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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

IEEE Transactions on Services Computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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Secrecy Performance Analysis of Space-to-Ground Optical Satellite Communications 16

Secrecy Performance Analysis of Space-to-Ground Optical Sate...

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16th International Conference on Advanced Technologies for Communications, ATC 2023

作者： Nguyen, Thang V. Phamz, Thanh V. Phamx, Anh T. Ngoc, Dang T. Posts and Telecommunications Institute of Technology Wireless Systems and Applications Lab. Hanoi Viet Nam Shizuoka University Department of Mathematical and Systems Engineering Japan The University of Aizu Department of Computer Science and Engineering Japan

ISBN: (纸本)9798350301328

To cope with the scarcity of radio frequency (RF) counterparts, free-space optics (FSO) based on satellite communications technologies have recently gained a lot of interest. Firstly, this investigates the security of space-to-ground intensity modulation/direct detection FSO satellite communications in various effects from the physical world, such as propagation loss, beam misalignment, cloud attenuation, and fading caused by atmospheric turbulence. Next, A wiretap channel composed of a genuine broadcaster Alice (i.e., the satellite), a legitimate user Bob, and an eavesdropper Eve is considered over turbulent channels characterized by the Fisher-Snedecor mathcal{F} distribution. In addition, the closed-form expressions of the secrecy performance metrics, including average secrecy capacity, secrecy outage probability, and strictly positive secrecy capacity, are derived in our study. Finally, the numerical findings indicate that the satellite altitude of 600 km should be used to achieve a secrecy outage of roughly 50% for the whole turbulence regime. In addition, high levels of cloud liquid water content (CLWC) could improve the secrecy performance as it is shown that, the average secrecy capacity increases by about 12% when the CLWC increases from 1mathrm{~mg}/mathrm{m}{3} to 2mathrm{~mg}/mathrm{m}{3}. © 2023 IEEE.

关键词： Atmospheric turbulence

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A SPB based LB Algorithm in Order to Schedule the Sequence Task in CCS 4

A SPB based LB Algorithm in Order to Schedule the Sequence T...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bindu, G. B. Hima Manjula, L. Tellur, Anil Vincy Devi, V.K. Nasrulaah, Najlaa Muhsin, Omar The Apollo University Department of Cse School of Technology Chittoor India The Oxford College of Engineering Department of Computer Science and Engineering Bangalore India Karnataka Bengaluru India Adi Shankara Institute of Engineering&Technology Department of Computer Applications kalady India National University of Science and Technology Faris Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

The increasing pervasiveness of computers in almost every aspect of our everyday lives has led to a commensurate rise in the need for computing resources, including bandwidth, processing power, and storage space. Originally, computer networks were created to address this difficulty, which helped ideas and paradigms like distributed computing, grid computing, and cluster computing to develop. But providing such services calls for advanced methods to effectively handle workloads and quickly attend to customer demands. One of the major problems with cloud computing is load balancing, which is a vital procedure that distributes work among virtual computers to avoid excess or underuse. In order to support the increasing number of users, the algorithms controlling this work assignment need to balance robustness with efficient use of resources. The related costs and hazards increase, especially for larger organisations, which emphasises how crucial it is to evaluate the risks involved in deploying load balancing algorithms in order to achieve desired service levels. Assessing the algorithms, applications, and rules in a virtualized setting is a useful method for risk assessments. With the use of simulation tools, one may practically mimic the intricacies of actual cloud settings and gain insights into possible problems and performance. As a powerful simulation toolset, CloudSim is intended to fully represent and simulate various cloud environments. It makes experimentation and optimisation easier by providing the ability to design and test different load balancing algorithms and cloud broker policies. © 2024 IEEE.

关键词： Cluster computing

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Cloud-Connected Wearable Devices for Lone Worker Protection With Wireless Sensor Networks 4

Cloud-Connected Wearable Devices for Lone Worker Protection ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mohankumar, N. Manivannan, K.K. Babu, A. Ramesh Sampoornam, M. Maria Kovarasan, Rajesh Kambattan Srinivasan, C. Nagpur Campus Maharashtra Pune India Kcg College of Technology Department of Mechatronics Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Various sectors continue to prioritize lone worker safety, which requires creative ways to provide quick support and monitoring. To safeguard lone workers, this paper introduces a new method that makes use of cloud-connected wearable devices that are coupled with Wireless Sensor Networks (WSNs). The system design relies on sensors embedded in wearable devices that can monitor a range of environmental elements as well as the vital signs of workers. A central cloud platform receives data in real-time via wireless communication from these devices and processes and analyzes it. The system may detect possible dangers or crises using smart techniques and immediately notify those who are supposed to react or the proper authorities. Cloud computing also makes it easy to store, analyze, and monitor large amounts of data over time, which is great for doing proactive risk assessments and improving safety standards. It demonstrates solution's capacity to improve lone worker safety, reduce reaction times, and mitigate hazards via simulations and actual deployment scenarios, efficacy and dependability. This growing body of knowledge on how to protect lone workers in various workplaces by using the power of IoT and cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Estimating Fetal Gestational Age from Ultrasound Images Using Deep Learning Techniques

