementing the opening of a door using tags. The mechanism described above leads to an additional level of security and control over physical access in an IoT ecosystem. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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