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Electrical, Energy, and artificial intelligence (EICEEAI), International engineering Conference on

作者： Khosro Rezaee Mohammad R. Khosravi Hossein Ghayoumi Zadeh Mohammad Kazem Moghimi Ghassan Samara Hani Attar Sattam Almatarneh Deprtement of Biomedical Enginering Meybod University Meybod Iran Weifang University of Science and Technology Weifang Shandong China Department of Electrical Engineering Vali-E-Asr University of Rafsanjan Rafsanjan Iran Department of Communication Engineering University of Sistan and Baluchestan Zahedan Iran Department of Computer Science Zerqa University Zerqa Jordan Department of Energy Engineering Faculty of Enineering Zarqa University Zerqa Jordan Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zerqa Jordan

The autism spectrum disorder (ASD) is a developmental disability caused by abnormalities in the brain. Different methods have been used to diagnose ASD in the past, each with its own advantages and disadvantages. Prior research has focused mainly on algorithms and processing methods, rather than identification tools, to improve the automated diagnosis of ASD. This article identifies and describes diagnostic tools in the ASD literature, including facial features, EEG recordings, speech signals, and neuroimaging, in order to assist researchers interested in developing statistical, computational, and sound clinical approaches to ASD data mining. Accordingly, based on the responses obtained, this review study intends to assess several aspects of the key automatic diagnostic tools for autism spectrum disorders, including diagnostic accuracy, privacy protection, uncertainty, cost, efficiency, absence of methodological interference, and prospective clinical and therapeutic conditions.

关键词： Neuroimaging Autism Privacy Costs Uncertainty Brain modeling Electroencephalography

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Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting

arXiv

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arXiv 2022年

作者： Panja, Madhurima Chakraborty, Tanujit Kumar, Uttam Hadid, Abdenour Center for Data Sciences International Institute of Information Technology Bangalore India Department of Science and Engineering Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Sorbonne University Abu Dhabi United Arab Emirates

Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. In this study, we introduce the Probabilistic AutoRegressive Neural Networks (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. PARNN is constructed by improving autoregressive neural networks (ARNN) using autoregressive integrated moving average (ARIMA) feedback error, combining the explainability, scalability, and "white-box-like" prediction behavior of both models. Notably, the PARNN model provides uncertainty quantification through prediction intervals, setting it apart from advanced deep learning tools. Through comprehensive computational experiments, we evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models, including Transformers, NBeats, and DeepAR. Diverse real-world datasets from macroeconomics, tourism, epidemiology, and other domains are employed for short-term, medium-term, and long-term forecasting evaluations. Our results demonstrate the superiority of PARNN across various forecast horizons, surpassing the state-of-the-art forecasters. The proposed PARNN model offers a valuable hybrid solution for accurate long-range forecasting. By effectively capturing the complexities present in time series data, it outperforms existing methods in terms of accuracy and reliability. The ability to quantify uncertainty through prediction intervals further enhances the model’s usefulness in decision-making processes. © 2022, CC BY.

关键词： Neural network models

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Distributed energy trading management for renewable prosumers with HVAC and energy storage

arXiv

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arXiv 2021年

作者： Yang, Qing Wang, Hao Shenzhen University Guangdong Province Shenzhen China Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia

Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users' privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users' private information locally for solving the primal subproblem hence preserves the users' privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users' peak load and reduce the overall energy cost of the system by 23% on average. © 2021, CC BY.

关键词： Automation

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Self-Replicating Imperative Node Evaluator for Improved Network Intrusion Detection 2

Self-Replicating Imperative Node Evaluator for Improved Netw...

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2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024

作者： Babitha, B.S. Banchhor, Chitrakant O. Bhuvaneshwari, K.S. Khurana, Saniya Senthilkumar, K.K. Marwaha, Shuthanshu School of Mangement - Ug Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350368949

Cyber-attacks occur when someone attempts to gain unauthorized access to another person's or company's computer network. Network resources are considered compromised when several activities are made that potentially jeopardize their availability, anonymity, or reliability. Any time a system that detects intrusions notices questionable activity on an organization or system, for example, a potential breach of security policy, it notifies a management station. It is hard to efficiently identify threats in real-time due to the massive amount of data sent via cyber networks. The focus of this study is on developing an AI network for the purpose of detecting cyber threats. Checking user behavior for anomalies is an excellent way to spot fraud. When it comes to cyber defense, one of the biggest mysteries is how systems can detect previously unseen vulnerabilities and take action to mitigate or eradicate them. In order to stop cyber assaults, new cyber security measures are needed to identify malicious nodes before they interact. This study introduces an auto-triggering mode architecture and a replicating itself code for an aggressive node evaluator. As soon as it detects an intrusion, it may fix the network immediately, securing it and reducing the number of false alerts. The proposed model has a 98% success rate in both intruder identification and self-replication. Though progress has been made, there is still room for enhancement in reducing the number of false alarms. © 2024 IEEE.

关键词： Neural networks

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A Blockchain-Based Secure Communication Framework For IOMT With Improved Security Via Hybrid Metaheuristic Models

A Blockchain-Based Secure Communication Framework For IOMT W...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Parikshit N. Mahalle V.P. Arul Kumar Ansh Kataria S Srividhya Sudhanshu Dev H Malathi Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Information Technology Karpagam Institute of Technology Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Biotechnology and Genetics JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Multiple healthcare devices are integrated into an IoMT network to enhance monitoring of patients and real-time treatment. These networks make crucial health care choices using many devices. To protect these devices and networks, researchers have used encoding, hashing, confidentiality safeguarding, attribute-based admittance controller, and more. However, continuous monitoring security frameworks are complicated or unchangeable. Present methods’ security relies on their interior setup, which is seldom extendable for new assaults. This explore recommends a hybrid metaheuristic approach for health care IoT safety. Changing encryption and hashing standards may dynamically rearrange the blockchain paradigm. EHO and GWO are used to continually optimize blockchain-based IoMT implementation security and QoS performance. In the proposed paradigm, dual fitness functions increase safety and QoS for numerous attacks. Fitness functions adjust encryption and hashing settings to enhance performance against various attacks. Blockchain-based deployment may be scaled and used in real time by integrating EH and GW optimization models for dynamic attack situations. The prototypical is evaluated beneath masquerade, Sybil, MITM, and DDoS assaults and associated to leading models.

