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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89784 条记录，以下是311-320 订阅

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Short Paper: AI-Driven Disaster Warning System: Integrating Predictive Data with LLM for Contextualized Guideline Generation 24

Short Paper: AI-Driven Disaster Warning System: Integrating ...

引用

11th international conference on Networking, systems and Security, NSysS 2024

作者： Faiaz, Md. Abrar Nawar, Nowshin Bangladesh University of Engineering and Technology Dhaka Bangladesh University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9798400711589

Early warning systems are the backbone of disaster management, but traditional systems often send static alerts with no personalized advice. This work investigates an AI-powered disaster warning system using predictive analytics and LLMs to design personalized recommendations over efficient response strategies. This system ensures that real-time information on context-specific and geographically relevant preparedness is tailored to the needs of varied populations. It was built using the LangChain framework and features an architectural composition with a predictive layer in disaster monitoring and an advisory layer for proactive safety guidelines. It integrates current weather conditions with advanced machine learning algorithms to enhance disaster prediction and utilizes LLMs in constructing customized actions, hence streamlining emergency messaging with enhanced clarity and relevance. Among the tested models, it turned out that the Gemini Pro LLM is the most effective, consistently generating outputs appropriate in context and accurate. Analysis under different temperature settings demonstrated the ability of the model to maintain high precision on various types of crises. The findings indicate that this approach can transform traditional early warning systems into more adaptive, user-centered frameworks, hence enhancing disaster management practices. © 2024 Copyright held by the owner/author(s).

关键词： Alarm systems

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Smart Wellness: AI-Driven IoT and Wearable Sensors for Enhanced Workplace Well-Being

Smart Wellness: AI-Driven IoT and Wearable Sensors for Enhan...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Sujanthi, S. Abishek Raghavan, V.B. Poovizhi, M. Kayathri, T.L. Balaji, P. Hariharan, M. M. Kumarasamy College of Engineering Tamil Nadu Karur India M. Kumarasamy College of Engineering Tamil Nadu Karur India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Professionals deal with a lot of physical and mental stress in today's fast paced work environments, which frequently results in long-term health problems, decreased productivity, and financial difficulties. This project presents a smart wellness solution for office workers that leverages wearable technology to track activity levels and health metrics utilizing an MLP algorithm powered by machine intelligence and IoT based alert notification using GSM. To encourage healthier habits, the system actively monitors posture, recognizes weariness, and recommends timely breaks. The incorporation of a Galvanic Skin Response (GSR) sensor into an Internet of Things framework to gauge stress and dehydration levels is a crucial component. The gadget uses integrated AI algorithms to spot anomalous trends in daily routines or wellness metrics, warning users to take preventative action for better health management. The method seeks to improve general well-being and promote healthier habits to reduce stress-related risks, increase productivity, and cultivate a more positive and sustainable work environment. © 2025 IEEE.

关键词： Wearable technology

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An Intelligent and Dynamic Replication Scheme for Reliable Cloud Environment 3

An Intelligent and Dynamic Replication Scheme for Reliable C...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Damodhar, M. Rao, Ch.D.V. Subba Sri Venkateswara University College of Engineering Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331508456

Recently, cloud computing research, especially Virtual Machine based replication schemes and their applications, were becoming prevalent. A cloud data center (DC) comprises of hosts with an enormous of virtual machines (VMs) where determining replica quantity and replication order is a tedious task to minimize cost and bandwidth. Majority of the existing dynamic replication techniques rely on budget constraints and server failure rate to guarantee customer requirements. But, there is a strong need to determine replication order of servers to retain the performance and system stability. In this paper, we suggest two different algorithms for better selection with proper and intelligent placement of replicas in the cloud region. The proposed algorithms towards dynamic replication are request based replica selection (RRS) and ant colony optimization (ACO). The foremost proposed algorithm was request based replica selection used for obtaining the best selected replicas depends upon the VM performance ranking and failure probability based on the task requirement. Conversely, the subsequent offered algorithm is ACO that is helpful to find the best location to place the designated replica depends on the least-cost distance metric, and the replicas availability in order to encompass and regulate with the entire network load and the link bottlenecks. The performance of proposed techniques using CloudSim was evaluated against various pro-active fault tolerance strategies. The simulation results gives that dynamic replica selection provides efficient task replication when compared with the existing pro-active algorithms. Additionally, ACO realizes high availability, low cost, and less bandwidth utilization. © 2025 IEEE.

