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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Ajibola, Temitope Ezekiel Adeniran, Opeyemi Taiwo Taiwo, Peter Morgan State University Dept. of Electrical and Computer Eng. Baltimore United States

ISBN: (纸本)9798331513269

This study introduces SmartPattern, a novel machine learning-based framework to detect reentrancy attacks in smart contracts, a critical threat to blockchain security. Analyzing 40,000 smart contract, SmartPattern achieves 94% detection accuracy with Random Forest and Support Vector Classifier, outperforming Bidirectional Encoder Representations Transformers embeddings, which produced inconsistent accuracies of 84% and 78% Random Forest and Support Vector Classifier, respectively. Unlike traditional tools like Slither, which rely on static analysis and predefined heuristics, SmartPattern overcomes limitations related to dynamic invocation patterns and non-linear state changes. By leveraging machine learning models and targeted pattern recognition, SmartPattern effectively detects obfuscated attack patterns and generalizes to unseen vulnerabilities. This scalable, automated framework significantly enhances blockchain security by safeguarding billions of dollars in digital assets and promoting trust in decentralized ecosystems. The results demonstrate that SmartPattern is a viable alternative to state-of-the-art models, including those using Graph Convolutional Networks, and provides a comprehensive solution for fortifying smart contract ecosystems against reentrancy attacks. © 2025 IEEE.

关键词： Adversarial machine learning

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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Finite Control Set Model Predictive Control Combined with Online Junction Temperature Estimation for Reliability Enhancement of Voltage Source Inverters 14

Finite Control Set Model Predictive Control Combined with On...

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

作者： Mu, Qiang Zhou, Jiale Mustakin, Zaheen Pereira, Lucas Parkhideh, Babak Zhao, Tiefu University of North Carolina at Charlotte Dept. of Electrical and Computer Engineering CharlotteNC United States

ISBN: (纸本)9798331516116

As the efficiency of the power converter has significantly improved, its long-term reliable operation has become a critical factor in most power electronics applications. Among these factors, monitoring real-time temperature variations in the power converter during operation is a key priority for assessing reliability and predicting its lifetime. However, limited research has been conducted on the online monitoring of junction temperatures in power semiconductors using on-resistance measurements. Online temperature monitoring can help prevent individual device failure caused by transient thermal overstress, which could otherwise undermine the reliability of the entire converter. The power semiconductor junction temperature prediction method discussed in this paper still relies on the less commonly used direct measurement of on-resistance. Building on this approach, a combined method is proposed that integrates finite control set model predictive control (FCS-MPC) to minimize power loss and thermal stress in power semiconductors, thereby improving the overall reliability of the converter. Above all, acknowledging the variations in parameter distribution among identical power semiconductor devices, this method aims to estimate the online junction temperatures of all SiC power MOSFETs in a three-phase, two-level voltage source inverter. The simulation results demonstrate that the proposed method enables online estimation of the junction temperature of all SiC power MOSFETs in a three-phase inverter, and even allows closed-loop regulation of the junction temperature of the SiC power MOSFETs. Simultaneously, the reliability of the power MOSFET is notably enhanced due to the considerable reduction in junction temperature achieved by the proposed method. In addition, the single-vector MPC under finite set model predictive control, along with the double-vector and triple-vector approaches under continuous set model predictive control, are used for comparative analysis with

关键词： Silicon carbide

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Classifying Weed Development Stages Using Deep Learning Methods: Classifying Weed Development Stages with DenseNET, Xception, SqueezeNET, GoogleNET, EfficientNET CNN Models Using ROI Images

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International Journal of Advanced computer science and Applications 2025年第2期16卷 619-626页

作者： Çiçek, Yasin Gülbandilar, Eyyüp Çiray, Kadir Uludağ, Ahmet Dept. of Computer Engineering Eskişehir Osmangazi University Eskişehir Turkey Sinanpaşa Vocational School Afyon Kocatepe University Afyonkarahisar Turkey Dept. of Plant Protection Çanakkale Onsekiz Mart University Çanakkale Turkey

