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检索条件"任意字段=International Conference on Advances in Computer Science and Engineering"

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A Quarter Century Journey: Evolution of Object Detection Methods 4

A Quarter Century Journey: Evolution of Object Detection Met...

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4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Jain, Shreya Gajbhiye, Samta Jain, Achala Tiwari, Shrikant Naithani, Kanchan Shri Shankaracharya Technical Campus Department of Computer Science and Engineering Chattisgrah Bhilai490020 India Shri Shankaracharya Technical Campus Department of Electrical Engineering Chattisgrah Bhilai490020 India Galgotias University School of Computing Science and Engineering Uttar Pradesh Gautam Budha Nagar203201 India

ISBN: (纸本)9798350343670

One of the most important tasks in computer vision is object detection, which is identifying and recognizing things in pictures or video frames. The field of object detection algorithms has advanced significantly over the years, especially with the introduction of Convolutional Neural Networks (CNNs) and, more recently, Transformers. The survey begins by reviewing the pioneering methods that laid the foundation for modern object detection. It explores the watershed moment in 2012 when the arrival of deep learning-based approaches, specifically Convolutional Neural Networks (CNNs), transformed the field. We delve into the pivotal role played by datasets namely ImageNet, PASCAL VOC, and COCO in driving progress through benchmark challenges. The objective of this survey study is to present an in-depth analysis of the development of object detection techniques., starting from the conventional CNN-based methods to the cutting-edge Transformer-based architectures, and finally exploring the emerging hybrid models that integrate the best features of both CNN and transformers. Furthermore, the survey investigates challenges in object detection, such as handling occlusions, scale variations, and real-world deployment issues, while also discussing evaluation metrics and benchmarks used to assess performance. The paper also sheds light on the ethical implications of object detection, particularly concerning privacy and bias. © 2024 IEEE.

关键词： CNN computer Vision Deep Learning Object Detection Transformer

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Data-Driven Recycling Transformation for Enhancing Paper and Cardboard Bin Efficiency through IoT and Random Forest

Data-Driven Recycling Transformation for Enhancing Paper and...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Srinivasan, S. Swathy, R. Srividhya, V. Murugan, S. Srinivasan, C. Muthulekshmi, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Department Of Networking And Communications Chenagalpattu Chennai Tamil Nadu Kattankulathur India Meenakshi College Of Engineering Department Of Electrical And Electronics Engineering Tamil Nadu Chennai India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

This study uses data to enhance paper and cardboard recycling bin efficiency for sustainable waste management. It uses Internet of Things (IoT) and Random Forest algorithms to dynamically optimize bin use to improve recycling. It starts by installing IoT sensors on paper and cardboard recycling bins to track fill levels and use. A Random Forest system trained on past data predicts bin fill levels from this continuous data stream. The predictive algorithm adapts collection schedules and resource distribution based on temporal trends, weather, and community events. IoT and Random Forest increase fill-level forecasts and enable data-driven recycling bin management. This reduces wasted collections, fuel use, and carbon emissions, making recycling more sustainable and cost-effective. The study also analyzes the system's real-world urban application, demonstrating its scalability and flexibility to varied waste management circumstances. Our empirical study and case studies show that the technique improves paper and cardboard recycling efficiency, contributing to data-driven sustainability programs. © 2024 IEEE.

关键词： Predictive analytics

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advances in sentiment analysis: A comprehensive review of technique and application 4th

Advances in sentiment analysis: A comprehensive review of te...

引用

4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Naki, Ali Ghosh, Gopal School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9781032911571

Sentiment Analysis is a part of NLP that aims to identify the sentiment or opinion from textual data. This review paper gives a comprehensive study of the sentiment analysis method. From traditional methods to modern approaches. here we begin with understanding the foundational concepts of sentiment analysis. We understand various methods including lexicon based, machine learning, Deep learning. Where we are highlighting their strengths, weaknesses, and applications. Apart from this we examine the problems and future work in sentiment analysis. This paper act as a valuable resource for researcher and enthusiasts interested in understanding the sentiment analysis. © 2025 the Author(s).

关键词： Contrastive Learning

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Radio resource management and path planning in intelligent transportation systems via reinforcement learning for environmental sustainability

Radio resource management and path planning in intelligent t...

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international conference on Innovation and Technological advances for Sustainability, ITAS 2023

作者： Norouzi, S. Azarasa, N. Abedi, M.R. Mokari, N. Seyedabrishami, S.E. Saeedi, H. Jorswieck, E.A. Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Civil and Environmental Engineering Tarbiat Modares University Tehran Iran College of Engineering Doha University for Science and Technology Doha Qatar Institute of Communications Technology TU Braunschweig Lower Saxony Germany

ISBN: (纸本)9781032803722

Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework. © 2025 The Author(s).

