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检索条件"任意字段=14th International Conference on Computer Systems and Technologies, CompSysTech 2013"

共 1923 条记录，以下是71-80 订阅

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Intelligent Anti-theft System with Automated Spray Control using ESP32 WROOM

Intelligent Anti-Theft System with Automated Spray Control u...

引用

international conference on Communication systems and Network technologies (CSNT)

作者： Geetha Shanmugam Deepa Balusamy Elamparithi S Vetriselvan R Mourin Praamoth S Department of Computer Science and Engineering Nandha Engineering College Erode India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

this project demonstrates a low-cost anti-theft solution built around the ESP32-WROOM microcontroller, with integrated chemical spray for security. the system is designed to detect unauthorized entry and respond with spraying disinfectant chemicals such as pepper spray, thus ensuring security as well as being effective against attackers. the ESP32-WROOM microcontroller is known for its Wi-Fi and Bluetooth capabilities, which plays a key role in providing real-time monitoring and control. Users can also access the system via mobile or web applications. In case of any abnormal situation, the user will be notified immediately. this hospitality provides a living environment for its users. therefore, they can move with peace of mind and even safeguard their valuable things from anywhere. this system also has a modular design that saves energy for easy installation and is suitable for residential, small business and commercial environments.

关键词： Bluetooth Microcontrollers Communication systems Spraying Object detection Real-time systems Security Chemicals Wireless fidelity Business

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From Misinformation to Truth: Fake News Detection with Transformer-Based Models

From Misinformation to Truth: Fake News Detection with Trans...

引用

international conference on Communication systems and Network technologies (CSNT)

作者： Arvind Mewada Mohd. Aquib Ansari Sushil Kumar Maurya Computer Science & Engineering Bennett University Greater Noida India Computer Science & Engineering Siksha ‘O’ Anusandhan Bhubaneswar India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Nowadays, technological advancements have significantly transformed information dissemination and consumption strategies. People acquire news through online portals such as social media and news websites. However, this change is also accompanied by the rapid spread of fake news based on false and misleading information. the main reason is that the coverage of digital platforms and social media is wide, and news can spread quickly without any authenticity verification. this problem poses a serious challenge to social trust, the ability to make wise decisions, and democratic stability. therefore, in this study, we developed an advanced artificial intelligence (AI) model to detect fake news using Transformer (BERT, DistilBERT, and RoBERTa ) models and evaluated their performance based on indicators such as accuracy, recall, and F1 score. the results show that RoBERTa performs best due to the use of a large dataset and better contextual understanding and achieved an accuracy of 93.84%, a precision of 91.80%, a recall of 95.28%, and an F1 score of 90.82%.

关键词： Support vector machines Accuracy Social networking (online) Communication systems Machine learning Transformers Stability analysis Fake news Portals

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Intrusion Detection at the Edge Computing: A Deep Learning Approach Using the UNSW-NB15 Dataset

Intrusion Detection at the Edge Computing: A Deep Learning A...

引用

international conference on Communication systems and Network technologies (CSNT)

作者： Vipin Kumar Vivek Kumar Abhay Pratap Singh Bhadauria Jay Dixit Amit Kumar Department of Computer Science Gurukula Kangri (Deemed to be University) Haridwar India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Engineering & Applications GLA University Mathura UP

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. the findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. this study investigates the application of edge computing, Internet of things (IoT), and deep learning to improve the performance of Intrusion Detection systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. the results demonstrate better performance, showing their suitability for real-time security applications. these findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments.

关键词： Deep learning Image edge detection Computational modeling Intrusion detection Real-time systems Internet of things Security Servers Long short term memory Edge computing

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks

A Bayesian Optimization Framework for Adaptive Node Deployme...

引用

international conference on Communication systems and Network technologies (CSNT)

作者： Rahul Priyadarshi Jayant Kumar Rout Anish Kumar Vishwakarma Nikhil Dhengre Rakesh Ranjan Amit Verma Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Dehradun Uttarakhand India Department of ECE IIIT Nagpur Maharashtra India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. this paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. the results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. these findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations.

