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An Environmentally Friendly Fishing Android-Based Mobile Device App for Penghu Islands (Pescadores), Taiwan

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An Environmentally Friendly Fishing Android-Based Mobile Dev...

Consumer Technology (ICCT-Pacific), International Conference on

作者： Hung-Tu Lin Ke-Qing Yan Ying-Zun Fang Xiang-Rui Huang Liang-Bi Chen Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Penghu Islands (Pescadores), Taiwan's outlying islands surrounded by the sea, boast rich fishing resources that attract many anglers. However, successful fishing requires more than just gear: it involves understanding the varying fish species available in different locations, times, and climates, as well as adhering to relevant laws and regulations. To address these needs, this paper proposes an environmentally friendly fishing Android-based mobile device App specifically for the Penghu Islands. The proposed Android-based mobile device App aims to gather essential information, including local fishing regulations, aquatic safety tips, and important precautions. Its goal is to equip fishermen with the resources necessary to fish successfully and with peace of mind.

关键词： Sea measurements Length measurement Fish Regulation Mobile applications Tides Protection Smart phones Meteorology Lenses

Advancing IoV Security Through Machine Learning: Design and Evaluation of the CICIoV2024 Benchmark dataset

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Advancing IoV Security Through Machine Learning: Design and ...

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Arafah Asma Adnane Wael Hadi Ghayda Al-Hyasat Mohammad Al-Habahbeh Department of Information Security University of Petra Amman Jordan Department of Computer Science Loughborough University Loughborough UK Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Computer Science Zarqa University Zarqa Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The Internet of Vehicles (IoV) faces growing cybersecurity challenges as vehicles become more connected by networks, which requires efficient Anomaly Intrusion Detection Systems (AIDS). This paper uses CICIoV2024, a comprehensive modern dataset for IoV security research. Also reflects real-world attacks conducted on a Ford vehicle's Controller Area Network (CAN) bus system. The dataset covers popular attack scenarios, including Denial-of-Service (DoS) and spoofing (steering wheel, RPM, gas, and speed controls) attacks. We implemented a systematic machine learning pipeline to build AIDS based on a comprehensive dataset evaluation. The evaluation includes binary classification (Benign vs Attack), categorical classification (Benign, DoS, Spoofing), and multiclass classification (Benign, Steering Wheel Spoofing, Gas Spoofing, RPM Spoofing, Speed Spoofing) where the Logistic Regression, Random Forest, AdaBoost, and Deep Neural Networks are used in implementation. Results demonstrated exceptional performance across the mentioned models. In categorical classification, the ensemble methods have high scores, compared to the Extra Trees Classifier, Decision Tree, and Histogram Gradient Boosting algorithms achieving excellent performance (precision = 1.0, recall = 1.0, F1 score = 1.0), while Gaussian Naive Bayes reported lower performance (precision = 0.83, recall = 0.80, F1 score = 0.79). For multi-class classification, most advanced models maintained perfect metrics, notably outperforming traditional approaches like LDA (precision=0.95, recall=0.95, F1-score=0.94) and Gaussian Naive Bayes (precision=0.80, recall=0.73, F1-score=0.73). This research advances IoV security by providing an AIDS based on a realistic and modern dataset to develop and test robust AIDS, which is valuable for the security of smart transportation infrastructure.

关键词： Histograms Systematics Computational modeling Pipelines Wheels Boosting Bayes methods Controller area networks Ensemble learning Random forests

GPU-accelerated Evolutionary Many-objective Optimization Using Tensorized NSGA-III

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arXiv 2025年

作者： Li, Hao Liang, Zhenyu Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

NSGA-III is one of the most widely adopted algorithms for tackling many-objective optimization problems. However, its CPU-based design severely limits scalability and computational efficiency. To address the limitations, we propose TensorNSGA-III, a fully tensorized implementation of NSGA-III that leverages GPU parallelism for large-scale many-objective optimization. Unlike conventional GPU-accelerated evolutionary algorithms that rely on heuristic approximations to improve efficiency, TensorNSGA-III maintains the exact selection and variation mechanisms of NSGA-III while achieving significant acceleration. By reformulating the selection process with tensorized data structures and an optimized caching strategy, our approach effectively eliminates computational bottlenecks inherent in traditional CPU-based and naïve GPU implementations. Experimental results on widely used numerical benchmarks show that TensorNSGA-III achieves speedups of up to 3629× over the CPU version of NSGA-III. Additionally, we validate its effectiveness in multiobjective robotic control tasks, where it discovers diverse and high-quality behavioral solutions. Furthermore, we investigate the critical role of large population sizes in many-objective optimization and demonstrate the scalability of TensorNSGA-III in such scenarios. The source code is available at https://***/EMI-Group/evomo. Copyright © 2025, The Authors. All rights reserved.

