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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Yazhini, A. Kalpana Devi, M. Anitha, M. Praveena, R. Mol, Maheswari K Lawrence, Jinsha Anna University Faculty of Electrical Engineering College of Engineering Chennai600025 India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore10 India KIT - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore641402 India Jai Shriram Engineering College Department of Computer Science and Engineering Tirupur638660 India Veterinary College and Research Institute Thanjavur614625 India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

In order to lower death risks, provide the most effective course of treatment, and improve community healthcare, the majority of recent research has concentrated on examining prevalent illnesses in the population. One of the prevalent ailments that has impacted our society is kidney disease. Around the world, kidney tumors (KT) rank tenth in terms of frequency in both men and women. The organization of cancer is a problem that is attracting increasing attention in the domains of bioinformatics and computational biology. This study presents a comparative analysis of the latest developments in deep learning (DL) and machine learning (ML). While several methods have been proposed to tackle the cancer classification challenge, new research indicates that supervised and deep learning-based methods are the most effective. Healthcare has advanced as a result of the creation of numerous machine learning techniques for awareness evaluation in cancer categorization. It seems that there is a high requirement for the ongoing development of efficient categorization algorithms in order to handle the growth in healthcare applications. The comparative analysis of the proposed classification shows the accuracy as 0.92%. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

This study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. The analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. The model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

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SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration

SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Shen, Yuanhao Zhu, Xiaodan Chen, Lei Department of Electrical and Computer Engineering Ingenuity Labs Research Institute Queen’s University Canada Rakuten Institute of Technology

ISBN: (纸本)9798891761667

The tool-use ability of Large Language Models (LLMs) has a profound impact on a wide range of industrial applications. However, LLMs’ self-control and calibration capability in appropriately using tools remains understudied. The problem is consequential as it raises potential risks of degraded performance and poses a threat to the trustworthiness of the models. In this paper, we conduct a study on a family of state-of-the-art LLMs on three datasets with two mainstream tool-use frameworks. Our study reveals the tool-abuse behavior of LLMs, a tendency for models to misuse tools with overconfidence. We also find that this is a common issue regardless of model capability. Accordingly, we propose a novel approach, SMARTCAL, to mitigate the observed issues, and our results show an average of 8.6 percent increase in the QA performance and a 21.6 percent decrease in Expected Calibration Error (ECE) compared to baseline models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques 3

Real-Time Facial Recognition System for Secure College Bus T...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, M.N. Saranya, S.S. Pragatheeswari, E. Priya, N. Kaviya Ranjith, S.R. Vanchivel, N. Rahul Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongu Engineering College Department of Csd Tamil Nadu India

ISBN: (纸本)9798331509675

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability. © 2025 IEEE.

关键词： Students

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Software-Defined-Storage Performance Testing Using Mininet

Software-Defined-Storage Performance Testing Using Mininet

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Naik, Deepak Ranoji Ghonge, Vilas D. Thube, Smita Mangesh Khadke, Abhijit Mali, Yogesh Kisan Borate, Vishal Kisan G.H. Raisoni College of Engineering and Management Department of Computer Engineering Pune India Viit Department of Computer Engineering Pune India Nutan Maharashtra Institute of Engineering and Technology Department of Computer Engineering Pune India G H Raisoni College of Engineering & Management Department of Aiml Pune India Dr D y Patil College of Engineering & Innovation Department of Computer Engineering Pune India

ISBN: (纸本)9798350382693

Customary capacity the board methods are turning out to be less powerful for dealing with this huge volume of information because of the improvement of server farms and the unforeseen ascent away requirements. By eliminating the capacity control exercises out of the genuine information stockpiling media and placing them in the product layer of a concentrated regulator, Programming Characterized Stockpiling offers an answer for this issue. An insightful programming stack named 'programming characterized foundation' may deal with reasonable product equipment. A software engineer known as programming characterized capacity controls information capacity assets without the utilization of real equipment. Making the capacity engineering of a genuine Programming Characterized Stockpiling framework with no reenactment or imitating is an exorbitant and unsafe technique. Subsequently, the framework should be recreated prior to being utilized, in actuality. In this manner, virtualized proving grounds for such frameworks should be reenacted before genuine execution and sending. In this article, we evaluate software-defined storage engineering using the Programming Described Association (PDA) test framework, Mini-net. This framework is designed, assembled, and ready to advance without revisiting the established assembly structure. The core components of Mini-net have been modified to address data distribution challenges in contemporary SD storage and to scale storage capacity appropriately within this simulated environment. © 2024 IEEE.

