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

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Unveiling the Truth: Detecting Fake News Using SVM and TF-IDF

Unveiling the Truth: Detecting Fake News Using SVM and TF-ID...

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2025 international conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Rizal, Randi Faturahman, Ade Impron, Ali Darmawan, Irfan Haerani, Erna Rahmatulloh, Alam Faculty of Engineering Siliwangi University Department of Informatics Tasikmalaya Indonesia Universitas Muhammadiyah Sampit Faculty of Engineering and Agriculture Departement of Informatics Central Kalimantan Indonesia Telkom University Faculty of Industrial Engineering Department of Information Systems Bandung Indonesia Faculty of Engineering Siliwangi University Department of Information Systems Tasikmalaya Indonesia

ISBN: (纸本)9798331513320

The spread of fake news on social media platforms such as Facebook, Instagram, and X has become a significant issue in the digital era due to its negative societal and informational impacts. The rapid dissemination of unverified information often leads to misinformation and confusion, highlighting the critical need for reliable detection methods. This study aims to classify and detect fake news by optimizing the Support Vector Machine (SVM) machine learning algorithm using the Term Frequency-Inverse Document Frequency (TFIDF) technique. Two publicly available Kaggle datasets, containing labeled fake and real news, were utilized as the foundation for this research. The research results demonstrate that the SVM algorithm, when optimized with TF-IDF, achieved the highest accuracy of 99.6% compared to other commonly used machine learning algorithms such as Random Forest and Logistic Regression. This highlights the effectiveness of TF-IDF in improving the feature extraction process, enabling the model to better differentiate between fake and real news. These findings underscore the potential of SVM as a reliable tool for combating misinformation. To further enhance fake news detection, future research should explore the use of more comprehensive, multilingual, and complex datasets to better reflect real-world scenarios. Additionally, employing transformer-based approaches, such as BERT, GPT, or similar models, could provide a deeper understanding of linguistic context and semantic relationships, ultimately improving the robustness and accuracy of fake news detection systems. © 2025 IEEE.

关键词： Support vector machines

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Mobile Edge Computing for Efficient Vehicle Management in Smart City 14

Mobile Edge Computing for Efficient Vehicle Management in Sm...

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14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Chanu, Asem Debala Shelar, Saylee Nath, Shubha Brata Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Engineering Ajeenkya DY Patil University Pune India

ISBN: (纸本)9798331531935

Mobile edge computing (MEC) promises to be a useful technology to enable applications in a smart city. However, the task offloading becomes challenging as the MEC infrastructure has limited resources. To solve the challenges of MEC, we have proposed a system architecture with an optimization algorithm to minimize the task delay in MEC. A Genetic Algorithm based task allocation algorithm finds the optimal set of edge servers for task deployment and processing. The proposed system efficiently optimizes the task delay in MEC as analyzed from the experiments performed by taking virtual machines as MEC servers. We show the usefulness of the proposed system by taking an application scenario for efficient vehicle management in a smart city. © 2025 IEEE.

关键词： Genetic algorithms

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Critical Review of Automated Traffic Signal systems Leveraging IoT and Machine Learning 2

Critical Review of Automated Traffic Signal Systems Leveragi...

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2nd international conference in Civil engineering for a Sustainable Planet, ICCESP 2024

作者： Abhijith, R.P. Christy, Freeda Simi, M.R. Renju, C.M. Dept. of Civil Engineering Kalasalingam Academy of Research and Education India Dept. of Civil Engineering Mahaguru Institute of Technology India Dept. of Civil Engineering Marian Engineering College India

ISBN: (数字)9780784486047

ISBN: (纸本)9780784486047

The integrated use of Internet of Things (IoT) and machine learning (ML) in automated traffic signal systems marks a significant leap forward in optimizing urban traffic flows. By leveraging real-time data gathered from interconnected devices and applying advanced machine learning algorithms, these systems can dynamically adjust traffic signals to enhance efficiency and alleviate congestion. This technological innovation underscores the transformative potential of incorporating state-of-the-art technologies into urban traffic management practices. However, despite the promising advantages offered by IoT and machine learning, several implementation challenges are also be addressed. That includes ensuring the secure and reliable transmission of the data across interconnected devices, managing the scalability of the system to accommodate varying traffic volumes and urban environments, and effectively handling the complexity of real-time decision-making processes. Going through these types of challenges is very severe for the widespread adoption and successful deployment of automated traffic signal systems powered by IoT and machine learning. Continuous research and development efforts are essential to refine algorithms, improve data integration capabilities, and validate system performance in diverse real-world conditions. By overcoming these hurdles, cities can unlock the full potential of automated traffic signal systems to create more efficient, sustainable, and liveable urban environments. © ASCE.

关键词： Internet of Things machine learning traffic flow traffic signal systems

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Electric Vehicle Charging Navigation Using Waterwheel Plant Algorithm for Coordinating Smart Grids and Intelligent Transportation systems 2

Electric Vehicle Charging Navigation Using Waterwheel Plant ...