关键词： Resource allocation

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Anomaly Detection from Web Log Data Using Machine Learning Model 7

Anomaly Detection from Web Log Data Using Machine Learning M...

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7th international conference on computer Applications in Electrical engineering-Recent advances, CERA 2023

作者： Mishra, Amit Kumar Bagla, Piyush Sharma, Ravi Pandey, Neeraj Kumar Tripathi, Neha Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India National Institute of Technology Department of Computer Science & Engineering Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305005

The information in the logs produced by the servers, devices, and applications can be utilized to assess the system's health. It's crucial to manually review logs, for instance, during upgrades, to verify whether the update and data movement went smoothly. Manual testing is insufficiently trustworthy, and manual log examination takes much time and effort. In this paper, we propose to search log files for anomalous sequences using the machine learning methods K-Means and DBSCAN. The two data representation approaches examined in this study were feature vector representation and IDF representation. The effectiveness of the deployed machine learning algorithms was examined using evaluation measures like F1 score, recall, and precision. The study found considerable differences in the algorithms' capacities to spot anomalies, with some algorithms being better at seeing various types of abnormal arrangements than their overall prevalence. By using the study's findings, the user might be able to spot strange arrangements after manually sifting through the log file. © 2023 IEEE.

关键词： Anomaly detection

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advances in Convolutional Neural Networks for Object Detection and Recognition

Advances in Convolutional Neural Networks for Object Detecti...

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2024 international conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Dhananjay Kumar Kumari, Neeraj Harron, Syed Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Convolutional neural networks (CNNs) have emerged as a powerful tool for object detection and recognition. Recent advances in CNNs have improved their performance on object detection by incorporating innovative convolutional layers and architectures. These advances include the inception architecture, region proposal networks (RPNs), and fully convolutional networks (FCNs). Additionally, these architectures have enabled object detection and recognition with significant improvements in accuracy and speed. Furthermore, recent research has focused on applying deep transfer learning techniques to CNNs for object detection and recognition, which have shown promising results in terms of precision and accuracy. Overall, these ongoing advancements have further improved the state of the art in object detection and recognition tasks. © 2024 IEEE.

关键词： Additionally Architectures Convolutional Improvements Layers Promising Recognition

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Tomato Leaf Diseases Prediction Using Deep Learning Algorithms

Tomato Leaf Diseases Prediction Using Deep Learning Algorith...

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2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Ananthi, P. Devi, K. Nirmala Gopinath, D. Shanmugapriya, P. Gowtham, S. Kongu Engineering College Department Of Computer Technology Tamilnadu Perundurai India Bangaluru India Kristu Jayanti College Department Of Computer Science Bangaluru India Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798350364828

In the realm of agriculture, infections on tomato leaves pose a worldwide danger to established tomato production, impacting a large number of farmers worldwide. To ensure healthy tomato plant growth and food security for the world's expanding population, early disease detection, treatment, and solution identification are essential. The use of computer-assisted technologies for disease detection in plant leaf is prevalent in modern agriculture. The goal was to develop an effective deep learning model for the detection of diseases in tomato plant leaf. To address memory and computational resource constraints, the proposed model incorporates a lightweight 'Region Convolutional Neural Network (R-CNN)' head and conducted experiments with various neural network architectures, including Densenet-169, optimized Mobilenet-V2 and Resnet-50, to enhance detection accuracy and computational efficiency. The research extends the capabilities of these neural network architectures by optimizing anchor proportions in the Conventional Neural Network (CNN) and modifying feature extraction topologies. These optimizations resulted in improved detection accuracy and computational efficiency and compared proposed technique against cutting-edge models to evaluate its viability and robustness. The outcomes of the research were promising. Impressive outcomes were obtained by the proposed approach regarding accuracy, F1-score, recall, and precision. © 2024 IEEE.

关键词： Computational efficiency

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Designing Secure Communication Protocols for Industrial Systems

Designing Secure Communication Protocols for Industrial Syst...

