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Expert Clouds and Applications (ICOECA), International Conference on

作者： V. Senthil Kumar C. Karpagavalli S. Pradeep S. Divya Syed Noeman Taqui Nadanakumar Vinayagam Department of Computer Science and Engineering SRM Institute of Science and Technology Tiruchirapalli Tamil Nadu India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Electronics and Communication Engineering S.A. Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Dr. N. G.P. Institute of Technology Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Automobile Engineering Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Epilepsy is a neurological disorder characterized by repeated seizures throughout the day. Epilepsy affects around 65 million people worldwide. For people with focal epilepsy, surgery is a possibility but medicine is the standard treatment for generalized epilepsy. Unfortunately, these medications fail to control epileptic seizures in more than 30% of patients. Yet, this ends up in accidents and significantly shortens the patient's life expectancy. An electroencephalogram (EEG) is a critical diagnostic tool for epilepsy. The conventional method for detecting epileptic activity is expert human interpretation of multi-channel EEG readings. This is a complex and time-consuming procedure, thus many researchers have tried to automate it using Machine Learning (ML) and Deep Learning (DL) methods. Unfortunately, the investigation did not produce satisfactory results. This study aims to fill this research gap by proposing a new CNN-BLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) based hybrid DL model for epileptic detection. The EEG data from Kaggle was collected and processed. This study uses the same amount of data and epochs for training, validation, and testing all four DL models such as AlexNet, VGG-16, InceptionV3, and the proposed model. According to the experiment findings, the proposed model has a better level of accuracy (98.37 % ) than the other three models.

关键词： Training Accuracy Epilepsy Surgery Brain modeling Electroencephalography data models

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The EmpkinS-EKSpression Reappraisal Training Augmented With Kinesthesia in Depression: One-Armed Feasibility Study

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JMIR Formative Research 2025年 9卷 e65357页

作者： Keinert, Marie Schindler-Gmelch, Lena Rupp, Lydia Helene Sadeghi, Misha Richer, Robert Capito, Klara Eskofier, Bjoern M. Berking, Matthias Department of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum Muenchen Neuherberg Germany

Background: Harboring dysfunctional depressogenic cognitions contributes to the development and maintenance of depression. A central goal of cognitive behavioral therapy (CBT) for depression is to invalidate such cognitions via cognitive reappraisal (CR). However, relatively low remission rates and high dropout rates in CBT demonstrate the need for further improvement. Potentially, the effects of CBT could be enhanced by addressing not only dysfunctional depressogenic cognitions but also body states associated with depression. This may be done, for example, by systematically pairing the invalidation of depressogenic cognitions with the performance of antidepressive kinesthesia. Objective: This study aimed to examine the feasibility and clinical potential of a smartphone-based cognitive restructuring task that required users to deliberately perform antidepressive kinesthesia in conjunction with the rejection of depressogenic statements and the affirmation of antidepressive statements. This feasibility study was conducted as a precursor to a large-scale randomized controlled trial. Methods: In total, 10 healthy participants engaged in a single 90-120-minute session of smartphone-based CR training. During the training, they completed 2 phases in which they were required to reject 20 depressogenic and affirm 20 antidepressive statements, respectively. Diagnostic assessments were conducted 1 week (T1) before and directly prior (T2) to the training, and again directly posttraining (T3) and at a 2-week follow-up posttraining (T4). Feasibility outcomes assessed at T3 included intervention safety recorded by study therapists, compliance, technical feasibility, usability assessed using the Short Version of the User Experience Questionnaire (UEQ-S), and acceptability assessed using the UEQ-S and self-developed items. Preliminary clinical potential was evaluated via single-item ratings of current depressed and positive mood assessed continuously during the training. Feasibility

关键词： cognitive reappraisal depression facial expression kinesthesia mobile phone smartphone-based intervention

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Regression for Predicting Effort in Object-Oriented Software Projects

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SSRN 2022年

作者： Alrababeh, Ne'meh BaniMustafa, Ahmed Mahmood Software Engineering Department Isra University Amman Jordan Data Sceince & Artificial Intelligence Department Isra university Queen Alia International Airport Road Amman Jordan P.O. Box 22 33 Isra Post Office Amman11622 Jordan

