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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

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computers, Materials & Continua 2025年第4期83卷 1023-1039页

作者： Faten S.Alamri Tariq Sadad Ahmed S.Almasoud Raja Atif Aurangzeb Amjad Khan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan AIDA Lab Department of Information SystemsCollege of Computer&Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science International Islamic UniversityIslamabad44000Pakistan

Mango farming significantly contributes to the economy,particularly in developing ***,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial *** development of deep learning models for detecting crop diseases is an active area of research in smart *** study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)*** datasets were *** first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery *** second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and *** datasets were obtained from publicly available *** proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS *** results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more *** system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual ***,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.

关键词： ConvNeXt model fusion mango disease smart agriculture vision transformer

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Broadband single-polarization single-mode low confinement loss hollow core anti-resonant fiber with semi-elliptical nested tube

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Optical and Quantum Electronics 2025年第4期57卷 1-12页

作者： Lin, Wen Fei, Yihong Wang, Ning Jia, Hongzhi Engineering Research Center of Optical Instruments and Systems Ministry of Education Shanghai Key Laboratory of Modern Optical Systems School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

In this paper, a hollow-core anti-resonant optical fibre containing a semi-elliptical nested tube is proposed, which has the characteristics of single-polarization, large bandwidth, single-mode and low confinement loss. By optimizing the structural design, the polarization extinction ratio (PER) of the fiber reaches 21,0183 at a wavelength of 1.55 μm, and the y-polarized fundamental mode loss is only 0.00086 dB/m. By changing the material and thickness of the semi-ellipsoidal nested tube, a large bandwidth of 280 nm was achieved in the wavelength range of 1.51-1.78 μm, with loss limited to less than 0.001 dB/m within the bandwidth range. The PER and higher-order mode extinction ratio of 4870, 1487, respectively. The loss remains below 0.01 dB/m when the bending radius exceeds 4.5 cm. Therefore, it is expected that this optical fiber can be used in optical devices such as fiber optic sensors, fiber optic lasers and fiber optic gyroscopes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Single mode fibers

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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computer Modeling in engineering & sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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HRMG-EA: Heterogeneous graph neural network recommendation with multi-level guidance based on enhanced-attributes

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Applied Intelligence 2025年第6期55卷 1-22页

作者： Wang, Longtao Yuan, Guiyuan Li, Chao Zhao, Yufei Duan, Hua Zeng, Qingtian College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Electronic and Information Engineering Shandong University of Science and Technology Shandong Qingdao266590 China College of Mathematics and Systems Science Shandong University of Science and Technology Shandong Qingdao266590 China

Heterogeneous Graph Neural Networks are an efficient and powerful tool for modeling graph structure data in recommendation systems. However, existing heterogeneous graph neural networks often fail to model the dependencies between user and item attribute preferences, limiting graph structure optimization and consequently reducing the accuracy of recommendations. To overcome these issues, we propose a Heterogeneous graph neural network Recommendation with Multi-level Guidance based on Enhanced-Attributes (HRMG-EA). First, we design an attribute enhanced gated network to model user-item interaction attribute scenarios and obtain enhanced-attributes by capturing complex attribute dependencies. It effectively avoids the expansion of the graph scale in attribute graph scenarios and further covers personalized attribute relationship distribution characteristics of users and items. Then, we propose a novel multi-level graph structure guidance strategy based on enhanced-attributes. It guides graph structure learning from three optimization levels, optimizing from two perspectives: explicit (heterogeneity and homogeneity) and implicit (contrast enhancement). The former can screen higher-quality heterogeneous neighbor nodes in a direct interaction environment, and filter out redundant or erroneous edges under different similar semantic interest paths to improve the quality of the neighborhood environment. The latter aligns representation embeddings of enhanced-attributes and graph structure in a latent space, explores their potential commonalities, and obtains more comprehensive, fine-grained semantic and beneficial structural information. Finally, on two real-world datasets, HRMG-EA significantly outperforms the state-of-the-art baseline algorithms in both recall and normalized discounted cumulative gain. A large number of ablation experiments and analytical verifications also verify its effectiveness. © The Author(s), under exclusive licence to Springer science+Business Med

关键词： Graph neural networks

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Minimum Initial Marking Estimation in Labeled Petri Nets Using Minimum Token Number Prediction

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IEEE Transactions on Automatic Control 2025年第5期70卷 3296-3302页

作者： Yue, Hao Xu, Yakun Hu, Hesuan Wu, Weimin Li, Lingxi College of Computer Science and Technology Qingdao266580 China Xidian University School of Electro-Mechanical Engineering Xi'an710071 China Nanyang Technological University School of Computer Science and Engineering College of Engineering 639798 Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China Purdue University Elmore Family School of Electrical and Computer Engineering College of Engineering IndianapolisIN46202 United States

