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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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Traffic Prediction Using GPS Based Cloud Data Through RNN-LSTM-CNN Models: Addressing Road Congestion, Safety, and Sustainability in Smart Cities

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SN computer science 2025年第2期6卷 1-18页

作者： Selvan, C. Senthil Kumar, R. Iwin Thanakumar Joseph, S. Malin Bruntha, P. Amanullah, M. Arulkumar, V. School of CSE REVA University Karnataka Bangalore India Department of Computer Science and Engineering (AIML) Faculty of Engineering and Technology Jain (Deemed-to-be) University Bangalore India Department of CSE Koneru Lakshmaiah Education Foundation Andhrapradesh Vaddeswaram India Department of ECE Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

Smart cities that use technology and data for efficiency optimization, sustainability, and well-being of citizens face a lot of challenges. Because all of the aforementioned challenges share a common characteristic of complexity, achieving success will need careful preparation and coordinated effort. This research presents a novel approach utilizing deep learning models to address issues about road congestion, specifically by offering secure routes for pedestrians and cyclists. The Global Positioning System (GPS) data stored in the cloud is used as input for the proposed work. In the proposed work, the flow of vehicles, their speed, and the occupancy have been predicted. The need for deep learning to resolve the traffic problem is that deep learning methods are highly efficient when compared to statistical techniques as they provide more than 90% of accuracy in forecasting. The novel approaches used in this paper are integrated Recurring Neural Networks (RNN)-Long Short Term Memory (LSTM)- Convolutional Neural Networks (CNN) to form RLC (RNN-LSTM-CNN) models. The system encompasses appropriate methods for improving the transportation system’s efficiency by mitigating environmental impacts. The implementation of Recurrent Neural Networks (RNN) along with Long Short-Term Memory (LSTM) are used to analyze historical traffic flow data by predicting future traffic conditions by optimizing traffic signal timings, traffic flow, and public transportation schedules to reduce idling time and fuel consumption, leading to lower emissions by predicting Electric Vehicle (EV) charging demand patterns, optimize charging stations’ locations and driver routes, and manage energy distribution more efficiently. The proposed Deep Learning-based models perform better when compared to the other methods and hold the potential to transform urban mobility, making it more efficient, safer, and environmentally friendly in the smart cities of the future as it provides higher forecasting accuracy

关键词： CNN LSTM Occupancy RNN Speed Traffic flow prediction

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Scalable Subspace Clustering via Purity Kernel Tensor Learning 3

Scalable Subspace Clustering via Purity Kernel Tensor Learni...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Adhvaryu, Rachit Kannan, S. Rajes Sanjay, C.P. Dange, Pravin Parul University Department of Computer Science and Engineering Faculty of Engineering and Technology Gujarat India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Sri Balaji University Pune India Pune India

ISBN: (纸本)9798331508456

Subspace clustering is a fundamental task in machine learning, particularly for high-dimensional datasets where the goal is to group data points that lie in distinct subspaces. Traditional methods, such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR), have demonstrated good performance but often suffer from high computational costs, particularly when applied to large-scale data. To address these limitations, this paper introduces a novel technique called Purity Kernel Tensor Learning (PKTL). PKTL combines the power of tensor decomposition with a purity kernel function to improve both the efficiency and effectiveness of subspace clustering. By utilizing tensor-based representations, the method captures higher-order relationships in the data, allowing for a more robust clustering process. The purity kernel function measures the coherence within the clusters, enhancing the identification of subspaces in complex datasets. The proposed approach not only reduces computational complexity but also improves the clustering accuracy compared to existing methods. Experiments on synthetic and real-world datasets, including ORL, Yale, and MNIST, show that PKTL outperforms conventional subspace clustering algorithms, both in terms of clustering accuracy and scalability. The results demonstrate the potential of PKTL in handling large datasets while maintaining high performance, making it a promising approach for scalable subspace clustering in real-world applications. © 2025 IEEE.

