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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Komosinski, Maciej Leszczyński, Marcin Miazga, Konrad Siera, Dawid Poznan University of Technology Institute of Computing Science Poznan Poland

ISBN: (纸本)9798400704956

In evolutionary computation, diploid genotypes (i.e., genotypes with two chromosomes) are traditionally used mostly in the context of optimization of non-stationary problems. Recent research, however, suggested that the use of diploid genotypes with mechanisms such as best-chromosome-wins can improve the performance of evolutionary algorithms even for stationary problems. In this paper we test the effectiveness of diploidy and polyploidy (i.e., genotypes with more than two chromosomes) with best-chromosome-wins on mathematical benchmarks. We verify the effect and the importance of the crossover operator on the behavior of evolutionary algorithms with diploidy and polyploidy. We explore the inner workings of evolutionary algorithms with diploidy and polyploidy in order to better understand their performance. We find that the results reported in previous papers on the best-chromosome-wins dominance for stationary functions may have been overly optimistic, and the use of diploidy with best-chromosome-wins does not enhance the search process for such functions. © 2024 Copyright held by the owner/author(s).

关键词： Optimization algorithms

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AR-GeoShapes: Development and Evaluation of a Marker Less Augmented Reality Based Geometrical Shapes Learning Framework on Memory Retention and Learning Achievement 18

AR-GeoShapes: Development and Evaluation of a Marker Less Au...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Azam, Ayesha Naz, Zubaira Khan, Muhammad Usman Ghani Din, Irfanud Al-Khwarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Information Technology University Lahore Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan New Uzbekistan University Department of Computer Science School of Computing Tashkent Uzbekistan

ISBN: (纸本)9798331508692

A large number of students struggle to grasp geometric principles, especially in three-dimensional (3D) space. In turn, this is why digital educational tools have been integrated more and more into the learning environment: because they entertain students. One of these technologies is Augmented Reality (AR) which has become a widely researched and effective tool for education. Where learning is situated with content embedded within the physical environment (support), AR better enables offerings to be adapted. This paper presents AR-GeoShapes, an augmented reality-based educational tool developed to help students learn solid geometry concepts. Built with the Vuforia marker less SDK in Unity, AR-GeoShapes is an exercise that runs on mobile devices for users to learn shapes interactively. Further to evaluate whether using AR-GeoShapes helps increase memory retention and improve learning achievement, a comparative study involving 58 students was conducted. The results demonstrate that the experimental group, utilizing AR-GeoShapes, exhibited significantly higher memory retention and learning outcomes compared to the control group. These findings suggest that AR-GeoShapes are a valuable tool for enhancing students' comprehension of geometric concepts. © 2024 IEEE.

关键词： Contrastive Learning

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Meta Auxiliary Learning for Top-K Recommendation

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IEEE Transactions on Knowledge and Data Engineering 2023年第10期35卷 10857-10870页

作者： Li, Ximing Ma, Chen Li, Guozheng Xu, Peng Liu, Chi Harold Yuan, Ye Wang, Guoren Beijing Institute of Technology School of Computer Science and Technology Beijing100811 China City University of Hong Kong Department of Computer Science Hong Kong

Recommender systems are playing a significant role in modern society to alleviate the information/choice overload problem, since Internet users may feel hard to identify the most favorite items or products from millions of candidates. Thanks to the recent successes in computer vision, auxiliary learning has become a powerful means to improve the performance of a target (primary) task. Even though helpful, the auxiliary learning scheme is still less explored in recommendation models. To integrate the auxiliary learning scheme, we propose a novel meta auxiliary learning framework to facilitate the recommendation model training, i.e., user and item latent representations. Specifically, we construct two self-supervised learning tasks, regarding both users and items, as auxiliary tasks to enhance the representation effectiveness of users and items. Then the auxiliary and primary tasks are further modeled as a meta learning paradigm to adaptively control the contribution of auxiliary tasks for improving the primary recommendation task. This is achieved by an implicit gradient method guaranteeing less time complexity compared with conventional meta learning methods. Via a comparison using four real-world datasets with a number of state-of-the-art methods, we show that the proposed model outperforms the best existing models on the Top-K recommendation by 3% to 23%. © 1989-2012 IEEE.

关键词： Gradient methods

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DSAFF-Net:A Backbone Network Based on Mask R-CNN for Small Object Detection

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computers, Materials & Continua 2023年第2期74卷 3405-3419页

作者： Jian Peng Yifang Zhao Dengyong Zhang Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India

Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.

关键词： Small object detection classification RPN MS COCO COVID-19

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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A Reliable Real-Time Traffic Sign Detection Model for Diverse Environmental Challenges 5

A Reliable Real-Time Traffic Sign Detection Model for Divers...

