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IEEE Southeastcon

作者： Eric Scott Sisson Steven C. Puckett Dept. of Electrical and Computer Engineering Mississippi State University Starkville AL USA Computer Science and Information Systems University of North Alabama Florence AL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The current capabilities of deepfake and Artificial Intelligence (AI) technologies make it difficult to distinguish what is original from what is fabricated. Electronic documents can be digitally signed to verify and authenticate their information, making them admissible as evidence in court. In contrast, images and videos from Internet of Things (IoT) devices-such as police body cameras, security cameras, and smartphones are susceptible to deepfake manipulation, which can undermine their authenticity in legal proceedings. This research hypothesizes that digitally signing image and video data utilizing hardware cryptographic coprocessors could render them admissible in court. Utilizing our processes will also identify if any manipulation of the image data has occurred. Our approach addresses a critical need in the justice system to combat the challenges deepfakes pose and could pave the way for enhanced trust in digital evidence, bolstering the integrity of the judicial process.

关键词： Deepfakes Law enforcement Media Cameras Hardware Cryptography Internet of Things Artificial intelligence Digital signatures Coprocessors

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A Bi-LSTM Based Model for Social Media Extremism Detection in Context of Bangladesh

A Bi-LSTM Based Model for Social Media Extremism Detection i...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sajib Saha Zahidul Islam Tanvirul Islam Md Monzur Morshed Tithee Chakma Mama Subhenur Latif Md Walid Bin Jashim Dept. of Computer Science & Engineering Daffodil Internation University Dhaka Bangladesh Department of Accounting & Information Systems University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s modern world, digital technology has advanced social networking with revolutionary changes. People use some most popular platforms like Facebook, YouTube and some other social media platforms to engage with each other for business, information sharing and so on. Attacking someone on social media platforms through comments, in response to their speech, has become a common occurrence in Bangladesh as well as worldwide. Very little research has been done in this related field for Bengali NLP. In this study we primarily focus on classifying these comments into two categories: extremist and non-extremist. Comments were collected from two of the most popular platforms, YouTube and Facebook, used in Bangladesh to build our "BEC (Bangla Extremist Comments)" dataset. To classify the comments, a CNN + Bi-LSTM hybrid model was employed considering extremist comments detection systems using deep learning-based sentiment analysis techniques. The proposed model achieved an accuracy rate of 85% in identifying the extremist comments. This research opens opportunities for future researchers to take advantage of contributing and collaboration in this field.

关键词： Deep learning Analytical models Sentiment analysis Video on demand Social networking (online) Computational modeling Information sharing Collaboration Web sites Context modeling

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Predicting Optimal Placement of Electric Vehicle Charge Stations Using Machine Learning: A Case Study in Glasgow, UK

Predicting Optimal Placement of Electric Vehicle Charge Stat...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Arash Mousaei Yahya Naderi dept. Computer and Electrical Engineering University of Tabriz Tabriz Iran Sustainable Energy Systems Department Ricardo AEA Glasgow U.K

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

Given the reduced costs of establishing Electric Vehicle (EV) charging stations and the need to improve their accessibility, it is essential for EV developers and governments to identify optimized locations for new stations. This study presents a predictive modeling approach using Machine Learning to determine the optimal sites for EV charging stations in Glasgow. While various machine learning methods are available for this purpose, we focus on Linear Regression due to its simplicity and high accuracy. By analyzing a comprehensive dataset of 207 AC charging stations-including factors latitude, longitude, population density, number of kinds of plugs, total number of plugs, price per kWh, traffic conditions, distance to the nearest station, Air Quality Index (AQI), and land value - we aim to provide urban planners and policymakers with actionable insights. The study evaluates the performance of the Linear Regression model in predicting the most suitable locations for charging stations. The results indicate that the proposed model achieved impressive accuracy, with R 2 , Mean Absolute Error (MAE), and Mean Squared Error (MSE) values of 0.89, 0.05, and 0.08, respectively. This research contributes to the ongoing efforts in urban planning for sustainable mobility by offering a data-driven methodology for EV infrastructure development.

关键词： Accuracy Atmospheric modeling Linear regression Machine learning Predictive models Charging stations Electric vehicle charging Indexes Population density Plugs

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Reinforcement Learning for Unrelated Parallel Machine Scheduling with Release Dates, Setup Times, and Machine Eligibility 24

Reinforcement Learning for Unrelated Parallel Machine Schedu...