关键词： Ant colony optimization

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Comparison Analysis of Transfer Learning Models for Deep Fake Image Detection 2

Comparison Analysis of Transfer Learning Models for Deep Fak...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Sujanthi, S. Priya, R. Ranga Shree, S. Suruthika, S. Sushmitha, S. M.Kumarasamy College of Engineering Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Deepfake identifies fake videos and photographs. To create deepfakes, machine learning algorithms change video or reputation features like faces. Deepfake identifies fake videos and photographs. New deep learning generative algorithms may make fake films and photos seem real, worrying many. Personal and social safety are jeopardized. We urgently need computational methods to detect fake material and alert consumers of picture and video changes. We evaluate deep learning-based deepfake detection. We want to automate field development fake content detection. Assessing the work's strengths and faults, we find deepfakes. AI, NLP, and computer vision use deep machine learning for efficiency and usability. Deepfakes create realistic images and videos using deep learning. We test multiple deep learning-based deepfake detection and generation approaches. Several deepfake detection techniques are examined. Researchers benefit from our paper's social media deepfake detection. Compare current and previous industrial practices and datasets. © 2025 IEEE.

关键词： Deep learning

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A Framework for Real-Time Monitoring of Power Consumption of Distributed Calculation on Computational Cluster 15

A Framework for Real-Time Monitoring of Power Consumption of...

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15th international conference on Cloud Computing and Services Science, CLOSER 2025

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology al. 1000-lecia PP 7 Kielce Poland

ISBN: (纸本)9789897587474

This paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. The framework allows to visualize and analyze the provider results based on the context information about the performed calculation. The first part of the framework is devoted to monitoring the power consumption during the execution of machine learning algorithms and the performance of NoSQL storage. The second part is dedicated to the testing of distributed data storage, called Scalable Distributed Two-Layered Data Structure (SD2DS). The results show that the framework can be used in the development of a management system that could schedule computations to take full advantage of renewable energy. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cluster computing

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Optimizing Forest Fire Detection systems: A Deep Learning Approach with UAVs and Binary Sand Cat Swarm Optimization 3

Optimizing Forest Fire Detection Systems: A Deep Learning Ap...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Kumar, T. Rajesh Devi, K. Komala Subramanian, P. Mahaveerakannan, R. Balamanigandan, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering India Cmr Institute of Technology Department of Ise Bengaluru India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Networking Institute of Cse India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Devastating forest fires are an international environmental crisis that endangers both people and their valuable natural resources. Forest fires are becoming more common and more destructive, leading to significant depletion of natural resources. A combination of human interference with natural processes and the effects of climate alteration have contributed to a recent uptick in the frequency of wildfires. Combating this issue requires firefighters to respond swiftly and identify fires from early smoke so they can prevent the fires from spreading. An efficient system for detecting and monitoring fires is essential for reducing this risk. The purpose of this study is to investigate and assess the state of the art in forest fire detection and monitoring with the use of deep learning algorithms and drones or unmanned aerial vehicles (UAVs). An efficient and economical way to monitor in real-time and detect fires early is to use drones that are outfitted with specialised sensors and cameras. Using the suggested customised Convolutional Neural Network models, the datasets were tested. Deep Q learning (DQL) is used to extract features, which enhances the accuracy of classification. When compared to other transfer learning methods, such as VGG16, Xception, ResNet50, DenseNet121, Inception ResnetV2, besides Inception V3, their outcomes are evaluated. To analyse and solve the optimal parameter selection problem, the authors suggest using Binary Sand Cat Swarm Optimisation (bSCSO) to fine-tune the parameters of the prototypical. The suggested custom VGG16 model outstripped state-of-the-art models by a significant margin (98.14%), which was measured by its ability to generalise to unknown images. © 2025 IEEE.

关键词： Drones

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Classification and Analysis of EEG Montage

Classification and Analysis of EEG Montage

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Sivaranjani, S. Sneha, R. Sowmiga, B. Sruthikaa, K.V. Yuvashree, R. M. Kumarasamy College of Engineering Department of Electronics and Communication Engineering Thalavapalayam Tamilnadu Karur639113 India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Electroencephalography (EEG) has gained considerable traction in diverse research and clinical applications due to its non-invasive nature, real-time brain activity monitoring capabilities, and increasing affordability. This literature survey explores the applications and advancements of EEG technology in fields such as sleep studies, seizure detection, Alzheimer's disease diagnostics, user authentication, intensive care monitoring, and brain-computer interfaces (BCIs). Innovations such as reduced-channel setups and subdermal electrodes have improved EEG accessibility, while machine learning algorithms have enhanced signal interpretation and diagnostic accuracy. For instance, simplified EEG configurations show promise in seizure detection and home sleep monitoring, while TMS-EEG-based methods improve Alzheimer's diagnostics. EEG-based user authentication methods present a unique approach to secure identification, though privacy remains a concern. Additionally, EEG-driven BCIs are evolving, offering new ways for individuals with motor impairments to interact with technology. This review highlights EEG's transformative potential across these domains, identifies current limitations, and suggests future research directions, particularly in enhancing signal quality, data privacy, and diagnostic accuracy. © 2025 IEEE.