Estimating Fetal Gestational Age from Ultrasound Images Usin...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Suganthi, N. Mohana Subhashni, R. Chitra, A. Rajakumari, S. Priya, T. Anitha, P. Caroline Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India St.Peter's Institute of Higher Education and Research Department of Computer Applications Avadi Chennai54 India EastPoint College of Higher Education Department of Computer Application Jnana Prabha East Point Campus Karnataka Bengaluru560049 India

ISBN: (纸本)9798331509828

Monitoring fetal development and guaranteeing mother health depend on correct estimate of fetal gestational age. Conventional approaches of gestational age estimate mostly depend on manual estimations from ultrasonic scans, which may be laborintensive and prone to human mistake. Variability in operators, picture quality, and fetal position might potentially affect current methods and cause possible errors and inconsistent outcomes. We offer the Ultrasound Images Using Deep Learning Techniques (UI-DLT) framework, a strong, automated method for fetal gestational age estimate, to handle these problems. UI-DL T analyzes ultrasonic pictures and enhances the accuracy and consistency of gestational age estimations by using deep learning-more especially, convolutional neural networks (CNNs). UI-DLT reduces human error and standards outcomes across several clinical environments by automating the analytic process. CNNs are used in the proposed system to categorize and examine ultrasonic pictures, therefore spotting important information on fetal development and growth. By extracting deep picture elements that match developmental phases, our method guarantees accurate and consistent gestational age estimations, thereby beyond the capacity of conventional manual techniques. Experimental data on ultrasonic image datasets shows significant increases in estimating accuracy of the UI-DLT framework. This approach not only improves the accuracy of gestational age prediction but also helps to progress clinical decision-making and mother-fetal health care. © 2025 IEEE.

关键词： Ultrasonic imaging

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Smart Auto Image Captioning Using LSTM and Densenet Network 6

Smart Auto Image Captioning Using LSTM and Densenet Network

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arumugam, M. Vijayakumar, P. Siddharrth, G.M. Rohini, J. Karthikeyan, A. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350304480

The integration of semantic attention increase photo captioning ability. The premise underlying semantic attention-based techniques is that the model should prioritize words or qualities with high semantic value. Prior studies focused on the attribute detector and the captioning network as distinct entities. This hampers the best use of semantic data. Image captioning is the computational process of producing accurate written descriptions of an image's or video's visual content. Captioning images makes it easier to construct contextualized interpretations, allowing people to situate photographs within a larger context. Using photo captions can be useful in a variety of contexts, such as conducting research with an unlabeled image collection or attempting to identify. previously unknown patterns. Subtitles added to the photos using a Deep Learning Model. Recent advances in the fields of deep learning and natural language processing have made it much easier to generate captions for input photographs. This study will use neural networks to generate image captions on their own. The Long Short-Term Memory (LSTM) model, a type of recurrent neural network, used as an encoder, together with a pre-trained lexicon and visual attributes. This enables the extraction of image data and the generation of captions. © 2023 IEEE.

关键词： Deep Learning Model Densenet Network Long Short-Term Memory Remote Sensing

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mHAR: a novel convolutional recurrent model for recognizing motion-based human activity

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Procedia computer science 2023年 229卷 218-227页

作者： Prabhat Kumar S. Suresh Department of Computer Science Institute of Science Banaras Hindu University Varanasi 221 005 India Department of Computer Engineering & Applications GLA University Mathura 281406 India Department of Computer Applications National Institute of Technology Kurukshetra 136119 India

With sensors built into smartphones and wearable devices, Human Activity Recognition (HAR) makes it possible to identify regular activities. One of the challenges is to access the motion information from activity data and extract the features from this information. A general model trained without motion features may perform poorly while recognizing the contextual patterns in human activities. In this paper, we proposed the mHAR (motion HAR) model for recognizing static, gradual, and dynamic motion activity to address this challenge. We examine the impact of the proposed model against the general non-convolutional and convolutional recurrent networks in terms of accuracy rates and F1 score. As an experimental dataset, the publicly accessible WISDM was employed. Additionally, the findings of the experiments demonstrate that, in contrast to the unbalanced class problem, motion features play a significant role in activity recognition by achieving accuracy rates of 92.29% and an average F1 score of 83.33%.

