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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Shivam Tiwari, Naveen Kumar Bajpai, Abhishek Yadav, Shashank Dept. of Computer Science and Engineering Rajkiya Engineering College Kannauj UP Kannauj India

ISBN: (纸本)9798331531935

In Today’s world or from Past Scenario’s Fake News has a Huge Impact on Everyone’s Lives including Politics, Sports, Education, External Foreign Affairs, Defense, etc. This project aims to develop a model for identifying and detecting fake news. In this model, we implement an ensemble method that leverages the TF IDF vectorization technique to extract meaningful textual features and employ a Bagging Classifier with Logistic Regression as the base estimator to enhance classification accuracy. To build this classifier, we utilized the scikit learn library. Our work was implemented using Jupyter Notebook, an online platform. For feature extraction, we employed a well-known vectorizer called Term Frequency Inverse Document Frequency (TF-IDF). We split our data into training sets (75%) and test sets (25%). The proposed model achieves an accuracy of 99.15% on the test dataset, with a precision of 99.45%, a recall of 99%, and an F1-score of 99.17%. It outperforms standalone Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting classifiers. © 2025 IEEE.

关键词： Logistic regression

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Evaluating Visual Transformers for Wafer Defect Detection: A Feasibility Study

Evaluating Visual Transformers for Wafer Defect Detection: A...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Lafrance, Samantha Bernadin, Shonda FAMU-FSU College of Engineering Dept. of Electrical and Computer Engineering Tallahassee United States

ISBN: (纸本)9798331504847

Wafer defect detection is a critical aspect of semi-conductor manufacturing, where even minor defects can lead to significant performance issues or device failures. Traditional methods, such as rule-based approaches and Convolutional Neural Networks (CNNs), have achieved moderate success but face limitations in scalability, adaptability, and the ability to model complex spatial patterns in high-resolution wafer imagery. These limitations make it critical to investigate advanced architectures that can accurately capture complex spatial relationships. Vision Transformers (ViTs) have emerged as a promising alternative within computer vision, offering global attention techniques that are particularly effective in recognizing long-range dependencies. This study evaluates the feasibility of ViTs for wafer defect detection through a literature-based empirical validation approach, synthesizing findings from prior research on ViT-based classification, defect localization, and industrial feasibility. Results indicate that ViTs offer superior accuracy and interpretability compared to CNN s, particularly in detecting rare and mixed-type defects. However, challenges remain in terms of computational cost and real-time deployment in high-throughput manufacturing settings. The findings of this study underscore the potential of ViTs as a transformative technology for semiconductor defect inspection while highlighting key areas for further development. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimizing DC-Stabilized Direct Antenna Modulation Transmitters for Electrically-Small Antennas

Optimizing DC-Stabilized Direct Antenna Modulation Transmitt...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Mayekar, Miheer P. Dusenbury, Joseph K. Schab, Kurt R. Adams, Jacob J. Dept. of Electrical and Computer Engineering North Carolina State University RaleighNC United States Dept. of Electrical and Computer Engineering Santa Clara University Santa ClaraCA United States

ISBN: (纸本)9781946815200

Linear time-invariant (LTI) electrically small antennas have high radiation quality factors, leading to a challenging tradeoff between efficiency and bandwidth. Bandwidth limitations distort broadband signals, limiting overall data throughput. To circumvent the narrowband limit of LTI antennas, researchers have investigated non-LTI systems. In this paper, we study one time-varying antenna technology called direct antenna modulation (DAM), which is based on storing and releasing energy on a small, capacitive, dipole-like antenna synchronously with an input digital sequence. One challenge in DAM systems is parasitic oscillations that occur when the antenna is switched between states. This ringing significantly influences signal quality in DAM transmitters and leads to spurious radiation. To mitigate the ringing and ensure better transmitted signal quality, the use of an auxiliary DC source was recent proposed to stabilize the parasitic ringing during state transitions. In fact it has been shown that this DC-DAM technique can improve key signal quality metrics like EVM by as much as 10 dB. However, little work has been done to optimize the DC source for best performance in a variety of scenarios. © 2025 USNC-URSI.

