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32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2024

作者： Kabir, Md Arafat Kamucheka, Tendayi Fredricks, Nathaniel Mandebi, Joel Bakos, Jason Huang, Miaoqing Andrews, David University of Arkansas Department of Electrical Engineering and Computer Science United States University of South Carolina Department of Computer Science and Engineering United States United States

ISBN: (纸本)9798350372434

Many recent FPGA-based Processor-in-Memory (PIM) architectures have appeared with promises of impressive levels of parallelism but with performance that falls short of expectations due to reduced maximum clock frequencies, an inability to scale processing elements up to the maximum BRAM capacity, and minimal hardware support for large reduction operations. In this paper, we propose a 'Standard' set of design objectives for PIM array-based FPGA designs. We then propose a PIM array-based GEMV accelerator architecture as a case study to show the proposed Standard can be realized in practice. The GEMV accelerator serves as existence proof that dispels several myths surrounding what is normally accepted as clocking and scaling FPGA performance limitations. Specifically, the proposed accelerator clocks at the maximum frequency of the BRAM and scales to 100% of the available BRAMs. Comparative analyses show execution speeds over existing PIM-based GEMV engines on FPGAs and achieving a 2.65×-3.2× faster clock. An AMD Alveo U55 implementation achieves a system clock speed of 737 MHz, providing 64K bit serial multiply-accumulate (MAC) units for GEMV operation. © 2024 IEEE.

关键词： Memory architecture

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AI-based Online Proctoring System with YoLo-v3 & MMOD CNN 9

AI-based Online Proctoring System with YoLo-v3 & MMOD CNN

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Negi, Divyanshu Bhandari, Ambuj Gaur, Abhishek Sindhwal, Abhishek Chauhan, Rahul Kapruwan, Akanksha Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350394474

The popularity of remote learning has increased recently. However, there has never been a better alternative for academic exams. Certain colleges have compiled assignments that allow students to simply copy and paste using the internet, although others have adopted remote proctoring, in which an observer uses a manual proctor to keep track of student's activities. In this paper, we have suggested a method to create an integrated AI-based proctoring system that could help in stopping exam cheating. The technology detects and reports mal practices, and the system is economical and safe. © 2024 IEEE.

关键词： Students

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Blockchain-Assisted Trust Management in Vehicular Ad-Hoc Network 15

Blockchain-Assisted Trust Management in Vehicular Ad-Hoc Net...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Banerjee, Suman Kuila, Pratyay Naik, Banavath Balaji National Institute of Technology Department of Computer Science and Engineering Sikkim737139 India National Institute of Technology Department of Computer Science and Engineering Patna800005 India

ISBN: (纸本)9798350370249

Vehicle-to-everything (V2X) communication is crucial in Intelligent Transportation Systems (ITSs) for enhancing road safety, traffic efficiency, and convenience. To address the need for trust in V2X communications, a trust management system for Vehicular Ad Hoc Networks (VANETs) using blockchain technology has been proposed. This system calculates dynamic trust scores for vehicles based on direct communication, recommendations from other vehicles, and proximity to roadside units (RSUs). The trust scores are stored in a transparent and tamperproof blockchain, ensuring the immutability and transparency of trust scores. Simulations demonstrate that this blockchain-based trust management system significantly improves the security and reliability of V2X communications. This system is essential for the widespread acceptance of VANETs by fostering a trusted communication environment. © 2024 IEEE.

关键词： and Edge computing Blockchain Trust V2X VANET

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Revolutionizing Rice Agriculture: A Machine Learning Approach to Fungal Disease Management for Economic Sustainability

Revolutionizing Rice Agriculture: A Machine Learning Approac...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sharma, Richa Aarti Department Of Computer Science And Engineering Lovely Professional University Punjab Phagwara India Department Of Computer Science And Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350366846

This research presents an innovative Decision Support System (DSS) utilizing machine learning (ML) techniques, tailored for the effective control of fungal diseases in rice farming, an essential component of global food security. Rice, a cornerstone in sustaining billions, grapples with persistent threats from diverse fungal infections, leading to substantial losses in yield and quality. Our innovative DSS harnesses ML techniques, drawing from an extensive dataset encompassing images and metadata related to various fungal diseases affecting rice plants. Engineered for real-time functionality, our DSS furnishes swift and precise diagnoses coupled with tailored management recommendations, elevating the efficacy of crop disease mitigation. Rigorously trained on a diverse dataset, the system exhibits impressive accuracy, validated through meticulous evaluations on a dedicated test dataset containing images of rice plants afflicted with known fungal infections. The practical ramifications of our ML-based DSS are profound, holding the potential to transform rice farming practices and make substantial contributions to global food security. By delivering timely and accurate diagnoses, the system empowers rice farmers and agricultural extension workers to promptly implement targeted management strategies, thus minimizing losses and optimizing yields. This user-friendly tool, positioned at the nexus of technology and agriculture, aims to streamline decision-making processes, diminish reliance on traditional methods, and fortify the resilience of rice crops against fungal diseases. As a beacon of innovation in crop disease management, our ML-based DSS promises to be an invaluable resource for the agricultural community. By augmenting the capabilities of those at the forefront of rice cultivation, it ensures not only sustainable food production but also underscores the transformative potential of advanced technology in addressing pressing challenges within global agriculture. ©

