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Hybrid Channel Attention Regression U-Net (ARU-Net): An Enhanced Architecture for Brain Tumour Segmentation in Magnetic Resonance Imaging

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IEEE Access 2025年 13卷 102278-102298页

作者： Siddiqui, Noman Ahmed Qadri, Muhammad Tahir Ali, Zain Anwar Akhter, Muhammad Ovais Alsanad, Ahmed Alhogail, Areej Abdullah Gumaei, Abdul H. Sir Syed University of Engineering and Technology Electronic Engineering Department Karachi75290 Pakistan Co. Kildare MaynoothW23 A3HY Ireland Bahria University Electrical Engineering Department Karachi73500 Pakistan King Saud University College of Computer and Information Science Riyadh11543 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Engineering and Sciences Al-Kharj11942 Saudi Arabia

This research presents an accurate segmentation of brain tumours which is important in diagnosis and treatment planning of magnetic resource imaging (MRI) scans. Despite existing methodologies based on deep learning models, it remains difficult to distinguish between a tumour region and a normal brain tissue. To tackle this problem, a recent work proposed a new brain tumour segmentation technique on MRI, known as the Hybrid Attention Regression U-Net (ARU-Net) architecture. To enhance the effectiveness of segmentation, the proposed architecture utilises modern approaches including attention mechanisms, feature fusion processes, and regression-based output layer. It specifically uses Spatial and channel-wise attention mechanisms to capture important regions and inter channel dependencies. Spatial attention module in this architecture is based on improved Convolutional Block Attention Module which constrain attention on informative areas and suppress irrelevant or noisy ones. On the other hand, the channel-wise attention part, the Squeeze-and-Excitation (SE) blocks, recalibrates channel-wise feature responses for higher emphasis on salient features. The performance of proposed method has been evaluated comprehensively on the BraTS-2021 dataset where it shows enhanced accuracy over existing methods in detecting the tumour area. From the point of epoch curve, F1 score, the area under the curve, specificity, sensitivity, and accuracy in augmented and non-augmented datasets validated the stability of the proposed ARU-Net. This underlines its roles in segmentation of brain tumour, which if optimally developed holds the promise to significantly contribute for diagnosis and treatment planning. © 2013 IEEE.

关键词： Image segmentation

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Scalable and Reliable Data Framework for Sensor-Enabled Virtual Power Plant Digital Twin

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 108-120页

作者： Singh, Amritpal Demirbaga, Umit Aujla, Gagangeet Singh Jindal, Anish Sun, Hongjian Jiang, Jing Northumbria University Department of Computer and Information Sciences Newcastle Upon TyneNE1 8ST United Kingdom Bartin University Department of Computer Engineering Bartin74110 Turkey Durham University Department of Computer Science DurhamDH1 3LE United Kingdom Durham University Department of Engineering DurhamDH1 3LE United Kingdom Northumbria University Department of Mathematics Physics and Electrical Engineering NewcastleNE1 8ST United Kingdom

Sensor-enabled distributed energy resources (DERs) provide various advantages, including a lower carbon footprint, yet effective management of millions of DERs is still an issue. Virtual power plants (VPP) can integrate several DERs into a unified operational digital twin to enable real-time monitoring, analysis, and control. VPP may utilize advanced solutions to improve operational efficiency by combining substantial measurement data from DERs. However, effectively managing the quantity and complexity of data flows, whether streaming data or high-impact low-frequency data, is essential in maintaining the performance of DERs at sustained levels. The vast amounts of diverse data generated from various DERs pose significant challenges for storage, processing, and resource management. This article proposes a comprehensive framework that employs a distributed big data cluster to ensure scalable and reliable data storage and utilizes a robust message broker system for efficient data queuing. In addition, we present innovative load-balancing strategies within the VPP digital twin system. A decision tree algorithm is implemented to calculate the forthcoming workload collected by various deployed sensors at various DERs. The required resources are identified per workload, and the numbers are forwarded to the orchestrator. The orchestrator scales up and down resources based on resource utilization suggested by the decision tree algorithm when the resources or nodes are insufficient to handle the sensor data, optimizing the utilization of computing resources. The framework also features a failure detection component that performs root cause analysis to provide actionable insights for system optimization. Experimental results show that this framework ensures high efficiency, reliability, and real-time operational capability in VPP digital twin by addressing critical challenges in data storage, streaming data analysis, and load balancing. © 2025 The Authors.