关键词： Large datasets

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SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing

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Discover Computing 2025年第1期28卷 1-17页

作者： Mohanty, Subhasish Mishra, Jyotirmaya Mohapatra, Sudhir Kumar Bejo, Seifu Detso Deferisha, Aliazar Deneke Computer Science and Engineering GIET University Odisha Gunupur India Faculty of Engineering and Technology Sri Sri University Cuttack India Department of Software Engineering CCI Wolkite University Wolkite Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the Squirrel Search Algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic Algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems. © The Author(s) 2025.

关键词： First-order mutants Higher-order mutants Mutation score Mutation testing Software testing Squirrel search algorithm

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of Information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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Empowering SMEs in the Fourth Industrial Revolution: A Framework for Maintenance 4.0 Adoption 6

Empowering SMEs in the Fourth Industrial Revolution: A Frame...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Nasirinejad, Majid Afshari, Hamid Sampalli, Srinivas Faculty of Computer Science Dalhousie University Halifax Canada Department of Industrial Engineering Dalhousie University Halifax Canada

SMEs play a vital role in driving economic growth, creating jobs, and fostering innovation. However, unlike larger businesses, SMEs often struggle to adopt Industry 4.0 technologies due to limited resources. Addressing these challenges is essential to help SMEs leverage advanced technologies, enhance competitiveness, and support economic development. This paper presents a framework for SMEs to adopt Industry 4.0 technologies in maintenance operations. The framework leverages Reliability, Availability, Maintainability, Safety, and Sustainability (RAMS2) benefits and offers optimization opportunities to enhance production efficiency, reduce costs, and improve product quality. Based on the comprehensive literature review the gaps are identified and technical components of the proposed framework are matched with RAMS2 objectives. A case study illustrates its practical application, including a mathematical model to balance cost and reliability in maintenance. The proposed framework, compared to traditional systems, provides SMEs with a competitive edge by achieving operational and financial objectives. © 2025 Elsevier B.V.. All rights reserved.

关键词： Scheduled maintenance

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Data-Driven Evolutionary Algorithms for Optimizing Pumping Stations in Water Distribution Networks: Classifier-Guided Search Space Reduction 27th

Data-Driven Evolutionary Algorithms for Optimizing Pumping S...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Faúndez-Lizama, Thalía Gajardo-Sepúlveda, Nicolás Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766060

In the context of increasing water scarcity and the need to enhance water distribution network efficiency, this study focuses on optimizing the design of pumping stations using data-driven evolutionary algorithms guided by classification models. These facilities are critical components in water networks due to their high energy consumption and role in maintaining necessary water pressure. The proposed approach integrates a machine learning classifier model within the evolutionary process to pre-label solutions as feasible or infeasible, thereby reducing search space and computational costs associated with hydraulic simulations. Classification models include support vector classifier, artificial neural networks, and random forest. These models are trained on data generated from initial evolutionary optimization, enabling the classifier to predict the viability of new solutions. Results show that all tested classification models reduced the number of objective function evaluations and consequently, required hydraulic simulations. In some cases, the optimal solution defined by the original method was improved, demonstrating dual benefits by reducing both cost and computational effort. This work highlights that data-driven evolutionary optimization can effectively tackle highly complex problems, offering potential future applications in other areas of civil engineering and water resource management. Implementing this methodology promises significant time and resource savings in large-scale optimization processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hydraulic models

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Data-Driven Genetic Algorithm for the Optimization of Water Distribution Networks: A New Surrogate Model for Estimating Investment and Operational Costs in Pumping Stations 27th

Data-Driven Genetic Algorithm for the Optimization of Water ...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Gajardo-Sepúlveda, Nicolás Faúndez-Lizama, Thalía Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766039

