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arXiv 2025年

作者： Sun, Shengyin Yu, Wenhao Ren, Yuxiang Du, Weitao Liu, Liwei Zhang, Xuecang Hu, Ying Ma, Chen Department of Computer Science City University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Advance Computing and Storage Lab Huawei Technologies China Academy of Mathematics and Systems Science Chinese Academy of Sciences China Department of Electronic Engineering Tsinghua University China

Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. However, we argue that most existing methods suffer from two limitations in the two phases: (i) Existing models do not adequately capture the "face" information in molecular graphs for the reaction center identification. (ii) Current approaches for the reactant generation predominantly use sequence generation in a 2D space, which lacks versatility in generating reasonable distributions for completed reactive groups and overlooks molecules’ inherent 3D properties. To overcome the above limitations, we propose GDiffRetro. For the reaction center identification, GDiffRetro uniquely integrates the original graph with its corresponding dual graph to represent molecular structures, which helps guide the model to focus more on the faces in the graph. For the reactant generation, GDiffRetro employs a conditional diffusion model in 3D to further transform the obtained synthon into a complete reactant. Our experimental findings reveal that GDiffRetro outperforms state-of-the-art semi-template models across various evaluative metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular docking

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Enhancing Healthcare Data Security in Cloud Computing systems through Advanced Homomorphic Encryption Techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： I. Sudha P.S. Ramesh A. Ponmalar J. Jagannathan Rajkumar Pandiarajan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India R.M.K. Engineering College Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient’s data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems.

关键词： Cloud computing Machine learning algorithms Metaheuristics Distributed databases Medical services Organizations Machine learning Homomorphic encryption Protection Electronic medical records

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Deep Ear Biometrics for Gender Classification

arXiv

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arXiv 2023年

作者： Singh, Ritwiz Kashyap, Keshav Mukherjee, Rajesh Bera, Asish Chakraborty, Mamata Dalui Department of Computer Science and Engineering Haldia Institute of Technology WB India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333 031 India Dept. of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances, and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset. © 2023, CC BY.

关键词： Biometrics

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Efficient Coordination and Synchronization of Multi-Robot systems Under Recurring Linear Temporal Logic

arXiv

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arXiv 2025年

作者： Peron, Davide Fernandez-Ayala, Victor Nan Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Department of Information Engineering University of Padova Padova35122 Italy Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work. © 2025, CC BY.

关键词： Multipurpose robots

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A Lightweight Encoder-Decoder Framework for Carpooling Route Planning

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IEEE Transactions on Mobile Computing 2025年

作者： Gao, Yucen Ma, Li Yu, Zhemeng Zhang, Songjian Gao, Hui Fang, Jun Gao, Xiaofeng Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems College of Computer Science and Engineering Shanghai200240 China Didi Global Inc. Beijing100190 China

Carpooling Route Planning (CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a novel, meaningful and challenging scenario for CRP in industry, called Multi-Candidate Carpooling Route Planning (MCRP) problem, where each passenger may have several potential positions to get on and off the car. We surprisingly notice that this problem can be easily generalized for similar services such as express, takeout, or crowdsensing services, which means MCRP is a new fundamental combinatorial optimization problem. Traditional graph search algorithms or indexing methods are usually time and space consuming or perform poorly, which are not suitable for solving the problem. In this paper, we propose an end-to-end encoder-decoder model to plan a route for each many-to-one carpooling order with various data-driven mechanisms such as graph partitioning and feature crossover. The encoder is a filter-integrated Graph Convolution Network with external information fusion combining a supervised pre-training classification task, while the latter mimics a pointer network with a rule-based mask mechanism and a domain feature crossover module. We validate the effectiveness and efficiency of our model based on both synthetic and real-world datasets. Our code is available at https://***/GaoYucen/Lite-GD © 2002-2012 IEEE.

关键词： Network coding

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Welcome to Artificial Intelligence science and engineering

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人工智能科学与工程 2025年第1期1卷封2,前插1页

作者： Tingwen Huang Faculty of Computer Science and Control Engineering at Shenzhen University of Advanced Technology World Academy of Sciences European Academy of Sciences and Arts International Academy of Systems and Cybernetic Sciences IEEE IAPR AIAA

Over the past decade,artificial intelligence(AI)has evolved at an unprecedented pace,transforming technology,industry,and *** diagnosing diseases with remarkable accuracy to powering self-driving cars and revolutionizing personalized learning,AI is reshaping our world in ways once thought *** fields such as machine learning,deep learning,natural language processing,robotics,and ChatGPT,AI continues to push the boundaries of innovation.

