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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Blockchain-Based Privacy-Aware Voting System

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SN computer science 2024年第8期5卷 1079页

作者： Bendjemaa, Isra Cherifi, Katia Ines Benarous, Leila Boudjit, Saadi Department of Computer Science Faculty of Science University of Amar Telidji Laghouat Algeria LIM Laboratory Faculty of Science University of Amar Telidji Laghouat Algeria LITIS Lab Intelligent Transportation Systems Group University of Rouen Normandy Rouen France

Traditional voting methods, conducted at physical ballot boxes, incur numerous expenses related to campaign logistics, equipping voting centers, and compensating workers. However, these methods may not guarantee the honesty, traceability, and integrity of vote results. In contrast, centralized electronic voting streamlines the process, saving resources and time while automating ballot counting for faster and more accurate outcomes. Despite these advantages, centralized systems are vulnerable to forgery and lack transparency due to their reliance on private servers controlled by the voting authority. To address the issue of single points of failure associated with private servers and to enhance transparency, a distributed, secure, and fully connected technology is required. Therefore, we propose a blockchain-based electronic voting system with a web application as its frontend. The implementation of blockchain technology helps prevent forgery and ensures immutability, as votes are openly monitored by all participants. However, the transparency and traceability provided by blockchain—essential for ensuring that no voter casts multiple votes and that no unauthorized votes are injected—contradict the confidentiality and privacy requirements of the voting process. To reconcile the need for transparency with confidentiality, our proposal incorporates additional security measures, including key images, group addresses, and group signatures. This privacy-aware blockchain-based solution guarantees the uniqueness of each vote and prevents multiple voting attempts without compromising voter confidentiality. We evaluated the security of our designed solution using an attack tree to identify potential threats and estimate their likelihood of occurrence. The results shows that the proposed system is 3.2 time more secure compared to the traditional centralized voting system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain e-Voting Forgery-resilient Privacy Security Transparency

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Service Oriented Fuzzy Decision Model for Road Traffic Engineering 9

Service Oriented Fuzzy Decision Model for Road Traffic Engin...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Utama, Ditdit Nugeraha Zaki, Fauzan Affan Munjeri, Intan Juliani Putri, Nidya Utami Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia UIN Syarif Hidayatullah Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350370126

A number of attempts have already been implemented formally to solve road traffic congestion. However, the objective strategy type of road traffic engineering could not be proven truly. Try and error is one inefficient way in road traffic engineering to degrade the level of congestion. The study was conducted to propose a new service-oriented model (SOM) that combines fuzzy-logic and water flow algorithm methods (called FWFA). The method combination was operated as the main method to construct the decision model for selecting the objective strategy in road traffic engineering. Also, service-oriented architecture (SOA) is realized to implement such a constructed model practically. The Model can suggest the most optimal strategy decision in road traffic engineering. Here, a main traffic road of Juanda in area Ciputat, Tangerang Selatan, province Banten, Indonesia;was selected as a research object in this study. The constructed SOM for road traffic engineering was structurally delivered in this paper. © 2023 IEEE.

关键词： Fuzzy logic

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Classification of Alzheimer's Disease using Random Oversampling and Albumentations on Convolutional Neural Network 8

Classification of Alzheimer's Disease using Random Oversampl...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Kamardi, Calvin Laksana, I Kadek Perry Bagus Anggreainy, Maria Susan Fitriawati, Nora Yonatan, Michiko Amaya Mangkang, Tesalonika Abigail Eikwine Pengesa, Vianny Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350342604

Alzheimer's Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition. © 2023 IEEE.

关键词： Convolutional neural networks

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 IEEE.

关键词： Undirected graphs

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors 36

AI-enabled Software Engineer: A Taxonomy of Challenges and S...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Shameem, Mohammad Niazi, Mahmood Nadeem, Mohammad Kumar, Ankur Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Saudi Arabia Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (纸本)1891706594

Artificial intelligence (AI) is revolutionizing software development by enabling high-quality products at lower costs and faster delivery. While existing research highlights ChatGPT’s role in various phases of software development, little attention has been given to its use in end-to-end project delivery. This study explores the challenges and success factors for using AI tools in complete project workflows through interviews with software practitioners. Key challenges identified include knowledge gaps in LLMs, programming languages, and automated testing, while success factors include cost efficiency, early prototyping, and continuous improvement. These insights offer a framework for implementing AI-assisted software engineering in end-to-end projects. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Software design

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Real-Time Human Fall Detection Using a Lightweight Pose Estimation Technique 5th

Real-Time Human Fall Detection Using a Lightweight Pose Est...