Software estimation is one of the pivotal activities in software engineering projects. However, predicting effort is challenging due to the complexity, intangibility, and diversity of software solutions and their involved expertise and applied technologies. This work aims at improving the accuracy of software estimation using regression algorithms and based on the Use-Case Points analysis (UCP). Five regression algorithms were applied including Random Forests, Stochastic Gradient Decent (SGD), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Neural Networks (NN). These algorithms were applied to a historical dataset that represents several object-oriented software projects. The dataset consists of 70 projects that are described using 37 attributes which are related to software requirement specifications, development process, and domain application in addition to a set of technical and environmental factors. The experimental results in this work showed that Random Forests and SGD algorithms were successful in achieving significant improvement in the predicting software effort estimation compared to Use-Case Point analysis. The random forest model scored 0.86 in R2 , while the SGD model scored 0.83. It was concluded that regression improves software effort prediction compared to other estimation methods, particularly if adequate data is collected, processed, and analysed using the right algorithms. © 2022, The Authors. All rights reserved.

关键词： Project management

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A Smart Eyewear using IoT and CNNs for Visual Assistance

A Smart Eyewear using IoT and CNNs for Visual Assistance

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Chitra Sabapathy Ranganathan V. Kannagi R C Karpagalakshmi N V Shibu Ishwarya M. V S. Murugan Mphasis Corporation Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The development of Convolutional Neural Networks (CNNs) and the growth of the Internet of Things (IoT) have made smart eyewear a potentially revolutionary platform for visual assistance. This research introduces a new method for real-time object detection and navigation assistance using IoT connection and CNNs algorithms. The proposed solution improves users' ability to see and understand their environments by combining a small camera with smart glasses. The smart glasses assist the user by identifying items in their range of vision using CNN-based object recognition algorithms and giving contextual information. Using IoT connection, the system allows easy integration with navigation services, providing users with real-time assistance and direction in new areas. It presents the planned smart eyewear system, including its design, implementation, and assessment. It emphasizes the system's uses in helping people with vision impairments or navigating complicated situations more independently. The experimental findings show that the system is efficient and effective, with reliable visual and navigational help, proving it can be used in the real world.

关键词： Visualization Navigation Visual impairment Object detection Real-time systems Internet of Things Reliability Object recognition Convolutional neural networks Smart glasses

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Advanced Dual Module Weapon Detection for Public Safety and Surveillance System

Advanced Dual Module Weapon Detection for Public Safety and ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Manikanta Sirigineedi M. Sowmiya HemavathI. U V.C. Bharathi Sayali S Karmode R. Sathya Department of Information Technology Vishnu Institute of Technology Vishnupur Bhimavaram Andhra Pradesh India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology AVADI School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Information Technology Mahatma Gandhi Mission's College of Engineering & Technology Kamothe Navi Mumbai Maharashtra India Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments.

关键词： YOLO Weapons Surveillance Real-time systems Public security Classification algorithms Security Convolutional neural networks Feeds Expert systems

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AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing

AI-Enhanced Public Safety Lighting for Smart Sustainable Cit...

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Multi-Agent Systems for Collaborative intelligence (ICMSCI), International Conference on

作者： Bharat Tidke S. Pragadeeswaran R. Poornima Lakshmi G Bhuvaneswari G Manikandan S. Murugan Symbiosis Institute of Technology Nagpur Campus Symbiosis international (Deemed) University Pune India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Saveetha Engineering college Saveetha Nagar Thandalam Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This paper presents a novel method that combines cloud computing infrastructure and Reinforcement Learning (RL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's RL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating RL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. RL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of RL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities.