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

作者： Siva Ramkumar, M. Sivaramkrishnan, M. Uday, Kaparthi Kanan, Mohammad Mohan Raj, C. Giri, Jayant SNS College of Technology Dept of ECE India Karpagam College of Engnieering Dept of EEE India CVR College of Engineering EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

The efficient charging of electric vehicles (EVs) remains essential to create sustainable mobility while EVs transform into crucial elements of upcoming transportation systems. The present EV charging navigation systems deal with three main issues including grid congestion and inadequate charging station (CS) selection and delays from traffic *** paper proposes a novel Waterwheel Plant Algorithm (WWPA) for optimizing EV charging navigation within the Smart Grid (SG) and Intelligent Transportation systems (ITS). The suggested method strives to enhance SG-EV coordination through demand balancing in addition to charge price reduction and decreased grid congestion levels. WWPA algorithm serves as the proposed method to optimize EV charging schedules. The MATLAB platform serves as the implementation environment for the proposed method which demonstrates superior performance than Multi-agent Reinforcement Learning (MARL), Deep Reinforcement Learning (DRL), Coati-Northern Goshawk Optimization (Coati-NGO) and Grey Sail Fish Optimization (GSFO). Results demonstrate that the WWPA algorithm reduces average charging expenses by 18.14% throughout diverse charging operations as it produces superior performance than existing algorithms operating in the SG ITS. © 2025 IEEE.

关键词： Reinforcement learning

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The Role of IoT and Machine Learning in Automating Space Docking: Challenges, Advancements, and Future Prospects 2

The Role of IoT and Machine Learning in Automating Space Doc...

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

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Space docking is an important aspect of modern-day space exploration, enabling crewed missions, shipment transport, and spacecraft refuelling. Traditionally, manual docking has trusted astronaut precision and floor manipulation coordination, which has grown to be inefficient and blunders-susceptible in deep-space missions because of conversation delays. Integrating the Internet of Things (IoT) and Machine Learning (ML) is reworking area docking by introducing self-reliant, actual-time selection-making structures. IoT-enabled sensors continuously monitor spacecraft role, pace, and orientation, while ML algorithms optimize docking manoeuvres, beautify predictive upkeep, and make certain collision avoidance. Reinforcement learning similarly allows adaptive management strategies, making self-sufficient docking more efficient and dependable. This paper explores the improvements in IoT and ML for automating space docking, reading real-global programs along with SpaceX Dragon, NASA's AR&D systems, and China's Tianzhou cargo deliver. By addressing key demanding situations-precision, pace, and safety-those technologies pave the way for sustainable space exploration, including future Mars missions and autonomous area stations. The observation highlights how AI-driven docking may be integral for deep-space operations, making sure safer and more green missions are made. © 2025 IEEE.

关键词： Reinforcement learning

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Optimizing Crop Recommendation System Using Threshold and Decision-Level Fusion

Optimizing Crop Recommendation System Using Threshold and De...

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

作者： Umamaheswari, R. Kannan, E. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Nowadays crop recommendation systems face many obstacles in recommending suitable crops. A main challenge that has to be encounter is changes in environmental conditions and handling diverse dataset like soil and climate oriented dataset. Existing algorithm's like decision tree, random forest have limitations in adapting to local variability. To overcome this and to enhance the agricultural activities, this paper presents an innovative crop recommendation system based on threshold (CRST) values, through which crops are chosen by considering properties of soil, climatic parameters, and season factors such as Kharif, Rabi, and Zaid. Critical limit threshold values are set for most important features like fertility of soil, nutrition of the soil, pH, temperature, humidity, and rainfall to ensure optimal growth conditions. In order to recommend the suitable crop, a rule-based decision-fusion method is tied with confidence level analysis. To evaluate the effectiveness of the threshold method, ordinary least squares (OLS) regression is used. CRST model achieves an accuracy of 90% compared to existing algorithms. Integration of seasonal adaptation and threshold determination greatly enhances crop management decision-making and provides a strong answer to agricultural problems. © 2025 IEEE.

关键词： Temperature Rain Decision making Crops Humidity Soil Decision trees Recommender systems Random forests Faces

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Integration of Renewable Energy systems Into Utility Grid: A Review on Power Quality Issues, Mitigating Devices, and Control algorithms

Integration of Renewable Energy Systems Into Utility Grid: A...

