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Modified U-Net with attention gate for enhanced automated brain tumor segmentation

Neural Computing and Applications 2025年第7期37卷 5521-5558页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Wielgosz, Maciej Caesarendra, Wahyu Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Faculty of Computer Science Electronics and Telecommunications AGH University of Krakow Krakow30-059 Poland Faculty of Mechanical Engineering Opole University of Technology 76 Proszkowska St. Opole45-758 Poland

This study addresses the formidable challenges encountered in automated brain tumor segmentation, including the complexities of irregular shapes, ambiguous boundaries, and intensity variations across MRI modalities. Manual segmentation, plagued by subjectivity and time constraints, further exacerbates the problem. To address these issues, we propose a modified U-Net architecture with an integrated attention gate. The proposed model demonstrates high performance, with notable Dice Similarity Coefficient (DSC) and Jaccard Index (JI) values across various tumor classes, consistently exceeding 0.93 and 0.87, respectively. Incorporating Contrast-Limited Adaptive Histogram Equalization and Histogram Equalization improves segmentation accuracy, particularly in cases of Meningioma. Comparative analyses against established models reveal a DSC of 0.9521 and a JI of 0.9093, underscoring the superiority of our method. Validation in the BraTS 2021 dataset underscores the robustness of the method, achieving high DSC and JI scores in four MRI modalities, with the T2 modality demonstrating the highest performance (DSC: 0.9216, JI: 0.8556). While acknowledging these achievements, we recognize challenges related to dataset specificity and computational intensity associated with the attention gate. Future research efforts should address these issues to improve the generalizability and applicability of the method in real-world scenarios. In addition to presenting a novel automated brain tumor segmentation method, this study contributes comprehensive result values and comparative analyses with previous research, providing valuable insights into the evolving landscape of medical image analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review

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Artificial Intelligence Review 2025年第8期58卷 1-78页

作者： Al-antari, Mugahed A. Salem, Saied Raza, Mukhlis Elbadawy, Ahmed S. Bütün, Ertan Aydin, Ahmet Arif Aydoğan, Murat Ertuğrul, Bilal Talo, Muhammed Gu, Yeong Hyeon Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Artificial Intelligence College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Computer Engineering Faculty of Engineering Fırat University Elazığ Turkey Department of Computer Engineering Faculty of Engineering Inonu University Malatya Turkey Department of Software Engineering Faculty of Technology Fırat University Elazığ Turkey Department of Neurosurgery Faculty of Medicine Fırat University Elazığ Turkey Department of Computer Science & Engineering University of North Texas DentonTX76205 United States

Lumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews AI-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various AI tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable AI frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of science, scienceDirect, and IEEE Xplore (2005–2024). The Rayyan AI tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking AI relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to AI-based LSS prediction. This review provides a comprehensive analysis of AI techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable AI and large language models (LLMs). © The Author(s) 2025.

关键词： Explainable artificial intelligence (XAI) Harmonization Large language models (LLMs) LSS prediction Lumbar spinal stenosis (LSS) Spinal LSS indices measurements

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Detection of Mobile Phone Usage Among Motorcycle Riders: A computer Vision Approach 5th

Detection of Mobile Phone Usage Among Motorcycle Riders: A C...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Charef, Ayoub Jarir, Zahi Quafafou, Mohamed LAMIGEP EMSI Moroccan School of Engineering Marrakesh Marrakesh Morocco Department of Computer Science Faculty of Science Cadi Ayyad University Marrakech Marrakesh Morocco Department of Computer Science University of Aix-Marseille Marseille Marseille France

ISBN: (纸本)9789819791118

This study examines the prevalence of mobile phone use among motorcycle riders at signalized intersections in Marrakech, utilizing advanced computer vision techniques. Employing the YOLOv8 object detection model and the DeepSORT tracking algorithm, our approach effectively detects and tracks mobile phone usage in real-time traffic scenarios. By analyzing data from strategically placed cameras at high-traffic intersections, the research identifies patterns in mobile phone usage among motorcyclists and evaluates the impact on traffic safety. The findings highlight significant times of day for phone usage and offer insights into behavioral trends, providing a foundation for developing targeted interventions to enhance road safety. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motorcycles

