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检索条件"机构=Computer Applications in Science and Engineering"

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Industry 4.0 based Machine Learning Models for Anomalous Product Detection and Classification

Industry 4.0 based Machine Learning Models for Anomalous Pro...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Kumar, Sourabh Chandra, Saroj Kumar Shukla, Ram Narayan Panigrahi, Lipismita Computer Science and Engineering Op Jindal University Center of Excellence in Industry 4.0 Raigarh India Kalinga Institute of Industrial Technology Department of Computer Applications Bhubaneswar India

ISBN: (纸本)9781665492942

Automation has made tremendous changes in the industries. It has been used to automate the manual processes involved in different physical units of the industries. The purpose was to increase the production in the manufacturing. Now, computers are being used in the industries to monitor functionalities of different production units with the help of artificial intelligence and internet of things (IoT). The IoT has revolutionized the industries. It is an interconnected network system of physical units. The core purpose of it to gather and share information among different physical units. The IoT has great impact on the many areas such as business, industry, medicine, the economy, transport, industrial robots and automation systems. IoT with artificial intelligence has wide range of industrial applications. Industry 4.0 is used in the industries where different industrial units are connected over the internet and interacting to make decisions via machine-to-machine communication. It has increased the benefits of industries in terms of production and supply chain management. Manufacturing industry monitors its production units in every 10 milliseconds to capture features of the product that is being produced. The features generated in this process are huge in amount. Critical observation is performed on the generated features to categorize the product as anomalous or good. Product classification is difficult task in the labeled datasets due to human bias in labeling the final product as anomalous or good. In this work, machine learning models is being used to detect and classify faulty product produced by manufacturing industry. Both qualitative and quantitative study will be carried out to compare various machine learning models. © 2023 IEEE.

关键词： Internet of things

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A Novel Machine Learning-Enhanced Swarm Intelligence Algorithm for Cost-Effective Cloud Load Balancing

A Novel Machine Learning-Enhanced Swarm Intelligence Algorit...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Satheesh, Nimmati Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Cloud computing refers to the instant accessibility of shared computer resources over the internet on-demand, thereby revolutionizing the way organizations deploy and manage their IT infrastructure. Efficient load balancing in a cloud environment scales up, utilizing resources with optimized cost-effectiveness. This paper introduces a new Machine Learning-guided Improved Swarm Intelligence Algorithm for reliable load balancing in cloud computing. Methodology It spans on three main elements. The first is the data center controller for the management of tasks. VM allocation uses an Improved Beetle Swarm Optimization Algorithm and one-dimensional convolutional probabilistic neural network is used for host load prediction. To propose an approach that looks to optimize the key metrics such as make span, average cost of VM, resource utilization, and value response time through iterative optimization process. The results of the experiments reflect the success of the proposed algorithm, because an improved performance of load balancing is obtained in comparison with those achieved using PSO, Grey Wolf Optimization, and Genetic Algorithm. The convergence diagram displays the iterative optimization process achieved by the proposed algorithm in obtaining a balance among various performance metrics. Overall, the methodology proposed in this work will provide promising facilitation in the increased efficiency, scalability, and cost-effectiveness of environments based on cloud computing with a reliable load balancing. © 2024 IEEE.

关键词： Genetic algorithms

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Enhancing the Compressed Video Quality through a Neural Network Approach 4

Enhancing the Compressed Video Quality through a Neural Netw...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Panneerselvam, Karthick Rajalakshmi, K. Sornalatha, P. Kpr Institute of Engineering and Technology Department of Computer Science & Engineering Tamil Nadu Uthupalayam641407 India Cmr Institute of Technology Department of Mca Karnataka Bengaluru560037 India Alagappa University Department of Computer Applications Tamil Nadu Karaikudi630003 India

