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A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method 19th

A Novel Deep Federated Learning-Based and Profit-Driven Ser...

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19th EAI International Conference on Collaborative Computing: Networking, applications and Worksharing, CollaborateCom 2023

作者： Ouyang, Zhaobin Xia, Yunni Peng, Qinglan Li, Yin Chen, Peng Wang, Xu College of Computer Science Chongqing University Chongqing China School of Artificial Intelligence Henan University Zhengzhou China Guangzhou Institute of Software Application Technology Guangzhou China School of Computer and Software Engineering Xihua University Chengdu China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

ISBN: (纸本)9783031545306

Service caching is an emerging solution to addressing massive service request in a distributed environment for supporting rapidly growing services and applications. With the explosive increases in global mobile data traffic, service caching over the edge computing architecture, Mobile edge computing (MEC), emerges for alleviating traffic congestion as well as for optimizing the efficiency of task processing. In this manuscript, we propose a novel profit-driven service caching method based on a federated learning model for service prediction and a deep reinforcement learning mode for yielding caching decisions (FPDRD) in an edge environment. The proposed method is temporal service popularity and user preference-aware. It aims to ensure quality of service (QoS) of delivery of cached service while maximizing the profits of network service providers. Experimental results clearly demonstrate that the FPDRD method outperforms traditional methods in multiple aspects. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Traffic congestion

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Exploring Sentiment Analysis Algorithms for Brand-Product Reputation Enhancement 15

Exploring Sentiment Analysis Algorithms for Brand-Product Re...

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

作者： Kumar, Ashwini Sharma, Sunil Nanduri, Sireesha Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Mangement - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper affords an overview of sentiment analysis algorithms for boosting brand-product reputation. The point of interest is on supervised and unsupervised systems getting to know strategies, which might be usually used for predicting sentiment. Specifically, the authors examine 4 forms of algorithms - Naïve Bayes classifiers, support vector machines, recurrent neural networks, and sentiment analysis with deep mastering - and how they can be used for boosting emblem-product popularity. Moreover, the look evaluates the constraints of the algorithms, in addition to capacity destiny studies instructions. The authors finish that while sentiment analysis with deep getting to know can also be the maximum accurate choice, cautious consideration must be made while choosing an algorithm as specific algorithms may also produce distinctive outcomes. This paper presents an informative and comprehensive assessment of sentiment analysis algorithms and their usefulness for boosting logo-product reputation. © 2024 IEEE.

关键词： Support vector machines

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Research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm 7

Research on Mining Association Rules for Classroom Concentra...

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7th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2023

作者： Bai, Jingqiu Kong, Huafeng Hu, Wenhan Wuhan Business University Department of Information Engineering Hubei China Major in Data Science and Big Data Technology China The Main Research Direction Is the Computer Application China

ISBN: (纸本)9798350334197

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future. © 2023 IEEE.

关键词： Association rules

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Rupa Saha Koushik Karmakar Suparna Biswas Department of Computer Application Narula Institute of Technology Department of Computer Science and Technology Narula Institute of Technology Department of Computer Science & Engineering MAKAUT W B

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis.

关键词： Legged locomotion Machine learning algorithms Accuracy Tracking Reviews Real-time systems Biomedical monitoring Gait recognition Wearable sensors Smart phones

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BlockWhisper: A Blockchain-Based Hybrid Covert Communication Scheme with Strong Ability to Evade Detection 24th

BlockWhisper: A Blockchain-Based Hybrid Covert Communication...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Zehui Xu, Yuwei Huang, Ranfeng Fan, Xinhe Xu, Jingdong Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Software College Northeastern University Shenyang China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9789819615247

In light of the pervasive significance of privacy protection in contemporary society, the exploration of covert communication technology has been extensive, which is designed to safeguard communication behaviors and contents from disclosure. The decentralization, anonymity, and robustness of blockchain technology render it a more suitable medium for covert communication than traditional networks, which are vulnerable to tracing and destruction. Nevertheless, state-of-the-art blockchain-based covert communication schemes tend to focus on the concealment of a single type of constituent element in a blockchain transaction. These methods are able to resist detection methods for a particular element, yet are easily exposed under the joint detection of multiple classes of methods. The absence of resistance to joint detection methods targeting all elements presents a significant challenge that impedes the advancement of blockchain-based covert channels. Consequently, BlockWhisper is proposed as a blockchain-based hybrid covert channel that is capable of evading the recognition of various detection methods. Following the probability distribution model obtained from a blockchain transaction distribution analysis method, BlockWhisper is implemented using a top address list and a special hybrid coding method. The experiments demonstrated that Graph-based Detection (GD), Similarity-based Detection (SMD), Shape-based Detection (SPD), Entropy-based Detection (ETD), and Machine Learning-based Detection (MLD) yielded inferior results on this scheme, which substantiates the assertion that BlockWhisper possesses a strong ability to evade detection. Moreover, a comparison of BlockWhisper with other schemes reveals that it has a superior channel capacity on the basis of the guarantee of channel feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Covert communication Evading detection

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Time-stretching and genetic zooms in OutGene: Detecting Undefined Network Attacks 5

Time-stretching and genetic zooms in OutGene: Detecting Unde...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Shekhawat, Jyoti Khurana, Rashmi Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350398267

The study introduces OutGene, a method for detecting malicious behaviour in real time without prior knowledge of assaults or training data. OutGene gathers hosts with comparable behaviour via clustering. We offer the idea of genetic zoom, which involves utilizing a genetic algorithm which select the attributes that is more pertinent to characterize cluster in order to aid human analysts in locating malevolent clusters. Attacks carried out slowly or below thresholds are frequently successful in evading machine learning-based attack detection. We offer the idea of time stretching as a way to identify such stealth attacks. The goal is to discover assaults regardless of how quickly they are carried out by analyzing the stream of events over various time frames. We evaluated OutGene experimentally using data sets currently publicly available and data sets collected at critical military installations. Both genetic zoom and temporal stretching achieved high recall and precision scores, indicating that both are effective. © 2022 IEEE.

