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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Binju Saju V Asha Jincy C Mathew Shreya N Sangeetha SanganagoudaDambala Shruti Department of Computer Science and Engineering(Artificial Intelligence) Adi Shankara Institute of Engineering and Technology Kalady India Department of Master of Computer Applications New Horiznon College of Engineering Bengaluru India

Plant diseases can be found using an analysis of these observable symptoms. The traditional approach to disease diagnosis entails professionals visually evaluating plants. The study proposes a hybrid model to classify plant leaf diseases. Initially, images are pre-processed which includes Image resizing, Gray-scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an an accuracy of 99.4%, sensitivity of 98.9%, specificity of 99.1%, precision of 99%, and an F1 score of 98.98%.

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Exploring the Benefits of DNS-Based Authentication for Wireless Network Security 3

Exploring the Benefits of DNS-Based Authentication for Wirel...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Swarup, Lakshya Jayadurga, R. Borade, Monali R. Ramachandran, T. Singh, Akhilendra Pratap Prabha, Savita Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Wi-wireless networks are becoming more pervasive and are frequently the primary goal of malicious attackers. Therefore, it is miles crucial that wireless networks are nicely secured to defend their users from malicious moves. DNS-based total authentication is a promising answer for attaining ease of authentication of wi-wireless customers in an open Wi-Fi community. This paper surveys numerous current works that discover the wi-fits wireless of DNS-based total authentication for Wi-Fi wireless community safety. especially this paper opinions three one-of-a-kindwireless strategies of using DNS for authentication purposes: making use of DNS's cache for identifying windows-primarily based Wi-Fi wireless clients;Open Mobile Alliance (OMA)'s tool control (DM) projects to relaxed get right of entry to;and a modern scheme for protecting the use of the DNS-based techniques. Wi-finally, this paper discusses a few safety-demanding situations and future studies directions. © 2023 IEEE.

关键词： authentication initiatives management protecting secured

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Implementing Catboost Algorithm for Allergen Cross Contamination Detection in Food Industry 1

Implementing Catboost Algorithm for Allergen Cross Contamina...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Sudharson, D. Kumar, D. Satheesh Dubey, Aman Kumar Kumar, B. Arun Vaishali, V. Balavedhaa, S. Kumaraguru College of Technology Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Hindusthan College of Engineering &technology Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Master of Bussiness Administration Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Cse Tamil Nadu Coimbatore India Kumaraguru College of Engineering Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359756

In the food industry, allergen cross-contamination is a serious problem that necessitates innovative approaches to detection and prevention. In order to tackle this problem, this paper investigates the possibilities of machine learning (ML) techniques, focusing on CatBoost, XGBoost, and LightGBM in particular. Through an analysis of datasets from food processing establishments, the research develops prediction models that leverage on the distinct advantages of these machine learning methods. The models' comparative evaluations demonstrate how well they identify allergen cross-contamination risks, improving food safety protocols. This paper demonstrates the significant role of CatBoost, XGBoost, and LightGBM in strategically controlling allergen cross-contamination and offers insightful information about the applicability of ML. In the final analysis, these results provide a framework for the advancement of risk-mitigating intelligent systems that ensure consumer safety and support the industry's broader food safety regulations. © 2023 IEEE.

关键词： Food safety

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Intelligent Energy Management for Electric Vehicles: Fuzzy Logic and Neural Network Integration 10

Intelligent Energy Management for Electric Vehicles: Fuzzy L...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Thirumalaikumari, T. Princy, D. Lita Uma, S. Sadaieswaran, R. Malathi, P. Legapriyadharshini, N. Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Tamil Nadu Chennai India Meenakshi College of Engineering Department of Master of Computer Applications Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Mahabalipuram India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Data Analytics Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Application Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

This study suggests a novel methodology for intelligent energy management in electric vehicles (EVs) through the integration of neural networks and fuzzy logic. Achieving enhanced energy efficiency for electric vehicles is a critical concern that is tackled in this research through the integration of neural networks' decision-making and pattern recognition functionalities with fuzzy logic's adaptability and learning capabilities. The proposed method seeks to optimize energy consumption, enhance overall performance, and extend driving range. The integrated system is subjected to rigorous testing using a meticulously planned experimental setup and simulation environment, which enables it to surpass the performance of more conventional approaches to energy management. The results emphasize the potential of the novel methodology to improve environmentally sustainable transportation options and propel the development of electric vehicle technology. This study not only offers valuable insights into efficient energy management but also establishes the groundwork for subsequent developments in the domain. © 2024 IEEE.