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2024 international conference on advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Dheer, Meenakshi Sinha, Deepak Kumar Kaushik, Naresh Vivekananda Global University Department of Agriculture Jaipur India Faculty of Engineering and Technology Department of Computer Science Engineering Bangalore India Atlas SkllTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

Data transmission inside industrial systems must be encrypted to protect sensitive information and guarantee the trustworthiness of mission-critical processes. Here, we offer three novel approaches to the special problems and risks that industrial systems must deal with. The Dynamic Key Exchange Algorithm (DKEA), Efficient Lightweight Encryption Algorithm (ELEA), and Adaptive Threat Mitigation Algorithm (ATMA) are three methodologies that provide state-of-the-art solutions that excel above more conventional approaches. Our trials clearly show that DKEA is superior in the realm of real-time key management. Its dynamic nature guarantees that cryptographic keys are always up to date, making it more difficult for hackers to get access to sensitive infrastructure. Consistently high levels of accuracy and precision demonstrate DKEA's ability to protect sensitive information. ELEA, which was designed for low-power industrial equipment, uses its resources very efficiently without sacrificing safety. The results of our trials show that ELEA is very accurate and precise, making it an excellent option for systems with constrained computing resources. ATMA, with its adaptive threat mitigation capabilities, delivers comprehensive protection against changing cyber threats. The security threats to industrial systems are always growing, but our novel ideas offer a comprehensive and flexible answer. © 2024 IEEE.

关键词： Network security

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international conference on computer science, Electronics and Industrial engineering, CSEI 2020

International Conference on Computer Science, Electronics an...

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international conference on computer science, Electronics and Industrial engineering, CSEI 2020

ISBN: (纸本)9789813345645

The proceedings contain 18 papers. The special focus in this conference is on computer science, Electronics and Industrial engineering. The topics include: Design of a Synchronous Generator of Permanent Magnets of Radial Flux for a Pico-Hydropower Station;preface;a Prototype Electronic Toy for the Development of Mathematical Logical Reasoning in Children from Five to Seven Years Old Using Python;smart University: Key Factors for an Artificial Intelligence Adoption Model;assessment of Supply Chain Performance in an Assembly Company: Evaluation of Evolutionary Algorithms;construction of a Dataset for Static and Dynamic Hand Tracking Using a Non-invasive Environment;monitoring of Industrial Variables Based on LoRa Communication Protocols;Optimization of the Mechanical Properties Responses of SBR 1502 Rubber/Amorphous Silicon Dioxide/Others by DOE-MSR Methodology;interactive Application for Learning Electric Motors Using Leap Motion Controller;low-Cost Assistive System for Deaf People Based on Artificial Vision;selection of Classifiers for Hand Gesture Recognition Using Analytic Hierarchy Process: A Systematic Literature Review;toward a Sustainability Balanced Scorecard for Managing Corporate Social Responsibility: A Conceptual Model;reconfigurable Manufacturing System Based on the Holonic Paradigm for the Die-Cutting Process in a Sports Shoes Company;a Model DevOps Framework for SaaS in the Cloud;selection of Optimal Lodging Site in the City of Baños, Ecuador;decision Support System for the Location of Retail Business Stores.

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Artificial Intelligence-Driven Power Management System for Enhanced Efficiency in Smart Grids

Artificial Intelligence-Driven Power Management System for E...

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2024 IEEE international conference on Recent advances in science and engineering Technology, ICRASET 2024

作者： Deepak, B.L. Sabin, T.T. Darshana, A. Vindya, L. Suma Manjunath, N. Mallikarjunaswamy, S. Shilpa, M. Rekha, V. Sjc Institute of Technology Dept of Information Science and Engineering Chickballapur India Sjc Institute of Technology Dept of Ai & Ds Chickballapur India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Christ University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350388602

Smart grids are crucial in modern energy systems, integrating sensors, communication networks, and control mechanisms to improve energy distribution. However, conventional methods like Time-Based Scheduling (TBS), Demand Response Mechanism (DRM), and Static Load Distribution (SLD) struggle with inefficiencies such as poor real-time load balancing, limited adaptability, and inaccurate power usage predictions. These drawbacks hinder the overall effectiveness of smart grid *** address these challenges, the proposed AI-Driven Power Management System (AI-PMS) leverages machine learning to optimize energy distribution and predict demand more accurately. By using historical and real-time data, AI-PMS dynamically adjusts power flow, ensuring better load distribution and efficiency. This method surpasses the limitations of TBS, DRM, and SLD. The implementation of AI-PMS has resulted in a 0.25% improvement in energy efficiency and a 0.15% boost in grid reliability, significantly reducing power losses. The system also enhances the integration of renewable energy sources, making it a more sustainable and adaptive solution for modern smart grids. These advancements promise better power management and lower operational costs for grid operators. © 2024 IEEE.

关键词： Smart power grids

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