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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Sajja Suneel Reema Rallan R Jayakarthik R. Naveenkumar Alok Dubey Shivaputra Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of Computer Application Lovely Professional University Phagwara Punjab Dept of Computer science Saveetha College of Liberal Arts and Sciences SIMATS Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Mohali Punjab India Department of Preventive Dental Sciences College of Dentistry Jazan University Jazan Saudi Arabia Department of Electronics and Communication Engineering Dr. Ambedkar Institute of Technology Bengaluru

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

DynamicVisionCore is a novel predictive object tracking framework designed for real-time robotics applications. The system integrates YOLOv8 for object detection and DeepSORT for multi-object tracking, ensuring high accuracy and low latency. The framework achieves an impressive MOTA of 72.5% and MOTP of 80.1% on the MOT17 dataset, outperforming traditional methods such as SORT and ByteTrack. Motion prediction is enhanced using a hybrid Kalman Filter and LSTM-based model, reducing RMSE from 9.7 to 6.3 in occlusion scenarios. Additionally, the system ensures robust performance under challenging conditions, achieving 91.3% accuracy in normal lighting and 85.6% in low-light environments. The implementation is optimized for edge computing platforms like Jetson Xavier, where it achieves real-time processing at 9.7ms per frame. The system's robustness is further validated against varying lighting conditions, occlusions, motion blur, and extreme environments. This research demonstrates the efficacy of DynamicVisionCore in real-time robotic vision applications, ensuring reliable object tracking for autonomous systems. Future work will explore a daptiv e reinforcement learning strategies and improved sensor fusion techniques to further enhance tracking robustness.

关键词： Accuracy Lighting Reinforcement learning Sensor fusion Predictive models Robot sensing systems Real-time systems Robustness Object tracking Kalman filters

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Enhancing Aspect-based Sentiment Analysis with ParsBERT in Persian Language

arXiv

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arXiv 2025年

作者： Ariai, Farid Mahmoudi, Maryam Tayefeh Moeini, Ali Department of Computer Engineering University of Tehran Kish International Campus Kish Iran Data Analysis & Processing Research Group IT Research Faculty ICT Research Institute Tehran Iran School of Engineering Science College of Engineering University of Tehran Tehran Iran

In the era of pervasive internet use and the dominance of social networks, researchers face significant challenges in Persian text mining including the scarcity of adequate datasets in Persian and the inefficiency of existing language models. This paper specifically tackles these challenges, aiming to amplify the efficiency of language models tailored to the Persian language. Focusing on enhancing the effectiveness of sentiment analysis, our approach employs an aspect-based methodology utilizing the ParsBERT model, augmented with a relevant lexicon. The study centers on sentiment analysis of user opinions extracted from the Persian website 'Digikala.' The experimental results not only highlight the proposed method's superior semantic capabilities but also showcase its efficiency gains with an accuracy of 88.2% and an F1 score of 61.7. The importance of enhancing language models in this context lies in their pivotal role in extracting nuanced sentiments from user-generated content, ultimately advancing the field of sentiment analysis in Persian text mining by increasing efficiency and accuracy. © 2025, CC BY.

关键词： Latent semantic analysis

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Fuzzy Preference Completion with Ranked and Unranked Preferences

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Cognitive Computation 2025年第3期17卷 1-21页

作者： Li, Lei Liu, Pan Zhang, Renjie Tao, Zhenchao Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Radiation Oncology The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Department of Radiation Oncology Anhui Provincial Cancer Hospital Hefei China

As for social choice, all alternatives are ranked by agents to form preferences as linear orders. However, in applications, sometimes some alternatives cannot be ranked, or it is unnecessary to rank them, which leads to unranked alternatives. Hence, without loss of generality, by dividing the set of alternatives into three ranked and unranked subsets, including top-k alternatives, intermediate-r alternatives, and last-l alternatives, the Mallows model on ranked and unranked preferences can be analyzed systematically. Technically, a repeated insertion model is adopted during sampling, and probability distributions are derived for ranked and unranked preferences of alternatives. Experimental results verify the accuracy of the probability distributions for different ranked and unranked preferences of alternatives. Furthermore, in order to solve the preference completion problem where agents have multiple partial rankings, a fuzzy preference completion algorithm, Fuzzy-Multi-Rankings, is proposed, which introduces a fuzzy ranking to complete the target agent’s preference in addition to the traditional nearest-neighbor-based methods. Based on the three ranked and unranked preferences, seven cases can be classified and analyzed for fuzzy preference completion. Experiments on the synthetic datasets and MovieLens dataset confirm the effectiveness and efficiency of our proposed Fuzzy-Multi-Rankings algorithm and also verify the accuracy of the evaluated probability distributions for the proposed seven cases.

