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检索条件"任意字段=International Conference on Energy, Communication, Data Analytics and Soft Computing"

共 734 条记录，以下是1-10 订阅

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2017 international conference on energy, communication, data analytics and soft computing, ICECDS 2017

2017 International Conference on Energy, Communication, Data...

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2017 international conference on energy, communication, data analytics and soft computing, ICECDS 2017

ISBN: (纸本)9781538618868

The proceedings contain 700 papers. The topics discussed include: a cloud-based dashboard for time series analysis on hot topics from social media;software defined network for conservation of energy in wireless sensor network;home automation using Internet of Things;implementation of blackhole attack under Aodv routing protocol;cluster analysis of Delhi crimes using different distance metrics;power management of hybrid AC-DC microgrid with Li-ion battery for pulse loads;optimization of vehicular adhoc network using cloud computing;a novel threshold-based clustering method to solve k-means weaknesses;sag compensation in smart grid with distributed generation;estimation of field of view on human fundus image;operation and control of wind/solar/diesel generator based hybrid microgrid in grid connected mode under fault conditions;PAPR optimization in Mimo-OFDM system for Wimax;enhanced pyramid image fusion on visible and infrared images at pixel and feature levels;predictive modeling for identifying the human brain developmental stages;and recovery based TPA in cloud for providing security to outsourced data using CloudSim.

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data-Driven Decision Management: Multi-Modal analytics in a Health Case Study 19

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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19th international conference on Ubiquitous Information Management and communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Uzoma, Agomuo Kingsley Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework’s flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes. © 2025 IEEE.

关键词： Electronic health record

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Real-Time energy Efficient communication for Large-Scale Industrial Internet of Things 2nd

Real-Time Energy Efficient Communication for Large-Scale Ind...

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2nd international conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khan, Musfira Khan, Muazzam A. Farman, Haleem Malik, Asad W. Ramzan, Muhammad Sher Islamabad Pakistan Department of Computer Science Quaid-I-Azam University Islamabad Pakistan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-I-Azam University Islamabad Pakistan Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789819783441

Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in intermediate devices responsible for routing data. Consequently, the deployed network becomes sporadic, hindering connectivity with the industrial controller system, especially critical during emergencies. Furthermore, IoT node failures lead to a short network lifetime, which is intolerable in real-time industrial applications. This paper introduces a multi-hop clustering framework designed for reliable communication in industrial environments. The framework dynamically forms clusters, employing cluster heads and backup nodes to stabilize communication in the absence of a cluster head. Each cluster selects a backup node during the initial cluster head selection and later through data piggybacking. The proposed framework provides efficient communication across the IIoT network with the help of a backup and multi-hop clustering mechanism. The proposed framework is compared with two existing protocols i.e. cluster-based coordination routing (CRC) and energy-efficient quality of service routing (EEQSRP) protocols. The results show a considerable decrease in sensor energy consumption and packet delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： energy utilization

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25th international conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

25th International Conference on Parallel and Distributed Co...

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25th international conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

ISBN: (纸本)9789819642069

The proceedings contain 55 papers. The special focus in this conference is on Parallel and Distributed computing: Applications and Technologies. The topics include: A Meta-reinforcement Learning Framework for Adaptive Quadrotor UAV Attitude Control;Securing energy Transactions for Electric Vehicles: The Blockchain Approach and Encrypted NFTs;optimizing Task Allocation in Heterogeneous Agent Manufacturing Systems;MPG: Multi-modal Personal Health Graph for Alzheimer’s Disease Diagnosis;SMAC: A Secure Multi-authority Access Control Scheme with Attribute Unification for Fog Enabled IoT in E-Health;Convolutional Neural Networks Parameter Training for SCM Algorithm Based on Hausdorff Difference;handling Non-stationarity with Distribution Shifts and data Dependency in Time Series Forecasting;the Two-Stage Stochastic Facility Location Game;regularized Non-monotone γ-weakly Submodular Maximization;fed-MoE: Efficient Federated Learning for Mixture-of-Experts Models via Empirical Pruning;WaitIO-Hybrid: communication for Coupling MPI Programs Among Heterogeneous Systems;the Material Delivery Route Prediction Method Based on Deep Reinforcement Learning;privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications;research on Task Migration Problem Based on Link Uncertainty in Adversarial Scenarios;optimizing Production Component Scheduling in Multivariate Industrial Networks with Dynamic Changes in Production Costs;multi-agent Collaboration for Time-Sensitive Tasks in Multiple Networked Adversarial Scenarios;containerized data-Flow Processing for Scalable Real-Time analytics on Edge Devices;fast Approximation for Scheduling Malleable Jobs on Parallel Batch Machines with Rejection;real-Time and In-Situ Temperature Profiling for Determining Detonation of White Dwarf Mergers;accparser: A Standalone OpenACC Parser and Its Usage on Mapping OpenACC to OpenMP Directives;Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams;long-Term and Periodicity-Aware Spatio

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Detection and Prevention Various Network Attacks Using soft computing Approaches: An Overview 1

Detection and Prevention Various Network Attacks Using Soft ...

