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2022 IEEE Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2022

作者： Shanthini, M. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665477192

During the Covid-19 pandemic, the insurance industry's digital shift quickened, resulting in a surge in insurance fraud. To combat insurance fraud, a system that securely manages and monitors insurance processes must be built by combining a machine learning classification framework with a web application. Examining and identifying fraudulent features is a frequent method of detecting fraud, but it takes a long time and can result in false results. One of these issues is addressed by the proposed solution. By digitalizing the paper-based workflow in insurance firms, this paper intends to improve the efficiency of the existing approach. This method also aimed to improve the present approach's data management by integrating a web application with a machine learning stacking classifier framework experimented on a linear regression-based iterative imputed data for detecting fraud claims and making the entire claim processing and documentation process more robust and agile. © 2022 IEEE.

关键词： Information management

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Book-Bot - An AI Powered Library Assistance Tool 2

Book-Bot - An AI Powered Library Assistance Tool

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2nd IEEE International Conference on Emerging Trends in engineering and Medical sciences, ICETEMS 2024

作者： Nimbarte, Mrudula Manekar, Nilay Prashantrao Shivhare, Divya Jain, Ketki Raina, Gurjyot Singh Badjate, Kushal S. B. Jain Institute of Technology Management & Research Department of Computer Science & Engineering Nagpur India

ISBN: (纸本)9798331529352

This literature survey aims to analyze the implementation of AI-based chatbots, developed via the Telegram platform, into today's libraries. In the conventional library environment, systems like Online Public Access Catalogs (OP AC) present several drawbacks including;it is face off the user, it possesses inherent hard coded search capabilities and has low user interaction. On the converse, artificial intelligence chatbots are a more creative, real-life approach that can facilitate easier access, eBooks, and other electronic materials. In turning workflow automation and real-time assistance, chatbots could enhance user satisfaction and also operational effectiveness of the library. The present cross-sectional survey sums up the maj or advancements in using AI for library systems, the use of chatbots in learning institutions and the effective use of Telegraph API to develop intelligent interface system. Our results show how the mentioned technologies help libraries to address the current limitations, offering a sustainable solution for meeting new user requirements. © 2024 IEEE.

关键词： API Chatbot OPAC

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Archer: Adaptive Memory Compression with Page-Association-Rule Awareness for High-Speed Response of Mobile Devices 23

Archer: Adaptive Memory Compression with Page-Association-Ru...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

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Deep learning model for Classifying leaves of Plants using VGG-16 14

Deep learning model for Classifying leaves of Plants using V...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Abinaya, K. Ishwarya, K. Saraswathi, S. Kasinathan, Prabakaran Nandhini, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Semmancheri TamilNadu Chennai600119 India Veltech Rangarajan Dr. Sagunthula R & D Institute of Science and Technology Department of Cse School of Computing TamilNadu India Bharath Institute of Higher Education and Research Biher Department of Computer Science and Engineering Selaiyur Chennai India

ISBN: (纸本)9798350335095

Systematic plant-based categorization has never been difficult. Targeted crop safety is dependent on automated technologies that detect and classify plants. Classical machine learning approaches have been used to categorize leaves using handcrafted features from the shape of plant leaves, which have yielded promising results. However, we concentrate on classifying plant leaves using non-handcrafted traits. To accomplish this, we employ the use of deep learning for extraction of features and categorization. Deep Convolution Neural Networks have recently demonstrated impressive achievements in picture classification and object detection challenges. This paper proposes using the built-in, compact VGG-16 model to detect and classify ten plant species from the Plant dataset. The proposed models are utilized to classify ten other classes. When contrasted with other pre-trained models, VGG -16 fared better in categorizing leaf images. computerized plant species categorization could be beneficial to food engineers, agricultural professionals, researchers, and the general public. © 2023 IEEE.

关键词： Object detection

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Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities:A Survey

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Machine Intelligence research 2022年第3期19卷 184-208页

作者： Xiao-Qing Zhang Yan Hu Zun-Jie Xiao Jian-Sheng Fang Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Tomey Corporation Nagoya 4510051Japan Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315300China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and TechnologyShenzhen 518055China

Cataracts are the leading cause of visual impairment and blindness *** the years,researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading,aiming to prevent cataracts early and improve clinicians′diagnosis *** survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic *** summarize existing literature from two research directions:conventional machine learning methods and deep learning *** survey also provides insights into existing works of both merits and *** addition,we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.