关键词： Biological system modeling Computational modeling Metaheuristics Medical services Quality of service Throughput Real-time systems Safety Security Monitoring

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A Fog Cluster-based Interpretable AI Framework for Human Activity Recognition and Personalized Healthcare Monitoring

A Fog Cluster-based Interpretable AI Framework for Human Act...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： K.S. Bhuvaneshwari Mukul Mishra V Janakiraman Shreya Ghosal Saritha Srinivasmurthy Raghotham Shilpa M. Katikar Department of Artificial Intelligence and Data science Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mech Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The innovations made possible by the Internet of Things have been of enormous use to the healthcare sector. These developments have made it feasible for a multitude of applications to collect huge amounts of information from users for evaluation in the cloud. On the other hand, immediate medical care need very low latency as well as which is something that traditional cloud computing is not able to supply. Fog computing is a solution that has emerged as a result of this issue. It allows for the processing of data that is nearer to its origin, which in turn reduces latency and the amount of bandwidth that is used. In the paper, a novel system is proposed that takes use of fog clusters. Fog clusters are groupings of fog nodes that collaborate with one another to accelerate and distribute the processing of tasks. According to the findings, fog clusters significantly cut down on latency and energy usage, which demonstrates the usefulness of fog technology in Internet of Things (IoT) systems that have stringent latency requirements. This study was carried out with the assistance of a simulation of an individual with a COPD monitoring system.

关键词： Hands Cloud computing Technological innovation Medical services Chronic obstructive pulmonary disease Internet of Things Human activity recognition Low latency communication Monitoring Edge computing

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Self-Replicating Imperative Node Evaluator for Improved Network Intrusion Detection

Self-Replicating Imperative Node Evaluator for Improved Netw...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： B.S. Babitha Chitrakant O. Banchhor K.S. Bhuvaneshwari Saniya Khurana K.K. Senthilkumar Shuthanshu Marwaha School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

关键词： Technological innovation Codes Accuracy Signal processing Real-time systems Security Servers artificial intelligence Time complexity Cyberattack

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

引用

engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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DEPS: AN IMPROVED DEEP LEARNING MODEL FOR DE NOVO PEPTIDE SEQUENCING

arXiv

引用

arXiv 2022年

作者： Ge, Cheng Lu, Yi Qu, Jia Xie, Liangxu Wang, Feng Zhang, Hong Kong, Ren Chang, Shan Institute of Bioinformatics and Medical Engineering School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China School of Computer Science and Artificial Intelligence Aliyun School of Big Data Changzhou University Changzhou213164 China School of Mathematics China University of Mining and Technology Xuzhou221116 China

De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of the represetative models. In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing even with missing signal peaks or large number of noisy peaks in tandem mass spectrometry data. It is showed that, for the same test set of DeepNovoV2, the DePS model achieved excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino acid precision and peptide recall respectively. Furthermore, the results suggested that DePS outperforms DeepNovoV2 on the cross-species dataset. © 2022, CC BY.

关键词： Amino acids

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Advanced Battery Management for Electric Vehicles: A Deep Dive into Estimation Techniques Based on Deep Learning for the State of Health and State of Charge of Lithium-Ion Batteries

Advanced Battery Management for Electric Vehicles: A Deep Di...

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Electrical, Electronic and Information sciences (eStream), Open Conference of

作者： Saad El Fallah Jaouad Kharbach Jonas Vanagas Živile Vilkelytė Sonata Tolvaišienė Ghita Ikmel Assia Redouani Saloua Lagnaoui Khaoula Boumais Mohammed Ouazzani Jamil Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fez Morocco Université Sidi Mohamed Ben Abdellah Faculté des Sciences Dhar El Mahraz Laboratoire de Physique de Solide Fez Morocco Dept. Electrical Engineering Vilnius TECH Vilnius Lithuania Signals Systems Components Laboratory (LSSC) Faculty of Science and Technology Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Artificial Intelligence Data Science and Emerging Systems (IASSE) ENSA Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

The precision of state of charge (SoC) prediction prediction of the SoC is necessary to avoid deep discharging and remains an important challenge in the field of electric vehicles and overcharging, which can damage batteries and shorten their life. the energy storage industry. The SoC is the percentage of energy The degradation of lithium-ion batteries is a key area of research, available in a battery relative to its total capacity. A precise as these type of batteries are extensively employed in many sectors, in particular electric vehicles, electronic devices and renewable energies. Evaluation systems based on deep learning, like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are becoming increasingly popular for their ability to process complex data and make accurate predictions. As lithium-ion batteries degrade over time, their reliability in storing and delivering energy is diminishing. To effectively monitor and manage this degradation, researchers are turning to evaluation systems based on deep learning. These approaches enable the prediction of battery state of health (SoH) and SoC, making it easier to optimize battery use and extend battery life. This article presents various techniques for predicting the SoH and SoC of batteries to evaluate the degradation of cells.

关键词： Lithium-ion batteries Degradation Deep learning Recurrent neural networks Estimation Battery management systems Electric vehicles

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