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Detection, Classification and Counting of Moving Vehicles from Videos 1

2nd International Conference on data Analytics and Learning, DAL 2022

作者： Sunny, Alfina Manohar, N. Department of Computer Science Amrita School of Arts and Sciences Mysore India

ISBN: (数字)9789819963461

ISBN: (纸本)9789819963454

Detecting and Classifying vehicles into several categories demonstrates a considerable possibility in the field of intelligent transportation systems. In this paper, we aim to propose a Convolutional Neural network integrated with YOLO framework to detect, classify and count vehicles from videos considering different climatic conditions like rainy, foggy, night and sunny weathers. Experimental results show that on applying this proposed network on MIO vision Traffic Camera dataset for training and testing the model with different traffic videos, the network has a good testing and classification ability taking into consideration of the complex features of the image dataset. Our algorithm achieves an average accuracy of 94.4% in the detection, classification and counting of vehicles depending on the visibility of the image in the frame. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vehicles

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Concept, Technology, Barriers To Smart Village: A Systematic Literature Review 11

Concept, Technology, Barriers To Smart Village: A Systematic...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Utamajaya, Joy Nashar Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Ramadhan, Arief Bina Nusantara University Computer Science Department Binus Graduate Program - Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350305968

Smart villages that utilize digital technology are a solution to assist rural community services in meeting their needs, especially for developing countries. However, financial problems, namely the high cost of infrastructure, the size of the informal economy and governance are challenges in limiting the government's aspirations regarding the smart village concept. The presentation of this article examines the state of smart village development in developing countries, which includes conceptual understanding, motivations, drivers and barriers to smart villages. The purpose of this study is to find answers to research questions by reviewing several selected study articles. The method used was to search for selected studies and there were 60 selected studies identified from a search of five literature databases which initially contained 3394 articles. The results of the data were analyzed using synthetic analysis and thematic analysis. The results of this study found data related to Concept, Technology, Barriers To Smart Village which came from several articles by answering questions from the research, namely RQ1: How many articles from the 10 most populous countries discussed Smart Village, RQ2: What are the basic dimensions of Smart Village, RQ3: What new technologies are discussed in Smart Village digitization, RQ4: What are the Challenges in Smart Village. The results of this SLR can also later be used as a reference for further researchers to find out the background of research related to smart villages. The conclusion obtained in this research is the end of the explanation of this article concludes that there are still many shortcomings in this research and it is hoped that in the future many studies will be able to explore other aspects such as regulations and others that are not discussed in the article so that it is expected to be a reference for further researchers to examine information related to smart villages. in order to find an ideal and up-to-date con

关键词： Developing countries

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High-Performance Chip Design With Parallel Architecture for Magnetic Field Imaging System

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-13页

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Lin, Chun-Hung Yuan Ze University Department of Computer Science and Engineering Taoyuan320315 Taiwan National Pingtung University of Science and Technology Program of Artificial Intelligence and Mechatronics Pingtung912301 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan International Games System Company Ltd. New Taipei City248 Taiwan

To address the limitations of typical coil detection systems and enhance the performance of traditional magnetic field imaging (MFI) systems, we propose a MFI system that uses a 4×4 array of anisotropic magnetoresistance (AMR) sensors. The core controller of the proposed system was designed using a pipelined parallelization algorithm and implemented on a high-speed field-programmable gate array (FPGA) in a fully hardware-based architecture. The designed MFI system can achieve parallel acquisition of magnetic field components from the 16-channel AMR sensor array, parallel data processing, parallel data transmission, and parallel computations in a single cycle of only 4 ms. Compared with typical detection systems, the proposed solution not only exhibits superior performance but can also provide dynamic visual representations of sensed information. © 1963-2012 IEEE.

关键词： Parallel architectures

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Blood Test Report-Based Prediction and Classification of Diseases Using Artificial Neural Networks 1

Blood Test Report-Based Prediction and Classification of Dis...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Kodipalli, Ashwini Prajwal, N. Vignesh, Vamshi B. Rao, Trupthi Gargi, N. Devamane, Shridhar B. Global Academy of Technology Dept. Artificial Intelligence & Data Science Karnataka Bangalore India School of Computing Amrita Vishwavidyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Through early intervention and individualized treatment plans, timely disease detection and personalized healthcare can advance patient results and reduce healthcare costs. With the aim to categorize medical conditions, we present a dataset in this study that includes blood sample parameters like haemoglobin, cholesterol, and glucose. To analyse these parameters, we created a three-layered artificial neural network (ANN). Achieving an accuracy of 93.42% on the test set, the ANN was trained and validated on the dataset. With the aim of reducing overfitting and enhance model generalization, dropout layers were added. Furthermore, a comparative analysis with various optimizers was carried out. The results highlight the ANN's potential as a means for timly diagnosis, personalized healthcare, improving patient outcomes and lowering long-term healthcare costs. They also demonstrate the ANN's ability to accurately classify diseases. © 2024 IEEE.

