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

作者： Raju, Ayalapogu Ratna Ramadevi, R. Babu, Padamata Ramesh Vetriselvi, D. Mahatma Gandhi Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur Chennai India

ISBN: (纸本)9798350325799

The primary objective of this study is to construct an Internet of Things (IoT)-based stress detection system that uses Galvanic Skin Response (GSR) and pulse sensors. In today's contemporary world, stress has developed into a widespread problem and early recognition of the condition is necessary for efficient intervention and treatment. The suggested system utilizes GSR and pulse sensors in conjunction with the IoT technologies to continuously monitor and analyze an individual's stress levels. The GSR sensor analyzes changes in the electrical conductance of the skin, which is known to occur during times of stress;in contrast, the pulse sensor identifies patterns in heart rate related to stress. Signal processing methods are used to process the newly collected sensor data, after which useful characteristics are retrieved. After the data has been analyzed, it is wirelessly sent to a cloud platform so that machine learning algorithms may be used for further analysis and categorization. The system displays the current amount of stress in real-time and can send warnings if the current stress level reaches a certain threshold. To guarantee that the system conforms to the rules, the system has established privacy and data security precautions. Our goal is to contribute, via this study, to the creation of an accurate and reliable stress detection system that may aid people in efficiently controlling the amounts of stress that they are experiencing in their lives. © 2023 IEEE.

关键词： Machine learning

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Personalized Smart Grocery Delivery with IoT Technology Using Collaborative Filtering Algorithms 11

Personalized Smart Grocery Delivery with IoT Technology Usin...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kulkarni, Shirish Dattatreya Raman, Ramakrishnan Sutaria, Kruti Kamal Mishra, Nilamadhab Sujatha, S. Nandagopal, V. Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India School of Engineering Mohan Babu University Department of Electrical and Electornics Engineering Andrapradesh Tirupathi India

ISBN: (纸本)9798350350357

Personalized suggestions may enhance the use of online food shopping, an already popular and handy service. But data sparsity and scalability problems restrict the current recommendation systems rely on user-based collaborative filtering algorithms. Smart grocery delivery with personalized suggestions is proposed in this system using item-based collaborative filtering algorithms and Internet of Things (IoT) technologies. In the system, smart appliances like fridges and scales track food supplies and consumption, and then, according to the user's preferences, they automatically place purchases on internet platforms. It makes use of item-based collaborative filtering algorithms to examine other users' evaluations and purchase histories in order to provide the user with relevant and varied product recommendations. The method is tested on a real-world dataset and contrasted with collaborative filtering methods rely on user input. Techniques outperform the competition in terms of suggestion accuracy, variety, and coverage while simultaneously decreasing delivery time and costs. By implementing our strategy, not only can online grocery shopping be made more enjoyable for users, but it can also spur the growth of e-commerce and the IoT. © 2024 IEEE.

关键词： Food supply

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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Computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt 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 Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Optimizing Convolutional Networks Using a Modified Metaheuristic for Apple Tree Leaf Disease Detection

Optimizing Convolutional Networks Using a Modified Metaheuri...

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Telecommunications Forum (TELFOR)

作者： Aleksandar Petrovic Luka Jovanovic Kavitha D Nebojsa Bacanin Miodrag Zivkovic Snezana Malisic Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia Department of Mathematics Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

The global demand for food is continuously increasing. The use of artificial intelligence can vastly improve the field of agriculture. Computer vision can help detect issues with crops. This work focuses on detecting plant diseases in apple tree leaves through the analysis of images. The proposed approach consists of a convolutional neural network (CNN) optimized by a hybrid optimization algorithm. The optimized models showcase favorable outcomes with the best models attaining an accuracy of 0.772408.

关键词： Computer vision Plant diseases Accuracy Image analysis Computational modeling Metaheuristics Refining Telecommunications Convolutional neural networks Artificial intelligence

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Pettifor maps of complex ternary two-dimensional transition metal sulfides

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npj Computational Materials 2022年第1期8卷 1692-1702页

作者： Andrea Silva Jiangming Cao Tomas Polcar Denis Kramer Faculty of Engineering and Physical Sciences University of SouthamptonUniversity RoadSO171BJSouthamptonUK National Centre for Advanced Tribology Study University of SouthamptonSouthamptonUK Faculty of Mechanical and Civil Engineering Helmut-Schmidt-Universität/Universität der Bundeswehr HamburgHolstenhofweg 8522043HamburgGermany Advanced Materials Group Department of Control EngineeringFaculty of Electrical EngineeringCzech Technical University in Prague(CTU)PragueCzech Republic Department of Heterogeneous Catalysis Helmholtz-Zentrum HereonMax-Planck-Strasse 1GeesthachtGermany

Alloying is an established strategy to tune the properties of bulk compounds for desired *** the advent of nanotechnology,the same strategy can be applied to 2D materials for technological applications,like single-layer transistors and solid *** we present a systematic analysis of the phase behaviour of substitutional 2D alloys in the Transition Metal Disulfides(TMD)*** phase behaviour is quantified in terms of a metastability metric and benchmarked against many-body expansion of the energy *** show how the metastability metric can be directly used as starting point for setting up rational search strategies in phase space,thus allowing for targeted further computational prediction and analysis of *** results presented here also constitute a useful guideline for synthesis of TMDs binary alloys via a range of synthesis techniques.