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5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Jayan, Keerthi Muruganantham, B. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chengalpattu India

ISBN: (纸本)9798350379884

This paper presents the development and evaluation of the Real-Time Traffic Sign Detection Model (RTTSDM), lever-aging the YOLOv4 framework with a CSPDarknet53 backbone, trained on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The RTTSDM exhibits significant advancements in traffic sign detection, showing substantial improvements across key performance metrics including average Intersection over Union (IoU), precision, recall, F1 Score, and mean Average Precision (mAP) throughout training. Notably, the model achieves a peak mAP of 0.9685 at iteration 4000, reflecting its superior ability to accurately identify and classify traffic signs. The model demonstrates robust performance under various lighting conditions, effectively detecting prohibitory, danger, mandatory, and priority signs with high accuracy. The RTTSDM shows strong potential to improve road safety and traffic management through its precise and reliable traffic sign detection capabilities. © 2024 IEEE.

关键词： Deep learning

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EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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Leveraging the Potential of Large Language Models

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Informatica (Slovenia) 2024年第8期48卷 1-16页

作者： Prasad, Shreya Gupta, Himank Ghosh, Arup Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Information Technology Graceland University LamoniIA United States

This study focuses on enhancing Natural Language Processing (NLP) in generative AI chatbots through the utilization of advanced pre-trained models. We assessed five distinct Large Language Models (LLMs): TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B, and LLAMA-2-13B to identify the most effective one. Our findings revealed that the LLAMA Model excels in comprehending user queries and delivering precise responses during conversations. The article elucidates the methodology employed to evaluate and select various models for our chatbot. Through rigorous testing, we determined that the LLAMA-2-13B model exhibits enhanced response time and accuracy. Additionally, we employed tools such as Facebook Artificial Intelligence Similarity Search (FAISS) and experimented with user interfaces like Streamlit and Chainlit to enhance the chatbot's user-friendliness. The research underscores the significance of selecting the appropriate model for crafting efficient chatbots. Ultimately, the LLAMA-13B model emerged as the standout performer, showcasing superior performance. Benchmark assessments, including HellaSwag and WinoGrande, which gauge common sense reasoning, were employed to evaluate our chatbot's capabilities. The study concludes that LLAMA-based models hold significant promise for the development of innovative and user-friendly chatbots in the future. Povzetek: Študija se osredotoča na izboljšanje obdelave naravnega jezika (NLP) v generativnih AI klepetalnicah z uporabo naprednih vnaprej usposobljenih modelov. Analizirali so pet modelov: TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B in LLAMA-2-13B. Model LLAMA-13B se je izkazal kot najboljši z vrhunskimi rezultati na testih HellaSwag in WinoGrande. Raziskava uporablja orodja, kot je FAISS, in uporabniške vmesnike, kot sta Streamlit in Chainlit za izboljšanje uporabniške izkušnje. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Machine learning

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Cricket Performance Analysis System Using Machine Learning Techniques 5

Cricket Performance Analysis System Using Machine Learning T...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mishra, Suraj Garg, Animesh Batra, Vansh Saxena, Devansh Om Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications 603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications India

ISBN: (纸本)9798331530013

The Cricket Performance Analysis System based on big data tools, like Hadoop and Apache Spark, gives realtime deep insights into performances from players and teams and conditions in matches based on large-scale cricket match data. It scales up speedily and finds patterns using distributed computing capabilities of Spark, which makes it possible to take decisions around performance improvements and strategies. The system applies a set of machine learning techniques including K-means clustering, Logistic Regression, and Random Forest classifiers to be used for the analysis of player performance metrics and match outcomes. Thus, through the conversion of raw data into actionable insights, teams can improve their strategies, make better improvements in performance, and thus gain an upper hand against other competing professional cricket teams. As a result, teams are best positioned to make adjustments in good time and direct finer tactics toward further enhancement of overall performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Markov enhanced I-LSTM approach for effective anomaly detection for time series sensor data

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International Journal of Intelligent Networks 2024年第1期5卷 154-160页

作者： Shanmuganathan, V. Suresh, A. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur ChengalpattuTamil Nadu603203 India

Users could engage and interact with their immediate surroundings without effort in smart settings. The emergence of intelligent technologies along with software-based services has made this possible. It is clear that technical advancements have ushered in a new era for both computer processing and sensor technology, facilitating the concept of smart surroundings. Even though their implementation faces a number of obstacles, numerous expansive projects are working to advance their adoption. The problem of anomalies in the sensor data could result inappropriate decisions and could lead unamicable situations to the users. Many such algorithms are already there, which does not provide satisfactory predictions for the sensor data for the time series data. Time series anomaly detection problems are typically stated as finding outlier data points in comparison to some norm or typical signal. Better anomaly detection in time series data is provided by the proposed Markov and enhanced LSTM technique. The Markov model and the enhanced LSTM offer accurate predictions for extra- and short-term data, which is highly useful in situations involving intelligent environments. When compared to the KNN algorithm, the technique offers reduced MAE, RMSE, MSE and MAPE errors. The algorithm also performs better than other LSTM and RNN methods. The proposed algorithm provides 0.00047 reduced error in humidity data, 0.00416 reduced error in temperature and 0.01771 reduced MAE value in case of light intensity when comparing with the KNN algorithm. © 2024 The Authors

关键词： Anomaly detection

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