The control of harmful weeds holds a significant place in the cultivation of agricultural products. A crucial criterion in this control process is identifying the development stages of the weeds. The technique to be used is determined based on the weed's growth stage. This study addresses the application of deep learning methods in classifying growth stages using images of various weed species to predict their development periods. Four different weed species, obtained from seeds collected in Turkey-Afyonkarahisar-Sinanpaşa Plain, were used in the study. The images were captured with a Nikon D7000 camera equipped with three different lenses, and the ROI extraction was performed using Lifex software. Using these ROI images, deep learning models such as DenseNet, EfficientNet, GoogleNet, Xception, and SqueezeNet were evaluated. Performance metrics including accuracy, F1 score, precision, and recall were employed. In the 4-class dataset with ROI annotations, DenseNet and Xception achieved an accuracy of 86.57%, while EfficientNet demonstrated the highest performance with an accuracy of 89.55%. Following the initial tests, it was concluded that classes 3 and 4 exhibited extreme similarity caused most of the prediction errors. Merging the said classes significantly increased the accuracy and F1 scores across all models. In image classification tests, SqueezeNet and GoogleNet demonstrated the shortest processing times. However, while EfficientNet lagged slightly behind these models in terms of speed, it exhibited superior accuracy. In conclusion, although the use of ROI improved classification performance, class merging strategies resulted in a more significant performance enhancement. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep reinforcement learning

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Adaptive Prescribed Time Path Following Controller for Underactuated Surface Vessels 13

Adaptive Prescribed Time Path Following Controller for Under...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Ghommam, Jawhar Saad, Maarouf Zhu, Quanmin Rahman, Mohammed H. Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Engineering Ecole de Technologie Superieure Montreal Canada Dept. of Engineering Design and Mathematics University of the West of England Bristol United Kingdom Mechanical Engineering Department University of Wisconsin-Milwaukee Wisconsin United States

ISBN: (纸本)9798331516147

Heading control of marine vehicles is an important issue in navigation and control engineering. This paper investigates the design of a prescribed-time robust adaptive path following control problem of an underactuated surface vessel with parametric uncertainties, unknown disturbances and control input constraints. The establishment of prescribed-time property consists of a prescribed-time regulation of the steering control systems and novel prescribed-time Line of Sight (LOS) guidance control, both of which satisfy the prescribed-time requirement. The feasibility of separate independent control design for the steering and the guidance control systems is guaranteed through the development of new results on nonlinear prescribed-time cascade systems with non-vanishing uncertainties. The Lyapunov method is employed to show that the equilibrium point of the whole system is prescribed-time input to state stable. Numerical simulations are carried out to validate the performance of the proposed control strategy. © 2025 IEEE.

关键词： Steering

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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IoT Based Smart Waste Bins for Smart Waste Management in Sri Lanka 5

IoT Based Smart Waste Bins for Smart Waste Management in Sri...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Hemapriya, H.P.B.D. Hewaranaweera, D.S. Sajith, N.T.M. Aslam Saja, A.M. Aashiq, M.N.M. South Eastern University of Sri Lanka Dept. of Electrical and Telecommunication Engineering Oluvil Sri Lanka South Eastern University of Sri Lanka Dept. of Computer Science and Engineering Oluvil Sri Lanka

ISBN: (纸本)9798331530983

This paper presents a research study on the development of smart solutions for waste collection and monitoring adapted for Sri Lankan cities. The proposed system includes a user reward mechanism, real-time monitoring of bins, identification of filled bins, and optimized route suggestions for collecting them. A unified mobile application has been developed to serve both the general public and authorities. Users are rewarded based on the weight of the recyclable waste they dispose of, and they can track their points and locate bins through the mobile application. Authorities can monitor the filling level of each bin in real time with help of the application. When a bin reaches its maximum capacity, a notification is displayed in the app and provides an optimized route for collecting the filled bins. Additionally, this system is designed to be seamlessly integrated with existing recyclable waste collection bins, making it affordable. It allows authorities to gather waste generation data from each area, enabling informed decision-making while encouraging community members to actively engage with the system. © 2025 IEEE.

