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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

computer Modeling in Engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Sensor nodes

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Enhancing Skin Disease Diagnosis: Light GBM-DMS Algorithm for Accurate Image Classification

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Kalpana, B. Senthilselvi, A. Muruganandam, S. Kumar, S. Vinodh Department of Information Technology RMD Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai India

Globally, skin diseases are emerging as the most common health problem. It initiates depressive disorder, and it also causes physical health distress. It rarely led to skin cancer in extreme cases. Diagnosing skin disease is subjective and time-consuming when it is performed by health experts manually. Hence, automatic skin disease prediction is required by both dermatologists and patients to enhance and accelerate treatment plans. Even though many machine-learning techniques are available to detect skin diseases, the timely prediction and accurate classification of skin lesions are still in demand. Hence, the Discrete Mycorrhizal Search Algorithm is used to optimize the novel LightGBM to meet this objective. Firstly, intrusions like noise elimination and hair removal are performed and then the image resizing takes place by utilizing the preprocessing pipelines. Subsequent to data pre-processing, the prime features are extracted using the convolutional self-attention based L2 regularization, thus minimizing the data complexity. At last, the extracted features are shifted to the final categorization phase which is done by the novel approach for effective classification of skin diseases. Two skin disease classification images, including datasets, the Skin Lesions Dermatoscopic Image dataset and Skin Cancer MNIST HAM10000, are selected to validate the classification performance. Comprehensive analyses are performed with significant measures that showed the proposed model attained better performances of 98.6%, 0.95, and 10.43 s from accuracy, intersection of union, and execution time, respectively. The stability and robustness under different environmental conditions for clinical applications in skin disease detection and classification tasks are analyzed for making decisions in real-time scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Dermatitis

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MATM-SN: A Deep-Reinforcement-Learning Model for Ocean Sensing Task Allocation Based on Generative Social Network

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IEEE Internet of Things Journal 2025年第10期12卷 13477-13486页

作者： Guo, Shuai Xia, Menglei Xue, Huanqun Song, Yutong Zhang, Zhitian Liu, Chao Qingdao University of Technology College of Information and Control Engineering Qingdao266520 China Qingdao University of Technology College of Science Qingdao266520 China Ocean University of China Department of Computer Science and Technology Qingdao266100 China

Due to the complexity of ocean sensing tasks, buoy detection in traditional ocean observation methods has the disadvantages of high cost and insufficient real-time performance. Ocean mobile crowd sensing technology collects high-resolution data in real-time through shipborne sensors and has significant potential for future development in the ocean Internet of Things. In this article, we consider a cooperative vessel activity that performs sensing tasks, incentivizing multiple vessels through dynamic external task profits. To spontaneously incentivize the team to take part in tasks and maximize the total profit of the vessels, we propose a multiagent task allocation model (MATM-SN) based on deep reinforcement learning (DRL). This model uses a generative adversarial network to predict the trajectory of the vessels, and STGCN is used to extract synergistic features between vessels effectively. DRL training provides an optimal task allocation strategy for vessels. Finally, we compared four baselines and demonstrated that the MATM-SN model outperformed other baselines regarding task completion rate and total efficiency. © 2014 IEEE.

关键词： Deep reinforcement learning

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Exploring image data association: A hybrid mining approach

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Multimedia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is generated using feature vectors obtained from the fine-tuned CNN architecture. The proposed FIAR algorithm is used to generate hash-indexed image association rules, which are further optimized using the proposed OFIAR algorithm. This methodology combines the strengths of the CNN model to extract histogram features from images, the FIAR algorithm to efficiently mine frequent image itemsets, and the OFIAR algorithm to optimize image association rules. The proposed methodology can be used to discover hidden relationships among images, leading to new insights in image processing and analysis. Efficient results were obtained with a minimum support of 0.50 and a minimum confidence of 0.50. Experiments were performed on the fruits image dataset consisting of 2618 images from six different classes, and the results show that image mining is feasible and can produce strong optimized image association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Association rules

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HDEL: a hierarchical deep ensemble approach for text-based emotion detection

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Multimedia Tools and Applications 2025年第4期84卷 1799-1820页

作者： Vora, Shivani Mehta, Rupa G. Information Technology Department C.K.Pithawala College of Engineering and Technology Gujarat Surat India Department of Computer Science and Engineering SVNIT Gujarat Surat India