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Deore, Mahendra Gosavi, Chhaya MKSSS's Cummins College of Engineering for women Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

The Internet of Things (IoT) has emerged as a powerful tool for remote environmental monitoring, attracting considerable interest from the research community. Through IoT-enabled wireless technologies, various applications such as Intelligent Transport Systems (ITS), healthcare, home automation, and precision agriculture have benefited from enhanced monitoring capabilities. The functionality of IoT applications is centred around sensor nodes, which are deployed in the field to collect data and transmit it to a base station (BS) for processing. However, IoT networks that utilize Wireless Sensor Networks (WSN) face significant challenges. Two major issues are energy efficiency, which affects the longevity and performance of the network, and security vulnerabilities, which expose the system to a variety of threats such as sinkhole attacks, black hole attacks, and Denial of Service (DoS) attacks. These security concerns can severely disrupt communication and compromise the integrity of the data being transmitted. This survey paper focuses on examining recent security strategies designed to mitigate such threats in IoT networks. A systematic review was conducted to explore lightweight security solutions, with particular attention to trust-based and probe route-based mechanisms. These mechanisms offer promising approaches to safeguarding IoT networks while maintaining efficiency. The paper also presents a comparative analysis of these methods, evaluating them based on security techniques, types of attacks they address, and key performance metrics such as energy consumption and response times. The findings of this paper serve as a critical evaluation of the existing research, highlighting the strengths and limitations of the security measures reviewed. Through this comparative analysis, the study provides valuable insights into the effectiveness of various security approaches in protecting IoT-enabled networks from evolving cyber threats. © 2025 IEEE.

关键词： Probes

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Dynamic Entropy Constrained soft Actor-Critic Based Task Scheduling in Cloud Resource Management

Dynamic Entropy Constrained Soft Actor-Critic Based Task Sch...

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Integrated Circuits and communication Systems (ICICACS), IEEE international conference on

作者： Prakash Murugesan Sairam Madasu Artificial Intelligence and Data Verizon USA Cloud Computing and Artificial Intelligence Microsoft USA

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Resource scheduling in cloud computing refers to the process of allocating computational resources for efficient task completion, aiming to minimize resource usage while ensuring timely task completion. Traditional approaches of resources allocation face challenges such as energy constraints and strive for dynamic scheduling to meet unpredictable workload demands. To address these limitations, the Dynamic Entropy Constrained soft Actor-Critic (DESAC) method has been proposed to enhance resource utilization and reduce energy consumption. The DESAC facilitates entropy regularization during training, promoting exploration by adjusting entropy to ensure the strategy remains less deterministic, and facilitating comprehensive exploration of the state-action space. The system's performance and energy efficiency are evaluated using a fitness function based on energy consumption, processing 1,000 tasks (each with a value of 35.47), and comparing the results to those of the existing Actor-Critic (A3C) method.

关键词： Training energy consumption Cloud computing Costs Processor scheduling Heuristic algorithms Dynamic scheduling Entropy energy efficiency Resource management

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Predictive analytics for Power Transformers and energy Consumption Using Machine Learning Approaches

Predictive Analytics for Power Transformers and Energy Consu...

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Advances in Electrical, computing, communication and Sustainable Technologies (ICAECT), international conference on

作者： Libina M Aswin R Mohamed Imthiyas M Department of Information Technology IFET College of Engineering Villupuram India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The growing global population, energy-hungry smart technologies, and increasing electricity consumption present numerous challenges. Nonlinear demand patterns have not yet been adequately model, even though numerous technical methods and simulation tools have been employed to determine energy consumption with high accuracy. While artificial intelligence techniques rely on historical data, conventional engineering techniques rely on dynamic equations. This research offers a novel method for projecting household power usage, which may be used to monitor and prepare for energy use more effectively. To prevent outages, power transformers, which are expensive but essential components of networks for the transmission and distribution of electricity, must be constantly monitored. An effective metric for assessing transformer status based on various factors is the health indicator (HI). While the HI approach has a wealth of documentation, there is a lack of extensive discussion of its reliability and how to handle uncertainty resulting from limited data. By using AI algorithms that account for data ambiguity, this study seeks to improve transformer health assessment accuracy. Transformer insulation is assessed using the suggested HI framework according to three criteria: paper condition, dissipated gas analysis (DGA), and oil quality. Furthermore, to enhance predictive performance, It can be provide a new hybrid algorithm called the Hybrid Insight Classifier (HIC), which combines of a contrast to performance are made an ml-based hi clone nave bayes decision tree and support vector machine a benchmark for this purpose is the scoring weighting HI approach.