关键词： Decision making

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Hybrid Offline Passive Grammatical Inference and Online Planning for Non-Markovian Tasks

Hybrid Offline Passive Grammatical Inference and Online Plan...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mahyar Alinejad Alvaro Velasquez Yue Wang George Atia Dept. of Electrical and Computer Engineering University of Central Florida Orlando Florida Dept. of Computer Science University of Colorado Boulder Colorado Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida Orlando Florida

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Planning in non-Markovian environments often requires inferring task structures, such as reward machines, through interactions with the environment. Traditional active grammatical inference methods, like Angluin’s L * algorithm, depend on continuous querying to learn task structures for the underlying planning objectives. In contrast, we propose a hybrid approach that combines passive grammatical inference, using the Regular Positive and Negative Inference (RPNI) algorithm, with online planning. By leveraging pre-collected positive and negative trajectories, RPNI learns a deterministic finite automaton (DFA) that captures the task structure, significantly reducing the need for real-time interactions. Subsequently, online planning is conducted over the product MDP, which integrates the environment with the learned DFA. This hybrid methodology minimizes the cost of online interactions and improves learning efficiency in complex environments. Our approach outperforms baseline algorithms in terms of runtime and sample complexity, and is well-suited for real-world scenarios where task structures are implicit, and interactions with the environment are expensive.

关键词： Runtime Learning automata Signal processing algorithms Signal processing Inference algorithms Real-time systems Planning Complexity theory Trajectory Speech processing

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Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance 15

Analyzing the Impact of AI Tools on Student Study Habits and...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Ward, Ben Bhati, Deepshikha Neha, Fnu Guercio, Angela Kent State University Dept. of Computer Science KentOH United States

ISBN: (纸本)9798331507695

This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA, suggesting positive academic outcomes. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified, emphasizing the need for AI tools to complement conventional educational strategies rather than replace them. Data were collected through a survey with a Likert scale and follow-up interviews, providing both quantitative and qualitative insights. The analysis involved descriptive statistics to summarize demographic data, AI usage patterns, and perceived effectiveness, as well as inferential statistics (T-tests, ANOVA) to examine the impact of demographic factors on AI adoption. Regression analysis identified predictors of AI adoption, and qualitative responses were thematically analyzed to understand students' perspectives on the future of AI in education. This mixed-methods approach provided a comprehensive view of AI's role in education and highlighted the importance of privacy, transparency, and continuous refinement of AI features to maximize their educational benefits. © 2025 IEEE.

关键词： Adaptive learning paths AI in education Personalized learning Real-time feedback Study habits

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An Intelligent Hybrid Algorithm for Smooth AUV Path Generation 3

An Intelligent Hybrid Algorithm for Smooth AUV Path Generati...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Singh, Paramjeet Talukdar, Bhaskar Jyoti Dash, Rudra Narayan Das, Bikramaditya BPUT Dept. of Computer Science and Engineering Odisha Rourkela India VSSUT Dept. of Electronics and Telecommunication Engineering Odisha Burla India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India BPUT Dept. of Electronics and Communication Engineering Odisha Rourkela India

ISBN: (纸本)9798331533205

The development of effective multi-objective optimization strategies for Autonomous Underwater Vehicles (AUVs) operating in uncharted territories constitutes a considerable challenge. This endeavor necessitates the simultaneous minimization of travel time, reduction of path length, enhancement of navigational safety, and smoothing of trajectories. In light of these challenges, this research presents an innovative real-time path planning framework that seamlessly integrates Particle Swarm Optimization (PSO) with Simulated Annealing (SA), thereby enhancing AUV navigation within static environments. The PSO algorithm is utilized to generate optimal temporary waypoints across the scenarios examined, by balancing the crucial optimization parameters. Subsequently, an optimal path is formulated, informed by both the initial and terminal points. The efficacy of the proposed methodology is assessed through simulations conducted across three distinct maps. Each obstacle map demonstrates varying densities of obstacles. The outcomes of these simulations emphasize the robustness and adaptability of the proposed strategy, highlighting its ability to formulate optimal paths within complex underwater environments, irrespective of the arrangement of obstacles. The AUVs display remarkable agility in maneuvering through intricate underwater landscapes, adept.y avoiding debris and obstacles while preserving operational efficiency. This enhanced path planning technique not only elevates the responsiveness of the AUVs during critical missions - such as search and rescue operations and structural assessments - but also significantly bolsters their overall navigational safety. Through this research, we introduce a formidable tool for enhancing AUV capabilities in practical applications, ultimately facilitating more efficient underwater exploration and intervention. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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