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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panda, Sanghamitra Sahu, Rekha Dash, Satya Ranjan Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Silicon University Department of Computer Science and Engineering Odisha Bhubaneswar India KIIT Deemed to Be University School of Computer Application Odisha Bhubaneswar India

ISBN: (纸本)9798350369175

Alzheimer's disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer's disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer's disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model. © 2024 IEEE.

关键词： Convolutional neural networks

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Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimized Pedestrian Detection Leveraging YOLOv9: A Thorough Deep Learning Framework for Autonomous Vehicle System

Optimized Pedestrian Detection Leveraging YOLOv9: A Thorough...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Kumar, Hemant Mamoria, Pushpa Dewangan, Deepak Kumar Chhatrapati Shahu Ji Maharaj University Department of Computer Science & Engineering Kanpur India Chhatrapati Shahu Ji Maharaj University Department of Computer Application Kanpur India ABV-IIITM Department of Computer Science & Engineering Gwalior India

ISBN: (纸本)9798350379525

Pedestrian detection for an autonomous vehicle system is the crucial and foremost important aspect for accurate identification of pedestrian on the road scene environment. For this study a YOLOv9-C state-of-the-art deep learning model is used. YOLOv9 is renowned for its accuracy and efficiency in real time application. Model is trained on KITTI dataset. This dataset has a number of classes but for this study only pedestrian class is used. Dataset provides a number of images of urban scene environment where number of objects are there and trained model is perfectly detecting pedestrian in the images. In the YOLOv9 model an additional backbone layer is added for achieving high efficiency and time bound detection. The primary aim of this research is to look for the possible technique by which detection accuracy can be improved. Additional added layer helps in detecting more complex features of pedestrian in the images. After careful observation findings are shows that proposed model has achieved better accuracy in comparison with another models. Experimental results also show that there is a chance for more reliability and responsiveness of object detection if model is trained on diverse dataset. Although this study demands high computational resources which may be a barrier in some sense. © 2024 IEEE.

关键词： Deep learning

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application of ML in Agriculture Field With Its Enhancement Methods 13

Application of ML in Agriculture Field With Its Enhancement ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Agrawal, Shobit Bhardwaj, Sonika Sharma, Nandini Mishra, Varun Gl Bajaj Group of Institutions Department of Computer Science and Engineering Uttar Pradesh Mathura India Gla University Department of Computer Engineering and Application Uttar Pradesh Mathura India

ISBN: (纸本)9798350305463

Food is essential for humanity and is primarily produced through agriculture. While addressing fundamental human needs, agriculture also serves as a global source of employment. Recent advancements in machine learning (ML) research have not only enhanced agricultural gains but also brought about additional benefits, including resource optimizations. ML, functioning as an advanced technology, provides farmers with detailed advice and insights into crop yield and resources optimization, aiding in the reduction of farming losses. This article comprehensively examines recent ML applications and trends in agriculture, showcasing its potential for more accurate and effective farming, yielding high-quality results with reduced human labor. The findings emphasize the considerable space for innovation in meeting the demands of businesses seeking sustainable and optimized agricultural practices. These technologies, can be crucially helpful in agriculture in automation Furthermore, the paper serves as a potential milestone for future research in various directions of agriculture. © 2024 IEEE.

关键词： Support vector machines

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Image Classification in Monkey Species using Image Classifiers of Keras 1

Image Classification in Monkey Species using Image Classifie...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Singh, Neelam Upreti, Manisha Dwivedi, Ayushi Rawat, Vandana Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350352931

A species is biologically basic classification and taxonomic rank of an organism. Among the many common names for the mammals that make up the suborder Simiiformes (sometimes known as the simians), 'monkey' is among the most common. The traditional classification of all members of the group currently known as simians as monkeys. Different classifiers are used to categorize the various kinds of monkeys based on their photos. Assigning a name or class to an image is the work of image classification. In the research paper, different image classifiers of Keras are used for image classification in monkey species. A dataset of 10 distinct monkey species was used to test out many different classifiers. Keras comes preloaded with a variety of Convolutional Neural Networks, including VGG16, VGG19, ResNet50, ResNet-50 V2, ResNeXt101, Inception V3, and Xception. We compared the performance of other classifiers and discovered that ResNeXt-101 achieved the best accuracy (99.98%). © 2024 IEEE.

