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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 IEEE.

关键词： Smart homes

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Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

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Intelligent Automation & Soft Computing 2023年第5期36卷 2111-2128页

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research CenterFaculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingﬁrm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new *** techniques for identifying and detecting worker performance in industrial applications are based on computer vision *** widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is *** the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction *** suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker *** proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work *** addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed *** an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalﬁndings revealed that the suggested model could accurately recognize the actions of construction ***,we examined the impact of window size and sensor position on the identiﬁcation efﬁciency of the proposed method.

关键词： Complex human activity recognition wearable inertial sensors deep learning construction workers automatic recognition

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End-to-end Multilingual Coreference Resolution with Headword Mention Representation 7

End-to-end Multilingual Coreference Resolution with Headword...

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7th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2024

作者： Pražák, Ondřej Konopík, Miloslav Department of Computer Science and Engineering NTIS - New Technologies for the Information Society Faculty of Applied Sciences University of West Bohemia Technická 8 Plzeň306 14 Czech Republic

ISBN: (纸本)9798891761711

This paper describes our approach to the CRAC 2024 Shared Task on Multilingual Coreference Resolution. Our model is based on an end-to-end coreference resolution system. Apart from joined multilingual training, we improved our results with headword mention representation and training large model mT5-xxl through LORA. We provide an analysis of the performance of our model. Our system ended up in 4th place. Moreover, we reached the best performance on three datasets out of 21. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

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computers, Materials & Continua 2022年第4期71卷 109-124页

作者： Sathish Babu Pandu A.Sagai Francis Britto Pudi Sekhar P.Vijayarajan Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mesfer Al Duhayyim Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Mechanical Engineering Rohini College of Engineering&TechnologyPalkulam629401India Department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyAndra Pradesh530046India Department of Electrical and Electronics Engineering University College of EngineeringBIT CampusTiruchirappalli620024India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the *** Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation *** models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of *** this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and ***,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting *** order to perform long-term collection of weather data,Hadoop MapReduce tool is *** proposed solar irradiance prediction model operates on different ***,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data ***,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive *** is divided into input layer,hidden layer,loadbearing layer,and output *** overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is *** order to validate the performance of the proposed model,a series of experiments was *** experimental values infer that the proposed model outperformed other methods used for comparison.

关键词： Solar irradiation prediction weather forecast artificial intelligence Elman neural network mayfly optimization

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Integration of Machine Learning Algorithms for Predictive Maintenance in IoT-Enabled Smart Safety Helmets 5

Integration of Machine Learning Algorithms for Predictive Ma...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gowda, Dankan V. Prasad, V. Nuthan Agme, Vaishali N. Prasad, K.D.V. Kawale, Sheetalrani Rukmaji Mallikarjun, Shruti Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of Electronic and Communication MSRIT Karnataka Bangalore India Department of Applied Science Bharati Vidyapeeth College of Engineering Maharashtra Navi Mumbai India Department of Research Symbiosis Institute of Business Management Hyderabad India Pune India Department of Computer Science Karnataka State Akkamahadevi Women University Karnataka Vijayapura India Department of Computer Science Karnataka State Akkamahadevi Women University Karnataka Vijayapura India

ISBN: (纸本)9798350361155

This paper focuses on the combination of ML algorithms for predictive maintenance through Internet of Things (IoT)-based smart safety helmets in order to increase not only the level of safety operations and but as well equipment life in industrial settings. We study the present helmet technologies and reveal the weaknesses by tomorrow foreseeing technologies which exist in current predictive maintenance strategies. Proposing a new technique, ML algorithms suite is applied as an analyzing tool to collected data from sensors within the helmets. The system architecture is real-time data processed by an IoT framework to predict and act on maintenance needs up front. The results of the experimental validation confirm the implementation of our approach, that shows the significant increase of the predictive accuracy and timeliness as compared to the traditional methods. The research has not only enabaled to create smart safety equipment but also a scalable approach that could be maximized across other Internet of things applications. © 2024 IEEE.

关键词： Accident prevention

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Constructing Varied and Attractive Shortlists from Databases: A Group Decision Approach 7th

Constructing Varied and Attractive Shortlists from Databas...