关键词： Clinical research

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Efficient Task Scheduling for Cloud Environments with MSA: Enhancing Makespan and Resource Utilization 5

Efficient Task Scheduling for Cloud Environments with MSA: E...

引用

5th international conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Veeramachaneni, Vinod Mallick, Pradeep Kumar Rout, Subhashree Pareek, Piyush Kumar Colorado Technical University Department of Information Technology United States Kiit Deemed to be University School of Computer Engineering India Nalanda Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Nitte Meenakshi Institute of Technology Yelahanka Karnataka Bengaluru560064 India

ISBN: (纸本)9798331522100

People still want the fastest and most effective answers, even as technology improves. Since the advent of cloud computing and its multiple substructures, many algorithms have been proposed to improve the presentation of each unit or structure. The massive cloud can solve any user-supplied task. Therefore, new methods are proposed to improve the process and reduce evaluation time. The task scheduler allocates jobs to virtual machines to maximize efficiency and minimize execution time in cloud computing. This paper introduces the Mantis Search Algorithm (MSA) as a scheduling solution and tests and compares it to other algorithms based on efficiency, makespan, and cost limits. Sexual cannibalism, prey pursuit, and prey assault comprised this optimization process. A multiobjective fitness purpose is created in the CC stage by reducing makespan and optimising resource utilization. There are several ways to test the strategy and evaluate the results. Simulations showed that the MSA technique outperformed alternatives in numerous situations. © 2025 IEEE.

关键词： Virtual environments

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Generative Waveform Design Methods in Complex Electromagnetic Spectrum Scenarios 24

Generative Waveform Design Methods in Complex Electromagneti...

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4th international conference on Signal Processing and Communication Technology, SPCT 2024

作者： Hu, Yuhao Hu, Weidong Du, Xiaoyong Lei, Gang Xi’an Institute of Electronic Engineering Shaanxi Xi’an China College of Electronic Science and Technology National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9798400710636

With the development of complex spectrum environments such as 5G and 6G, the utilization of electromagnetic spectrum resources has become increasingly intensive, imposing higher demands on the performance and adaptability of traditional radar systems. The fixed waveform design approach commonly used in traditional radar systems is easily identifiable and prone to interference in complex spectrum environments, making it difficult to meet dynamically changing mission requirements. To address this challenge, this paper proposes a generative waveform design method suitable for complex spectrum environments. This method constructs a performance envelope based on radar mission requirements under multiple constraints and generates a feasible waveform set through constrained rule-based searches, thereby enhancing the flexibility and adaptability of waveform design to the environment. Experiments demonstrate that the proposed generative waveform design exhibits greater robustness and anti-interference capabilities in complex spectrum environments. Comparative analysis using classic clustering and sorting algorithms further validates the effectiveness and superiority of this method over traditional fixed waveforms, providing new insights for the design and optimization of radar systems in complex spectrum environments. © 2024 Copyright held by the owner/author(s).

关键词： Radar interference

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Exploration of the generalization and hallucination in deep learning: a study based on imaging through scattering medium 17

Exploration of the generalization and hallucination in deep ...

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17th international conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Liu, Honglin Zhang, Xuyu Xie, Gengchen Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Science Beijing100049 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

With fast developments of computational power and algorithms, deep learning has made breakthroughs and been applied in many fields. However, generalization remains to be a major challenge, and the limited generalization capability severely constrains applications of deep learning in practice. The hallucinations issue is another unresolved conundrum faced by deep learning and large models. By leveraging a physical model of imaging through scattering media, we studied the lack of generalization to datasets and system response functions in deep learning respectively, identified their causes, and proposed universal solutions. The research also provides an explanation for hallucinations. In general, it enhances the interpretability of deep learning from a physics-based perspective. It will pave a way for direct interaction between deep learning and the physical world, facilitating the transition of deep learning from a demo model to a practical tool. © 2025 SPIE.

关键词： Education and training Deep learning Image restoration Imaging systems Associative arrays Image quality Scattering Scattering media Artificial intelligence Light sources and illumination

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