关键词： Cloud computing Pedestrians Smart cities Scalability Lighting Public security Real-time systems artificial intelligence Standards Meteorology

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An Evaluation and Implementation of Pretty Good Privacy (PGP) in Wireless Network Security

An Evaluation and Implementation of Pretty Good Privacy (PGP...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Arvind Hans M. Vigenesh Yuvaraj S Suvarna R. Bhagwat Hamza Zaki Chiging Yamang Faculty Of Business Mangagement & Commerce Usha Martin University Ranchi India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department Of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India

Top privateers (PGP) is an encryption protocol that shields statistics saved, exchanged, or transmitted over wireless networks. PGP provides a green manner to comfy WiFi networks and communications using symmetric and publickey cryptography. This paper evaluates and examines the utility of PGP in wireless network protection. The paper critiques the layout and implementation of PGP on specific varieties of Wi-Fi networks, which include the partnerships to be had for implementing PGP in pre-present and destiny wireless infrastructures. Similarly to the numerous partnerships, the paper explores PGP's unique protocols and technologies and how they can be integrated inside Wi-Fi networks based on their extraordinary capabilities and functions. The paper additionally discusses the commercial advertising of PGP, its effect on wireless community protection, and the packages of PGP, in addition to a number of the demanding situations and threats related to its use. Moreover, the paper highlights the advantages of implementing PGP in wireless community safety. It gives a diffusion of trying out and implementation strategies that can be used to ensure a successful implementation. The paper concludes with a dialogue about the possible destiny directions of PGP and how it could be used to ease and protect wireless networks.

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Assessing the Ability of AI-Driven Natural Language Processing to Accurately Analyze Unstructured Text data

Assessing the Ability of AI-Driven Natural Language Processi...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： M. Vigenesh RaviKumar V Vivek D. Patil Sharma Sonu Kumar Nyarik Geyi Subir Chattopadhyay Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Faculty of Business Mangagement & Commerce Usha Martin University Ranchi India

This technical abstract examines the ability of herbal language processing (NLP)-driven synthetic intelligence (AI) to analyze unstructured textual content facts. Unstructured textual content records include information from assets with webpages, social media, emails, and consumer comments. They are widely used in sentiment evaluation, hazard detection, and entity extraction programs. NLP-pushed AI answers along with natural language knowledge (NLU), herbal language knowledge (NLG), and natural language understanding (NLP) are used to manner and examine unstructured textual content information. The analysis technique starts evolving with tokenizing the facts, breaking the textual content into smaller factors, including phrases and phrases. After tokenizing the statistics, the AI device discovers styles inside the records. Those styles can then discover the emotions and entities inside the data. NLU and NLG models are used to expand the facts' expertise by assigning sentiment and issue-level scores. The accuracy of NLP-driven AI is similarly progressed through more excellent complex fashions consisting of deep studying and reinforcement.

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Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network 20

Search Result Diversification Framework Based on Dual Star-s...

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Qin, Xubo Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong School of Information Renmin University Gaoling School of Artificial Intelligence Renmin University of China Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Research shows that most queries issued by users are short queries. Those queries may be ambiguous or broad for specifying users' actual information need. The same query issued by different users may lead to different information needs. In order to satisfy different users' needs, the search engines need to rank the search result document diversely. This leads to the search result diversification technology. For those previous diversification approaches based on global document interactions, fully-connected self-attention networks are used for capturing the interactions among all the candidate documents, leading to well performance. However, those models only take the document interactions into consideration, ignoring the relevance signals of candidate documents. As the annotated datasets are not enough for model training, the effectiveness of the model will be limited. In this paper we prove a new implicit diversification framework based on self-attention network in the shape of dual-star. A star-shaped topological structure is deployed with an external query node, in order to effectively extract the global interaction signals relevant to the query. Using self-attention to learn the similarity between each document, this model can be trained easily, and perform significantly better than the state-of-the-art implicit approach indicating the potential of implicit methods. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Search engines

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Predicting Crops Based on Soil and Environmental Characteristics Using ARIMA and LSTM Model

Predicting Crops Based on Soil and Environmental Characteris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： U Naresh kumar K Madhana Mohan S. Pragadeeswaran Jobin M Scaria Baseera A D. Seenivasan Department of Artificial intelligence & Data science Annamacharya University Rajampet India Department of Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Mt. Eriama Campus School of Information Technology IBS University Port Moresby Papua New Guinea School of Computing Science and Engineering VIT Bhopal University Sehore India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact.

关键词： Training Accuracy Food manufacturing Biological system modeling Soil Predictive models Feature extraction Agriculture Long short term memory Meteorology

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