Emotion detection from social media data plays a crucial role in studying societal emotions concerning different events, aiding in predicting the reactions of specific social groups. However, it is complex to automatically extract implicit emotional information from noisy social media text data. This study introduces the Hierarchical Deep Ensemble Learning (HDEL) system to identify emotions in text data. The proposed HDEL model utilizes BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), BiGRU (Bidirectional Gated Recurrent Unit), and RCNN (Recurrent Convolutional Neural Network) in the first level of its hierarchy. The predicted probabilities of the four models are embedded with input data to prepare the intermediate hybrid data. This hybrid data is fed to the next layer of the proposed system, which utilizes a Random Forest (RF) algorithm to predict the emotion. The proposed approach is tested using three emotion datasets: the WASSA-2017 Emotion Intensity (EmoInt) dataset, the International Survey on Emotion Antecedents and Reactions (ISEAR) dataset, and the CrowdFlower (CF) dataset. EmoInt and ISEAR are clean and balanced, while CF is noisy and imbalanced. The results are compared with various state-of- the-art Machine Learning models. The outperforming results depict the superiority of the proposed approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization

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IEEE Access 2025年 13卷 72375-72388页

作者： Aldhahri, Eman Ali Almazroi, Abdulwahab Ali Alkinani, Monagi Hassan Ayub, Nasir Alghamdi, Elham Abdullah Janbi, Nourah Fahad University of Jeddah Collage of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah21959 Saudi Arabia University of Jeddah College of Computing and Information Technology at Khulais Department of Information Technology Jeddah21959 Saudi Arabia Air University Islamabad Department of Creative Technologies Islamabad44000 Pakistan

Optimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adaptive Depthwise Separable Convolutions, and HBA-optimized parameters. This model uses HBA's Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. Our system also employs Adaptive Synthetic Resampling with Environmental Context. Using novel measures including the Crop Type Generalisation Score (CTGS) and Environmental Sensitivity Index (ESI), the ResXceNet-HBA model achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score. These results beat ResNet, CNN, and Inception V2. The model executed in 50.9 seconds, faster than the alternatives. The confusion matrix exhibits minimal false positives and negatives, suggesting good prediction accuracy. ResXceNet-HBA's statistics and resource optimization value increases. Precision farming and sustainable agriculture benefit from our strategy's significant environmental stress and crop health assessments. © 2013 IEEE.

关键词： Monitoring Crops Temperature measurement Adaptation models Temperature sensors Plants (biology) Accuracy Predictive models Urban agriculture Deep learning

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A Lightweight Authentication Protocol for RFID-Assisted Supply Chain Management System

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IEEE Internet of Things Journal 2025年第9期12卷 12845-12852页

作者： Tariq, Tayyaba Kuo, Wen-Chung Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J. F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency. © 2014 IEEE.

关键词： Authentication Protocol

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Review on Meta-heuristic Algorithm-Based Priority-Aware Computation Offloading in Edge Computing System

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Journal of The Institution of Engineers (India): Series B 2025年 1-26页

作者： Pilli, Neelima Mohapatra, Debasis Reddy, Shiva Shankar Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela India Department of Computer Science and Engineering Parala Maharaja Engineering College Odisha Brahmpur India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India

Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge servers. So, a variety of offloading strategies are offered in previous research to enhance the efficiency of SDs by offloading their workloads to neighboring cloudlets or distant cloud computing resources. Consequently, optimization techniques are crucial for scheduling computation offloading tasks in (ED) networks. To the greatest extent of current understanding, an extensive analysis of meta-heuristic-based computation offloading (CO) techniques in the EC has not been systematically surveyed. This systematic survey creates an inventory of CO in EC and further summarizes the most advanced recently available EC technologies. In this survey, three types of optimization techniques are examined: Lyapunov, Meta-heuristic, and Convex. The objective functions, application domains, offloading methods, evaluation techniques, merits, and demerits of the existing algorithms are summarized. The proposed survey undergoes four main CO processes: task scheduling, priority-aware task scheduling, load balancing, and edge server selection. In addition, this research provides a comparative analysis of the offloading techniques. Subsequently, unresolved problems and upcoming unfocused or inadequately covered research tasks are explored. © The Institution of Engineers (India) 2025.

关键词： Computation offloading

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A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and Systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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