关键词： Cellular automata

EvoGP: A GPU-accelerated Framework for Tree-based Genetic programming

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arXiv 2025年

作者： Wang, Lishuang Wu, Zhihong Sun, Kebin Li, Zhuozhao Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

Tree-based Genetic programming (TGP) is a key evolutionary algorithm widely used in symbolic regression, feature engineering, and scientific modeling. Its high computational demands make GPU acceleration essential for scalable and high-performance evolutionary computation. However, GPU acceleration of TGP faces three key challenges: inefficient tree encoding, highly heterogeneous genetic operations, and limited parallelism in fitness evaluation. To address these challenges, we introduce EvoGP, a comprehensive GPU-accelerated TGP framework. First, we design a tensorized encoding scheme to represent tree with different structures as tensors with the same shape, optimizing memory access and enabling efficient parallel execution. Second, we propose a unified parallel framework for genetic operations by leveraging shared computational primitives and implementing dedicated CUDA kernels for scalable performance. Third, we present a fully parallel fitness evaluation strategy for symbolic regression, exploiting both population-level and data-level parallelism to maximize GPU utilization. Moreover, we implement a comprehensive library to provide rich algorithm operators and benchmark problems. EvoGP is extensively tested on various tasks, including symbolic regression, classification, and robotics control, demonstrating its versatility and effectiveness across diverse application scenarios. Experimental results show that EvoGP achieves up to a 140.89× speedup over the state-of-the-art GPU-based TGP implementation, while maintaining or exceeding the accuracy of baseline methods. EvoGP is open-source and accessible at: https://***/EMI-Group/evogp. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization

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arXiv 2025年

作者： Liang, Zhenyu Li, Hao Yu, Naiwei Sun, Kebin Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limitations due to insufficient parallelism and scalability. While most work has focused on algorithm design to address these challenges, little attention has been given to hardware acceleration, thereby leaving a clear gap between EMO algorithms and advanced computing devices, such as GPUs. To bridge the gap, we propose to parallelize EMO algorithms on GPUs via the tensorization methodology. By employing tensorization, the data structures and operations of EMO algorithms are transformed into concise tensor representations, which seamlessly enables automatic utilization of GPU computing. We demonstrate the effectiveness of our approach by applying it to three representative EMO algorithms: NSGA-III, MOEA/D, and HypE. To comprehensively assess our methodology, we introduce a multiobjective robot control benchmark using a GPU-accelerated physics engine. Our experiments show that the tensorized EMO algorithms achieve speedups of up to 1113× compared to their CPU-based counterparts, while maintaining solution quality and effectively scaling population sizes to hundreds of thousands. Furthermore, the tensorized EMO algorithms efficiently tackle complex multiobjective robot control tasks, producing high-quality solutions with diverse behaviors. Source codes are available at https://***/EMI-Group/evomo. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

Machine Learning Models for Speech-Based Depression Screening

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Machine Learning Models for Speech-Based Depression Screenin...

Cybernetics and Innovations (ICCI), International Conference on

作者： Kunanon Kongchatree Ongon Suriyo Natvara Pichedpan Rachada Kongkachandra Pokpong Songmuang Data Science and Innovation College of Interdisciplinary Studies Thammasat University Bangkok Thailand Department of Computer Science Thammasat University Bangkok Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

Mental health issues significantly affect society, requiring long-term treatment that is both costly and impacts personal relationships. This research paper proposes a speech-based method to screen for depression risk as an accessible alternative. The research included five machine learning models-Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naive Bayes, and Linear Discriminant Analysis (LDA)-were used to analyze speech data from 25 participants with varying mental health conditions. Three data preparation techniques-Leave-One-Out Cross-Validation, SMOTE, and raw data-were applied to optimize the models. Results showed that SVM with SMOTE achieved the highest accuracy (98 %), emphasizing the importance of effective data preparation. This research demonstrates the potential of using speech analysis as a cost-effective tool for early mental health screening and provides a foundation for practical applications in healthcare and beyond.

