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Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm for Social Media Content Classification and Community Detection

Stereoscopic Gated Graph Scalable Quantum Capsule Convolutio...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： A. Elumalai S. Rukmani Devi D. Rosy Salomi Victoria A. Jagadeeswaran Aparajita Mukherjee K. Vinoth Department of Mathematics R.M.K. Engineering College Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Information Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Annapoorana Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering Institute of Engineering and Management(IEM) University of Engineering and Management Kolkata West Bengal Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Social Media Content Classification and Community Detection (SMCCCD) classify content and identify communities through deep learning, and NLP. Traditional models are weak in scalability and interpretability. To solve all these problems, this paper introduces a Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm (SGGSQCCN2Nets+AHA) for social content classification and community detection. Data inputs are accessed from the Twitter dataset and are initially preprocessed by the Natural Language (NL) method. After preprocessing, content classification and community detection are done using the Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks (SGGSQCCN2Nets) and optimization is then performed with the Artificial Hummingbird Algorithm (AHA). The performance of the new SGGSQCCN2Nets+AHA model is tested on a Twitter dataset with a high accuracy rate of 99.9% and specificity of 99.8%. The proposed model is developed by utilizing Python programming language. The outcome of the proposed SGGSQCCN2Nets+AHA model enhances the performance of content classification and improves user community detection accuracy. It is effective in addressing scalability issues, obtaining complex features, and offering more interpretability. The model is also resilient against noisy data dynamically adapts to evolving social media trends and outperforms traditional deep learning techniques in real-time analysis.

关键词： Adaptation models Accuracy Social networking (online) Stereo image processing Scalability Heuristic algorithms Blogs Logic gates Classification algorithms Convolutional neural networks

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An Automated Multimodal Medical Image Fusion Framework for Alzheimer Detection using Deep Learning

Research Square

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Research Square 2025年

作者： Beatrice, S. Janaki Meena, M. Dhivviyanandnam, I. School of Computer Science and Engineering Department Vellore Institute of Technology Chennai600127 India Department of Mathematics North Bengal St. Xavier’s College Rajganj West Bengal India

Alzheimer's disease (AD) is a progressive neurodegenerative condition that affects the elderly population. The early detection and diagnosis of AD is critical for achieving effective treatment, as it can greatly improve the patient experience. AD can be viewed through imaging techniques like MRI, PET, and SPECT, providing valuable information about structural and functional changes. These findings are important in understanding this area. However, each imaging modality offers a different perspective. This information can be better collected from several of the other modalities as well as from some others to improve accuracy and reliability in AD detection. By combining information from different imaging modalities, such as MRI, PET, DTI, and fMRI, automated multimodal medical image frameworks aim to create a fused representation that preserves the relevant features from each modality. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), among other deep learning techniques, have been prevalent in these frameworks for learning discriminative and informative features from multi-modal data. In this paper, The Alzheimer's Disease Neuroimaging Initiative (ADNI) is used for experimental analysis. The proposed work gives 98.94 % of accuracy and 1.06% of error which is greater than the existing approaches. © 2025, CC BY.

关键词： Convolutional neural networks

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Real-Time Detection of Tea Chrysanthemums in Complex Outdoor Scenes 26

Real-Time Detection of Tea Chrysanthemums in Complex Outdoor...

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26th International Conference on Industrial technology, ICIT 2025

作者： Zang, Siyang Shu, Lei Han, Ru Zhang, Feiya Zhang, Zhijun Deng, Der-Jiunn College of Artificial Intelligence Nanjing Agricultural University Nanjing210031 China School of Environmental Science and Engineering Guangdong University of Petrochemical Technology Guangdong Provincial Key Laboratory for Green Agricultural Production and Intelligent Equipment Maoming525000 China School of Engineering University of Lincoln LincolnLN67TS United Kingdom National Changhua University of Education Department of Computer Science and Information Engineering Changhua50007 Taiwan

ISBN: (纸本)9798331521950

Picking robots have emerged as a pivotal focus in smart agriculture, with precise target position detection being a key element for realizing robotic picking. However, detecting tea chrysanthemums presents various challenges, including their dynamic appearance, complex environmental conditions, susceptibility to occlusion and lighting variations, and the requirement for real-time embedded systems. When applied to complex outdoor scenes for Tea chrysanthemum targets, existing detection models suffer from drawbacks such as missed detections, low accuracy, and slow processing speeds. To address these challenges, we introduce YOLO-VTC (You Only Look Once-Varieties of Tea Chrysanthemum), a lightweight deep-learning model optimized for the Jetson Xavier NX platform. Detection experiments were conducted on image data capturing eight varieties of tea chrysanthemums in complex outdoor scenes collected in 2023. Results demonstrate that YOLO-VTC achieves an average accuracy of 86.60% and a peak accuracy of 97.10%, surpassing other lightweight networks with remarkable performance. Notably, the model effectively maintains a compact size of 6.58MB and has been successfully deployed on edge device platforms. © 2025 IEEE.