关键词： Anaemia ANN Artificial Neural Networks Blood Test Classification Deep learning

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Blockchain-based Decentralized Digital Forensics Case Management System using IPFS 3

Blockchain-based Decentralized Digital Forensics Case Manage...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Viswanathan, Jayaraj Kumar, S. Udhaya Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350354348

Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Management System using the InterPlanetary File System (IPFS) protocol. The decentralized nature of blockchain ensures data integrity and transparency, while IPFS facilitates efficient and decentralized storage of case files. Our system allows for secure and fine-grained access control to case data, ensuring that only authorized users can view and manage cases. We present the architecture of the system, which includes smart contracts deployed on the blockchain to manage case data and access control rules. Additionally, we have discussed the implementation details and use case scenarios to demonstrate the functionality of the proposed system. Performance testing revealed that our system significantly enhances data integrity and transparency. Comparative analysis demonstrated that IPFS, despite having slightly higher latencies than traditional FTP, provides superior security and redundancy. Through our research, we aim to provide a secure, transparent, and efficient solution for managing cases in various domains, including legal, healthcare, and finance. © 2024 IEEE.

关键词： data integrity

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A Graph-Based Approach to Automatic Car Reliability System Prediction with Probabilistic Modeling and Machine Learning 3

A Graph-Based Approach to Automatic Car Reliability System P...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Varun, Bheema Sai Murali, K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369083

Reliability prediction in automotive systems undoubted represents a substantial part of safety and customer satisfaction. a new graph-based probabilistic method and machine learning algorithm for the automotive system reliability prognosis. By breaking complex dependency among vehicles' components into a hierarchy and using past failure data, the method improves on prediction error. Real-world automotive datasets show excellent results with better resolution and higher accuracy for the enhanced components compared to existing techniques while at the same time providing interpretability by visualizing the dependency of the components. © 2024 IEEE.

关键词： Prediction models

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Cyber Chronicles: Tracking Behavior Patterns for Detecting Threats in Large Networks 3

Cyber Chronicles: Tracking Behavior Patterns for Detecting T...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramasubbu, Senthilnathan Thangavel, Senthil Kumar Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

One of the primary challenges in cybersecurity is that even one un-detected, appropriately unanalyzed malicious security event can hide the attack vectors of a potential hacker. It is essential to detect the data breach at the earliest stage to reduce the impact on the business. The malicious actor's activities will have higher visibility during the initial attacks to compromises or exploitation at the beginning of the attack cycle. After the initial compromise, the attacker studies the environment and establishes the persistence and covert channel communication. At this successive phase of the attack cycle, the attacker camouflages with the production data flow, access, and noises in the network and application logs. It is essential to detect the attacks during the reconnaissance and initial attacks and should not miss any malicious activities. Most of our security tools focus on reducing false positives;however, reducing false negatives is critical to cyber security. Hence, this paper is focused on suitable deep learning to reduce the False Negatives (FNs) with minimal Type II error to detect the attacks well in advance. © 2024 IEEE.

关键词： Cybersecurity

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Enhanced Steel Defect Detection using U-Net Model with Attention Mechanism 5

Enhanced Steel Defect Detection using U-Net Model with Atten...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sai, D. Gowtham Sri Mani Maheswari, Uma Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In this research, detection of steel defects is a significant use of computer vision that can enhance industrial quality control. New prospects for automated defect segmentation are presented by recent developments in deep learning. This paper investigated the use of attention processes in a U-Net convolutional neural network architecture for improved steel flaw identification. Images of steel and matching defect masks from the Severstal Steel Defect Detection dataset were used to train the U-Net network. Preprocessing actions such as image rotation and scaling were performed in addition to data augmentation. When tweaked at various probability thresholds, the model produced defect mask predictions on a test set with good accuracy. When compared to ground truth masks, qualitative results showed that different defect types could be segmented well. Forecasts on fresh test pictures without any masks provided also shown a believable ability to identify flaws. According to this research, deep learning using attention blocks and U-Nets has the potential to improve automation in tasks related to semantic segmentation, such as steel inspection. To assess the model's efficacy on other defect types and steel materials, more work is required. The code and trained model serve as a basis for creating systems that are ready for production. © 2024 IEEE.

关键词： Attention mechanisms Convolutional neural networks Image processing Steel defect detection U-Net

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Multi-Stage Predict+Optimize for (Mixed Integer) Linear programs 38

Multi-Stage Predict+Optimize for (Mixed Integer) Linear Prog...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Stuckey, Peter J. Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science University of California Davis United States Department of Data Science and AI Monash University Clayton Australia

The recently-proposed framework of Predict+Optimize tackles optimization problems with parameters that are unknown at solving time, in a supervised learning setting. Prior frameworks consider only the scenario where all unknown parameters are (eventually) revealed at the same time. In this work, we propose Multi-Stage Predict+Optimize, a novel extension catering to applications where unknown parameters are instead revealed in sequential stages, with optimization decisions made in between. We further develop three training algorithms for neural networks (NNs) for our framework as proof of concept, all of which can handle mixed integer linear programs. The first baseline algorithm is a natural extension of prior work, training a single NN which makes a single prediction of unknown parameters. The second and third algorithms instead leverage the possibility of updating parameter predictions between stages, and trains one NN per stage. To handle the interdependency between the NNs, we adopt a sequential and parallelized versions of coordinate descent for training. Experimentation on three benchmarks demonstrates the superior learning performance of our methods over classical approaches. © 2024 Neural information processing systems foundation. All rights reserved.

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Household Waste Classification Algorithms Based on Various Convolutional Neural Networks: A Review of Performance Optimization 21

Household Waste Classification Algorithms Based on Various C...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Renxiang, Huang Jia, Chen Jianping, Li School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu635002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research. © 2024 IEEE.

关键词： Convolutional neural networks

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