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An Analysis of Various Classification Algorithms for Fake News Detection 2

An Analysis of Various Classification Algorithms for Fake Ne...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Khare, Sakshi Singh, Pushpa Kumar, Pankaj Dept. of Computer Science and Engineering G. L. Bajaj Institute of Technology & Management Greater Noida India

ISBN: (纸本)9798331530389

With the expansion of social media and advanced stages, the spread of fake news has ended up a noteworthy societal issue. This paper presents a comprehensive outline of machine learning strategies utilized for the detection of fake news. We talk about different approaches such as Natural Language Processing (NLP), unsupervised learning calculations, and machine learning models utilized within the recognizable proof of fake news. We dive into highlight designing, opinion investigation, and organize examination as key components of fake news detection frameworks. Moreover, we look at datasets, assessment measurements, and challenges related with detection of fake news. Through this analysis, we point to supply bits of knowledge into the current state-of-the-art techniques and headings for future investigate in combating fake news dispersal. This paper mainly used K-Nearest Neighbor, Jaccard similarity, LSTM, Decision Tree, and Logistic regression Classifiers for the classification. Main objective of this paper to verify the accuracy of models by performing the pre-processing the data and handle the unbalanced data more appropriately. The results of this papers show comparison of all classifiers with accuracy. Our results shows that KNN achieves 83%, 84% for Logistic Regression, 84% for Decision Tree, 85% for Jaccard Similarity & Random Forest is 85% and 98% for LSTM in our experiment. © 2025 IEEE.

关键词： Adversarial machine learning

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A Blockchain-Based Secure and Transparent Global Radioactive Source Lifecycle Management System

A Blockchain-Based Secure and Transparent Global Radioactive...

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IEEE Southeastcon

作者： Susan Poudel Firdous Kausar Debashis Das Pushpita Chatterjee Sajid Hussain Uttam Ghosh Dept. of Mathematics and Computer Science Fisk University Nashville TN USA Department of Computer Science and Data Science Meharry Medical College Nashville TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The management of radioactive sources is a critical process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone's safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain's decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real-time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials.

关键词： Access control Radioactive materials Smart contracts Supply chains Production Position measurement Real-time systems Blockchains Safety Testing

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Deep Learning-Based Spectrum Sharing for Dynamic Resource Allocation in 6G Cognitive Radio Networks 4

Deep Learning-Based Spectrum Sharing for Dynamic Resource Al...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Venu, Nookala Kukreti, Kamlesh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798331530402

We conduct an extensive study on deep learning-based spectrum sharing to resolve dynamic resource allocation in 6G cognitive radio networks in this paper. The approach uses modern machine learning models to optimize spectrum usage, increase prediction accuracy of spectrum availability, and reduce interference among the primary and secondary users. We need to evaluate the performance of deep learning algorithms against the traditional spectrum allocation methods in simulation. In the paper, regular quality of service metrics such as throughput distribution and fairness among users are demonstrated to be much improved with these results, where both latency and energy consumption have been cut down dramatically. This work demonstrates the paradigm-shifting role of deep learning in spectrum management for 6G networks, pointing towards intelligent and effective resource allocation mechanisms in tomorrow's communications. © 2025 IEEE.

关键词： Cognitive radio

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Improving Semi-Supervised Federated Learning with Limited Labeled data via Adaptive Batchsize and Pseudo Labeling 22

Improving Semi-Supervised Federated Learning with Limited La...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Park, Byoungjun De Gusmao, Pedro Porto Buarque Kim, Minhoe Korea University Dept. of Computer and Information Science Sejong Korea Republic of University of Surrey Computer Science Research Centre Guildford United Kingdom

ISBN: (纸本)9798331508050

Federated learning (FL) is a distributed learning method that leverages numerous edge devices for training while protecting data privacy. However, most of the data produced by distributed edge devices are unlabeled, leading to the emergence of semi-supervised federated learning (SSFL) to address this issue. Although state-of-the-art approaches perform well when labeled data is sufficient, it shows severe performance degradation and training becomes more challenging as labeled data becomes scarce. In this paper, we improve performance in environments with limited labeled data by dynamically adjusting batch sizes and applying an Adaptive Threshold (AT). Additionally, we propose methods to resolve issues arising when adopting Adaptive Thresholds method of the centralized approach and investigate their limitations. © 2025 IEEE.