关键词： Signal modulation

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Open RAN Slicing with Quantum Optimization

arXiv

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arXiv 2025年

作者： Keyela, Patatchona Cherkaoui, Soumaya Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) through virtual networks that share a single radio access infrastructure. Efficient resource allocation is crucial for RAN slicing, as each service has specific quality of service (QoS) requirements, and a balance between different services must be maintained. Although heuristic and reinforcement learning (RL) techniques have been explored to achieve efficient resource allocation, these approaches face notable limitations: heuristic algorithms face complexity issues that limit their effectiveness in large networks, RL solutions are constrained by their dependency on training data and struggle to adapt to new scenarios and environments. This paper proposes a framework that leverages quantum optimization techniques to optimize radio resource blocks allocation in Open RAN slicing for URLLC and eMBB services. We provide a classical problem formulation and the quantum implementation using the constrained quadratic model on Dwave quantum annealing platform, showcasing the potential of quantum optimization techniques to deliver in real-time optimal solutions for optimization problems in 5G and beyond networks. © 2025, CC BY.

关键词： Constrained optimization

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Unified Face Matching and Physical-Digital Spoofing Attack Detection

Unified Face Matching and Physical-Digital Spoofing Attack D...

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

作者： Kunwar, Arun Rattani, Ajita University of North Texas Dept. of Computer Science and Engineering Denton United States

ISBN: (纸本)9798331536626

Face recognition technology has dramatically trans-formed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating Hil:o attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model ro-bustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications. © 2025 IEEE.

关键词： digital attacks face matching physical attacks unified models

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Revolutionizing Diabetes Prediction Models: Leveraging Advanced Feature Reduction and Classification Methods 22

Revolutionizing Diabetes Prediction Models: Leveraging Advan...

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22nd International Learning and Technology Conference, L and T 2025

作者： Attiah, Ameera Alagha, Mawaddah Bukassim, Lujain El-Kafrawy, Passent College of Engineering Effat University Computer Science Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The global challenge of diabetes demands innovative approaches for early diagnosis and effective management. This paper investigates the integration of advanced dimensionality reduction and feature selection techniques to improve diabetes prediction models. Utilizing Principal Component Analysis (PCA) and the Artificial Bee Colony (ABC) algorithm, the study focuses on optimizing feature selection to enhance model accuracy, computational efficiency, and interpretability. Employing two diverse datasets - the Behavioral Risk Factor Surveillance System (BRFSS) and the Pima Indians Diabetes Dataset - the research evaluates the performance of Random Forest and Logistic Regression models. Results reveal significant improvements in predictive accuracy and computational efficiency, particularly with ABC-selected features, reducing feature dimensionality while maintaining high accuracy. This study underscores the potential of combining PCA and ABC to revolutionize predictive healthcare analytics, offering robust tools for diabetes management and prevention. © 2025 IEEE.

关键词： Dimensionality reduction

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A Physics-Based Temperature Dependent Analytical Model For 2DEG Density in AlGaN/GaN HEMT Devices 14

A Physics-Based Temperature Dependent Analytical Model For 2...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Nabila, Kashfia Tajmim Hudgins, Jerry L. University of Nebraska-Lincoln Dept. of Electrical and Computer Engineering Lincoln United States

ISBN: (纸本)9798331516116

Accurate modeling of the temperature dependent two-dimensional electron gas (2DEG) is very crucial in applied power electronics, given its critical role in High Electron Mobility Transistor (HEMT) characteristics. In view of this, the temperature dependency of 2 DEG has been analyzed for different gate voltages and modeled for the temperature range from 300K to 500K. Relying on the device physics, the temperature dependence of various parameters including bandgap variation, fermi level energy, barrier height reduction, threshold voltage changes and their effects on electron sheet charge density (2DEG) has been included. This model will also integrate the effects of sub-band energy variations, polarization changes, and material property adjustment due to temperature changes. The goal is to provide a robust analytical framework that can predict device behavior accurately across a spectrum of operational gate voltage and temperature conditions. This anticipated model will facilitate the development of power electronics that are more reliable and efficient, capable of operating effectively in diverse and demanding environments. © 2025 IEEE.

关键词： Threshold voltage

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Real-Time Object Detection from Surveillance using Deep Learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

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

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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Open RAN Slicing with Quantum Optimization

Open RAN Slicing with Quantum Optimization

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International Global Information Infrastructure Symposium (GIIS)

作者： Patatchona Keyela Soumaya Cherkaoui Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

关键词： Annealing Training data Open RAN Quality of service Reinforcement learning Ultra reliable low latency communication Quantum annealing Resource management Optimization Faces

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Fake Job Post Detection Using Machine Learning

Fake Job Post Detection Using Machine Learning

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

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