关键词： Learning systems Wireless sensor networks Energy consumption Adaptive systems Machine learning Energy efficiency Real-time systems Bayes methods Reliability Optimization

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Accelerated MR image reconstruction using non convex regularized non-subsampled Contourlet transform and total variation based prior

Accelerated MR image reconstruction using non convex regular...

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international conference on Communication systems and Network technologies (CSNT)

作者： Nikhil Dhengre Rakesh Ranjan Sagar Motdhare Department of ECE IIIT Nagpur Maharashtra India School of Computer Science UPES Dehradun Uttarakhand India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In the domain of compressive sensing MRI (CSMRI), MRI image is reconstructed using partially acquired k-space data, in order to shorten the acquisition time of MRI. Various priors and their combinations have been examined for compressive sensing MRI application. In this paper, we propose a CS-MRI method comprised of the combination of non convex regularized non-subsampled contourlet transform (NSCT) based sparsity and hybrid total variation as a prior. the NSCT captures multi-directional and multi-scale image features effectively, while hybrid total variation preserves edges better while suppressing artifacts. the non conex regularization provides better measure of sparseness and preserve structural details better compared to convex regularization. Alternating direction method of multipliers is used to solve the proposed method. the experimental results of the proposed method are analyzed for different under-sampling patterns.

关键词： TV Communication systems Magnetic resonance imaging Image edge detection Transforms Reconstruction algorithms Convex functions Image reconstruction Compressed sensing

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GlobalLearn: An Efficient Multilingual Textual Summary for Education Videos

GlobalLearn: An Efficient Multilingual Textual Summary for E...

引用

international conference on Communication systems and Network technologies (CSNT)

作者： Pratham R Shetty Prateek M Anirudh Krishna R Ranjive M. Farida Begum Dept. Computer Science and Technology PES University Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Educational videos have become a preferred medium for learning, offering advantages such as self-paced study, the ability to revisit concepts, and a distraction-free environment. However, the effective- ness of such videos can be amplified through concise, multilingual textual summaries. this paper introduces GlobalLearn, an AI powered educational video to text summarization application that integrates speech recognition, optical character recognition, and NLP-driven large language models(LLMs) to extract and summarize key concepts from educational videos. By integrating audio and visual transcripts, time-stamped summaries, and multilingual capabilities, GlobalLearn ensures accessibility for diverse global audiences. this user-centric approach not only supports efficient learning and revision, but also bridges linguistic barriers, fostering an inclusive educational ecosystem. the paper explores GlobalLearn’s design and technical architecture, and core functionalities to outline its potential impact on modern education.

关键词： Visualization Runtime environment Translation Optical character recognition Education Knowledge based systems Text summarization Speech recognition Multilingual Videos

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Video Trailer Generation using Multimodal Data Analysis

Video Trailer Generation using Multimodal Data Analysis

引用

international conference on Communication systems and Network technologies (CSNT)

作者： Pranathi Praveen Shreeja Rajesh Prajna Rama Murthy Nikhil Giridhar Surabhi Narayan Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Trailers have been a vital part of the entertainment industry to pique audience interest. the process of trailer generation has been evolving to provide effective trailers to the public. In this paper we propose a novel approach by utilizing the audio and video modalities to extract key trailer segments that are compiled to create an effective trailer which highlights the impactful scenes of the short film. In contrast to previous work in this domain, the novel approach gives greater emphasis to the auditory features, since auditory features have a significant impact on the film in the horror genre. the dataset is a compilation of 311 short films and their respective trailers from various reputable public sources. Our approach introduces an audio guided visual model that compliments the acoustic features ensuring that the extracted segments are key moments both in terms of audio and video. the results obtained indicate that a significant proportion of predicted segments are deemed highly trailer-worthy.