关键词： Support vector machines energy consumption Electricity Oil insulation Prediction algorithms Mathematical models Bayes methods Decision trees Monitoring Power transformer insulation

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Real-Time energy Consumption Insights for Hospitals with IoT and Machine Learning

Real-Time Energy Consumption Insights for Hospitals with IoT...

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Emerging Systems and Intelligent computing (ESIC), international conference on

作者： C. Uthayakumar N. Mohankumar Elakkiya K E Rama Lakshmi T. R. GaneshBabu C. Srinivasan Department of Electronics and Communication Engineering Vivekanandha College of Engineering For Women Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharastra India Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Computer Science and Design Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Reducing operating expenses and minimizing environmental effects requires efficient hospital energy management. This research provides a comprehensive approach for tracking and analyzing energy consumption in real time. With the Internet of Things (IoT) and Machine Learning, especially Support Vector Regression (SVR). energy consumption, environmental factors, and operating parameters are continually monitored by the proposed system's network of IoT sensors. A centralized cloud platform receives data streams from these sensors in real-time, with high resolution. A powerful machine learning method, SVR, processes and analyzes data. It is well known for its accuracy in predicting continuous variables. SVR models energy consumption trends, spot outliers, and predict future energy use. The predictive features of the system allow hospital managers to spot inefficiencies, forecast peak energy needs, and take proactive actions to optimize energy consumption. energy data can be easily interpreted, and decisions can be made with the help of the system's visualization and analytics features. This system's combination of IoT and SVR offers a scalable and inexpensive method to improve healthcare facilities' energy efficiency. According to experimental data from pilot installations, there was a notable improvement in overall energy management techniques and a considerable decrease in energy waste. According to this research, modern IoT and machine learning technology may drive sustainable hospital operations and environmental stewardship.

关键词： energy consumption Accuracy Hospitals Machine learning Predictive models Real-time systems data models Sensors Internet of Things Monitoring

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data-Driven Decision Management: Multi-Modal analytics in a Health Case Study

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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international conference on Ubiquitous Information Management and communication (IMCOM)

作者： Okechukwu Clement Agomuo Zohaib Khan Agomuo Ijeoma Otuomasirichi Agomuo Kingsley Uzoma Junaid Hussain Muzamal Department of Operations and Information Systems University of Utah Salt Lake City USA dept. of Software Engineering FAST - NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast - NUCES Fast School of computing Lahore Pakistan

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework's flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes.

关键词： Accuracy Decision making Mortality Medical services Organizations Predictive models Transformers data models Information management X-ray imaging

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Consumer Behavior in the Post-Pandemic Era: Insights from Machine Learning and AI-Driven analytics

Consumer Behavior in the Post-Pandemic Era: Insights from Ma...

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Advances in Electrical, computing, communication and Sustainable Technologies (ICAECT), international conference on

作者： Divya Nimma Ch. Anudeep Tripti Sahu Nusrat Hamid Shah Lubna Hamid Shah B Kiran Bala PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Management studies Shri Vishnu Engineering college for women (A) Bhimavaram India MIMA Institute of Management Pune Maharashtra India College of Engineering and Computer Science Jazan University Kingdom of Saudi Arabia Department of Management and Marketing College of Business Jazan University Kingdom of Saudi Arabia Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

COVID-19 has been the disruptor in the world, and the society has been changed from one perspective of the consumer behavior in terms of digital platforms and their preferences for safety, convenience and sustainability. It is therefore very important for any business to acquaint itself with these changes so that may be in a position to effectively compete for the market within the current ever-changing business environment. Conventional consumer behavior models, on the other hand, rely on fragmented data sources and analytical tools that may not reflect the current post-pandemic consumption patterns. Such models are also incapable of incorporating multiple data types and can also fail to address changing social factors. In response, a Multi-Modal Consumer Behavior Prediction Framework (MCBP) that considers a vast array of data sources is introduced. BERT and GNNs are used to give a detailed, real-time interpretation of consumer behaviour accomplished by the MCBP framework. It uses BERT to extract and analyze sentiment and topics of the textual data and GNNs to capture influence and peer effects on purchase decisions. The findings of the study show the effectiveness of the proposed framework with an accuracy of 99%. 5% and the implementation is done using Python. The high level of accuracy shown assures the success of the MCBP model in measuring and forecasting consumers' behaviors in the post-pandemic world.

关键词： Consumer behavior Accuracy soft sensors Predictive models data models Social factors Real-time systems Safety Sustainable development Business

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