关键词： Cataract classification and grading ophthalmic image machine learning deep learning

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Adversarial Attacks and Defenses Using Machine Learning for Cybersecurity in Corporates

Adversarial Attacks and Defenses Using Machine Learning for ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Gopinath, R. Sathiyamoorthy, C. Sugumaran, D. Giriprasath, K.S. Tripathi, Neha Arif, Mohammad K S.Rangasamy College of Technology Department of Computer Science and Engineering Tamil Nadu India Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Graphic Era Deemed to Be University Uttarakhand Dehradun India Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350375442

This article suggests a novel method for protecting corporate cybersecurity systems from malevolent attacks, based on Capsule Networks (CapsNets). The enhancement of hierarchical feature learning by Capital Networks is a critical component of its capacity to differentiate between authentic and fraudulent data. Robust optimization techniques and adversarial training are implemented to develop a model. The training seeks to be more resilient and beneficial in a larger environment by introducing perturbations one capsule at a time. CapsNets executed an effective operation, achieving 95% accuracy and 97% precision. In terms of managing adversarial assaults, CapsNets outperform baseline models greatly. The proposed approach exhibits potential as an improved cybersecurity defense method, as a result of its exceptional resilience and precision. This study demonstrates the efficacy of CapsNets in improving cybersecurity and also offers a glimpse into the adversarial defenses used in enterprise machine learning applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Forest Fire Detection using computer Vision 3

Forest Fire Detection using Computer Vision

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Prasanna Rahavendra, A. Praneash, G.P. Rashmika, T. Natarajan, Aravindhraj Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Every year, approximately 70,000 wildfires occur around the world. Forest fires can have a variety of negative effects on forest cover, soil, tree development, vegetation, and general flora and fauna. Fires destroy several hectares of forest and leave behind ash, rendering it unfit for vegetation growth. Animal habitats are destroyed by the heat created by the fire. Preventing fire disasters and saving people's lives and property requires early detection of fires before they become catastrophic. These forest fires must be detected and put out as early as possible. In this project a computer vision based deep learning model is proposed that can detect and alert in-case of forest fires. The model which is used here is called MobileNet Architecture which employs depth-wise separable convolutions and has greater advantages than using other currently existing models. The model is trained from a dataset that was scrapped from the internet. The computer vision model is built utilizing the packages in Python 3 such as NumPy, OpenCV, Pillow, Matplotlib, and TensorFlow. This method is created by training and testing the model to identify failures and establish recovery strategies. As a result, these computer vision-based systems can provide a warning at an early stage of fire, which is critical for forest fire early warning. © 2022 IEEE.

关键词： Deforestation

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FedCosA: Optimised Federated Learning Model Using Cosine Annealing

FedCosA: Optimised Federated Learning Model Using Cosine Ann...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： John, Binu Menon, Varun G. Adhikari, Mainak Scms School of Engineering and Technology Department of Computer Science & Engineering Kerala Ernakulam683576 India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India School of Data Science Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India

ISBN: (纸本)9798331507541

The Federated Learning paradigm allows multiple devices or nodes to contribute to model training without sharing raw data, ensuring that sensitive information remains on the client side while still benefiting from collective learning. However, FL suffers several problems, including slow convergence, overfitting, and communication cost, particularly in Non-Independent and Identically Distributed (Non-IID) data settings. To solve these concerns, we offer an improved Federated Averaging (FedAvg) method that includes the Adam optimizer, cosine annealing learning rate scheduler, and weight decay (L2 regularisation). The Adam optimizer generates adaptive learning rates, but the cosine annealing schedule refines the learning rate over time for greater stability. Weight decay reduces overfitting by penalizing big model parameters. Furthermore, picking a subset of customers in each cycle lowers communication costs. Experimental findings on the MNIST dataset show that our strategy outperforms existing FL algorithms like FedAvg, FedSGD, FedNova, and FedSAM in test accuracy and loss minimization while significantly lowering communication load and training time. These enhancements make the suggested approach more effective and scalable for FL across several settings. © 2025 IEEE.