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In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis

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computer Methods and Programs in Biomedicine 2025年 267卷 108793-108793页

作者： Shahrior, Rahat Tamkin, Salwa Khan, Mohammad Badhruddouza Meraj, Ahmed Jebail Bhuiyan, Hanif Department of Biomedical Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh Department of Computer Science and Engineering BRAC University Kha 224 Bir Uttam Rafiqul Islam Avenue Dhaka Merul Badda1212 Bangladesh Data Scientist Performance & Analytics Group City of Gold Coast BundallQLD4217 Australia

Background and Objective: In search of a key driver for the invasive growth of cancer metastasis, AKT2 is found to be exceptionally expressed in colorectal cancer and its metastasis. Again, exceeding genomic arrangements of AKT2 can be held responsible for HGSC (High-grade serous ovarian cancer) and breast cancer cell metastasis. FDA-approved capivasertib, a potential drug targeting the AKT signaling pathway, has a few side effects such as plausible alterations of liver function and gastrointestinal issues. Hence, this research aims to detect compounds with higher drug potency for selective AKT2 inhibition to encounter the incidence of different types of cancer cell metastasis. Methods: Eight machine-learning models were engaged to classify active and inactive drug candidates among 1148 collected compounds from the CHEMBL database. Potential drug candidates with greater IC50 value and no Lipinski violations were then addressed to molecular docking and molecular dynamics simulation using PyRx, AutoDock Vina and Desmond package. Results: From docking studies, three of the initial drug candidates provided greater binding affinities within a range from -10.9 to -9.8 kcal/mol, comparable to that of Capivasertib and backed up by post-docking MM/GBSA analysis. Again, the prediction of pharmacokinetic properties and bioactivity scores of drug candidates revealed their drug-likeliness and safer ADMET profiles for future clinical trials. Finally, 100 ns MD simulation computation for these lead compounds exhibited greater stability and drug potency during interactions with AKT2 protein, followed by PCA and DCCM analysis. Conclusion: However, future in-vivo research can ascertain whether our proposed drug candidates can pass the standard clinical trials as publicly accessible novel drug targets. © 2025 Elsevier B.V.

关键词： Drug interactions

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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

作者： Marvin Borsdorf Zexu Pan Pascal Himmelmann Haizhou Li Tanja Schultz Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time.

关键词： Training Measurement databases Signal processing data models Blind source separation Robustness Acoustics Speech processing

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Unsupervised Multi-View Outlier Detection via Optimal Graph Filtering

Unsupervised Multi-View Outlier Detection via Optimal Graph ...

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

作者： Zhiguo Hu Ning Wang Peng Zhou Liang Du Yuhua Qian Cheng Wang Yanming Zhang School of Computer and Information Technology Shanxi University Taiyuan China School of Computer Science and Technology Anhui University Hefei China Institute of Big Data Science and Industry Shanxi University Taiyuan China Department of Computer Science and Engineering Tongji University Shanghai China China Mobile Communications Group Shanxi Co. Ltd. Taiyuan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unsupervised multi-view outlier detection has garnered increasing attention in recent years, yet existing methods face persistent challenges. Many approaches rely predominantly on first-order neighborhood information, overlooking the richer insights offered by higher-order structures, which can degrade detection accuracy. Additionally, some methods suffer from outlier domination in their objective functions, leading to suboptimal performance. Integrating information effectively across multiple views also remains a significant hurdle. To address these challenges, we propose a novel Multi-View Outlier Detection method based on Optimal Graph Filtering (MODGF). Our approach detects outliers using a high-order graph filtering mechanism, ensuring consistency between feature and neighborhood spaces by sharing filtering parameters. Furthermore, we incorporate the Corr-entropy Induced Metric (CIM) to refine the objective function and introduce an efficient scoring strategy for enhanced detection reliability. Extensive experimental results demonstrate that our method is both stable and efficient across scenarios. The code is available at https://***/criticcc/MODGF.

关键词： Measurement Accuracy Filters Filtering Linear programming Feature extraction Reliability Speech processing Anomaly detection Faces

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Motion Planning in Automated Vehicles Based on Hierarchical MPC

Motion Planning in Automated Vehicles Based on Hierarchical ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Pooja Kapila Pankaj Kumar Rahul Negi Bhupender Kumar Bharti Akash Punhani Suraj Singh Bisht Department of Artifical Intelligence & Data Science IIMT College of Engineering Department of Statistics Amity University Kolkata India Department of ECE Tula's Institute Dehradun India Department of Mechanical Engineering VMSB UTU Campus Pithoragarh India Department of Computer Science KIET Group of Institutions Noida India Department of ECE Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In this study, a thorough hierarchical control structure that supports autonomous decision-making that arises in autonomous systems and robots is proposed. Distinct state and decision/control sets are frequently used to describe high-level decision making in a conventional hierarchical control architecture. To keep up with proficiency, wellbeing, and solace, robotized vehicles generally depend serious areas of strength for on powerful movement arranging calculations. The capacity of Model Prescient Control (MPC) to oversee imperatives and upgrade directions progressively has made it a strong system for movement arranging. To approve the proposed strategy, this exploration utilizes MATLAB recreations to look at various levelled MPC for movement arranging in robotized vehicles. With the low-level regulator guaranteeing exact direction observing and the undeniable level organizer computing a worldwide direction, a two-layer progressive MPC structure is fabricated. The discoveries show how well the progressive MPC handles street ebb and flow, dynamic hindrances, and continuous computational impediments. It is demonstrated that the suggested autonomous decision-making scheme's recursive practicality and stability are ensured by carefully crafting its essential components. An intelligent vehicle's autonomous lane-changing system is developed using the suggested framework. Simulation demonstrates that it has a potential capacity to manage a variety of behaviours in complex and dynamic contexts. It is anticipated that the suggested framework will be useful for autonomous decision-making in a variety of robotics along with autonomous technologies where safety as well as optimality are important factors.

关键词： Analytical models Autonomous systems Computational modeling Decision making Turning Planning Trajectory Safety Vehicle dynamics Robots

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

arXiv

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arXiv 2025年

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk Electrical and Computer Engineering Department The Ohio State University United States BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025, CC BY.

关键词： Deep reinforcement learning

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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