关键词： Support vector machines Visualization Image segmentation Accuracy Data analysis Communication systems Entertainment industry Motion pictures Feature extraction Robustness

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Machine Learning Models for Intrusion Detection System

Machine Learning Models for Intrusion Detection System

引用

international conference on Communication systems and Network technologies (CSNT)

作者： Sourabh Hiremath Rohit D B Shudhanshu Singh Sheela S V Radhika K R Dept of ISE BMSCE Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Protecting networks from mischievous attacks in the scenario of cybersecurity require Intrusion Detection System (IDS). Leveraging machine learning algorithms to develop IDS has gathered substantial consideration due to its capacity to adapt and study from vast datasets. In this research paper, an extensive analysis of intrusion detection has been conducted utilizing various machine learning models trained on the CICIDS2018 dataset. Fifteen distinct models have been explored, ranging from traditional methods to sophisticated deep learning architectures. Subsequently, Random Forest has been identified as the optimal classifier based on its superior performance metrics of F1 score 99.79%, precision 99.79%, and recall 99.81%. Moreover, a framework has been proposed for real-time intrusion detection using the Random Forest classifier, employing CICFlowMeter for data collection, which aligns with the dataset used for model training.

关键词： Training Analytical models Adaptation models Machine learning algorithms Intrusion detection Telecommunication traffic Network security Real-time systems computer security Random forests

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Leveraging Next-Generation Business Intelligence for Proactive Cybersecurity Defense

Leveraging Next-Generation Business Intelligence for Proacti...

引用

international conference on Communication systems and Network technologies (CSNT)

作者： Arijit Sengupta Vidyavati Ramteke Symbiosis Centre for Information Technology Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

this paper explores how Business Intelligence operates better for protecting cybersecurity through its capability to merge predictive analytics methods with real-time data detection. A qualitative research design enables us to study how BI systems fit into present-day cybersecurity practices, specifically through predictive threat prevention alongside real-time database data. the evaluation process of BI tools leads organizations to select the most valuable system for detecting threats and minimizing risks. We introduce a data-oriented defense system that uses BI tools to implement proactive cybersecurity protection methods. the assessment focuses on implementing obstacles by understanding the main obstacles that stem from data privacy regulations, data overload, and worker skill gaps. the research establishes an orga- nized method which helps organizations use data analytics and automated threat identification to boost their security position through BI-based security tactics. the research shows that BI tools should be incorporated into cybersecurity approaches since they build robust security protocols and decrease vulnerability risks while fostering better resistance against modern security threats.

关键词： Resistance Protocols Prevention and mitigation Organizations Real-time systems threat assessment Regulation Business intelligence computer security Protection

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A Hybrid Reinforcement Learning Framework for Optimizing Node Deployment in WSNs

A Hybrid Reinforcement Learning Framework for Optimizing Nod...

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international conference on Communication systems and Network technologies (CSNT)

作者： Rahul Priyadarshi Jayant Kumar Rout Akash Kumar Gupta Rakesh Ranjan Amit Verma Panduranga Ravi Teja Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India Dept of ECE BIT Mesra Deoghar India School of Computer Science UPES Dehradun India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Efficient node deployment in Wireless Sensor Networks (WSNs) is critical for maximizing network coverage, enhancing energy efficiency, and prolonging network lifetime. However, achieving optimal node placement is challenging due to the limited energy resources and dynamic environmental conditions in WSNs. this paper presents a novel hybrid approach that combines Reinforcement Learning (RL) with the Differential Evolution (DE) algorithm to optimize node deployment in WSNs. In this method, RL dynamically adapts node positions based on local network states, while DE performs global optimization to refine these placements and improve coverage. Simulation results demonstrate that the proposed RL-DE hybrid approach significantly outperforms traditional deployment methods, standalone RL, and DE in terms of coverage, energy conservation, and network longevity. the results suggest that the RL-DE approach provides an adaptive and robust solution for node deployment in WSNs, with potential applications in real-world sensor network deployments.

关键词： Wireless sensor networks Adaptive systems Recurrent neural networks Smart cities Simulation Scalability Metaheuristics Reinforcement learning Energy efficiency Real-time systems

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