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international conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Vijaychandra, Joddumahanthi Behera, Santi Dora, Lingraj Department of Electrical and Electronics Engineering Veer Surendra Sai University of Technology Odisha Burla India

ISBN: (纸本)9783031513374

Integration of renewable energy systems (RESs) into the utility grid is the trend in electrical power systems distribution networks. It is a fact that RESs are the major contributors to generating electricity and meeting the power demand. The main reasons for this integration include an increase in energy demand, environmental impacts due to the emission of carbon gases, extinguishment of fossil fuels, and so on. In fact, several power quality (PQ) issues will emerge after integrating into the utility grid. This study aims to provide various PQ issues and their mitigation methods of grid-integrated RESs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clean energy

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Encryption and decryption framework using elliptic curve cryptography 1st

Encryption and decryption framework using elliptic curve cry...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

Secure file encryption has become increasingly important in various disciplines, including finance, healthcare, and government, where protecting sensitive data is paramount. The proposed framework aims to meet this requirement by developing an encryption and decryption framework for files that employ Elliptic Curve Cryptography (ECC), a modern and practical cryptographic technique. The framework will employ ECC algorithms for key generation, encryption, and decryption, which offer several advantages over conventional methods such as RSA and AES. ECC provides equivalent security with shortened key lengths, resulting in faster encryption and decryption, decreased computational burden, and optimized resource utilization. A crucial aspect of the proposed framework is its user-friendly interface, making it easy for users to manage encrypted files. In addition, the framework will include key management and validation mechanisms to ensure the security and authenticity of encryption keys, thereby preventing unauthorized access to encrypted files. The proposed framework will be rigorously tested on a local machine and hosted on a server in the cloud to ensure its dependability and resiliency in real-world scenarios. The framework’s efficacy will be determined based on the encryption and decryption times for various file sizes. The outcomes will demonstrate the efficacy and efficiency of the proposed framework, demonstrating that it provides superior performance compared to conventional algorithms. © 2025 the Author(s).

关键词： decryption Elliptic Curve Cryptography Encryption

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Predicting Heat Transfer Coefficient Using Bidirectional Long Short-Term Memory 2nd

Predicting Heat Transfer Coefficient Using Bidirectional Lon...

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2nd international conference on Mechanical engineering, INCOM 2024

作者： Basu, Ankan Saha, Aritra Banerjee, Sumanta Department of Computer Science and Engineering Jadavpur University Kolkata India Fakultät für Elektrotechnik und Informatik Gottfried Wilhelm Leibniz Universität Hannover Hannover Germany Department of Mechanical Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9789819772957

The prediction of Heat Transfer Coefficient (HTC) plays a critical role in optimizing the thermal performance of systems during heat treatment processes. Traditional numerical methods struggle to solve the Inverse Heat Transfer Problem (IHTP) associated with HTC prediction. In this paper, a novel approach has been proposed that leverages the power of machine learning, specifically Bidirectional Long Short-Term Memory (BiLSTM) networks, to estimate the HTC value. The developed model demonstrates remarkable precision, achieving an impressive ≈ 98.75% accuracy. This outperforms conventional feed-forward networks. The proposed machine learning approach offers several advantages over traditional methods. It provides rapid estimations of the key characteristics of the HTC function, offering quick insights into the heat transfer process. Furthermore, machine learning algorithms have the capability to learn from data and capture complex relationships, making them highly suitable for HTC estimation tasks. Compared to heuristic search algorithms and swarm-based approaches, the present approach significantly reduces computational requirements and maintains excellent predictive performance. The results obtained highlight the potential of machine learning in optimizing heat treatment processes and improving overall performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Heuristic algorithms

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A MADQN-based resource optimization method for cognitive satellite-terrestrial network 3

A MADQN-based resource optimization method for cognitive sat...

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3rd international conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： He, Wenzheng Liu, Ying Zhai, Yifan Yang, Mingchuan Zhou, Bilei Shanghai Institute of Satellite Engineering Shanghai China Communication Research Center Harbin Institute of Technology Harbin China

ISBN: (纸本)9781510688179

The integrated satellite-terrestrial network represents a pivotal direction in the evolution of sixth-generation mobile communication. It integrates the low latency, high capacity, and large-scale machine access characteristics of fifth-generation mobile network with the extensive coverage and robust stability of satellite systems. A key trend in the development of future integrated network systems will be the shared user of a unified frequency band between satellites and terrestrial networks. To address this, the paper considers a cognitive satellite-terrestrial network scenario, where satellite networks serve as the primary network and terrestrial networks as the secondary. The paper applies deep reinforcement learning for multidimensional resource optimization, aiming to enhance user service quality while alleviating frequency resource scarcity. This paper delves into resource allocation methods of cognitive integrated satellite-terrestrial network based on deep reinforcement learning. The paper jointly optimizes the beam patterns, bandwidth allocation, terrestrial base station power allocation, and user association for low-orbit satellites within the system, thereby improving network throughput and ensuring user delay fairness. Initially, this paper systematically models the cognitive satellite-terrestrial network and formulated optimization objectives encompassing throughput and user delay fairness. Then a multi-agent based deep Q-network method was introduced to achieve these objectives. Through simulations, this paper validates that the proposed algorithm outperforms traditional resource allocation algorithms in terms of throughput and user latency fairness. © The Authors.

关键词： Deep reinforcement learning

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