This article proposes an approach to addressing the problem of minimum initial marking (MuIM) estimation for labeled Petri nets (LPNs). We introduce the important concept of a label synthesis net for LPNs and develop a method for predicting the minimum number of tokens. By using this prediction method, we develop an algorithm that has polynomial complexity in the length of the observed label sequence for estimating MuIMs. An illustrative example is provided to show the effectiveness and efficiency of our proposed approach. Moreover, experimental results demonstrate its advantage over existing work. Finally, we provide a comparison of some representative studies in the literature for MuIM estimation. © 1963-2012 IEEE.

关键词： Discrete event simulation

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Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

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IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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Optimization of 6G resource allocation using CyberTwin function-based service enhancement scheme

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Eurasip Journal on Wireless Communications and Networking 2025年第1期2025卷 1-27页

作者： Aldrees, Asma Min, Hong Daradkeh, Yousef Ibrahim Dutta, Ashit Kumar Anjum, Mohd Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha61421 Saudi Arabia School of Computing Gachon University Seongnam13120 Korea Republic of Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Al-Kharj16273 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences Almaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Computer Engineering Aligarh Muslim University Aligarh202002 India

The most innovative Service-Optimized Logging for Resource Allocation (SOL-RA) is in situations that support 6G connection. The CyberTwin architecture optimizes the quality of service provided to end users in 6G communications by using the dependability of terahertz connections and interactions of a machine-type nature. Elastically sharing elastic resources among users is made possible by the proposed resource allocation strategy, which makes use of proprietary logger functions. This ensures that efficient allocation is achieved without overlapping or lengthy wait periods. Within the framework of SOL-RA, a categorization mechanism is implemented for dense requests, which differentiates them as either stationary or priority services. Resource allocations are dynamically done based on this categorization, which enhances the organization's responsiveness to different needs. Through a tree classifier learning mechanism, CyberTwin logs play an essential part in the processing of requests and the preparation of resources. This helps to ensure that resources are distributed without inefficiency. Requests that are generating a backlog are identified and processed in parallel by the system, which significantly reduces the amount of time that is wasted waiting. In order to guarantee a specialized distribution for the categorized outputs, resource allocations are directed by CyberTwin data logs. An examination of the performance of the SOL-RA scheme takes into account important metrics such service latency, service backlog, resource usage, and request-to-response ratio. This research offers insights into the efficacy of the scheme in maximizing service quality and resource consumption in 6G settings. © The Author(s) 2025.

关键词： Resource allocation

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Explainable Artificial Intelligence Driven Segmentation for Cervical Cancer Screening

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IEEE Access 2025年 13卷 71306-71322页

作者： Sritharan, Niruthikka Gnanavel, Nishaanthini Inparaj, Prathushan Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa10400 Sri Lanka Ulster University School of Computing Engineering and Intelligent Systems LondonderryBT48 7JL United Kingdom

Cervical cancer remains an important global health challenge among women. Early and accurate identification of abnormal cervical cells is crucial for effective treatment and improved survival rates. This paper addresses the development of a novel weakly supervised segmentation framework that combines binary classification, Explainable Artificial Intelligence (XAI) techniques, and GraphCut to automate cervical cancer screening. Unlike traditional segmentation methods that rely on pixel-level annotations of medical images, which are costly, laborious, and require expertise in medical imaging, our approach leverages classification-driven insights to segment the nucleus, cytoplasm, and background regions. A key innovation of our framework is the use of XAI techniques such as Grad-CAM++ and LRP combined with GraphCut, to enable annotation-free segmentation using only classification-level labels. This represents a pioneering application of explainability techniques in the context of cervical cancer screening. Among the classification models explored, including fine-tuned variants of VGGNet and XceptionNet, VGG16-Adapted128 achieved the highest performance, marked by an accuracy of 0.94, precision of 0.94, recall of 0.94, and an F1 score of 0.94. This novel segmentation framework employed LRP and GradCAM++ as XAI techniques to gain insight into the decision-making process of classification models, with GradCAM++ demonstrating greater effectiveness. The performance of these XAI methods was assessed through both visual inspection and quantitative metrics, including entropy and pixel flipping. This innovative approach to segmentation is formally introduced through two algorithms detailed in this paper. The weakly supervised segmentation framework achieved a Dice Similarity Coefficient (DSC) of 62.05% and an Intersection over Union (IoU) of 61.89%. In addition, it has received high satisfaction ratings from expert evaluations and has been seamlessly integrated into a user-frie

关键词： Self-supervised learning

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