关键词： Deep learning

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AI-Driven Framework for Multi-Service Multi-Modal Devices in NextG ORAN Systems

arXiv

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arXiv 2025年

作者： Gain, Mrityunjoy Kim, Kitae Raha, Avi Deb Adhikary, Apurba Saad, Walid Han, Zhu Hong, Choong Seon Department of Artificial Intelligence School of Computing Kyung Hee University Yongin17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

Next-generation (NextG) networks will adopt cloud-based, disaggregated designs with open interfaces, thereby enabling tailored data-driven control. The open radio access network (ORAN) paradigm enhances RAN optimization by enabling intelligent and flexible network management. The ORAN Alliance standardizes open and virtualized architectures, supporting services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (uRLLC), and massive machine-type communication (mMTC). Traditionally, eMBB, uRLLC, and mMTC services are tied to specific use equipment (UE) optimized for one service. NextG networks, however, aim to enable multiple services on a single device, crucial for applications like the metaverse. To fill this, in this paper, an artificial intelligence (AI)driven efficient RAN management framework is proposed. This framework introduces the concept of the multi-service-modal UE (MSMU) system, which allows a single UE to handle both eMBB and uRLLC services. The proposed framework integrates traffic demand prediction, route optimization, RAN slicing, service identification, and radio resource management under uncertainty. The challenge of dynamic environments in such a system is addressed by decomposing the optimization problem into long-term (L-SP) and short-term (S-SP) subproblems. Using a long short-term memory (LSTM) model, the proposed approach allows the prediction of eMBB and uRLLC traffic demands and optimal routes for RAN slicing in the L-SP. For the S-SP, another LSTM model is employed to handle real-time service type identification and resource management based on long-term predictions. To support continuous adaptation, continual learning is incorporated into the S-SP framework, allowing the model to learn new service types while retaining prior knowledge. Experimental results show that the proposed framework efficiently manages dual-mode UEs, achieving low mean square error for traffic demand (0.003), resource block prediction

关键词： Resource allocation

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer Systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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AdptDilatedGCN: Protein-ligand binding affinity prediction based on multi-scale interaction fusion mechanism and dilated GCN

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International Journal of Biological Macromolecules 2025年 311卷 143751页

作者： Wang, Wei Ma, Linchong Liu, Dong Zhang, Hongjun Wu, Guangsheng Zhou, Yun College of Computer and Information Engineering Henan Normal University Xinxiang453000 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang453000 China Hebi Instiute of Engineering and Technology Henan Polytechnic University Hebi458030 China School of Mathematics and Computer Science Xinyu University Xinyu338004 China

Predicting protein-ligand binding affinity is crucial for drug discovery. However, existing prediction methods often make insufficient use of the features of proteins and ligands, lack interactions between different information, and have difficulty in capturing and integrating both fine- and coarse-grained information of proteins and ligands at the same time. Therefore, we propose a deep learning method called AdptDilatedGCN, which fully interacts with protein and ligand features through multi-scale interaction and fusion mechanism, mining potential fine-grained and coarsely grained information and fusing it, combining skip connections and multi-scale fusion strategy to improve feature utilization and enhance features. In the model, dilated Graph Convolutional Network(GCN) combines multi-head attention mechanism and dilated convolution to overcome the limitations of traditional GCN, while taking into account the interactions between amino acid nodes in the protein graph. In addition, an adaptive Gated Recurrent Unit(GRU) is designed to dynamically enhance the model's ability to integrate multi-source information in adaptive GCN. The Vina weights obtained from AutoDock Vina are used to supplement and further enhance the features learned from GCN. Finally, The model achieves Pearson correlation coefficients of 0.837 and 0.803 on the CASF-2016 and CASF-2013 public test sets, respectively. © 2025 Elsevier B.V.