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Proceedings of the Winter Simulation Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (纸本)9798331534202

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

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Natural Language Processing for Conversational AI: Chatbots and Virtual Assistants

Natural Language Processing for Conversational AI: Chatbots ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Neeraj Shrivastava Pushpa Tewari S. Sujatha Srinivasa Rao Bogireddy Neeraj Varshney Vinod Sharma Dept. of Engineering Medicaps University Indore India Dept. of Vocational Studies Guru Nanak College Dhanbad India Dept. of Civil Engineering K. Ramakrishnan College of Technology Trichy India Dept. of Software Engineer Horizon Systems Inc Phoenix Arizona Dept. of CSE GLA University Mathura India Dept. of Computer Science Jiwaji University Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The frontline in developing the capabilities of chatbots and virtual assistants involves the use of NLP, enabling these machines to evolve into truly advanced, interactive machines that are able to comprehend and respond to human language with an almost unimaginable degree of accuracy. The paper will analyze some of the major NLP techniques and methodologies used for such systems: machine learning algorithms, neural networks, and semantic analysis. This would also mean that, with the help of big data and continuous learning, the work of chatbots and virtual assistants will range from answering queries to personalized recommendations. Language understanding, generation, and context management are discussed in detail, underlining how NLP improves user experience by making interactions more natural and intuitive. The challenges of ambiguous language processing, conversational context, and ensuring data privacy and security are discussed as well. Case studies and applications in real life show the impact of NLP on different sectors: customer service, healthcare, and e-commerce. The findings bring out the potential of NLP to change human-computer interaction and allow for more responsive and intelligent digital assistants.

关键词： Ethics Technological innovation Sentiment analysis Virtual assistants Semantics Speech recognition Chatbots Natural language processing User experience Text processing

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Adaptive Control for Shunt Active Power Filter under Stochastic Solar Photovoltaics Behavior

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Prakash, Surya Alkhatib, Mohamed Sindi, Hatem F. Alghamdi, Sultan Behera, Ranjan Kumar Muduli, Utkal Ranjan Nit Jamshedpur Dept. of Electrical Engg Jharkhand Jamshedpur India Uae University Al Ain Dept. of Electrical and Communication Engg Coe Abu Dhabi15551 United Arab Emirates King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia Indian Institute of Technology Patna Dept. of Electrical Engineering Bihta801103 India Khalifa University Apec Dept. of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

The paper discusses the challenges in controlling a two-stage photovoltaic (PV)-based shunt active power filter (SAPF) under distorted grid conditions and stochastic PV system behavior. To address this, the paper proposes an adaptive proportional integral (API) regulator to regulate the DC-link voltage, which adapts its parameters based on the DC-link error voltage to provide low bandwidth for disturbance rejection and high bandwidth for dynamic performance enhancement. The paper also proposes an estimation method for the reference current using the sliding discrete Fourier transform and the instantaneous symmetrical component theory, which is suitable for non-ideal grid or load conditions. The accuracy of the proposed reference current estimation method is analyzed using time- and frequency-domain analysis. The performance of the PV-SAPF system with the proposed control algorithms is illustrated through simulation and real-time hardware validation under various grid conditions. Despite distorted grid conditions, the total harmonic distortion of the grid current is substantially within IEEE Std 519-2014. The proposed API regulator and reference current estimation method show promising results in improving the performance of the PV-SAPF system in the presence of internal and external disturbances. © 2013 IEEE.

关键词： Frequency domain analysis

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Co-Design of Network Topology and Qubit Allocation for Distributed Quantum Computing

Co-Design of Network Topology and Qubit Allocation for Distr...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Jiyao Liu Lei Fan Yuanxiong Guo Zhu Han Yu Wang Department of Computer and Information Sciences Temple University Philadelphia USA Dept. of Engineering Technology University of Houston Houston USA Dept. of Electrical and Computer Engineering University of Houston Houston USA Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.