关键词： alloys sulfide behaviour

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Blockchain-Enabled Energy Trading in Microgrids for Sustainable computing Infrastructure 5

Blockchain-Enabled Energy Trading in Microgrids for Sustaina...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kokila, M.L. Sworna Sunitha, R. Kalyanakumar, P. Raj, J. Relin Francis Krishnan, R. Santhana Gopikumar, S. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Rajarajeswari College of Engineering Department of Electronics and Communication Engineering Bangalore India Velammal College of Engineering and Technology Department of Information Technology Madurai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India

ISBN: (纸本)9798350379945

This research investigates Blockchain-enabled energy trading within microgrids to enhance sustainable computing infrastructure. A novel algorithm, GridCoin, is proposed and evaluated against existing methodologies including Optimal Power Flow (OPF), Microgrid Energy Management System (MEMS), and Hierarchical control Algorithms (HCA). The study employs simulation metrics such as Transaction Throughput, Energy Trading Efficiency, and Transaction Latency to compare the performance of these algorithms. GridCoin demonstrates superior capabilities in optimizing energy trading operations, achieving higher transaction throughput, improved efficiency in energy utilization, and reduced transaction latency. These results highlight the potential of Blockchain technology to revolutionize energy markets by providing secure, transparent, and decentralized transaction mechanisms. Future research directions include enhancing optimization techniques, integrating renewable energy sources, addressing scalability and security challenges, conducting real-world deployments, and analyzing economic and policy implications. This study contributes to advancing sustainable computing infrastructure through innovative Blockchain-based solutions. © 2024 IEEE.

关键词： Blockchain technology energy trading GridCoin algorithm microgrids optimization renewable energy integration sustainable computing

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Automated Pneumonia Classification Using DensePneumoNet in Chest CT Scans 5

Automated Pneumonia Classification Using DensePneumoNet in C...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Nithila, Ezhil E. Bibin Christopher, V. Raj, J. Relin Francis Srinivasan, A. Soundiraraj, N. Krishnan, R. Santhana Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350379945

'DensePneumoNct' is a specialized deep learning algorithm designed for accurate and efficient detection of pneumonia from CT chest images. Leveraging the DenseNet architecture within TensorFlow, this algorithm demonstrates superior performance across critical metrics including accuracy, recall, precision, and F1 score. The implementation utilizes the ImageNet dataset, preprocessed to ensure standardization and resizing, optimizing input uniformity for robust model training. The DenseNet model architecture features dense blocks and transition layers, fostering strong feature propagation and effective downsampling of feature maps. Hyperparameter tuning, including a learning rate of 0.001, a batch size of 64, and training over 100 epochs, enhances model convergence and performance refinement. Training enhancements such as early stopping and learning rate scheduling further stabilize model training and prevent overfitting. Evaluation on a validation set showcases 'DensePneumoNet''s capability to distinguish COVID-19 pneumonia, viral pneumonia, and healthy lung tissues with unparalleled accuracy. Comparative analysis against ResNet-50, Inception-v3, SqueezeNet, and LeNet confirms DensePneumoNet's superiority in all evaluated parameters, affirming its efficacy for critical medical imaging tasks. 'DensePneumoNet' represents a significant advancement in pneumonia detection, poised to enhance diagnostic accuracy and clinical decision-making in medical settings. © 2024 IEEE.

关键词： Medical imaging

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Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

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Neural computing and Applications 2024年第24期36卷 14727-14756页

作者： Jovanovic, Luka Zivkovic, Miodrag Bacanin, Nebojsa Dobrojevic, Milos Simic, Vladimir Sadasivuni, Kishor Kumar Tirkolaee, Erfan Babaee Faculty of Technical Sciences Singidunum University Danijelova 32 Belgrade11000 Serbia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Mathematics Saveetha School of Engineering SIMATS Tamilnadu Thandalam Chennai602105 India MEU Research Unit Middle East University Amman Jordan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Yuandong Rd. Zhongli Dist. Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Center for Advanced Materials Qatar University Doha Qatar Department of Industrial Engineering Istinye University Istanbul34396 Turkey Department of Industrial and Mechanical Engineering Lebanese American University Byblos Lebanon

This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.

关键词： Crops

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Parkinson’s Disease Induced Gain Freezing Detection using Gated Recurrent Units Optimized by Modified Crayfish Optimization Algorithm

Parkinson’s Disease Induced Gain Freezing Detection using G...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Nebojsa Bacanin Aleksandar Petrovic Luka Jovanovic Miodrag Zivkovic Tamara Zivkovic Marko Sarac Department of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia School of Electrical Engineering Belgrade Serbia

Parkinson’s disease belongs to the group of health problems that are incurable but can be mitigated if treated properly. While there is no way of curing the damage caused by the disease, patient’s life quality can be improved if diagnosed and treated properly and on time. The role of artificial intelligence (AI) in medicine is increasing. Deep learning algorithms may be utilized to automatically detect freezing of gait episodes. This study is focused on Parkinson’s disease diagnosis based on the disturbances in the patient’s gait which is affected by this disease. A hybrid deep and machine learning AI solution that employs the gated recurrent unit (GRU) neural network optimized by a hybrid swarm intelligence solution between the crayfish optimization algorithm and the firefly algorithm has been proposed. The proposed solution is compared to other high-performing optimization algorithms to establish objective grounds for comparison. The proposed framework results in the overall best performance and confirms the made improvements. The best-constructed model attained an accuracy of 87.08%.

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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Computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga 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 Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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