关键词： intelligence engineering science welcome artificial

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Systematic Review: Predictive Models for the Winning Team of Super Leagues (SL) 2

Systematic Review: Predictive Models for the Winning Team of...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Muneer, Salman Alvi, Muhammad Bux Al Sakhnani, Mahmoud Raza, Hamamd Ghazal, Taher M. Ahmad, Munir NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan The Islamia University of Bahawalpur Department of Computer Systems Engineering Bahawalpur Pakistan University City Sharjah School of Business Skyline University College Sharjah1797 United Arab Emirates National College of Business Administration & Economics School of Computer Science Lahore Pakistan Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia School of Information Technology Skyline University College University City Sharjah 1797 United Arab Emirates

ISBN: (纸本)9798350335644

The Pakistan Super League (PSL) is one of the most popular cricket leagues in the world, attracting millions of fans and spectators each year. With the 2023 edition of the league just around the corner, the use of predictive models to determine the winning team has gained much attention. This systematic review analyzed the existing literature on predictive models for determining the winning team of PSL 2023. The study examined multiple approaches, including machine learning, statistical modeling, and expert opinion. The results of this review suggest that machine learning models, particularly ensemble models, are the most accurate predictors of PSL match outcomes. These models typically use a combination of features, such as team rankings, player performance statistics, match-winning ratio, and previous match outcomes, to predict the result of a match. This paper discovered that expert opinion-based models could provide valuable insights into the likely winner of a match. These models typically use input from cricket experts and analysts to predict the outcome of a match. © 2023 IEEE.

关键词： Machine learning

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IoT-Based Smart Beehive Monitoring System

IoT-Based Smart Beehive Monitoring System

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Advancements in Computing (ICAC), International Conference on

作者： Nisal Liyanage Chamindra Attanayaka Tarinya Perera Daniru Neilkumara Isuru Sri Bandara Lakshman Chandrasiri Department of Computer Science and Data Science NSBM Green University Town Homagama Sri Lanka Department of Computer Security and Network Systems NSBM Green University Town Homagama Sri Lanka Department of Electrical Electronic and Systems Engineering NSBM Green University Town Homagama Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

In recent years, there has been an alarming decline in bee populations both globally and in Sri Lanka, severely impacting ecosystems and agriculture worldwide. Traditional methods in Sri Lankan beekeeping are often time-consuming and less efficient, making hive management and health assessment challenging. This paper introduces a smart IoT-based beehive monitoring system tailored to the Sri Lankan beekeeping industry. The system integrates various sensors to measure key hive parameters, such as temperature, humidity, and weight, offering beekeepers a user-friendly web interface for real-time monitoring of hive conditions. It also provides insights into hive management aspects, including feeding needs, optimal harvesting times, and abnormal temperature detection within the hive. This paper highlights the initial steps towards implementing a robust IoT-based beehive monitoring system, acknowledging its current limitations. By addressing these limitations in the future, the authors aim to develop a more effective and efficient monitoring method for Sri Lankan beekeepers, saving time and enhancing the productivity and health of bee colonies.

关键词： Temperature sensors Temperature measurement Weight measurement Productivity Humidity measurement Humidity Sensor systems Real-time systems Monitoring Intelligent sensors

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A Comprehensive Review of Real-Time Sign Language Interpretation Using Various Machine Learning Models

A Comprehensive Review of Real-Time Sign Language Interpreta...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Balaraman Sundarambal Maneesh J J Chokiyan Karthikeyini Suresh Subramanian Senthil Kumar G Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Excel Engineering College Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Real-time Sign Language interpretation is among the fastest-growing fields that can potentially minimize the communication gap between deaf people and the hearing population and prepare the ground for inclusivity and accessibility throughout social, educational, and professional contexts. The review discusses the development of technologies for sign languages interpretation in real time, having the main emphasis that have to be met such as high interpretation accuracy, low latency, and computational efficiency. This review methodology, which compared machine learning and deep learning algorithms and discussed different model architectures, analyzed the trends and emerging methodologies that have shaped the current state of the field and identified significant gaps in the literature. In particular, the tendency is such that the multimodality involving hand, face, and body movement participation is becoming increasingly more effective; the pre-trained models get adapted to perform sign language tasks. Most importantly, diversity in datasets promotes model robustness across languages and dialects. A bit further, practical problems are discussed with real-time interpretation deployment on mobile devices and other resource-limited platforms. The following review is designed to point out the likely best directions for future research and development that would generalize, through the advancement of real time sign language interpretation, to become an accessible and reliable technology for everyday use.

关键词： Deep learning Sign language Adaptation models Accuracy Recurrent neural networks Translation Reviews Real-time systems Convolutional neural networks Optimization

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Mental Healthcare Chatbots: Technological Advancements, Challenges, and Opportunities

Mental Healthcare Chatbots: Technological Advancements, Chal...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Balaraman Sundarambal Amirthavarshini H Suresh Subramanian Chokiyan Karthikeyini Senthil Kumar G Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electronics and Communication Engineering Excel Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Mental healthcare chatbots are artificially intelligent systems that have been built with the intent of simulating human conversation and provide emotional support to one’s user. They are also widely used as tools to help individuals cope with the stress, anxiety, and other mental health challenges they have been facing, including easily accessible and affordable solutions. These chatbots use NLP and emotion recognition technology to assist users in understanding their feelings and responding accordingly. This review unpacks the latest advancements of recently developed chatbots, such as GPT-4-based models, BERT and MBERT based models, sentiment analysis, and the study of multimodal systems combining text, voice, and facial expression recognition. Also addressed are important challenges, including privacy concerns, cultural inclusivity, and clinical validation. Finally, the review identifies opportunities to improve chatbots, including supporting multi-language and primary telehealth platforms and customizing user interaction with chatbots to create more inclusive and effective mental health support worldwide.

关键词： Emotion recognition Analytical models Sentiment analysis Reviews Text recognition Telemedicine Medical services Mental health Speech recognition Chatbots

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