关键词： Semi-supervised learning

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Advancing Drug Discovery through a Deep Learning-Based Protein-Ligand Interaction Prediction Tool 3

Advancing Drug Discovery through a Deep Learning-Based Prote...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Hunge, Sudhir S. Yadao, Aman Rajendra Shewate, Varsha Tekdas Lalsare, Prakash Namdeorao Prasannan, Shiji Ramulu, Kothuri Parashu Chintamani College of Science Pg Dept. of Chemistry Maharashtra Pombhurna India Shriram Arts And Science Junior College Dept. Of Chemistry Maharashtra Ramtek India Chintamani College of Science Dept. of Chemistry Maharashtra Pombhurna India Shri Prabhu Ramchandra Junior College Dept. of Chemistry Nanda Tah Korpana Maharashtra India Faculty of Management Studies Parul Institute of Management and Research Parul University Gujarat Vadodara India Indur Institute Of Engineering And Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331515911

In recent years, deep learning has emerged as a promising approach to advance drug discovery by accurately predicting interactions between proteins and small molecules, or ligands. The identification of these interactions is essential in developing drugs, as it informs us about a molecule's potential therapeutic efficacy. Traditional methods, like molecular docking, often struggle with accuracy and computational efficiency, limiting their application in high-throughput settings. In this paper, we propose a novel deep learning-based tool designed to predict protein-ligand interactions with enhanced precision and scalability. Our model leverages convolutional and graph neural networks to capture the complex, three-dimensional nature of these interactions. Using a large, curated dataset, we train and test the model to optimize binding affinity prediction, employing metrics such as mean squared error and accuracy to validate performance. The results show that our approach outperforms conventional techniques and recent machine learning models, demonstrating a significant improvement in predictive reliability. This tool has potential applications in screening vast compound libraries in early-stage drug discovery, reducing time and costs associated with experimental trials. By providing more accurate insights into protein-ligand affinities, our method represents a step forward in computational drug design, with implications for developing targeted therapies across various disease areas. © 2025 IEEE.

关键词： Graph neural networks

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Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance 7

Cross-Format Retrieval-Augmented Generation in XR with LLMs ...

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Nagy, Akos Spyridis, Yannis Argyriou, Vasileios Kingston University Dept. of Networks & Digital Media Kingston upon Thames United Kingdom Kingston University Dept. of Computer Science Kingston upon Thames United Kingdom

ISBN: (纸本)9798331521578

This paper presents a detailed evaluation of a Retrieval-Augmented Generation (RAG) system that integrates large language models (LLMs) to enhance information retrieval and instruction generation for maintenance personnel across diverse data formats. We assessed the performance of eight LLMs, emphasizing key metrics such as response speed and accuracy, which were quantified using BLEU and METEOR scores. Our findings reveal that advanced models like GPT-4 and GPT-4o-mini significantly outperform their counterparts, particularly when addressing complex queries requiring multi-format data integration. The results validate the system's ability to deliver timely and accurate responses, highlighting the potential of RAG frameworks to optimize maintenance operations. Future research will focus on refining retrieval techniques for these models and enhancing response generation, particularly for intricate scenarios, ultimately improving the system's practical applicability in dynamic real-world environments. © 2025 IEEE.

关键词： Search engines

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Difficulty, Diversity, and Plausibility: Dynamic data-Free Quantization

Difficulty, Diversity, and Plausibility: Dynamic Data-Free Q...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Hong, Cheeun Baik, Sungyong Oh, Junghun Lee, Kyoung Mu Hanyang University Dept. of Data Science Seoul Korea Republic of Seoul National University Dept. of Ece & Asri Seoul Korea Republic of Seoul National University Ipai Seoul Korea Republic of