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5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Alam, Ekram Sufian, Abu Dutta, Paramartha Leo, Marco Department of Computer Science Gour Mahavidyalaya West Bengal Mangalbari India Department of Computer Science University of Gour Banga Malda India Department of Computer and System Sciences Visva-Bharati University Bolpur India National Research Council of Italy Institute of Applied Sciences and Intelligent Systems Lecce73100 Italy

ISBN: (纸本)9783031488788

The elderly population is increasing rapidly around the world. There are no enough caretakers for them. Use of AI-based in-home medical care systems is gaining momentum due to this. Human fall detection is one of the most important tasks of medical care system for the aged people. Human fall is a common problem among elderly people. Detection of a fall and providing medical help as early as possible is very important to reduce any further complexity. The chances of death and other medical complications can be reduced by detecting and providing medical help as early as possible after the fall. There are many state-of-the-art fall detection techniques available these days, but the majority of them need very high computing power. In this paper, we proposed a lightweight and fast human fall detection system using pose estimation. We used ‘Movenet’ for human joins key-points extraction. Our proposed method can work in real-time on any low-computing device with any basic camera. All computation can be processed locally, so there is no problem of privacy of the subject. We used two datasets ‘GMDCSA’ and ‘URFD’ for the experiment. We got the sensitivity value of 0.9375 and 0.9167 for the dataset ‘GMDCSA’ and ‘URFD’ respectively. The source code and the dataset GMDCSA of our work are available online to access. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fall detection

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Affectivefusionnet: A Multimodal Emotion Recognition using Combination of Visual Transformers and Variational Autoencoders 23

Affectivefusionnet: A Multimodal Emotion Recognition using C...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Babu, Eaby Kollonoor Mistry, Kamlesh Anwar, Naveed Zhang, Li Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom Royal Holloway University of London Centre for Reliable Machine Learning Department of Computer Science Centre for Intelligent Systems Egham United Kingdom

ISBN: (纸本)9798331528041

AffectiveFusionNet showcases a new era in multimodal emotion recognition, ingeniously integrating the strengths of Visual Transformers (ViTs) and Variational Autoencoders (VAEs) with the advanced principles of COGMEN and V2EM. This state-of-the-art model is meticulously engineered to detect and decode intricate emotional cues from a combination of visual and conversational data, setting a new benchmark for precision in the field. ViTs are harnessed within AffectiveFusionNet to delve into the subtle emotional indicators present in visual inputs, capitalizing on their powerful self-attention mechanisms. Concurrently, VAEs are employed to encapsulate and regenerate the nuanced emotional content found in audio and textual data, ensuring a rich, multimodal emotional representation. The synergy of these technologies, along with the relational learning from COGMEN and the hierarchical attention from V2EM, positions AffectiveFusionNet at the forefront of emotion recognition which can be integrated into humanoid robots for emotional understanding of the subjects. Demonstrating superior performance on prominent datasets like IEMOCAP and CMUMOSEI, AffectiveFusionNet not only pushes forward the capabilities of emotion detection systems but also paves the way for more perceptive and real-time emotional intelligence in artificial intelligence and robotic platforms. Future work aims to refine its real-time analytical prowess and adaptability to complex environments. © 2024 IEEE.

关键词： Graph neural networks

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Based-on computer Vision Applications for Bus Stop Passenger Detection System 3

Based-on Computer Vision Applications for Bus Stop Passenger...

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3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chang, Yu-Cheng Tsai, Hua-Wen Huang, Chao-Yi Wu, Zong-Rong Department of Computer Information and Network Engineer Lunghwa University of Science and Technology Taiwan Department of Bachelor's Program in Intelligent Robot National Yunlin University of Science and Technology Taiwan

ISBN: (纸本)9798350333862

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the Bus Stop Passenger Detection System. Taking the object detection of Wheelchairs into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient. © 2023 IEEE.

关键词： Object detection

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