关键词： Invertebrates

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A Review on Breast Cancer Classification using Neural Networks 10

A Review on Breast Cancer Classification using Neural Networ...

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

作者： Rajput, Aashita Muskan Yadav, Sachin Chhabra, Megha Agarwal, Arun Prakash School of Engineering and Technology Sharda University Department of Computer Science and Application Greater Noida India

ISBN: (纸本)9789380544472

Breast cancer is the second most common type of cancer due to which females have such a high mortality rate. Several computer-assisted diagnostic systems have recently been proposed to operate with the least amount of data necessary due to the high number of unnecessary breast biopsies. The following review paper discusses the entire process of breast cancer classification along with various kinds of neural networks used to build such models. It further includes the various to-date research done in this domain and the different accuracy obtained by them. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Diseases

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Breast MRI Registration Using Gorilla Troops Optimization 1st

Breast MRI Registration Using Gorilla Troops Optimization

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Nayak, Somen Mondal, Subhodip Department of Computer Application University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur India

ISBN: (纸本)9789811954023

Metaheuristics assume a significant part in enhancing issues, and most of them are enlivened by the aggregate knowledge of living peculiarities in nature. In excess of 10% of ladies are experiencing bosom disease in their lives in the entire world. Registration of breast magnetic resonance imaging is a method to align pre- and post-contrast images to analysis and classifying of cancer category. In this research, breast magnetic resonance imaging registration using gorilla troops algorithm is proposed. GTO is a metaheuristics-based optimizations algorithms that tested to registering the image of breast magnetic resonance. After that, images are successfully registered using GTO algorithm. The result of GTO-based registration method is compared with registration based on PSO method. The results implicates, in case of registration of breast magnetic resonance images, the GTO-based registration methods beat the PSO-based registration method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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An Amalgamated CNN-Transformer Network for Lightweight Image Super-Resolution

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1376-1387页

作者： Fang, Jinsheng Lin, Hanjiang Zeng, Kun School of Computer Science and Engineering Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China School of Computer and Control engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Recently, Transformer-based methods for single image super-resolution (SISR) have achieved better performance advantages than the methods based on convolutional neural network (CNN). Exploiting self-attention mechanism to model global context definitely improves the SR results. However, the neglect of local information will bring inevitable reduction of the network performance. In this work, we propose an Amalgamated CNN-Transformer network for lightweight SR, namely ACTSR. Specifically, an amalgamated CNN-Transformer block (ACTB) is developed to extract the useful information of both local and global features. By employing stacked ACTBs, our ACTSR extracts more informative deep features beneficially for super-resolution reconstruction to improve network performance while keeps lightweight and flexible enough. Extensive experiments on commonly used benchmark datasets validate our ACTSR outperforms the advanced competitors. Our codes are available at: https://***/ginsengf/ACTSR. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Approach Towards Web for Exploring the Suitable Job for Individuals 1

Approach Towards Web for Exploring the Suitable Job for Indi...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Goyal, Vishal Tiwari, Akhilesh Jadhav, Sharad K. Yelapure, Shripad Yadav, Ravindra Narasimha Prasad, L.V. Ghaziabad India Krishna Institute of Computer Application and Management Karad India Department of Surgery Karad India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350360707

In light of future work challenges, true human resource management (HRM) must be rebuilt. This involves over time human resource development;it must also contain the concept of sustainability to move from consuming to generating human resources. The labor market is constantly changing, with nontraditional jobs becoming increasingly important, especially in light of the current COVID-19 legislation. A useful teaching strategy in a variety of academic fields, including career development, is experiential learning. Important elements for establishing experiential learning programs at the institutional level are also covered by researchers. Our framework may assist businesses in identifying the type of experiential learning that best fits their objectives and setting for professional training. It can also help ensure that the training is successfully designed and delivered. © 2024 IEEE.

关键词： Resource allocation

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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