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7th Seminar on Quantitative Methods of Group Decision Making, 2021

作者： Ramsey, David M. Mariański, Aleksander Department of Computer Science and Systems Engineering Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland Department of Applied Computer Science Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783662665961

Thanks to the Internet, basic information can be found about a very large number of offers at very little cost. Many sites give basic information about real estate offers, including price, size, location and number of rooms. Information of this kind is sufficient to assess whether an offer is potentially attractive or not, but does not suffice in making an ultimate decision. The number of real estate offers in a city may be very large. In such a case, it can be beneficial to use an automatic procedure to first eliminate offers that do not satisfy the basic criteria of an individual or family and then construct a shortlist of varied and potentially attractive offers. This article recalls an algorithm that derives such a shortlist for an individual searcher. The novelty of this article lies in adapting this algorithm to scenarios in which a group decision is made. A practical example based on real estate offers in Warsaw is used to illustrate the algorithm. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Software engineering

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Multimodal and Multi-Lingual Deep Neural Network for Interactive Behavior Style Recognition from Uncontrolled Video-logs of Children with Autism

Multimodal and Multi-Lingual Deep Neural Network for Interac...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Zhenhao Zhao Eunsun Chung Myungeun Lee Kyong-Mee Chung Chung Hyuk Park Department of Biomedical Engineering School of Engineering and Applied Science The George Washington University Department of Psychology College of Liberal Arts Yonsei University Department of Electrical and Computer Engineering School of Engineering and Applied Science The George Washington University Department of Computer Science School of Engineering and Applied Science The George Washington University

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

With the increase of prevalence in autism, the need for efficient public health support has been amplified. Socially-assistive robots (SARs) have been found effective in engaging and interacting with autistic children, however, the perception intelligence during interaction still needs more domain-specific knowledge in terms of understanding children’s behaviors. The Family Observation Schedule-Second Version (FOS-II) is one of the key methods in assessing parent-child interactions in developmental disabilities, yet its manual annotation demands considerable time and effort. This study proposes a multimodal artificial intelligence (AI) model using video and audio inputs for automated FOS-II annotation. Utilizing advanced deep learning for behavior recognition, this method offers rapid, cost-effective FOS-II scaling. It will thus enhance the capability of socially assistive robots to understand human behaviors and support the advancement of digital health research for children with autism. The visual perception in home settings are most likely based on uncontrolled environments, so it is crucial to develop algorithms that can robustly work with video-log data with uncontrolled quality. Ultimately, it aims to ease the burden on parents and caregivers, streamlining the monitoring and treatment of challenging behaviors in autism.

关键词： Autism Pediatrics Annotations Manuals Behavioral sciences Artificial intelligence Public healthcare Robots Monitoring Visual perception

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Classifying Psychiatric Patients Using Machine Learning 25

Classifying Psychiatric Patients Using Machine Learning

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alrjoob, Hmmam M. Alazaidah, Raed Batyha, Radwan Khafajeh, Hayel Elsoud, Esraa Abu Saeb Al-Sherideh, Ala'a Elrashidi, Ali Alqaraleh, Muhyeeddin Amer, Waleed Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyper Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan Zarqa University Faculty on Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798331540012

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection. © 2024 IEEE.

关键词： Contrastive Learning

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Thyroid Disease Detection Using Supervised Machine Learning Techniques 3

Thyroid Disease Detection Using Supervised Machine Learning ...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Tiwari, Aryan Singh, Gaurav Singh, Shailja Jain, Tarun Manipal University Department of Computer Science and Engineering Jaipur India Greater Noida Institute of Technology Department of Information Technology Greater Noida India Greater Noida Institute of Technology Department of Applied Science Greater Noida India

ISBN: (纸本)9798350303360

Thyroid ailment is a common endocrine disease that affects millions of people around the world. For effective treatment, accurate diagnosis and prediction are essential. The efficiency of various machine learning models for predicting thyroid disease is investigated in this research study. Decision Tree, Logistic Regression, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes, Kmeans, and an ensemble model were among the models used. The results showed that the Logistic Regression model was 96% accurate, while the Decision Tree and Random Forest models were both 99% accurate. SVM had an 87% accuracy rate, kNN had a 95% accuracy rate, and the ensemble model had a 97% accuracy rate. The accuracy rates of Naive Bayes and K-Means were lower. The outcomes of this study emphasise the potential of machine learning algorithms, notably Logistic Regression, decision tree, random forest, and ensemble models, in accurately diagnosing and predicting thyroid disease. © 2023 IEEE.

关键词： Decision trees

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