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Wavelet-Infused Convolution-Transformer for Efficient Segmentation in Medical Images

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3326-3337页

作者： Dutta, Pallabi Mitra, Sushmita Roy, Swalpa K. Indian Statistical Institute Machine Intelligence Unit Kolkata700108 India Alipurduar Government Engineering and Management College Department of Computer Science and Engineering Alipurduar736206 India

Recent medical image segmentation methods extract the characteristics of anatomical structures only from the spatial domain, ignoring the distinctive patterns present in the spectral representation. This study aims to develop a novel segmentation architecture that leverages both spatial and spectral characteristics for better segmentation outcomes. This research introduces the wavelet-infused convolutional Transformer (WaveCoformer), a computationally effective framework to fuse information from both spatial and spectral domains of medical images. Fine-grained textural features are captured from the wavelet components by the convolution module. A transformer block identifies the relevant activation maps within the volumes, followed by self-attention to effectively learn long-range dependencies to capture the global context of the target regions. A cross-attention mechanism effectively combines the distinctive features acquired by both modules to produce a comprehensive and robust representation of the input data. WaveCoformer outperforms related state-of-the-art networks in publicly available Synapse and Adrenal tumor segmentation datasets, with a mean Dice score of 83.86% and 79%, respectively. The model is feasible for deployment in resource-constrained environments with rapid medical image analysis due to its computationally efficient nature and improved segmentation performance. © 2013 IEEE.

关键词： Convolutional neural networks

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Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

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computer Modeling in engineering & Sciences 2025年第4期143卷 639-671页

作者： Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali Knowledge Unit of Systems and Technology University of Management and TechnologySialkot51310Pakistan Department of Computer Science University ofWahWah Cantt47010Pakistan Department of Computer Engineering College of Computer and Information Sciences(CCIS)King Saud UniversityRiyadh11451Saudi Arabia Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea

Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few *** devices are now easy targets for hackers because of their built-in security *** a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion *** proposed model is evaluated on the NSL-KDD *** hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world ***,this paper proposes a highly efficient deployment strategy for resource-constrained network *** results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)*** particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.

关键词： Intrusion detection self-organizing map Internet of Things dimensionality reduction

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Constructing Rectilinear Steiner Minimum Tree with Conditional Generative Adversarial Network 38

Constructing Rectilinear Steiner Minimum Tree with Condition...

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38th International Conference on VLSI Design, VLSID 2025

作者： Saha, Kritanta Banerjee, Pritha Sur-Kolay, Susmita Indian Statistical Institute Advanced Computing and Microelectronics Unit Kolkata India Sister Nivedita University Department of Computer Science and Engineering Kolkata India University of Calcutta Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331522445

Given n points in the 2D plane which are the pins of a net, a Rectilinear Steiner Minimum Tree (RSMT) which consists only of vertical and horizontal line segments is the shortest network to interconnect the pins along with a few extra (Steiner) points. An RSMT for a net has a minimum total wirelength in Manhattan distance and acts as a backbone to many state-of-the-art global routers. However, finding an RSMT is NPcomplete. In this work, we propose a method for constructing an RSMT with an image-to-image translation using a conditional generative adversarial network (cGAN) based model. Images of RSMTs with optimal wirelength for randomly generated nets form our training dataset. Experimental results show that, after training, our model generates RSMTs with = 0.348% wirelength error on average compared with the optimal solution for nets with degrees up to 50. This is the first successful attempt to solve the RSMT construction problem as an image-to-image translation problem using a cGAN based model. © 2025 IEEE.

关键词： Generative adversarial networks

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Ethical Aspects of ChatGPT in Software engineering Research

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 254-267页

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Lappeenranta-Lahti University of Technology Software Engineering Department Lappeenranta15210 Finland University of Oulu M3S Empirical Software Engineering Research Unit Oulu90014 Finland Wuhan University School of Computer Science Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Furthermore, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. In addition, we employed an interpretive structure modeling approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level-based decision model. We further conducted a cross-impact matrix multiplication applied to classification analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles by adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. © 2020 IEEE.

关键词： Software engineering

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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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Deep Learning Framework for Facial Reconstruction Outcome Prediction: Integrating Image Inpainting and Depth Estimation for computer-Assisted Surgical Planning

SSRN

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SSRN 2025年

作者： Manni, Guido Bini, Fabiano Marinozzi, Franco Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Italy Department of Mechanical and Aerospace Engineering Sapienza University of Rome Rome00184 Italy

Background: Facial reconstructive surgery requires preoperative planning to optimize functional and aesthetic outcomes. While current imaging technologies like CT and MRI provide detailed anatomical assessment, they do not offer visualization of expected post-surgical appearance, limiting surgical planning capabilities in facial reconstruction procedures like maxillofacial surgery, otolaryngology, and ophthalmic ***: To develop a deep learning framework that predicts surgical outcomes for facial reconstruction by integrating facial inpainting and monocular depth estimation models, enabling both 2D and 3D surgical planning from standard clinical ***: The framework integrates two key components: a deep learning facial inpainting model for predicting reconstructed facial features and a deep learning monocular depth estimation model for generating three-dimensional representations. We evaluated three state-of-the-art inpainting architectures (LaMa, LGNet, MAT) and two state-of-the-art monocular depth estimation approaches (ZoeDepth, DepthAnything) using the FFHQ dataset for inpainting and C3I-SynFace dataset for depth estimation. Models were evaluated using comprehensive quantitative metrics for both reconstruction quality and depth accuracy. Results: LGNet demonstrated optimal performance for facial reconstructions with missing regions covering 10-25% of the facial area, achieving superior scores in MSE (63.8104), PSNR (31.4162), and SSIM (0.9587). For depth estimation, ZoeDepth consistently outperformed DepthAnything, showing significantly better accuracy in absolute relative difference (0.8123 vs 1.6065) and RMSE-log (0.6721 vs 0.9512).Conclusion: This novel framework addresses a critical gap in surgical planning by providing a comprehensive preoperative tool that enables both 2D and 3D visualization of potential outcomes from standard clinical photographs, supporting various applications from maxillofacial reconstruction to orbital and