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A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

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Journal of Experimental and Theoretical Artificial Intelligence 2025年第2期37卷 307-342页

作者： Tabagchi Milan, Sara Jafari Navimipour, Nima Lohi Bavil, Hamed Yalcin, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Engineering University of Applied Sciences & Technology Tabriz Iran Department of Computer Engineering Nisantasi University Istanbul Turkey

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cloud computing

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Cross-Scenario Device-Free Wireless Sensing with A Free-Energy View

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IEEE Internet of Things Journal 2025年第11期12卷 16610-16621页

作者： Chen, Bo Wang, Jie Liu, Xiaokai Gao, Qinghua Pan, Miao Fang, Yuguang Dalian Maritime University School of Information Science and Technology Dalian116026 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116023 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Device-free wireless sensing (DFWS) has gained significant attention due to its high accuracy and privacy-preserving capabilities. DFWS systems work by analyzing the influence pattern of targets on the surrounding wireless signals. However, changes in the sensing scenario can alter signal propagation patterns, causing deep learning models to lose effectiveness in cross-scenario applications. To address this problem, we analyze the information content of different samples from a free-energy view, and provide a new idea to guide the alignment of target scenario samples with source scenario samples based on free-energy. We find that free-energy can measure the degree of scenario knowledge contribution of the samples. Based on this observation, we first perform unsupervised coarse alignment by minimizing the free-energy deviation between scenarios. Next, we iteratively select a few number of high free-energy samples near the decision boundary to fine-Tune the network, achieving scenario fine alignment with a small labeling effort. Extensive experiments on two public datasets and one self-collected dataset show that our proposed method achieves high accuracy in cross-scenario human activity and gesture recognition tasks. © 2014 IEEE.

关键词： Labeling Sensors Wireless sensor networks Wireless communication Feature extraction Costs Internet of Things Gesture recognition Metalearning Training

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Adaptive isomap feature extractive gradient deep belief network classifier for diabetic retinopathy identification

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Multimedia Tools and Applications 2025年第9期84卷 6349-6370页

作者： Singh, Alka Kumar, Rakesh Gandomi, Amir H. Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur India Faculty of Engineering & Information Technology University of Technology Sydney SydneyNSW Australia Óbuda University Budapest1034 Hungary

Diabetic retinopathy (DR) is an infection that bases eternal visualization loss in patients with diabetes mellitus. With DR, the glucose level in the blood increases, as well as its viscosity, this results in fluid leakage into surrounding tissues in the retina. In other words, DR represents the pathology of capillaries and venules in the retina with leakage effects, being the main acute retinal disorder caused by diabetes. Many DR detection methods have been previously discussed by different researchers;however, accurate DR detection with a reduced execution time has not been achieved by existing methods. The proposed method, the Shape Adaptive box linear filtering-based Gradient Deep Belief network classifier (SAGDEB) Model, is performed to enhance the accuracy of DR detection. The objective of the SAGDEB Model is to perform an efficient DR identification with a higher accuracy and lower execution time. This model comprises three phases: pre-processing, feature extraction, and classification. The shape adaptive box linear filtering image pre-processing is carried out to reduce the image noise without removing significant parts of image content. Then, an isomap geometric feature extraction is performed to compute features of different natures, like shape, texture, and color, from the pre-processed images. After that, the Adaptive gradient Tversky Deep belief network classifier is to perform classification. The deep belief network is probabilistic and generative graphical model that consists of multiple layers such as one input unit, three hidden units, and one output unit. The extracted image featuresare considered in the input layer and these images are sent to hidden layers. Tversky similarity index is applied in hidden layer 1 to analyze the extracted features with testing features. Regarding the similarity value, the sigmoid activation function is determined in hidden layer 2 so different levels of DR can be identified. Finally, the adaptive gradient method is