关键词： Intrusion detection

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Exploring Dimensionality Reduction Techniques for On-line Analytical Processing 15

Exploring Dimensionality Reduction Techniques for On-line An...

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

作者： Kumar, Anup Inzimam Patel, Srinivasan Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Management - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

Exploring Dimensionality discount strategies for Analytical Processing (OLAP) is an area of research that makes a specialty of the use of diverse techniques to reduce the variety of dimensions needed to investigate a records set. That is high-quality because it reduces processing time, garage area requirements, and simplifies the statistics and outcomes acquired. Diverse strategies are to be had for reducing the wide variety of dimensions, and those are based totally upon the application and the associated information set. The strategies include central factor assessment (PCA), characteristic desire, weighted length reduction, and Locality Sensitive Hashing (LSH). This paper provides a comparative evaluation of those strategies and discusses their strengths and obstacles. The outcomes of this observation will help researchers and practitioners understand each method's talents and limitations while using it in OLAP packages. © 2024 IEEE.

关键词： Analytical Processing application Dimensionality Locality Observation Researchers Technique

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Optimizing Solar-Energy Harvesting Communication Systems: An energy allocation scheme for maximizing data transmission 4

Optimizing Solar-Energy Harvesting Communication Systems: An...

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4th International Conference on Internet of Things and Smart City, IoTSC 2024

作者： Li, Shuai Yao, Zhonghao Chen, Shengbo Schoool of Software Henan University Henan Kaifeng China School of Computer Science & Technology Beijing Institute of Technology Beijing China School of Computer and Information Engineering Henan University Henan Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Kaifeng China

ISBN: (纸本)9781510681767

The solar energy harvesting communication system is the basic support layer that is associated with the Internet of Things (IoT). However, the inherent stochasticity of solar energy brings about extensive research on energy allocation in the system. In this paper, we propose an energy allocation algorithm for maximizing the data transfer (MDT) of a solar energy harvesting communication system while jointly considering the practical constraints of battery capacity and link capacity restrictions. We can solve it through the convex optimization problem, but the solution is extremely complicated. This complexity is the result of the coupling of energy allocation problems caused by restricted battery capacity and link capacity. To simplify the solution process, we propose a decoupled energy allocation (DEA) algorithm. Simulation experiments are conducted through real energy harvesting data sets, and the results show that DEA outperforms the other two heuristic schemes. © 2024 SPIE.

关键词： Convex optimization

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Implementing a Smart Monitoring System with Wireless Sensor and Actuator Networks 11

Implementing a Smart Monitoring System with Wireless Sensor ...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Sharma, Garima Tanisha Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9781665487320

In this article, we investigate how to build an intelligent network unit over a wireless network. To do so, we make use of a resilient routing strategy made available by the Protocol for power (RPL), the definition of which is currently being considered. Our architecture is based on a simple binary web service execution of the RE presentational State Transfer (REST) paradigm and an executing strategy in which every node makes a collection of components (such as atmospheric sensors) available to parties who are concerned with them. We present an evaluation of RPL by means of an experimental inquiry, with the focus being on how well it creates the routing structure to highlight how the efficiency of routing is influenced by the fundamental properties of RPL. © 2022 IEEE.

关键词： Web services

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Analysis of Nutritional Status of Children in Karnataka Based on Machine Learning Techniques Using Indian Demographic and Health Survey Data 1

Analysis of Nutritional Status of Children in Karnataka Base...

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1st International Conference on Computational Intelligence for Information, Security and Communication applications, CIISCA 2023

作者： Sharma, Anjali Rohitaksha, K. JSS Academy of Technical Education Bangalore Dept. of Computer Science and Engineering Karnataka Bengaluru India JSS Academy of Technical Education Bangalore Dept. of Master of Computer Application Karnataka Bengaluru India

ISBN: (纸本)9798350339727

Malnutrition is caused when an individual gets very little or too many nutrients, resulting in health problems. In particular, 'a deficit, excess, or imbalance of energy, protein, and other nutrients' negatively impacts the body's tissues and form. Malnutrition significantly contributes to infant mortality rates in developing nations, and India, as a developing country, experiences challenges associated with malnutrition in children under the age of five. The primary objective of this study is to analyze the factors influencing the nutritional status of under-five children in Karnataka, India, using data from the 2015-2016 Indian Demographic and Health Survey (IDHS). Logistic regression (LR), k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), and Naïve Bayes (NB) are five prominent techniques in machine learning that are being utilized to successfully predict the factors affecting children's nutritional status. Body Mass Index (BMI), Height for Age Z score (HAZ), Weight for Age Z score (WAZ), and Weight for Height Z score (WHZ) are the main factors considered. Additionally, accuracy is used to do a systematic evaluation of the algorithms. Finally, the key factor impacting nutritional status in children was discovered to be HAZ based on accuracy, and the Random Forest produced the greatest findings and it was moderately superior to any other ML algorithms applied in this analysis. © 2023 IEEE.

关键词： Support vector machines

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