关键词： Costs Network topology Computational modeling Qubit Topology Computational efficiency Resource management Quantum circuit Integrated circuit modeling Optimization

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Learning 3D Scene Analogies with Neural Contextual Scene Maps

arXiv

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arXiv 2025年

作者： Kim, Junho Bae, Gwangtak Lee, Eun Sun Kim, Young Min Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University Korea Republic of

Understanding scene contexts is crucial for machines to perform tasks and adapt prior knowledge in unseen or noisy 3D environments. As data-driven learning is intractable to comprehensively encapsulate diverse ranges of layouts and open spaces, we propose teaching machines to identify relational commonalities in 3D spaces. Instead of focusing on point-wise or object-wise representations, we introduce 3D scene analogies, which are smooth maps between 3D scene regions that align spatial relationships. Unlike well-studied single instance-level maps, these scene-level maps smoothly link large scene regions, potentially enabling unique applications in trajectory transfer in AR/VR, long demonstration transfer for imitation learning, and context-aware object rearrangement. To find 3D scene analogies, we propose neural contextual scene maps, which extract descriptor fields summarizing semantic and geometric contexts, and holistically align them in a coarse-to-fine manner for map estimation. This approach reduces reliance on individual feature points, making it robust to input noise or shape variations. Experiments demonstrate the effectiveness of our approach in identifying scene analogies and transferring trajectories or object placements in diverse indoor scenes, indicating its potential for robotics and AR/VR applications. © 2025, CC BY.

关键词： Adversarial machine learning

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AutoMediVision:A Deep Learning System for Accurate Pharmaceutical pill identification

AutoMediVision:A Deep Learning System for Accurate Pharmaceu...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： G Rajeswarappa B. Venkata Sivaiah M. Navya Y. Lasya Shaik Mohammad Sameer V. Varun Kumar Department of CSE G Pulla Reddy Engineering College Kurnool Dept. of Data Science School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India Dept. of Computer Science and Systems Engineering(CSSE) Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The goal of this project is to build a high performance system that offers prediction of pill information from images, the pill name, medical use and recommended dosage by using MobileNet which is a lightweight and efficient deep learning model. This system serves as an important tool for healthcare professionals and patients as it offers a means of reducing risk because of the large number of medications out there. For this task, MobileNet was chosen for its high accuracy and good balance between computational efficiency and high accuracy, and hence can be deployed on mobile devices. Having trained the model on a wide variety of pills with class wise annotations, it generalized well for diverse pills. The output of the system is the pill name, primary medical indications and common dosage instructions that help in safer medication management by supporting healthcare providers in providing better care and patient safety.

关键词： Deep learning Accuracy Computational modeling Medical services Mobile handsets Safety Standards Research and development Biomedical imaging Pharmaceuticals

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LEARNING EFFICIENT POSITIONAL ENCODINGS WITH GRAPH NEURAL NETWORKS

arXiv

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arXiv 2025年

作者： Kanatsoulis, Charilaos I. Choi, Evelyn Jegelka, Stephanie Leskovec, Jure Ribeiro, Alejandro Dept. of Computer Science Stanford University United States School of Computation Information and Technology Technical University of Munich Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Dept. of Electrical and Systems Engineering University of Pennsylvania United States

Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity of Graph Neural Networks (GNNs). However, designing powerful and efficient PEs for graphs poses significant challenges due to the absence of canonical node ordering and the scale of the graph. In this work, we identify four key properties that graph PEs should satisfy: stability, expressive power, scalability, and genericness. We find that existing eigenvector-based PE methods often fall short of jointly satisfying these criteria. To address this gap, we introduce PEARL, a novel framework of learnable PEs for graphs. Our primary insight is that message-passing GNNs function as nonlinear mappings of eigenvectors, enabling the design of GNN architectures for generating powerful and efficient PEs. A crucial challenge lies in initializing node attributes in a manner that is both expressive and permutation equivariant. We tackle this by initializing GNNs with random node inputs or standard basis vectors, thereby unlocking the expressive power of message-passing operations, while employing statistical pooling functions to maintain permutation equivariance. Our analysis demonstrates that PEARL approximates equivariant functions of eigenvectors with linear complexity, while rigorously establishing its stability and high expressive power. Experimental evaluations show that PEARL outperforms lightweight versions of eigenvector-based PEs and achieves comparable performance to full eigenvector-based PEs, but with one or two orders of magnitude lower complexity. Our code is available at https://***/ehejin/Pearl-PE. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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