The increase in water consumption and demand has highlighted the need to improve the efficiency of water distribution networks (WDNs). Pumping stations (PS) represent a significant challenge due to their high energy consumption and associated operational costs. This study presents a data-driven evolutionary optimization methodology focused on predicting cost and penalties in the design and operation of PS. The methodology integrates the functioning of genetic algorithms with machine learning techniques, developing a surrogate model capable of predicting associated costs and increasing the computational efficiency of optimization models. A case study is presented to validate the methodology, showing a significant reduction of 31.8% in evaluation times and a decrease in optimization time compared to traditional models. The results indicated that despite the reduction in computational effort, the increase in the final cost of the network was minimal, with only a 2.42% increase compared to the baseline model. These findings underscore the effectiveness of combining machine learning with genetic algorithms for the optimization of PS in WDNs, improving computational efficiency while maintaining high standards in solution quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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PlantCare AI: An AI based Solution for Plant Disease Diagnosis and Assistance 6

PlantCare AI: An AI based Solution for Plant Disease Diagnos...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ghogare, Snehal Dhupe, Shivkanya Kulkarni, Abhijit Shingane, Tanish Gound, Renuka Department of Computer Science and Business Systems JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune India

ISBN: (纸本)9798331522667

The advancement of Artificial Intelligence (AI) and machine learning (ML) has introduced trans- formative solutions in plant care. This review investigates the integration of deep learning and image recognition technologies for identifying and managing plant diseases. The proposed project focuses on developing a user-centric web application designed to detect plant diseases automatically, provide treatment recommendations, suggest nearby nurseries for obtaining necessary supplies, and offer chatbot assistance for user inquiries. Leveraging advanced image recognition techniques, this system aims to make plant care more efficient and accessible. The integration of Google Maps enhances navigation to local nurseries, further simplifying access to resources and promoting better plant care practices. This study outlines current methodologies, explores challenges and opportunities, and proposes a structured framework for creating an intelligent plant care platform to meet the evolving demands of farmers and horticulture professionals. © 2025 IEEE.

关键词： Convolutional neural networks

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AI-Driven Predictive Analytics for CRM to Enhance Retention Personalization and Decision-Making

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International Journal of Advanced computer science and Applications 2025年第4期16卷 552-563页

作者： Gaidhani, Yashika Ramesh, Janjhyam Venkata Naga Singh, Sanjit Dagar, Reetika Rao, T. Subha Mastan Godla, Sanjiv Rao Baker El-Ebiary, Yousef A. Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of MBA-Sanjivani College of Engineering Savitribai Phule Pune University Pune India GD Goenka University Haryana Gurugram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Faculty of Informatics and Computing UniSZA University Malaysia

The advent of Artificial Intelligence (AI) has dramatically altered Customer Relationship Management (CRM) by allowing organizations to anticipate customer behavior, customize interactions and automate service delivery. This research introduces an extensive AI-based predictive analytics framework aimed at improving customer engagement, retention and satisfaction using advanced Machine Learning (ML) and Natural Language Processing (NLP) methodologies. By using XGBoost for churn prediction and BERT-based models for sentiment analysis, the system efficiently handles both structured and unstructured customer data. The methodology involves sophisticated feature engineering, customer segmentation via K-Means clustering, and Customer Lifetime Value (CLV) prediction to aid data-driven business strategies. An NLP-driven chatbot offers real-time, personalized support, response time and improving user experience. Evaluation metrics such as accuracy, precision, recall and F1-score demonstrate the better performance of the proposed system compared to conventional CRM approaches. This work also addresses important issues such as data privacy compliance, algorithmic bias and explainability of AI decision-making. Ethical deployment and transparency of AI are emphasized for building confidence in automated CRM systems. Future evolution will tackle the use of reinforcement learning to facilitate learning-based interaction schemes and federated learning for trusted, decentralized management of data. This architecture does not only provide better CRM functionality but also builds a platform towards intelligent, responsible and scalable solutions for customer relations across industries. © (2025), (science and Information Organization). All rights reserved.

关键词： Reinforcement learning

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