关键词： Support vector machines Analytical models Accuracy Speech analysis data preprocessing Mental health Machine learning Predictive models Depression data models

Proposed Hybrid AI-Driven Framework for Stock Market Prediction

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Proposed Hybrid AI-Driven Framework for Stock Market Predict...

Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Rajini S Shreya Vijay Mayuri G Moses John Wesley Jyothi Lakshmi N Department of Computer Science Engineering Kumaraguru College of Technology Coimbatore India Department of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The Stock Market has developed as a revolutionary force, transforming global economies through democratizing wealth creation. With the use of Artificial Intelligence and Machine Learning, investors can navigate complicated financial terrain. While improving predictive accuracy, the underlying complexities and varied market opinions can render decision-making effective. This study emphasizes trend analysis and prediction of future values, enabling users to make smart investment decisions with little effort. Using a Hybrid Machine Learning Model, the method brings together several top-performing algorithms to improve predictive power and trend observation. The most important components of the project are Market data Retrieval, Time Series Forecasting, Sentiment Analysis and data Visualization, all of which assist in creating thorough reports that aid strategic investment decisions.

关键词： Accuracy Navigation Time series analysis data visualization Machine learning data retrieval Complexity theory Stock markets Forecasting Investment

MetaDE: Evolving Differential Evolution by Differential Evolution

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arXiv 2025年

作者： Chen, Minyang Feng, Chenchen Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence the Department of Computing The Hong Kong Polytechnic University Hong Kong

As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization problems. While the advantages of DE are well-recognized, achieving peak performance heavily depends on its hyperparameters such as the mutation factor, crossover probability, and the selection of specific DE strategies. Traditional approaches to this hyperparameter dilemma have leaned towards parameter tuning or adaptive mechanisms. However, identifying the optimal settings tailored for specific problems remains a persistent challenge. In response, we introduce MetaDE, an approach that evolves DE’s intrinsic hyperparameters and strategies using DE itself at a meta-level. A pivotal aspect of MetaDE is a specialized parameterization technique, which endows it with the capability to dynamically modify DE’s parameters and strategies throughout the evolutionary process. To augment computational efficiency, MetaDE incorporates a design that leverages parallel processing through a GPU-accelerated computing framework. Within such a framework, DE is not just a solver but also an optimizer for its own configurations, thus streamlining the process of hyperparameter optimization and problem-solving into a cohesive and automated workflow. Extensive evaluations on the CEC2022 benchmark suite demonstrate MetaDE’s promising performance. Moreover, when applied to robot control via evolutionary reinforcement learning, MetaDE also demonstrates promising performance. The source code of MetaDE is publicly accessible at: https://***/EMI-Group/metade. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Multi-Modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

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Multi-Modal Imaging Genomics Transformer: Attentive Integrat...

IEEE International Symposium on Biomedical Imaging

作者： Nagur Shareef Shaik Teja Krishna Cherukuri Vince Calhoun Dong Hye Ye Department of Computer Science Georgia State University Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech Emory

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and overlap with other psychiatric conditions complicate diagnosis, necessitating advanced systems. Existing research studies have mostly focused on imaging data, such as structural and functional MRI, for SZ diagnosis. There has been less focus on the integration of genomic features despite their potential in identifying heritable SZ traits. This study introduces a Multi-modal Imaging Genomics Transformer (MIGTrans) that integrates genomics with structural and functional imaging to identify SZ-related abnormalities. MIGTrans improved SZ classification accuracy to 86.05%, offering clear interpretations and identifying key genomic and brain morphological/connectivity patterns linked to SZ.

关键词： Brain Accuracy Magnetic resonance imaging Decision making Genomics Imaging Schizophrenia Transformers Bioinformatics Biomedical imaging