关键词： Carbon footprint

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Iceberg draft prediction using gradient boosting regression algorithm

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Marine Systems and Ocean Technology 2024年第3-4期18卷 151页

作者： Azimi, Hamed Shiri, Hodjat Mahdianpari, Masoud Civil Engineering Department Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X5 Canada Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X5 Canada C-CORE 1 Morrissey Rd St. John’sNLA1B 3X5 Canada

The Arctic area is one of the best destinations for the development of oil and gas loading equipment. However, the recent development of oil and gas facilities, including the submarine pipelines and wellheads crossing the Arctic area, has elevated the need for more attention to iceberg draft (under-water height of icebergs) estimation during an ice scouring event. This means if the draft of an iceberg is more than the ocean deepness, the iceberg tip can gouge the ocean floor and collide with the subsea assets;as a result, the operational integrity of the submarine infrastructures is threatened. Hence, the estimation of the iceberg drafts is vital for the oil and gas operators in the Arctic waters. In the present study, the use of the Gradient Boosting Regression (GBR) algorithm was proposed to simulate the iceberg drafts for the first time. Initially, the parameters governing the iceberg drafts were recognized, and nine GBR models were then developed. The premium GBR model along with the most important inputs was known by conducting a sensitivity analysis, e.g., the value of correlation coefficient (R) and Akaike Information Criteria (AIC) for the premium GBR model was obtained at 0.907 and 12.571, while the iceberg length ratio (L⁄H) and iceberg width ratio (W/ H) were recognized as the most influential input parameters to predict the iceberg drafts. The best GBR model was compared with Support Vector Regression (SVR) and K-Nearest Neighbors Regression (KNR) algorithms, rather the GBR algorithm had the highest degree of accuracy and correlation as well as the lowest amount of complexity. The error analysis demonstrated that roughly 34% of the iceberg draft predicted by the premium GBR model had an error of less than 10%;however, this value for the SVR and KNR models was almost 19%. Lastly, the performed uncertainty analysis proved that the superior GBR model possessed the narrowest bound of uncertainty, with an overestimated performance in the iceberg draft simulat

关键词： Forecasting

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Multilayer Satellite Network Collaborative Mobile Edge Caching:A GCN-Based Multi-Agent Approach

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China Communications 2024年第11期21卷 56-74页

作者： Yang Jie He Jingchao Cheng Nan Yin Zhisheng Han Dairu Zhou Conghao Sun Ruijin School of Telecommunications Engineering Xidian UniversityXi’an 710071China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210000China Department of Electrical and Computer Engineering University of WaterlooWaterloo ON N2L 3G1Canada

With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has *** emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user *** with the MEC and satellite networks,the network is empowered popular content ubiquitously and *** the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this ***,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale *** the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content ***,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale ***,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching ***,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.

关键词： cache placement coded caching graph convolutional network(GCN) mobile edge caching(MEC) multilayer satellite network

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Editorial Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年第4期21卷 516-520页

作者： Zhou, Xiaokang Leung, Carson K. Wang, Kevin I-Kai Fortino, Giancarlo Shiga University Faculty of Data Science Shiga Hikone522-0069 Japan University of Manitoba Department of Computer Science WinnipegR3T 2N2 Canada University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand University of Calabria DIMES Department Rende CS87036 Italy

Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of artificial intelligence (AI) and Internets of Things (IoT) technologies, deep learning (DL) for big data analytics - including affective learning, reinforcement learning, and transfer learning - are widely applied to sense, learn, and interact with human health. Examples of biomedical applications include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, DL and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, DL, and big data analytic technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and DL technologies. Hence, exploring the possibility of DL and big data analytics in the fields of biomedical applications and digital healthcare is in high demand. © 2004-2012 IEEE.

关键词： Big data

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Identification and Visualization of Spatial and Temporal Trends in Textile Industry

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computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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LLaMA-LoRA Neural Prompt engineering:A Deep Tuning Framework for Automatically Generating Chinese Text Logical Reasoning Thinking Chains

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Data Intelligence 2024年第2期6卷 375-408页

作者： Songlin Chen Weicheng Wang Xiaoliang Chen Peng lu Zaiyan Yang Yajun Du School of Computer and Software Engineering Xihua Universitychengdu 610039P.R.china Department of Computer Science and Operations Research University of MontrealMontrealQC H3C3J7Canada College of Artificial intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

The exption of Chinese natural language processing(NLP)has stimulated research in the broader NLP ***,existing large language models have limitations in comprehending and reasoning in *** paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource *** propose LLaMA-LoRA,a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation(LoRA)of Large Language Models technique for ***-of-Thought(CoT)are crucial for generating intermediate reasoning chains in language models,but their effectiveness can be limited by isolated language *** reasoning resulting from conventional prompts negatively impacts model *** prompts are introduced to encourage reasoning chain generation and accurate answer *** the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning *** LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks,surpassing benchmark performance achieved by related language models such as GPT-3.5,Chat-GLM,and OpenAssistant,delivering accurate,comprehensive,and professional *** availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.

