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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...

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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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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Enhancing Fault Tolerance and Load Balancing in Cloud computing for improved e-healthcare Systems Performance 2

Enhancing Fault Tolerance and Load Balancing in Cloud comput...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Priyadarshini, Aliva Pradhan, Sateesh Kumar Laha, Suprava Ranjan Kumar Nayak, Debasish Swapnesh Utkal University Department of Computer Science & Application Odisha Bhubaneswar India Utkal University Department of Computer Science and Application Odisha Bhubaneswar India Siksha 'O' Anusandhan University Department of Computer Science & Engineering ITER Odisha Bhubaneswar India

ISBN: (纸本)9798350344882

Cloud Computing is increasingly being recognized as a valuable virtual resource-sharing solution. Due to its ability to provide solutions across different industries, the number of Cloud computing users has grown substantially. Load balancing distributes the workload across multiple servers to optimize resource utilization and prevent overload, while fault tolerance ensures that the system can continue to function even in the event of failures. In an e-healthcare system, where downtime or errors can have severe consequences for patients, fault tolerance and load balancing become even more critical. Load-balancing techniques are of utmost importance in enhancing the effectiveness of cloud servers. We have comprehensively examined the primary elements that impact fault tolerance in load-balancing task migration within cloud computing. These factors have been assessed by employing various methods to evaluate their influence on fault tolerance in load balancing. By implementing these techniques, e-healthcare systems can improve their ability to handle traffic surges and ensure patient safety and satisfaction. © 2023 IEEE.

关键词： Cloud computing

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An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

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computer Modeling in engineering & sciences 2023年第6期135卷 2349-2371页

作者： Mingyong Li Lirong Tang Longfei Ma Honggang Zhao Jinyu Hu Yan Wei College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331China

The learning status of learners directly affects the quality of *** with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile ***,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information ***,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,*** integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is *** to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple ***,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

关键词： Deep learning fatigue detection facial expression recognition sentiment analysis information fusion

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Classification of Potato Leaf Disease using Deep Learning 3

Classification of Potato Leaf Disease using Deep Learning

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Singh, Neelam Sharma, Deeksha Dev, Saurav 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)9798350354171

Deep learning is one of the subdomains of Artificial Intelligence. With the advantages of automatic learning and feature extraction, in recent years, it has become a hot topic in academic and industrial circles. Meanwhile, this has emerged as one of the hotspots within agricultural plant protection research, including plant disease recognition and pest range assessment. Deep learning in plant disease recognition can avoid the shortcomings of artificial selection of features of the disease spot, making the feature extraction of plant diseases more objective, promoting research efficiency, and increasing technological transformation speed. The goal of this work is to analyze potato leaf diseases using CNN in order to determine the model's optimal performance using a large dataset (10,000 photos) of potato leaves in the analysis. The most effective model will support farmers in making decisions that will keep potato production from declining. This investigation revealed that the model worked better when forty epochs were applied to the dataset of 10,000 photos. Additionally, three different epochs were applied and achieved 100% accuracy for 30 and 50 as achieved 99.97% and 99.98% respectively. By offering recommendations for how to categorise Potato Leaf Disease with the best result, the study made a substantial contribution to the agricultural industry and farmers. While working with big volumes of data, researchers should be concerned about the system because running code takes longer time. To focus this, a dataset including 2152 photos was gathered from Kaggle, with an additional 7848 images sourced from various platforms such as Google and raw data from Potato Field. To determine which epochs performed the best, a range of epochs were applied and results shows that 40 epochs led to 100% accuracy. © 2024 IEEE.

关键词： artificial intelligence deep learning potato disease

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