关键词： Data visualization

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Predicting Aesthetic Outcomes in Breast Cancer Surgery: A Multimodal Retrieval Approach 1st

Predicting Aesthetic Outcomes in Breast Cancer Surgery: A M...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Zolfagharnasab, Mohammad Hossein Freitas, Nuno Gonçalves, Tiago Bonci, Eduard Mavioso, Carlos Cardoso, Maria J. Oliveira, Hélder P. Cardoso, Jaime S. University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal Breast Unit Champalimaud Foundation Lisbon Portugal

ISBN: (纸本)9783031777882

Breast cancer treatments often affect patients’ body image, making aesthetic outcome predictions vital. This study introduces a Deep Learning (DL) multimodal retrieval pipeline using a dataset of 2,193 instances combining clinical attributes and RGB images of patients’ upper torsos. We evaluate four retrieval techniques: Weighted Euclidean Distance (WED) with various configurations and shallow Artificial Neural Network (ANN) for tabular data, pre-trained and fine-tuned Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), and a multimodal approach combining both data types. The dataset, categorised into Excellent/Good and Fair/Poor outcomes, is organised into over 20K triplets for training and testing. Results show fine-tuned multimodal ViTs notably enhance performance, achieving up to 73.85% accuracy and 80.62% Adjusted Discounted Cumulative Gain (ADCG). This framework not only aids in managing patient expectations by retrieving the most relevant post-surgical images but also promises broad applications in medical image analysis and retrieval. The main contributions of this paper are the development of a multimodal retrieval system for breast cancer patients based on post-surgery aesthetic outcome and the evaluation of different models on a new dataset annotated by clinicians for image retrieval. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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PulmoNetX: A Hybrid Vision Transformer Approach for Multi-scale Spatial Feature Reduction in Pneumonia Classification 27th

PulmoNetX: A Hybrid Vision Transformer Approach for Multi-sc...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Lasker, Asifuzzaman Ghosh, Mridul Obaidullah, Sk Md Chakraborty, Chandan Roy, Kaushik Pal, Umapada Department of Computer Science and Engineering Aliah University Kolkata700160 India Department of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah711312 India Department of Computer Science and Engineering National Institute of Technical Teachers’ Training and Research Kolkata700106 India Department of Computer Science West Bengal State University Barasat700126 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783031781650

An innovative deep learning structure, PulmoNetX, integrates the capabilities of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance pneumonia detection in chest X-ray imagery. During preprocessing, images are normalized in size, converted to grayscale, and subjected to contrast amplification to emphasize essential features. PulmoNetX employs a hybrid methodology to capture both the local and global characteristics of images, leading to significant advancements in diagnosing different pneumonia types, such as COVID-19-induced, viral, and bacterial pneumonia. Comparative studies reveal that PulmoNetX surpasses leading Vision Transformer models in terms of precision, recall, F1-score, and overall accuracy, highlighting its advanced processing abilities and its promise as an effective diagnostic tool in X-ray lung disease detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Load-profile based sizing method for distributed energy resources in industrial microgrids 33

Load-profile based sizing method for distributed energy reso...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Jansen, Phillip Schoor, George Van Uren, Kenneth R. North-West University School of Electrical Electronic and Computer Engineering Potchefstroom South Africa North-West University Unit for Energy and Technology Systems Potchefstroom South Africa

ISBN: (纸本)9798331535162

The demand for microgrids is increasing. Many countries such as South Africa are experiencing difficulties with power reliability, whereas other countries are on a drive to minimise carbon footprint in the power generation industry. In addressing the difficulties mentioned above, optimal sizing of renewable energy resources within a microgrid is essential. Studies show that a variety of sizing approaches have proven viable for analysing and optimising the sizes of distributed energy resources (DERs) within a microgrid. This paper gives an overview of existing sizing approaches from the literature. This overview of existing methods will contextualize the developed method presented in this paper. This study proposes a DER sizing approach called the load-profile based sizing method (LPBSM). The method simplifies factors such as the cost saving, the meteorological area, the peak demands and the average consumption of the site being tested and then calculates the sizes of the DERs using real hourly load data. The LPBSM follows the same fundamentals as that of the intuitive sizing approach from literature but now uses real-time load data as well as the specific electricity tariff of the end user to verify the reliability and availability of power. In this paper, a case study is presented on a site in Cape Town to verify how the LPBSM simulates real results based on a constructed site. © 2025 IEEE.

关键词： Distributed energy

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Classification of energy graph-based signatures for FDI using machine learning 33

Classification of energy graph-based signatures for FDI usin...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nel, Luke Uren, Kenneth R. Van Schoor, George North-West University School of Electrical Electronic and Computer Engineering Potchefstroom South Africa North-West University Unit for Energy and Technology Systems Potchefstroom South Africa

ISBN: (纸本)9798331535162

This paper evaluates energy graph-based visualisation (EGBV) data structures for fault classification using machine learning (ML). EGBV transforms process variable (PV) data into node signature matrices (NSMs) and cost matrices. Tests with k-nearest neighbour (KNN), logistic regression, and feedforward neural networks (FNNs) show that transforming data into the NSM format achieves comparable performance to the PV format, indicating that EGBV offers similar classification characteristics. Future work could apply deep learning to the NSMs and cost matrices to leverage their structural features. © 2025 IEEE.

关键词： Contrastive Learning

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