关键词： Electric Mobility Electric Vehicles Energy Management Intelligent Systems Neural Networks Smart Grid

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Designing an AI driven intelligent Tutorial System

Designing an AI driven intelligent Tutorial System

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Recent Trends in computer science and technology (ICRTCST), International Conference on

作者： Md Shamsher Alam Surjeet Kumar Zeeshan Khursheed Hemant Kumar Mahato Shadan Bashar Anjali Suman Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in NSHM Durgapur India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science & Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. This research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. The system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. The paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. The paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. Through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. The findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Ethics Data privacy Machine learning algorithms Image recognition Decision making Tutorials Market research

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Augmented Reality With Authentic GeometryGo App to Help Geometry Learning and Assessments

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IEEE Transactions on Learning Technologies 2023年第5期16卷 769-779页

作者： Hwang, Wu-Yuin Nurtantyana, Rio Purba, Siska Wati Dewi Hariyanti, Uun Suprapto National Dong Hwa University Department of Computer Science and Information Engineering College of Science and Engineering Hualien974 Taiwan National Central University Graduate Institute of Network Learning Technology Taoyuan City320 Taiwan Telkom University School of Computing Bandung40257 Indonesia Universitas Pelita Harapan Department of Master of Educational Technology Kota Tangerang15811 Indonesia Brawijaya University Information Systems Department Kota Malang65145 Indonesia State University Yogyakarta Department of Electronics Engineering Education Yogyakarta55281 Indonesia

We developed an Authentic GeometryGo (AGG) app to help students application of geometry in authentic contexts through authentic measurements and peer assessments. The study included an experimental group (EG) using an AGG app and a control group (CG) using paper-based methods. The results showed that the EG outperformed the CG in terms of estimation. Measuring authentic geometry shapes using the app could foster students' estimation. Furthermore, meaningful peer assessments with visual representations in authentic contexts can facilitate the evaluation of peers' work and improve students' geometry understanding. Therefore, we encourage educators to use augmented reality with appropriate learning activities to sharpen students' measurement quality and visual representations by measuring real geometry shapes and applying mathematics knowledge in authentic contexts. © 2008-2011 IEEE.

关键词： Augmented reality

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Beamforming Design Toward Sum-Rate Maximization for Holographic Active RIS-Aided Uplink Near-Field Communications

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IEEE Internet of Things Journal 2025年第12期12卷 18582-18595页

作者： Singh, Sandeep Singh, Keshav Singh, Sandeep Kumar Shin, Hyundong Duong, Trung Q. National Sun Yat-sen University International Master’s Program in Telecommunication Engineering Kaohsiung80424 Taiwan National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Kyung Hee University Department of Electronic Engineering Gyeonggi Yongin17104 Korea Republic of Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Communication Engineering Prayagraj211004 India Memorial University Faculty of Engineering and Applied Science St. John’sA1C 5S7 Canada Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Holographically driven active reconfigurable intelligent surface (HARIS), leveraging densely packed subwavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multiuser uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of Quality-of-Service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the nonconvex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques, such as minimum-mean-square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm’s efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance. © 2014 IEEE.

关键词： Mean square error

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Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

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International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MSE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MSE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

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Cybersecurity Threat Detection using Machine Learning on Network Traffic

Cybersecurity Threat Detection using Machine Learning on Net...

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Rithi, R. Nithya, N. Akila, A. Saravanakumar, R. Vargheese, M. Jeya Lakshmi, S. Karpagam Academy of Higher Education Master of Computer Applications Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India R.M.K College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu India Iconix Software Solution Tamil Nadu Tirunelveli India PSN College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331512088

Web phishing is a prevalent online attack in which victims are tricked into revealing their personal information by visiting fraudulent websites. Phishers often use URL shortening tools to disguise malicious links, making it easier to steal sensitive data such as usernames, passwords, and banking details. These phishing websites are designed to mimic legitimate ones, making detection more challenging. As phishing techniques continue to evolve rapidly alongside technological advancements, the need for effective anti-phishing systems has become increasingly crucial. Machine learning has emerged as a powerful tool for recognizing and mitigating phishing scams. This article provides a comprehensive overview of machine learning techniques for phishing detection and their effectiveness in combating online threats. © 2025 IEEE.

关键词： Legitimate websites Online attack URL Web Phishing

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Feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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