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arXiv 2024年

作者： Heins, Jonathan Schäpermeier, Lennart Kerschke, Pascal Whitley, Darrell Big Data Analytics in Transportation TU Dresden Germany ScaDS.AI Dresden/Leipzig Dresden Germany Department of Computer Science Colorado State University Fort Collins United States

Solving the Traveling Salesperson Problem (TSP) remains a persistent challenge, despite its fundamental role in numerous generalized applications in modern contexts. Heuristic solvers address the demand for finding high-quality solutions efficiently. Among these solvers, the Lin-Kernighan-Helsgaun (LKH) heuristic stands out, as it complements the performance of genetic algorithms across a diverse range of problem instances. However, frequent timeouts on challenging instances hinder the practical applicability of the solver. Within this work, we investigate a previously overlooked factor contributing to many timeouts: The use of a fixed candidate set based on a tree structure. Our investigations reveal that candidate sets based on Hamiltonian circuits contain more optimal edges. We thus propose to integrate this promising initialization strategy, in the form of POPMUSIC, within an efficient restart version of LKH. As confirmed by our experimental studies, this refined TSP heuristic is much more efficient – causing fewer timeouts and improving the performance (in terms of penalized average runtime) by an order of magnitude – and thereby challenges the state of the art in TSP solving. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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Machine Learning Techniques for Autism Spectrum Disorder: current trends and future directions

Machine Learning Techniques for Autism Spectrum Disorder: cu...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Kainat Khan Rahul Katarya Department of Computer Science & Engineering Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD.

关键词： Autism Medical treatment Machine learning Market research Medical tests Biology data mining

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Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video dataset

arXiv

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arXiv 2025年

作者： Panchal, Aneesh Khan, Kainat Katarya, Rahul Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India Big Data Analytics and Web Intelligence Laboratory Department of Computer Science & Engineering Delhi Technological University Delhi110042 India

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remains undiagnosed. In this paper, we worked on a methodology for detection of ASD from 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and using nature-inspired optimization algorithms for feature extraction from dataset. The proposed methodology involve the classification of ASD using supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients for finding the initial leading particle. This selection of particle significantly reduces the computation time and hence, improving the total efficiency and accuracy for the ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combination of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of 100% using random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academia fields, providing a foundation for future research and advancements in ASD diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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Drone Detection and Tracking using Deep Convolutional Neural Networks from Real-time CCTV Footage

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 313-321页

作者： Allmamun, Md Akter, Fahima Talukdar, Muhammad Borhan Uddin Chakraborty, Sovon Uddin, Jia Department of Computer Science and Engineering European University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh AI and Big Data Department Woosong University Daejeon Korea Republic of

Drones are flying objects that may be controlled remotely or programmed to do a wide range of tasks, including aerial photography, videography, surveys, crop and animal monitoring, search and rescue missions, package delivery, and military operations. Unrestrained use, however, can pose a significant threat to safety, privacy, and security through eavesdropping, flying close to prohibited locations, interfering with public events, and delivering illicit items. Hence, real-time drone detection and tracking are indispensable and appropriate measures. This study developed real-time drone detection and tracking using the most efficient deep-learning approaches. The models were fine-tuned first to suit the required purpose and yield the desired outcome. The performance of the developed system was better than that of earlier endeavors in terms of accuracy and loss. Of the seven fined-tuned models, the Xception model constantly rendered the maximum accuracy with negligible loss. The model outperformed other state-of-the-art architectures, exhibiting an accuracy and loss of 99.18% and 3.83, respectively. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Aerial photography

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From Traditional to Deep: A Survey of Image Forgery Detection Techniques

From Traditional to Deep: A Survey of Image Forgery Detectio...

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IEEE Applied Signal Processing Conference (ASPCON)

作者： Parita Mer Reshma Sunil Anjali Diwan Computer Engineering Department Marwadi University Rajkot India CE - AI & Big Data Marwadi University Rajkot India

The detection of image forgeries is a crucial area of study within the discipline of digital image analysis, with the objective of identifying and accurately determining the location of modified regions inside images. The importance of developing strong and dependable forgery detection methods has grown increasingly significant due to the widespread availability of advanced image editing tools and the growing complexity of forgery techniques. This survey paper presents a thorough examination of the comprehensive classification of methods used for detecting image forgeries. The paper considers various factors, including the type of forgery, the detection methodology applied, the evaluation metrics used for performance analysis, and the datasets utilized. The primary objective of this paper is to stimulate further progress in the field by offering valuable insights and up-to-date information on contemporary techniques in forgery detection to researchers in the field.

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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

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4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative ai model. The designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. The state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. This prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. The implementation of ai in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of ai-integrated safety compliance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language Information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences through Multi-Task Learning

JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shih-Wei Guo Ya-Chun Wen Jui-Ling Yu Yao-Chung Fan Department of Computer Science and Engineering National Chung Hsing University Taichang Taiwan Department of Japanese Language and Literature Providence University Taichang Taiwan Department of Data Science and Big Data Analytics Providence University Taichang Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of Japanese language processing, the correct use and understanding of Keigo (honorific language) presents unique challenges due to its complex nature and context-dependent usage. This study introduces JH-Ranker, a novel multitask learning framework designed to enhance the recognition and understanding of Keigo in Japanese sentences. We are employing a multi-task learning approach by integrating Keigo Level, Contextual Field, and the Classification of Keigo Forms. This integration aims to foster a comprehensive understanding of Keigo usage from small data, leveraging the synergy between multiple tasks to achieve a deeper understanding with limited resources. Utilizing the BERT-base-japanese pre-trained model, JH-Ranker takes into account various Keigo Forms and contextual scenarios, going beyond mere syntactic analysis. Our methodology also emphasizes the importance of considering both the form and context of Keigo, addressing challenges such as label imbalance and overfitting in multi-label classification tasks. The experimental results demonstrate a significant improvement in Keigo recognition capabilities over existing baseline models, particularly in discerning the subtle nuances of Keigo Levels and Forms. This study contributes to the realm of natural language processing by presenting a refined and contextually sensitive method for Japanese Keigo. It demonstrates the language model’s adaptability and understanding in dealing with intricate honorific language scenarios.

关键词： Adaptation models Analytical models Neural networks Syntactics Multitasking Natural language processing Context modeling

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (ai) advances, it is essential to continuously comprehend its limitations to optimise the integration of ai into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and ai, focusing on the differences and identifying limitations of ai from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of ai into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each ai limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness ai’s computational power and algorithmic innovations to enhance ai’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Towards Seamless Connectivity: Implementing 6G Communication Technologies in Next-Generation Networks 3

Towards Seamless Connectivity: Implementing 6G Communication...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Kalaiselvi, K. Sankar, R. Supriya, S. Kaushik, G. Swamy, Hemanth Devika, M. Saveetha College of Liberal Arts and Sciences SIMATS Department of Data Analytics Tamilnadu Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of AI& DS Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can't meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, ai-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues. © 2024 IEEE.

关键词： Internet of Everything

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