关键词： Decision support systems

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Predictive Vigilance: AI And ML Methods For DDOS Attack Detection And Forecasting 2

Predictive Vigilance: AI And ML Methods For DDOS Attack Dete...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Ravikumar, S. Rajendran, P. Revathi, S. Kumar, K. Antony Saranraj, S. Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Cmr Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India Kongunadu College of Engineering and Technology Department of Computer Science & Engineering Trichy India

ISBN: (纸本)9798350388916

Distributed network attacks are referred to, usually, as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization's site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. They used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, the UNWS-np-15 dataset was extracted from the GitHub repository and Python was used as a simulator. After applying the machine learning models, the generated a confusion matrix for identification of the model performance. In the first classification, the results showed that both Precision (PR) and Recall (RE) are 89% for the Random Forest algorithm. The average Accuracy (AC) of the proposed model is 89% which is superb and enough good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are approximately 90% for the XGBoost algorithm. The average Accuracy (AC) of the suggested model is 90%. By comparing present work to the existing research works, the accuracy of the defect determination was significantly improved which is approximately 85% and 79%, respectively. © 2024 IEEE.

关键词： Network intrusion

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Customer Segmentation Analysis Using Clustering Algorithms 3rd

Customer Segmentation Analysis Using Clustering Algorithms

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Reddy, Biyyapu Sri Vardhan Rishikeshan, C.A. Dagumati, VishnuVardhan Prasad, Ashwani Singh, Bhavya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819939312

Customer segmentation has been deployed as a prudent marketing strategy by companies to ensure that their investments are less risky and more judicious. Segmenting customers helps the companies to divide the customers into groups that reflect similarity and maximize the value of each customer to the business. The main goal of this research is to use a machine learning clustering approach called K-means clustering to accomplish consumer segmentation. Besides, the research work is also focused on performing exploratory data analysis on the given dataset. To group the customers into clusters, a K-means clustering algorithm is performed on the customer dataset. To achieve optimization and validation of the clusters, popular heuristic, interpretation, and approximation methods have been included in this paper. Further, for analyzing and visualizing the important facets of the customer dataset and the operation of the K-means algorithm, the paper presents some colorful and informative representations. The implementation of this research work has been done in the R programming language. The outcome of this work includes visualizing the segments of the mall customers in the form of clusters based on their spending scores and annual incomes. Furthermore, a better customer segmentation could be achieved by taking product reviews and customer feedback into consideration. Nevertheless, customer segmentation remains a prospective topic for many researchers and companies due to dynamic customer behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Seeking a Sense of Meaning and Companionship in Life: Informal Learning on Douyin Among Chinese Older Adults

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Proceedings of the ACM on Human-computer Interaction 2025年第2期9卷 1-24页

作者： Qian, Zhigu Fu, Jiaojiao Zhou, Yangfan School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing China School of Information Science and Engineering East China University of Science and Technology China

This study examines how Chinese older adults leverage Douyin, a short video platform, for informal learning purposes, analyzing their usage patterns, motivations, and encountered challenges. Although Douyin was not explicitly designed with educational features, it has emerged as a significant informal learning platform for this demographic. Through a qualitative investigation comprising participant observations and semi-structured interviews with 17 participants, we reveal the distinctive learning experience that Douyin facilitates. The platform’s unique combination of short-form videos, live streaming capabilities, and interactive community features creates an engaging learning environment that particularly resonates with older adults. Our findings demonstrate that participants derive substantial social-emotional benefits beyond knowledge acquisition, including strengthened social connections, enhanced companionship, and a reinforced sense of purpose through their Douyin engagement. These social-emotional aspects emerge as crucial factors driving older adults’ selection of Douyin as their preferred learning platform. By providing comprehensive insights into older adults’ engagement with digital platforms for lifelong learning, this research offers valuable implications for HCI, particularly in understanding how technology can be optimized to support the learning needs of older populations. © 2025 Association for Computing Machinery. All rights reserved.

关键词： Social aspects

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Reinforcement Learning based Adaptive Healthcare Decision Support Systems using Time Series Forecasting 5

Reinforcement Learning based Adaptive Healthcare Decision Su...