ISBN: (纸本)9798331523923

Video compression mainly focuses on reducing the file size of videos with little loss of quality, but most video compression algorithms on the market do not focus on clarity. Data compression is increasingly being considered as a necessary issue in larger engineering communities. Data compression using neural networks has been the main topic of some related research. The main objective of this study would be to apply the video in an amazing way with the help of an Enhanced Generative Adversarial Network (EGAN). EGAN was better to manage and produced better consistent results with fewer artifacts than other Neural Networks (NN). Deep neural networks (DNNs), recurrent neural networks (RNNs), generational adversarial networks (GANs), and various autoencoder (AE) variants have all been used for various compression methods. It would be a potential option for incredible movies. Based on significantly reduced sample input films, the proposed neural network was able to maintain high-frequency information and generate pleasurable sensory videos. A Residual Dense Block (RDB) is the sort of block utilized in the design. In comparison to previous state-of-the-art approaches, the comprehensive Mean-Opinion-Score (MOS) test, and the Video Multi-Method Assessment Fusion (VMMAF) test, reveal extremely substantial increases in terms of human experiential accuracy. The researchers are also proposing a new data pre-processing method based on the Deep Recurrent Neural Network (DRNN) that has been combined with two leading-edge coding standards, and the Adaptable Video Coding (AVC). The results show consistent encoding benefits across all sequences examined at multiple spatial frequencies, with an average bit rate savings of 4.0 percent relative to AVC. © 2025 IEEE.

关键词： Image coding

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Evasion Techniques in Cybersecurity: An In-Depth Analysis

Evasion Techniques in Cybersecurity: An In-Depth Analysis

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Turukmane, Anil V Khekare, Ganesh Shelke, Nilesh Sakarkar, Gopal Buchade, Siddhant School of Computer Science & Engineering Vellore Institute of Technology-AP Amaravati India School of Computer Science & Engineering Vellore Institute of Technology Vellore India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798331517953

Because of the rising sophistication of cybersecurity threats, it is imperative to have a thorough grasp of the evasion strategies used by malevolent actors. This study offers a detailed analysis of evasion strategies in several cybersecurity sectors, such as intrusion detection systems, network security, and endpoint protection. This paper employs a methodical approach to examine the development of these methods, showcases actual case studies, and suggests approaches for detection and mitigation. The findings emphasize how crucial it is to conduct ongoing research and development to combat new cyber threats. Key discoveries are reviewed, and directions for further research are indicated. © 2024 IEEE.

关键词： Network intrusion

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Comparative Analysis of Different Types of WSN 10

Comparative Analysis of Different Types of WSN

引用

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bajaj, Shilpa Bala, Manju Angurala, Mohit I. K. Gujral Punjab Technical University Department of Computer Applications Kapurthala India Khalsa College of Engineering and Technology Department of Computer Science and Engineering Amritsar India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789380544472

Wireless sensor networks (WSNs) have been widely adopted in various fields such as environmental monitoring, industrial control, and military applications. Different types of WSNs have been developed to cater to the specific needs of these applications. This comparative study aims to examine the different types of WSNs including terrestrial, underwater, and underground, and their respective advantages and limitations. The study will also explore the unique challenges faced by each type of WSN and the solutions that have been proposed to overcome these challenges. The main aim of carrying out the comparative study is to make researchers aware of the advantages and challenges of each type of WSN so that they can overcome the limitations with innovative approaches in the future. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Wireless sensor networks

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

引用

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

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 IEEE.

关键词： Contrastive Learning

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Citrus Leaf Disease Prediction: Deep Feature Fusion Perspective 5th

Citrus Leaf Disease Prediction: Deep Feature Fusion Perspect...

引用

5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Palei, Shantilata Lenka, Rakesh Kumar Behera, Santi Kumari Sethy, Prabira Kumar Nanda, Sandeep Barik, Rabindra Kumar Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar751003 India Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Sambalpur768018 India Electronics Sambalpur University Odisha Sambalpur768019 India Computer Science and Engineering Sanskriti University Uttar Pradesh Mathura281401 India School of Computer Applications Kalinga Institute of Industrial Technology Odisha Bhubaneswar751024 India

ISBN: (纸本)9783031609343

Citrus disease is a major problem of economic and productive loss in agriculture worldwide. Crops are affected by uneven climatic conditions, which leads to lower agricultural yields. This will affect the world’s agricultural economy. This paper proposes an image recognition method based on machine learning (ML) and deep learning (DL) feature fusion techniques for citrus leaf disease prediction. The three most popular CNN models were considered. The CNN models were AlexNet, vgg16, and vgg19. The features of these CNN models were extracted and fed into the support vector machine (SVM) to classify citrus leaf diseases. These classification models were evaluated in terms of performance metrics calculation. Subsequently, the multilayer feature fusion technique was adapted to the best-performing CNN models. Here, vgg16 performed the best among AlexNet, vgg16, and vgg19. Then, the different combinations of feature fusion of vgg16 are adapted. These fused features were fed into the SVM for citrus leaf disease classification. The feature fusion of ‘fc6 plus fc7’ of vgg16 with SVM yielded the best results;that is, the maximum values of accuracy, sensitivity, specificity, precision, FPR, F1 score, MCC, and Kappa coefficient were 97%, 97%, 98.09%, 97.06%, 1.58%, 95.34%, 84.4%, and 86.79% respectively. This method is very much helpful to the farmer as well as to the customers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Citrus fruits

