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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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A Research Framework for Energy Efficient Distributed Flow Shop Scheduling in Intelligent Production systems

A Research Framework for Energy Efficient Distributed Flow S...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Ganesh, J. Regi Kumar, V. College of Engineering Trivandrum Apj Abdul Kalam Technological University Department of Mechanical Engineering Kerala Thiruvananthapuram695016 India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507541

In intelligent production scheduling, advanced algorithms are employed to develop optimal production schedules for the shop floor. Industries all over the world are now adopting various energy conservation policies due to the rapid depletion of energy sources. Energy efficient scheduling has emerged as an economical tool to conserve energy in intelligent production systems. To survive the fierce competition in the context of globalization, manufacturing companies have adopted the strategy of distributed manufacturing, where production facilities are located at different geographical locations. This article presents a research framework for energy efficient distributed scheduling in flow shop environment. The proposed framework has three phases, the formulation phase, the solution methodology phase, and the experimentation phase. The various inputs that need to be included in each phase are detailed with examples. This framework is proposed as an attempt to standardize the research works in the field of energy efficient distributed flow shop scheduling. © 2025 IEEE.

关键词： Location

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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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Next-Generation Embedded systems with Synergies VLSI with Deep Learning: Design Methodologies, Optimization Techniques, and Real-World Applications

Next-Generation Embedded Systems with Synergies VLSI with De...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kalarani, G. Mohanapriya, M. Pattunnarajam, P. Sasikala, G. Sivakumar, J. Suresh, M. Jaya Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Ece Tamil Nadu Sriperumbudur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Mathematics Tamil Nadu Thiruvallur India

ISBN: (纸本)9798331529833

New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration (VLSI) technology. Methodologies for design, optimisation strategies, and practical uses of next-generation embedded systems are the foci of this study, which investigates the ways in which VLSI and deep learning might work together. These systems have the potential to transform several industries, such as transportation, medicine, robotics, and the IoT, by harnessing the processing power of deep neural networks with the improvements in semiconductor fabrication. Prior to delving into the advantages of bespoke hardware design for deep learning inference and training, we trace the history of very large scale integration (VLSI) technology and its incorporation with deep learning algorithms. Investigated here are the design techniques that, when applied to very large scale integration (VLSI) architectures like FPGAs and ASICs, allow for the efficient mapping of deep learning models onto these devices. We show case studies that show how these methods work and talk about the trade-offs between performance, power consumption, and adaptability. The development of next-generation embedded systems relies heavily on optimisation approaches. Model compression, quantisation, and pruning are some of the optimisation strategies that we examine;they lessen the memory and computational demands of deep learning models without drastically altering their accuracy. For embedded devices with limited resources, these methods are crucial for implementing deep learning models. Additionally, we explore the practical uses of embedded systems augmented with VLSI and deep learning. By capitalising on the complementary strengths of VLSI and deep learning, applications like autonomous driving, medical imaging, and smart home automation are revolutionising entire industries. In this paper, we examine the design

关键词： Integrated circuit design

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Image Search Engine with Recognition 4

Image Search Engine with Recognition

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kumar, Jmsv Ravi Dheeraj, Bellaganti Gowtham, Geddada Babu, Dokala Vivek Kiran, Bonthu Asha Srikanth, M. Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331523923

The Image Recognition Search Engine is one of the modern advances in artificial intelligence and image analysis and processing. This advanced system surpasses typical functionalities in image identification as it captures features to solve new problems faced in existing systems of digital media authentication, content designing, and improvement of images. The system core relies on advanced AI algorithms that can rasterically recognize and analyze various forms of image objects ranging from objects, faces, scenes, and brand logos converting what appears to be simple images into indexical, analyzable data. One feature is that the system can integrate deepfake detection technology, which is relevant in the context of increasing the amount of manipulated content in the media space. Using special algorithms to learn even the least detectable abnormality in images including the presence of unnatural distinctive facial characteristics and changes in lighting conditions, we obtain a highly effective tool for improving the reliability of image identification. Furthermore, the platform allows for new features to convert an image into a video by implementing frame interpolation and motion estimation through animating the images to allow users to create interesting clips for applications regarding marketing or any personal use. © 2025 IEEE.

关键词： Motion estimation

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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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AI in the Detection of Breast Cancer and Related Ethical and Legal Issues 5th

AI in the Detection of Breast Cancer and Related Ethical and...

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5th international Ethical Hacking conference, eHaCON 2024

作者： De Mukhopadhyay, Keya Guha, Tapas Basu, Abhisikta Department of Biotechnology Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science Engineering Techno International New Town India University of Engineering and Management Kolkata India

ISBN: (纸本)9789819784561

Breast Cancer accounts for a significant percentage of malignancy among women globally. It is a disorder where abnormal breast cells develop uncontrollably and form tumours when left untreated, the tumour could spread throughout the body and become fatal. So, early detection is critical for survival. The clinical outcomes of breast cancer can be significantly improved with an accurate and timely diagnosis. Significant advancements in breast cancer diagnosis and customized treatments have been made possible, by the emergence of Artificial Intelligence particularly in the field of image analysis, the K-Nearest Neighbors algorithm is one of the simplest and easily interpretable supervised ML algorithms, which can determine whether the cancer lumps are malignant or benign. KNN, SVM, and GRNN are machine learning algorithms that make use of an openly accessible dataset, features taken from images, data pre-processing techniques, and Principal Component Analysis (PCA) in order to improve accuracy and decrease diagnostic time. Artificial Intelligence can identify both missed cancers and breast tissue features, which in turn, can help to predict future cancer development. Some Artificial Intelligence algorithms excel at predicting patients with a high risk of interval cancer, which is aggressive. When no cancer was clinically detected by screening mammography, even Artificial Intelligence algorithms trained for short time horizons can forecast the chance of developing cancer for up to five years in the future. The WHO reports that low- and middle-income nations account for over 80% of fatalities from breast and cervical cancer. In the developing economic scenario of India, one in 28 women is likely to develop breast cancer. The Artificial Intelligence detection model is a cost-effective precautionary measure for the early detection of breast cancer in women. The data protection laws should be convenient enough to carry out the test with the informed consent of the patients

关键词： Diseases

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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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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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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

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