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

作者： Shirley, C.P. Berin Jeba Jingle, I. Abisha, M.B. Venkadesan, R. Absin, S.J. Karunya Institute of Technology and Sciences Department of Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Healthcare decision-making is a complex and crucial process that must take into account changing patient situations and medical trends. It is frequently difficult for conventional decision support systems to adjust to these shifting circumstances. In this work, we present a novel method for creating adaptive healthcare decision support systems by combining time series forecasting and reinforcement learning (RL). The main goal is to create a system that can use reinforcement learning (RL) to develop optimal policies for making decisions by utilizing time series data of patients from the past. Our system uses RL in conjunction with time series forecasting methods like LSTM (Long Short-Term Memory) networks and ARIMA (Auto Regressive Integrated Moving Average) to estimate future patient outcomes and healthcare resource requirements. There are several different components in the system architecture. The time series forecasting models are trained using historical healthcare data, which includes patient demographics, medical histories, and treatment outcomes. Forecasts for important healthcare parameters like patient recovery rates, disease progression, and resource consumption are produced by these models. According to the anticipated results, RL algorithms are used to learn decision policies. While interacting with a simulated environment that represents patient care scenarios, the RL agent aims to maximize long-term rewards, which may include reduced expenses, better patient outcomes, or more efficient use of available resources. Using actual healthcare datasets, the adaptability and efficacy of suggested approach will be assessed. The efficacy of RL-based decision policies, accuracy of projected results, and the system's adaptability to shifting healthcare dynamics are all examples of performance measures. © 2024 IEEE.

关键词： Decision support systems Adaptive systems Accuracy Time series analysis Systems architecture Medical services Reinforcement learning Predictive models Forecasting Long short term memory

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Challenges and Solutions for Distributed File System 2

Challenges and Solutions for Distributed File System

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Richa Gupta, Amit Bhatla, Shruti Tripathi, Vikas Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350371055

A Distributed File System (DFS) is a file system that allows multiple users to access and share files and data across a network. In a DFS, files are stored on multiple servers, and users can access them from any location on the network as if they were stored on their local computer. DFS provides a way to organize and manage files and folders across multiple servers in a unified way, making it easier for users to access and share data without worrying about the physical location of the files. This can help to improve data availability, reliability, and scalability in a networked environment. DFS also provides a way to replicate data across multiple servers for redundancy and fault tolerance, so that if one server fails, the data can still be accessed from other servers. Some examples of popular distributed file systems include Hadoop Distributed File System (HDFS), Google File System (GFS), and Windows Distributed File system (DFS). LCCFS (light weight cloud computing file system) which help to reduce the overhead and increase the performance of the distributed system RADOS (Reliable autonomic distributed object storage) which utilized for implementing distributed file system for cloud computing Pangea is a new system which allow different units to combine into a single unit and PLSF (Virtual Parallel log structured file system) which pick up the performance by improving the bandwidth are some new terminologies introduced by some previous research are discussed in this research. © 2024 IEEE.

关键词： Ceph Cloud file system Distributed File System Gluster HDFS

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Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model

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computer Modeling in engineering & sciences 2024年第8期140卷 1507-1537页

作者： Haijian Shao Suqin Lei Chenxu Yan Xing Deng Yunsong Qi School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas VegasNV89154USA Zhejiang Geely Automobile Research Institute Ningbo315336China

This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance *** methodologies struggle with the challenges posed by luminosity fluctuations,especially in settings characterized by diminished radiance,further exacerbated by the utilization of suboptimal imaging *** envisioned approach mandates a departure from the conventional YOLOX model,which exhibits inadequacies in mitigating these *** enhance the efficacy of this approach in low-light conditions,the dehazing algorithm undergoes refinement,effecting a discerning regulation of the transmission rate at the pixel level,reducing it to values below 0.5,thereby resulting in an augmentation of image ***,the coiflet wavelet transform is employed to discern and isolate high-discriminatory attributes by dismantling low-frequency image attributes and extracting high-frequency attributes across divergent *** utilization of CycleGAN serves to elevate the features of low-light imagery across an array of stylistic *** computational methodologies are then employed to amalgamate and conflate intricate attributes originating from images characterized by distinct stylistic orientations,thereby augmenting the model’s erudition *** validation conducted on the PASCAL VOC and MS COCO 2017 datasets substantiates pronounced *** refined low-light enhancement algorithm yields a discernible 5.9%augmentation in the target detection evaluation index when compared to the original *** Average Precision(mAP)undergoes enhancements of 9.45%and 0.052%in low-light visual renditions relative to conventional YOLOX *** envisaged approach presents a myriad of advantages over prevailing benchmark methodologies in the realm of target detection within environments marked by an acute scarcity of lumi

关键词： Target detection extremely low-light wavelet transformation highly differentiated features YOLOX

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