关键词： Software testing

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Creating a Protected Virtual Learning Space: A Comprehensive Strategy for Security and User Experience in Online Education 4th

Creating a Protected Virtual Learning Space: A Comprehensive...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Boddapati, Mohan Sai Dinesh Desamsetti, Sri Aravind Adina, Karunasri Uppalapati, Padma Jyothi Murty, P T Satyanarayana P. B. V, RajaRao Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534202 India

ISBN: (纸本)9783031488870

The pandemic has a significant impact on how people conduct meetings, both in corporations and in schools. Online meetings have become a popular way to connect people from all over the world, lowering the expenses and time associated with travel. Various video conferencing systems and communication tools have aided in this trend towards online meetings. Many countries have moved to online classrooms as an alternative to traditional face-to-face instruction in the educational sector. It has also enabled educational institutions to adapt to changing circumstances and continue to educate students. One of the major concerns is security. As online platforms become more popular, the potential of infiltration activities such as hacking or unauthorized access increases. This study proposes a comprehensive strategy for improving security and user experience in online education. The framework focuses on detecting existing participants, detecting intruders, restricting intruders, and restricting abusive messages. It employs authentication mechanisms, user behaviour analysis, network monitoring, and machine learning algorithms to validate participant identities, differentiate legitimate users from prospective invaders, restrict unauthorized access, and promote courteous conversation. The framework proves its usefulness in minimizing security concerns and promoting a secure online learning environment through simulations and case studies. © 2024, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Intrusion detection

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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Advanced deep learning approach for enhancing crop disease detection in agriculture using hyperspectral imaging

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Journal of Intelligent and Fuzzy Systems 2024年第2期46卷 3281-3294页

作者： Zekrifa, Djabeur Mohamed Seifeddine Lamani, Dharmanna Chaitanya, Gogineni Krishna Kanimozhi, K.V. Saraswat, Akash Sugumar, D. Vetrithangam, D. Koshariya, Ashok Kumar Manjunath, Manthur Sreeramulu Rajaram, A. Department of Bioinformatics Higher School of Food Science and Agri-Food Industry Ahmed Hamidouche Av Oued Smar Algiers Algeria Department of Computer Science & Information Technology School of Computing and Information Technology REVA University Rukmini Knowledge Park Kattigenahalli Karnataka Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science Engineering Dr. K. N. Modi University Jaipur Rajasthan Newai India Department of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Deemed to be University Coimbatore India Department of Computer Science and Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Department of Plant Pathology School of Agriculture Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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A Single DC-source Asymmetrical Multilevel Inverter With Solid State Transformer With New Switching Approach

A Single DC-source Asymmetrical Multilevel Inverter With Sol...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Karunakaran, E. Suresh, Y. Rao, Bhukya Nageswar Vivek, P.S.S. Aditya, K. National Institute of Technology Department of Electrical and Electronics Engineering Karnataka Surathkal India Kolhapur Institute of Technology College of Engineering Department of Computer Science and Business Systems Kolhapur India

ISBN: (纸本)9798350309140

Cascaded H-bridge (CHB) MLI have emerged as the preferred choice due to their high quality in output waveforms with low harmonic distortion. However, a key limitation in these inverters lies in the requirement of a dedicated DC source for each bridge. To overcome this challenge, there is a transition towards asymmetrical cascaded H-bridge (ACHB) MLIs, enabling the use of a variable DC source for each bridge. The variability in DC supply is achieved through a high-frequency link. This paper introduces a novel optimized switching technique for asymmetrical cascaded H-bridge (ACHB) MLIs with a single DC input incorporating a high-frequency link (HFL). The effectiveness of the suggested approach in achieving a 27-level asymmetric multilevel inverter is substantiated by experimental outcomes obtained through MATLAB/Simulink simulations. © 2024 IEEE.

关键词： DC transformers

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An Evolution of Hybrid Bagging Technique in Chemoinformatic and Drug Discovery 2

An Evolution of Hybrid Bagging Technique in Chemoinformatic ...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Aluvala, Srinivas Kalshetty, Jagadevi N. Husain, Saif O. Kaur, Jagroop Thind, Harpreet Kaur Sr University School Of Computer Science And Engineering Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Punjabi University Department Of Computer Science And Engineering Patiala India Dayananda Sagar College Of Engineering Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

The drug deals with every living organism on the earth, for classification of drugs based on their action is an important element in drug development. This research paper presents a machine learning model that predicts the action of a drug, utilizing a large drug network. The model integrates J-48 algorithm and random forest algorithm, and form the hybrid bagging technique with the neural network model demonstrating. The model uses the struck2vec data feature, which derived from the large network. Then the data features and dataset are trained on the hybrid bagging technique for drug classification according to the drug properties. The study's findings highlight the important of machine learning models in drug classification which offers a valuable tool for researchers and pharmaceutical companies in the drug development process. The output includes the top classes of drugs, the prediction of action and aiding in new drugs discovery with higher accuracy of 95.83% which is superior over other learning models. © 2024 IEEE.

关键词： Classification (of information)

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