关键词： Deep learning

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Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy

arXiv

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arXiv 2025年

作者： Dobslaw, Felix Feldt, Robert Yoon, Juyeon Yoo, Shin Mid Sweden University Department of Communication Quality Management and Information Systems Östersund Sweden Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden KAIST School of Computing Daejeon Korea Republic of

Large Language Models (LLMs) and Multi-Agent LLMs (MALLMs) introduce non-determinism unlike traditional or machine learning software, requiring new approaches to verifying correctness beyond simple output comparisons or statistical accuracy over test datasets. This paper presents a taxonomy for LLM test case design, informed by both the research literature, our experience, and open-source tools that represent the state of practice. We identify key variation points that impact test correctness and highlight open challenges that the research, industry, and open-source communities must address as LLMs become integral to software systems. Our taxonomy defines four facets of LLM test case design, addressing ambiguity in both inputs and outputs while establishing best practices. It distinguishes variability in goals, the system under test, and inputs, and introduces two key oracle types: atomic and aggregated. Our mapping indicates that current tools insufficiently account for these variability points, highlighting the need for closer collaboration between academia and practitioners to improve the reliability and reproducibility of LLM testing. © 2025, CC BY-SA.

关键词： Taxonomies

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Next Generation Energy Storage Solutions for the Automotive Industry 3

Next Generation Energy Storage Solutions for the Automotive ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Chaudhary, Pavan Makhija, Himanshu Nayak, Ajit Kumar Jain, Romil Sandeep, V. Fiza, Samreen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre for Interdisciplinary Research for Business and Technology Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798331542375

The automobile industry is shifting closer to electrification;the need for dependable and efficient answers to electricity garages has become increasingly important. The present-day era of batteries, specifically lithium-ion, is attaining its limitations in terms of overall performance, price, and protection. Consequently, the development of next-era power storage answers is crucial to fulfill the growing demands of the enterprise. Subsequent-technology energy garage answers aim to enhance the constraints of present-day batteries by making use of new materials, designs, and technologies. It includes stable-state batteries, which use strong electrolytes as opposed to liquid ones, presenting better electricity density and improved protection. Other solutions include metallic-air batteries that use air as the cathode, promising extensively higher power densities. There are also traits in power garage devices that use ultra-capacitors, promising faster-charging instances and longer lifetimes. Those improvements are only some of the most effective advantages for the automobile enterprise, but they also have potential for packages in other regions, including renewable power storage. However, there are nevertheless challenges to conquer, including improving production procedures to reduce charges and increasing the durability and reliability of these next-technology solutions. © 2025 IEEE.

关键词： Forming

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Innovations in Battery Design for Improved Cycle Durability and Flexibility in Automotive Applications 3

Innovations in Battery Design for Improved Cycle Durability ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Garg, Avni Eti, Muktha Srilakshmi K, H. Yadav, Dhananjay Kumar Kaushik, Vaibhav Sahu, Prabhat Ku. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre for Interdisciplinary Research for Business and Technology Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

The increasing call for electric-powered and hybrid cars has led to the want for greater efficient and durable battery designs. In response, improvements in the battery era have targeted improving cycle sturdiness and versatility in automotive programs. One essential innovation is the use of lithium-ion batteries, which offer higher power density and longer cycle life compared to traditional lead-acid batteries. Inside the realm of lithium-ion batteries, improvements have been made in electrode materials and cell designs. For instance, using silicon-based, total anodes has substantially increased the strength density of lithium-ion batteries, allowing for longer use tiers and improved cycle existence. Additionally, advancements in battery control structures have progressed the potential to reveal and manipulate the charging and discharging of batteries, growing their usual durability and lifespan. This has caused the development of fast-changing technologies that could rate a battery to eighty in only half an hour, making it an extra handy and realistic alternative for electric car proprietors. © 2025 IEEE.

关键词： Lithium-ion batteries

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Gaurav Kumar G. Devayani C Saraswathi R. Melba Kani Sandeep Dongre M. S. Mohamed Mallick School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Master of Business Administration Mar Ephraem college of Engineering and Technology Kanyakumari India Symbiosis Institute of Business Management (SIBM) Constituent of Symbiosis International (Deemed University) Nagpur India Department of Computer Science Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences.

关键词： Consumer behavior Accuracy Data preprocessing Transforms Market research Electronic commerce Convolutional neural networks Integrated circuit modeling Artificial intelligence Long short term memory

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