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Predictive Autoscaling for Containerized applications Using Machine Learning

Predictive Autoscaling for Containerized Applications Using ...

引用

Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： Amit K. Mogal Vaibhav P. Sonaje Department of Computer Science & Applications School of Computer-Science and Engineering Sandip University Nashik India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

As containerization continues to gain prominence in modern application deployment, the need for efficient autoscaling mechanisms becomes paramount to ensure optimal resource utilization and adherence to service level objectives. This research paper proposes a Predictive Auto-scaling framework leveraging machine-learning(ML) techniques to anticipate and proactively adjust the resources allotted to containerized applications. The aim is to enhance the scalability and responsiveness of applications running in dynamic and cloud-native environments. An auto-scaling technique with burst awareness for containerized applications is proposed in this research. This innovative method optimizes container utilization while meeting the Quality-of-Service requirement by taking into account a mix of both vertical and horizontal skills. The proposed proactive autoscaling strategy addresses the challenges posed by unpredictable workloads and varying application demands, fostering adaptability to dynamic environments. By highlighting the significance of machine learning in improving the predictability and effectiveness of resource scaling for containerized systems, this study adds to new work of research on auto-scaling methodologies.

关键词： Scalability Supervised learning Green products Reinforcement learning Quality of service Predictive models Containers

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Exploring the Integration of Generative AI in Modern Education Systems: A Comprehensive Analysis

Exploring the Integration of Generative AI in Modern Educati...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Dey, Avick Kumar Chauhan, Vineet Kumar Singh, Pradeep Kumar Sarkar, Ananya Dept. of Computer Applications West Bengal Durgapur India Galgotias College of Engg. & Tech Dept. of Information Technology Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India DSMS College of Tourism and Management Dept. of Computer Applications West Bengal India

ISBN: (纸本)9798331517953

This paper aims at filling this gap through dissecting the applicability of the Generative AI in the current learning and teaching system. The paper offers an introduction to Generative AI technologies: GPT and DALL-E, and describes the chronological history and current progress of its educational applications. Other objectives include discussing about the uses of Generative AI in adaptive learning environment, smart learning systems, automation of organizational work and in talented minds improvement. Analyzing key successful AI cases, the paper reveals the challenges implementers encountered, and provides debates about the appropriate practices of AI. Research evidence points to the prospect of Generative AI benefiting students and educators in teaching and learning, which indicates the innovative potential of the technology, yet at the same time reveals the potential risks and challenges that need to be addressed concerning the ethical and privacy implications of Generative AI in education. Others may follow general recommendations for policymakers and educators on how to integrate artificial intelligence responsibly, as well as future work for this quickly burgeoning domain. © 2024 IEEE.

关键词： Contrastive Learning

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Potential Impacts of Online-Based Learning 2.0 and Certification on Employability 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Saini, Dilip Kumar Jang Bahadur Singh, Sumeshwar Negi, Neerja Bhandari, Sonam Kumar, Raj Joshi, Kapil Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of School of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Dehradun India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Rapid advancement in technology innovation has seen an expansion in online-based learning programs as a major aspect of numerous organizations’ course offerings around the globe. Online learning in colleges has gotten more interactive, adaptable, collaborative, and participative. It has gained a fundamental part to play later on as a classroom teaching tool and a self-study platform for skill improvement. The reason for this examination is to consider the likely effects of online-based learning and accreditation regarding employability. The term employability implies a set of abilities, understanding, and individual qualities that make a graduate bound to pick up work and prevail in their chosen occupation. This paper has especially investigated the issues of online certification real availability, their impacts on employability, and their overall points of interest and weaknesses. Recommendations for further work and refinements of the pedagogical implementation that builds on these findings are presented in the final stages of this research paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-learning

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