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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Suganya, R. Ashok, P. Lekshitha, J. Shanmuga Priya, S. Monica, K. Somasundaram, K. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India Sri Muthukumaran Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

The crime monitoring system is a unique and authentic project which functions with the concepts of block chain language. Blockchain technology has the potential to revolutionize the management of criminal records by providing a secure network where documents are readily accessible and immune to tampering. This innovation ensures the safety and integrity of sensitive information, offering a robust solution to the challenges posed by traditional recordkeeping system. The basic use of the crime identification system is to maintain and process the criminal records and data that are given by the user. This can be used in the security system during the arrival of dignitary people for the occasional events by recognizing the people based upon the criminal records. Thus, criminals are restricted from entering inside the event. This can also be embedded for the security cameras of the event and are constantly monitored. © 2024 IEEE.

关键词： Records management

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P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

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Journal of computer science & Technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

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NR-Router+: Enhanced Non-Regular Electrode Routing With Optimal Pin Selection for Electrowetting-on-Dielectric Chips

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第9期43卷 2606-2619页

作者： Pan, Youlin Liu, Genggeng Huang, Xing Li, Zipeng Huang, Hsin-Chuan Liang, Chi-Chun Wang, Qining Kim, Chang-Jin Ho, Tsung-Yi Northwestern Polytechnical University School of Computer Science Xi'an710072 China Fuzhou University College of Computer and Data Science Fuzhou350116 China Apple Inc. Silicon Engineering Group CupertinoCA95014 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan University of California at Los Angeles Mechanical and Aerospace Engineering Department Los AngelesCA90095 United States The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

With the advances in microfluidics, electrowetting-on-dielectric (EWOD) chips have widely been applied to various biological and chemical laboratory protocols. Glass-based EWOD chips with nonregular electrodes are proposed, which allow more reliable droplet operations and facilitate the integration of optical sensors for many biochemical applications. Furthermore, nonregular electrode designs are utilized in EWOD chips, e.g., interdigitated electrodes for more reliable droplet manipulation, custom shaped electrodes for specific applications like concentric heating, etc. However, due to the technical challenges of fabricating multilayer interconnection on the glass substrate, e.g., unreliable process and high cost, both control electrodes and wires are fabricated with a single-layer configuration, which poses significant challenges to pin selection for nonregular electrodes. In this article, we propose a minimum-cost flow-based routing algorithm called NR-Router+ that features efficient and robust routing for single-layer EWOD chips with nonregular electrodes. To the best of our knowledge, this is the first work that overcomes the aforementioned challenges. We construct a minimum-cost flow algorithm to generate optimal routing paths followed by a light-weight model to handle flow capacity. A grid reduction strategy is proposed to reduce the computational overhead. Additionally, a flow collocation algorithm based on integer linear programming is presented to efficiently prevent wire overlapping. Experimental results show that NR-Router+ achieves 100% routability while minimizing wirelength with shorter run time. Moreover, NR-Router+ can generate mask files feasible for manufacturing via adjustments of design parameters, thus demonstrating its robustness and efficiency. © 1982-2012 IEEE.

关键词： Electrodes

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A Comparative Analysis of the Imputing of Missing data on Air Pollution 2

A Comparative Analysis of the Imputing of Missing Data on Ai...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Gayathri, M. Kavitha, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350365269

Air pollution is a significant environmental hazard in modern society because of its serious impact on human health and the environment. In point of fact, there has been a substantial rise in the levels of pollution in the environment over the course of the past few years. data analyst is commonly encountering the issue of data missing. It is crucial to handle them efficiently to get more accurate and dependable data analysis findings. This study aims to improve understanding of data missing mechanisms, data imputation techniques, and examine the efficacy of frequently utilized data imputation techniques on quantitative datasets. Analysts and data scientists frequently face the challenge of missing data in their investigations. Therefore, it is essential to handle them in an efficient manner so as to gain improved and more trustworthy results through the analysis of the data. The objective of this research is to enhance comprehension of data missing processes and data imputed methods, while also evaluating the effectiveness of data imputed approaches often employed in quantitative datasets. A complete comparison of 5 different data imputation methods is presented in this article. These approaches include mean imputation, median imputation, mode imputation, kNN imputation and Linear Regression. For the purpose of analysing and contrasting the efficacy of the various data imputation approaches, we have utilized five distinct numeric datasets that were gathered from several air pollution repositories. Standardized Root Mean Square Error (RMSE) is the technique for assessing the efficacy of data imputation techniques. According on the findings of the analysis, the kNN imputation approach offers superior performance than the other methods. There is no correlation between the length of the dataset or the number of values that are missing in the dataset and the accuracy of the data imputed approach. © 2024 IEEE.

关键词： Air pollution

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Real-Time Traffic Flow Optimization using Adaptive IoT and data Analytics: A Novel DeepStreamNet Model 4

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Saranya, V.S. Subbarao, Garimidi Balakotaiah, Dega Bhavsingh, Maloth Babu, K. Suresh Dhanikonda, Srinivasa Rao School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai603 203 India India Department of Computer Science and Engineering India Ashok: Womens Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Department of Computer Science and Engineering A.P Narasaraopet India Department of Artificial Intelligence and Data Science Telangana Hyderabad India

ISBN: (纸本)9798331540364

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Weld Defect Categorization via Nature-Inspired Optimization-Driven Neural Networks

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SN computer science 2024年第8期5卷 1035页

作者： Antony Vigil, M.S. Maheswari, K. Minu, M.S. Kulkarni, Gururaj L. Chandra Sekhar Reddy, L. Satishkumar, P. Haldar, Barun Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Ramapuram Chennai India Department of Computer Science and Engineering CMR Technical Campus Telangana Kandlakoya Hyderabad India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Shamshabad India Department of Computer Science and Engineering (Data Science) CMR College of Engineering & amp Technology Kandlakoya(V) Hyderabad Medchal India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Mechanical and Industrial Engineering Department College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

In many different sectors, the safety and dependability of welded structures are crucially dependent on the accurate identification of weld defects. To address this, we present a novel method for categorizing weld defects named as Pelican Optimization-based convolutional Bi-Long Short-Term Memory Networks (POCBi-LSTM). With this integrated strategy, weld defect categorization is intended to be more accurate and effective. To train and assess a suggested classification algorithm, a collection of 220 samples with 5various types of weld faults is utilized. The study's technique comprised denoising with a Modified Median Filter (MMF) for noise reduction and binary histogram equalization (BHE) for image enhancement. The Scale Invariant Feature Transform (SIFT) was used to extract features. The outcomes demonstrate that the proposed strategy performs greater than conventional methods and delivers superior recognition accuracy, precision, recall, and F1 scores of 92.9%, 94.5%, 89.5%, and 92.8%, respectively. Experiment findings demonstrate the effectiveness and advantage of the recommended approach in classifying weld faults. Through the use of an integrated strategy, it is hoped to increase the dependability and effectiveness of weld defect categorization by handling local pattern recognition and long-term interdependence efficiently. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Modified median filter Pelican Optimization-Based convolutional Bi-Long Short Term Memory Networks (POCBi-LSTM) Weld defects Weld structures

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

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5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

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