关键词： Feature extraction

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Density-Clustering Aggregation for Personalized Federated Learning With AI-Enabled Aerial and Edge Computing in UAVs

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IEEE Internet of Things Journal 2025年第9期12卷 11220-11232页

作者： Chien, Wei-Che Lin, Chih-Hsun Zhu, Tianli Dai, Cheng Garg, Sahil Mukherjee, Amrit National Dong Hwa University Department of Computer Science and Information Engineering Hualien City974301 Taiwan Sichuan University College of Computer Science Sichuan Chengdu610017 China École de Technologie Supérieure Electrical Engineering Department MontrealH3C 1K3 Canada Applied Science and Technology Dubai United Arab Emirates Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India University of South Bohemia Faculty of Science Department of Computer Science České Budějovice370 05 Czech Republic

This research introduces the density-clustering-based aggregation for personalized federated learning (DCPFL) algorithm, which utilizes DBSCAN clustering to enhance model accuracy in AI-enabled aerial and edge computing contexts, particularly for uncrewed aerial vehicles (UAVs). The DCPFL framework promotes model sharing among clients, fostering the development of personalized and optimized models. DBSCAN is beneficial in automatically determining cluster numbers using EPS neighborhoods and MinPts, with parameter optimization achieved through cross-experimental analysis. We further refined the model exchange mechanism by integrating a moving average prediction model to optimize the timing of these exchanges. Tests conducted on three public datasets covering two different machine learning (ML) tasks show that DCPFL surpasses existing methods, offering greater accuracy and enhanced adaptability in varied data environments. Implementing this algorithm in UAV networks leverages AI capabilities in aerial and edge computing to efficiently balance personalized modeling requirements with high performance, showcasing its potential to push federated learning (FL) forward in complex and dynamic settings. © 2014 IEEE.

关键词： Federated learning

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： Data quality

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A Case Study of Intelligent Street Lighting in Rural Areas for Energy Savings 33rd

A Case Study of Intelligent Street Lighting in Rural Areas f...

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Križanović, Višnja Pracny, Patrik Pejković, Ana Grgić, Krešimir Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9783031805967

The aim of this paper is to analyze the implementation of intelligent lighting within the concept of smart energy based on the possibility of saving and efficient use of energy, which is largely based on non-renewable sources. In selected case studies of rural environments, the possibility of implementing smart lighting was analyzed through simulation procedures, from the point of view of energy saving, showing the importance of this aspect in the context of building smart energy lighting systems. The conclusions in the paper show that the analyzed solution provides useful guidelines for the development of the concept of smart lighting, bringing benefits and reducing costs. Considerations given in the paper show a quantitative assessment of energy savings through the replacement of traditional lighting systems in rural areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy saving

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An Overview of Telemedicine: Evolution, Application, Challenges and Future Direction 3

An Overview of Telemedicine: Evolution, Application, Challen...

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

作者： Kawde, Dhanshri Gourshettiwar, Palash Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Telemedicine is of great importance as it increases the availability of health care for people living in remote or undeveloped areas. It reduces the cost of health care, allows for early diagnosis and treatment of chronic conditions, and saves time for both patients and healthcare providers. Telemedicine also ensures continuity of care in situations like pandemics while reducing reducing the need for face-to-face consultations can reduce the chance of catching contagious diseases.. This innovation will combine wearable devices and artificial intelligence with personalized care and mental health support. This way, healthcare services would be available and efficient for everyone around the world. However, technological challenges include those stemming from poor system architecture, poor connectivity limitation in diagnosis from a physical examination privacy issues among others and diverse legal regulations. Lack of access and integration into mainstream health systems are also among the causes of discontinuities in care. Overcoming such barriers is critical, and telemedicine needs to be easy to integrate into mainstream frameworks for better access, equity, and health outcome. © 2025 IEEE.

关键词： Telemedicine

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