关键词： Chinese natural language processing Neural prompt engineering Large language models Low-Rank adaptation Chain-of-thought

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Impact of Gamification on Student Learning: An Empirical Evidence 5th

Impact of Gamification on Student Learning: An Empirical Evi...

引用

5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Garg, Anchal Madhulika Centre for Applied Computer Science School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom Department of Computer Science and Engineering Amity School of Engineering and Technology Uttar Pradesh Noida India

ISBN: (纸本)9789819716814

Learner diversity is a matter for universities enrolling international students. Hence, learner engagement then becomes a major concern for instructors. This study uses gamification techniques to determine its impact on engagement and learning when it comes to diverse cohort of learners. A survey questionnaire method was used to identify the effect of gamification on engagement, motivation, performance, and learning. This empirical study revealed that quizzes supported by gamification enhanced engagement, performance, and learning but did not motivate learners to study outside the lecture theatre. This paper also summarizes the issues while using gamified quizzes in the classroom settings and provides potential solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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RP-SWSGD: Design of sliding window stochastic gradient descent method with user’s ratings pattern for recommender systems

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Multimedia Tools and Applications 2024年第14期83卷 41083-41120页

作者： Khan, Zeshan Aslam Raja, Hafiz Anis Chaudhary, Naveed Ishtiaq Iqbal, Sumbal Mehmood, Khizer Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering International Islamic University Islamabad Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

To offer relevant and useful recommendations, the crucial role of recommender systems in e-commerce industry is to predict the users’ concern for various items by estimating items’ attributes and users’ preferences. The reliability of a recommender system is usually assessed through accuracy and speed of relevant recommendations for a variety of items. The matrix factorization-based stochastic gradient descent (SGD) methods proposed by researchers lack memory needed to capture the ratings history hidden in the previous iterations. Recently, sliding window-based SGD strategies designed for Recommender systems and Hammerstein nonlinear systems gained attention due to the improved performance in terms of convergence speed and estimated accuracy. The memory impact with regard to the historical information enhances the performance of sliding window-based SGD techniques. However, sliding window-based methods are deficient in capturing the ratings history based on the users’ rating patterns. Hence utilizing the same window length for the set of observed ratings rated by users. Therefore, we propose an improved sliding window-based SGD strategy to acquire historical information of the ratings with respect to a user’s rating patterns for efficient matrix factorization of recommender systems. The proposed strategy performs significantly by accomplishing fast convergence speed and accuracy for window sizes greater than 1. The accuracy of the suggested technique is verified for two benchmark datasets such as ML-100 K and Film-Trust. However, the authenticity of the proposed method as compared to the standard counterpart (window size = 1) is confirmed through Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The average improvements achieved by the proposed strategy in terms of RMSE and MAE over the baseline for ML-100 K dataset are 0.726% and 2.245% respectively. Whereas the proposed method accomplishes considerable average improvement of 7.89% and 9.41% for RMSE an

关键词： Recommender systems

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Parameter-Efficient Federated Cooperative Learning for 3-D Object Detection in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 20314-20325页

作者： Chi, Fangyuan Wang, Yixiao Nasiopoulos, Panos Leung, Victor C. M. The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Toronto AI Lab LG Electronics TorontoONM5V 1M3 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

In the rapidly evolving field of autonomous driving, accurately detecting and understanding dynamic environments remains a challenge. Federated learning (FL) offers a promising approach by integrating decentralized models from multiple connected autonomous vehicles (CAVs) to enhance the performance of deep-learning (DL)-based object detection methods. However, traditional FL faces hurdles, such as extensive data synchronization requirements, limited data variance, and high communication costs. This article introduces a federated cooperative learning framework that addresses these challenges by combining data from both CAVs and roadside units. The framework combines local cooperative perception with global FL through a parameter-efficient FL adapter and a lazy communication strategy, improving DL-based object detection capabilities across diverse driving scenarios while significantly reducing bandwidth requirements. We also present a novel multiagent-multitown dataset Vehicle-to-Everything-Fed, specifically developed to validate the effectiveness of our approach under various conditions. Notably, our framework retains 97.51% of the detection accuracy achieved by full-model FL, while utilizing only 1.4% of the bandwidth typically required, demonstrating substantial improvements over conventional FL strategies. This study underscores the potential of our tailored approach to substantially enhance autonomous vehicle technologies with minimal resource utilization. © 2014 IEEE.

关键词： Bandwidth

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