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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Rajendar, Janga Kishore Kumar, Botcha Verma, Toran Lavanya, Mangalampalli Sasha Sai Lakshmi Department of Computer Science Engineering - Data Science CMR Engineering College Kandlakoya Telangana Hyderabad501401 India

ISBN: (纸本)9789819971367

Blockchain is a distributed database that multiple parties can maintain and share. This new technology is expected to greatly impact the healthcare industry. It can help address various issues related to patient care. Through the use of blockchain technology, data can be moved in real-time without exposing it to various risks such as theft and forgery. This paper aims to introduce the general idea of the Blockchain and its applications in the healthcare industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Roughness Analysis of Stainless Steel Bearing Using computer Vision 6

Roughness Analysis of Stainless Steel Bearing Using Computer...

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6th International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2024

作者： Das, Shounak Tarafdar, Arundhati Mondal, Prabir Computer Science and Engineering Jadavpur University Kolkata India School of Computer Science Data Science cluster UPES University Dehradun India Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9798350380040

Current technology utilizes a sophisticated standard room measurement system to analyze the average roughness of Stainless Steel Bearing Surface. However, this process relies on random sampling at infrequent intervals, resulting in inefficiency and inadequate analysis of a large number of samples. To address this issue, we propose a solution using computer vision via laser scanning. The scanning process produces an amplified speckle pattern of the ring surface. This pattern is then analyzed to verify the number of edges that cross a predefined threshold and exhibit symmetrical patterns, particularly on smoothed ring surfaces. Remarkably, this system operates with a turnaround time of less than a second. Moreover, unlike the standard room setup, this technology can be seamlessly integrated directly into production/manufacturing units. © 2024 IEEE.

关键词： Speckle

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Unsupervised social network embedding via adaptive specific mappings

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Frontiers of computer science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

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Precise control of page cache for containers

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Frontiers of computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Plant Fettle Detector: Unifying Fruit Quality Tracking and Plant Disease Detection through Advanced Image Analysis 3

Plant Fettle Detector: Unifying Fruit Quality Tracking and P...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Nair, Sruthi Bajpai, Khushi Fadnavis, Sakshi Jain, Shruti Sheikh, Zeenat Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering - Data Science Nagpur44013 India

ISBN: (纸本)9798350381931

India being an agricultural country, food quality tracking is a major challenge faced by common farmers across the country. This research presents an innovative integration of Convolutional Neural Networks (CNNs) to address key challenges in agriculture - fruit quality tracking and plant disease detection. The unified system comprises the Fruit Quality Tracker and the Plant Disease Detector, leveraging advanced image analysis for precision agriculture. The Fruit Quality Tracker uses computer vision to assess ripeness and defects in real-time, empowering farmers with informed decisions. Simultaneously, the Plant Disease Detector swiftly identifies and classifies diseases, mitigating economic losses. The research explores architectural nuances, development methodologies, and deployment on Google Cloud Platform. Testing shows high accuracy rates of 98.5% for fruit quality and 98.83% for plant disease detection. The system's transformative potential in agriculture is highlighted, bridging the gap between technology and sustainable food production systems. © 2024 IEEE.

关键词： Image analysis

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Bridging the Gap: Evaluating the Symbiotic Relationship of Cloud Computing with data science and data engineering

Bridging the Gap: Evaluating the Symbiotic Relationship of C...

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Delhi Section Conference (DELCON), IEEE

作者： Rajesh Sharma R. Imamhussain Naikwade Akey Sungheetha Ketema Adere G. S. Pradeep Gandasala Sumit Kumar Banshal Department of Computer Science and Engineering Alliance University Bangalore India Department of Data Science Alliance University Bangalore India Department of Computer Science & Engineering Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This paper examines the convergence of cloud computing, facts science, and facts engineering, providing a primer for college kids getting into those fields. The examine highlights the synergistic courting among those domains, displaying how cloud infrastructure complements collaboration and efficiency. By illuminating those interconnections, the paper presents college students with a holistic view of the current facts landscape, making ready them to leverage current equipment withinside the cloud era.

关键词： Symbiosis Cloud computing Quantum computing Soft sensors Decision making data science data engineering Automated machine learning Real-time systems Servers

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Ensemble Based Precautionary Detection of Parkinson's Disease from Voice Signal 1

Ensemble Based Precautionary Detection of Parkinson's Diseas...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Bhanusree, Yalamanchili Adhvy, Vodnala Sharan Affan, Mohammed Shaji Praneetha, Kanvapuri Sai Sahithi, Katikireddy Sai Computer Science - Data Science VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India

ISBN: (纸本)9798331518943

Parkinson's Disease is a neurological progressive disorder that affects the nervous system of a human being, which effects the parts of the body. Tremors, imbalance, slowed movement, stiffness, difficulty in walking and talking etc. are observed. The reasons being, brain disorder, injuries, toxins certain drugs etc. The disease has no cure however treatment can be offered for relief from symptoms. However early detection can cure the disease in the first stages and extend the life span of the patient. This paper proposes a Ensembled learning model for early detection of Parkinsons disease through voice features. Random forest ensemble models have outperformed the Machine learning and Neural Network models in detecting disease from voice. An accuracy of 94.8% is achieved with this model with proper preprocessing techniques and training. As a future work the proposed model's software prototype can be released for mobile application for cost effective early detection of Parkinsons Disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts 22

An Intelligent LLM-Powered Personalized Assistant for Digita...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Easin, Arafat Md Sourav, Saha Tamas, Orosz Data Science and Engineering Dept. Budapest Hungary Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385601

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital-agent. © 2024 IEEE.

关键词： Information management

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Identification of domain-specific euphemistic tweets using clustering

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International Journal of Information Technology (Singapore) 2024年第1期16卷 21-31页

作者： Devi, Maibam Debina Saharia, Navanath Data Engineering Lab Department of Computer Science & Engineering IIIT Senapati Manipur Imphal 795002 India

Social media platforms (SMPs) are frequently utilised as a readily accessible and comprehensive medium for expressing personal opinions nowdays. The use of euphemism, a linguistic strategy in which the underlying feeling of expressive content is veiled by the use of mild language, has been a longtime practise in the realm of SMPs for the purpose of reducing harshness or to discuss sensitive topics [1]. The identification of masked contents [2] in euphemism is challenging due to their inherent nature. This study presents a proposed identification mechanism aimed at detecting domain-specific euphemisms through the utilisation of clustering techniques. The pattern categorization feature is created utilising domain-specific lexical features combined with frequency-based features. In order to identify the most suitable match, the hybrid feature extraction algorithms incorporate uni-gram and bi-gram features dependent on frequency based feature, in conjunction with a lexicon. The objective of the dimension reduction phase is to address the issue of sparsity and to identify the most significant words for each sample in order to classify them into different domains using centroid and density-based clustering techniques. The DBSCAN algorithm is employed with an epsilon value of 2.5 and a minimum number of points set to 6, resulting in the identification of 7 distinct clusters. To calculate the optimal value for k in the K-means algorithm, the Silhouette score is utilised. The clusters that were obtained are examined by manual means. We compare our model to FLUTE dataset with epsilon value of 0.2, minpoints of 5 for DBSCAN, and obtain validation score of 0.55. The DBSCAN clustering algorithm generates distinct clusters that extend beyond the scope of the inquiry domain. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： DBSCAN Domain-specific euphemistic text Euphemism K-means Silhouette score

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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