ISBN: (纸本)9798331510831

Without access to the original training data, data-free quantization (DFQ) aims to recover the performance loss induced by quantization. Most previous works have focused on using an original network to extract the train data information, which is instilled into surrogate synthesized images. However, existing DFQ methods do not take into account important aspects of quantization: the extent of a computational-cost-and-accuracy trade-off varies for each image, depending on its task difficulty. To handle such varying trade-offs, several efforts have been made to dynamically allocate bit-widths for each image. Such dynamic quantization, however, remains challenging and unexplored in the data-free domain, because synthesized images of previous works fail to possess properties in natural test images that are crucial for learning the appropriate dynamic allocation policy: difficulty, its diversity, and its plausibility. By contrast, we propose a data-free quantization framework that is dynamic-friendly, by modeling varying extents of task difficulties with plausibility. We generate plausibly difficult images with soft labels, whose probabilities are allocated to a group of similar classes. Images with diverse and plausible difficulties enable us to train the framework to dynamically handle the varying trade-offs. Consequently, our framework achieves better accuracy-complexity Pareto front than existing data-free quantization approaches. © 2025 IEEE.

关键词： Pareto principle

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Temporal Fusion Transformer -LSTM-based Improved State of Charge Estimation of Li-ion battery for Real Drive Cycle 4

Temporal Fusion Transformer -LSTM-based Improved State of Ch...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Pandian, Anbumalar Shruthi, S. Dhanush, V. Sundarajan, Sriranganayaki Abishek, P. Subbian, Sutha Natarajan, Pappa Dept. of Instrumentation Engineering Anna University MIT Campus Chennai India Dept. of Artificial Intelligence and Data Science Meenakshi Sundararajan Engineering College Chennai India

ISBN: (纸本)9798331530402

Precise measurement of State of Charge (SoC) in Li-ion battery is crucial for effective utilization of energy storage system. However, it is challenging with physics-based model due to computation complexity while data-driven model often lacks generalization for unseen data. To improve the robustness of data-driven model, hybrid algorithms are employed. This paper focuses on combining Temporal Fusion Transformer (TFT)Long Short-Term Memory (LSTM) to improve the generalization of state-of charge estimation of unseen drive cycles. By leveraging the attention mechanism feature, temporal feature extraction of TFT along with memory capability of LSTM TFT-LSTM model is developed with optimal hyperparameters using Optuna framework. This model is used to estimate the SoC during the discharging cycle of a Turnigy Graphene Li-Ion Battery (5000mAh, 65C) for the LA92 drive cycle at an ambient temperature of 40°***, uncertainty analysis is performed against US06 drive cycle. Finally, the performance of TFT-LSTM is compared with LSTM model. The results demonstrate that TFT-LSTM achieves better performance compared to LSTM with Root Mean Square Error (RMSE) of 1.1962 for unseen US06 drive cycle. These findings demonstrate the robustness of hybrid TFT-LSTM against unseen drive cycle and highlight its potential under realistic conditions with variability. © 2025 IEEE.

关键词： Mean square error

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Term Deposit Customer Prediction: A Blending Approach Using Bank Marketing data

Term Deposit Customer Prediction: A Blending Approach Using ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Prasad, Rahul Choubey, Mohit Baghel, R.K. Naruka, Aaditya Upes Dehradun Cluster of Computer Science India Maulana Azad National Institute of Technology Dept. of Ece Bhopal India Poornima University Dept. of Se Jaipur India

ISBN: (纸本)9798331529833

The global COVID-19 pandemic led to a significant economic downturn, severely impacting financial systems and economies around the world. Widespread lockdowns and travel restrictions disrupted supply chains, forced business closures, and triggered job losses, highlighting the importance of sound financial planning. Term deposits have emerged as a preferred strategy for risk mitigation. In marketing, direct customer outreach has increasingly replaced traditional intermediary-based approaches. Using data from a Portuguese bank, this study explores the predictive accuracy of various machine learning models in identifying customers most likely to invest in term deposits. Machine learning techniques applied include Support Vector Machine (SVM), Gaussian Naïve Bayes, bagging, blending, Light Gradient Boosting Machine (GBM), bagging, and Extreme Gradient Boosting (XGBoost). Results demonstrate that bagging algorithms provide superior accuracy in predicting customer behavior, offering insights to enhance targeted marketing efforts." © 2025 IEEE.

关键词： Sales

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