关键词： Federated learning

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A machine learning-based framework to Classify X-rays anomality 5

A machine learning-based framework to Classify X-rays anomal...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Saranya, V.S. Kanagajothi, D. Rajina Begam, S. Sharma, Sunil Dubey, Nilesh Rehan, Sumaiya School of Computing Department of Computing Technologies Srm Institute of Science and Technology Kattankulathur Chennai India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & D Institute of science and technology Avadi. India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan university Tamil Nadu Trichy India Department of Computer Science and Engineering Vivekananda global University Jaipur India Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research FTE CHARUSAT University India Department of Computer Science and Engineering Babu Banarasi Das University Lucknow India

ISBN: (纸本)9798400709418

In recent years, the intersection of healthcare and technology has witnessed transformative advancements, particularly in medical imaging. The integration of machine learning techniques has empowered the medical community to enhance diagnostic accuracy and streamline healthcare processes. This paper introduces a comprehensive machine learning-based framework designed for classifying X-ray anomalies within the dynamic and scalable environment of cloud computing. The motivation behind this framework stems from the need for scalable, accurate, and cost-effective solutions in healthcare. Leveraging cloud computing's elastic resources, our framework aims to accelerate the deployment of machine learning models for X-ray anomaly classification, ensuring accessibility and affordability across diverse healthcare settings. Gather a diverse dataset of X-ray images with both normal and abnormal cases. Ensure a balanced representation of different anomalies. Utilize the validation set to fine-tune hyperparameters and prevent overfitting. Develop a robust machine learning model capable of accurately classifying X-ray anomalies, leveraging advanced convolutional neural networks (CNNs) and transfer learning techniques. The hardware and software required are discussed in relation to the functioning and integration of Cloud Computing and Blockchain. Then we have gone through the model of machine learning for detection of anomality in X-rays(infection) step by step and in detail. The model discussed in the research paper can be of great importance to society as many people in society lose their lives to pneumonia every year. With the help of image classification, we can detect the areas of lung infected with infection, hence helping doctors in swift treatment of the patients. © 2023 ACM.

关键词： Cloud computing

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Multi-level self attention for unsupervised learning person re-identification

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Multimedia Tools and Applications 2024年第26期83卷 68855-68874页

作者： Zheng, Yi Zhao, Jiaqi Zhou, Yong Liu, Fayao Yao, Rui Zhu, Hancheng El Saddik, Abdulmotaleb Engineering Research Center of Mine Digitization of the Ministry of Education of the People’s Republic of China China University of Mining and Technology No. 1 Daxue Road Jiangsu Xuzhou221116 China School of Computer Science and Technology China University of Mining and Technology No. 1 Daxue Road Jiangsu Xuzhou221116 China Institute for Infocomm Research Agency for Science Technology and Research Singapore Singapore University of Ottawa 800 King Edward OttawaONK1N 6N5 Canada

In recent years, the task of person re-identification (ReID) has placed a critical demand on accurately describing image features. Attention mechanisms, particularly Transformer-like self-attention (TLSA), have gained favor among researchers due to their outstanding feature descriptive performance. However, due to their intricate structures, TLSA models typically require more computational resources. Simultaneously, contrastive learning has significantly enhanced the performance of unsupervised person re-identification. Nevertheless, contrastive learning originates from deep exploration of relationships among multiple samples, making batch size a crucial factor influencing deep learning methods based on the contrastive learning paradigm. Therefore, under the constraint of limited computational resources, traditional TLSA models often struggle to effectively adapt to unsupervised person ReID methods based on the contrastive learning paradigm. In response to the aforementioned issues, we propose a novel and lightweight Multi-Level Attention (MLA) method in this paper, which effectively mitigates the computational resource conflicts of the TLSA model during training under the contrastive learning paradigm. MLA comprises a lightweight multi-head attention module, complemented by a spatial feature weighting module, and an inter-feature cross-attention module to assist it. By fully leveraging the complementary strengths of these attention mechanisms, our approach achieves significant performance improvements in the ReID task. We evaluated the proposed approach on three large-scale real person ReID datasets, namely Market-1501, DukeMTMC-reID, MSMT17, and the virtual person ReID dataset, PersonX. The experimental results demonstrate that our method outperforms state-of-the-art approaches without relying on supplemental pre-training procedures or additional training data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natur

关键词： Unsupervised learning

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