关键词： Human Activity Recognition (HAR) Deep learning (DL) Motion Features Smartphone Sensors Convolutional Recurrent Non-Convolutional Recurrent

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An Artificial Intelligence Network based-Host Intrusion Detection System for Internet of Things Devices 4

An Artificial Intelligence Network based-Host Intrusion Dete...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Jain, Ashish Srinivasa Rao, B. Chattopadhyay, Saumitra Kumar, Aniruddh Muthuraman, M.S. Manjula, A. Bsss College Department of Computer Applications Madhya Pradesh Bhopal India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Telangana Hyderabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Electronics Government Polytechnic Aurai Uttar Pradesh Bhadohi India Psna College of Engineering and Technology Department of Mathematics Tamilnadu Dindigul India Jyothishmathi Institute of Technology and Science Karimnagar India

ISBN: (纸本)9798350300093

Internet of Things (IoT) is currently employed in almost all the areas, including applications in smart cities, smart homes, e-Wellbeing, and others. Due to its wider utilization, IoT security has become a serious concern. A secure Intrusion Detection System (IDS) for the Internet of Things is often built using artificial intelligence (AI) and its subsets, deep learning (DL), and machine learning (ML). Industrial IoT devices, which are readily available, are regularly used by researchers and industry experts. This research study investigates the possibility of deploying a DL-Based Host-IDS (DL-HIDS) on specific commercial IoT devices. In this study, an optimized Convolutional Neural Network (O-CNN) based on DL is used. The proposed model's efficiency is evaluated by utilizing performance metrics like recall, precision, accuracy, and f1score. The proposed model's effectiveness is verified by analyzing the promising results obtained from the implementation of the proposed DL-HIDS on various existing models. © 2023 IEEE.

关键词： Internet of things

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A Study on Transcontinental Diabetes Datasets Using a Soft-Voting Ensemble Learning Approach 1

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Pradhan, Gaurav Thapa, Gopal Pradhan, Ratika Khandelwal, Bidita Visalakshi, S. Department of Computer Applications Sikkim Manipal Institute of Technology SMU Majitar Rangpo East Sikkim India Department of Computer Science and Engineering Sikkim Institute of Science and Technology Chisopani South Sikkim India Department of Medicine Sikkim Manipal Institute of Medical Sciences SMU Tadong East Sikkim Gangtok India

ISBN: (数字)9789819919833

ISBN: (纸本)9789819919826

Worldwide, diabetes mellitus (DM) is one of the primary causes of illness and death. Diabetes has a well-established hereditary link, which is widely acknowledged. Diabetes is a disease that occurs when the blood glucose level in the body is too high. Diabetes can result in serious cardiovascular problems. According to the WHO, there are 3.7 million fatalities by the age of 70, and cardiovascular disease is increasing day by day which leads to a high death rate (https://***/diabetes/basics/*** [16]). Therefore, the detection of diabetes at an early stage can help patients’ lives. Various research is going under the healthcare sector in the prediction of disease in the field of data mining. Data mining techniques are frequently utilized in early disease diagnosis. In this paper, five diabetes datasets have been used and the main objective is to compare the various machine learning algorithms for detecting and classifying diabetes. The algorithms used for classification are Random Forest, Logistic Regression, Naïve Bayes, XGBoost, and the proposed ensemble approach. The proposed ensemble approach provided better accuracy as compared with individual classification algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network 8

Optimized Smart Agricultural Production Systems using Enhanc...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, S. Gomathi Goyal, Himanshu Rai Lavanya Muthukumar, E. Megha, Mamtaben Kamleshbhai Parikh, Swapnil Peri College of Arts and Science Pg Department of Computer Science and Computer Applications Tamil Nadu Mannivakkam600048 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Viruthunagar626126 India Nehru College of Management Department of Mba Coimbatore Tamil Nadu Thirumalayampalayam641105 India Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of Cvm University Department of Computer Science Gujarat Vallabh Vidyanagar388120 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India

ISBN: (纸本)9798350376425

Smart agriculture, driven by IoT technologies, is becoming increasingly essential due to global population growth and rising food demands. However, challenges such as energy supply constraints and routing issues often result in data transmission failures, high energy consumption, reduced network lifetime, and delays. To address these issues, this study proposes a novel solution: an enhanced smart agricultural production system based on an Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network (EPar-GrAnCNN). This system enhances energy management by incorporating the Emperor Penguin Optimizer (EPO) for the formation and selection of cluster heads, aiming to increase power conservation and extend network lifespan. Additionally, the system leverages deep learning techniques, including the Enhanced Parallel Convolutional Neural Networks (EPar-CNN) and Green Anaconda Optimization (GAO), to analyze agricultural data and extract valuable insights. The EPar-GrAnCNN model accurately predicts crop health, the severity of water deficits, and other factors affecting crop growth, guiding farmers in the optimal utilization of resources. computer experiments conducted on the Python platform demonstrate that the proposed approach outperforms existing methods, achieving higher throughput (98.4%), a superior packet delivery ratio (99.3%), and lower energy consumption (0.30 mJ). These results highlight the efficiency of the approach and its potential for further advancement in smart agriculture. © 2024 IEEE.

关键词： Emperor Penguin Optimizer (EPO) Enhanced Parallel Convolutional Neural Network (EPar-CNN) Green Anaconda Optimization (GAO) Internet of Things (IoT) Smart Agricultural Systems

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