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arXiv 2025年

作者： Hasan, Md. Zahid Tamim, Abdullah Asadujjaman, D.M. Rahman, Md. Mahfujur Mollick, Md. Abu Ahnaf Dristi, Nosin Anjum Al-Noman, Abdullah Dept. of Computer Science & Engineering University of Rajshahi Bangladesh Dept. of Computer Science & Engineering Khulna University of Engineering & Technology Khulna Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh

Brain tumors require an assessment to ensure timely diagnosis and effective patient treatment. Morphological factors such as size, location, texture, and variable appearance complicate tumor inspection. Medical imaging presents challenges, including noise and incomplete images. This research article presents a methodology for processing Magnetic Resonance Imaging (MRI) data, encompassing techniques for image classification and denoising. The effective use of MRI images allows medical professionals to detect brain disorders, including tumors. This research aims to categorize healthy brain tissue and brain tumors by analyzing the provided MRI data. Unlike alternative methods like Computed Tomography (CT), MRI technology offers a more detailed representation of internal anatomical components, making it a suitable option for studying data related to brain tumors. The MRI picture is first subjected to a denoising technique utilizing an Anisotropic diffusion filter. The dataset utilized for the model’s creation is a publicly accessible and validated Brain Tumour Classification (MRI) database, comprising 3,264 brain MRI scans. SMOTE was employed for data augmentation and dataset balancing. Convolutional Neural Networks(CNN) such as ResNet152V2, VGG, ViT, and EfficientNet were employed for the classification procedure. EfficientNet attained an accuracy of 98%, the highest *** Codes 68T07 © 2025, CC BY-NC-SA.

关键词： computerized tomography

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Mahdi Alehdaghi Pourya Shamsolmoali Rafael M. O. Cruz Eric Granger Dept. of Systems Engineering ETS LIVIA ILLS Montreal Canada Dept. of Computer Science University of York UK

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments 1 1 Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance.

关键词： Training computer vision Accuracy Codes Prototypes Feature extraction Identification of persons

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An Intelligent Hybrid Algorithm for Smooth AUV Path Generation 3

An Intelligent Hybrid Algorithm for Smooth AUV Path Generati...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Singh, Paramjeet Talukdar, Bhaskar Jyoti Dash, Rudra Narayan Das, Bikramaditya BPUT Dept. of Computer Science and Engineering Odisha Rourkela India VSSUT Dept. of Electronics and Telecommunication Engineering Odisha Burla India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India BPUT Dept. of Electronics and Communication Engineering Odisha Rourkela India

ISBN: (纸本)9798331533205

The development of effective multi-objective optimization strategies for Autonomous Underwater Vehicles (AUVs) operating in uncharted territories constitutes a considerable challenge. This endeavor necessitates the simultaneous minimization of travel time, reduction of path length, enhancement of navigational safety, and smoothing of trajectories. In light of these challenges, this research presents an innovative real-time path planning framework that seamlessly integrates Particle Swarm Optimization (PSO) with Simulated Annealing (SA), thereby enhancing AUV navigation within static environments. The PSO algorithm is utilized to generate optimal temporary waypoints across the scenarios examined, by balancing the crucial optimization parameters. Subsequently, an optimal path is formulated, informed by both the initial and terminal points. The efficacy of the proposed methodology is assessed through simulations conducted across three distinct maps. Each obstacle map demonstrates varying densities of obstacles. The outcomes of these simulations emphasize the robustness and adaptability of the proposed strategy, highlighting its ability to formulate optimal paths within complex underwater environments, irrespective of the arrangement of obstacles. The AUVs display remarkable agility in maneuvering through intricate underwater landscapes, adept.y avoiding debris and obstacles while preserving operational efficiency. This enhanced path planning technique not only elevates the responsiveness of the AUVs during critical missions - such as search and rescue operations and structural assessments - but also significantly bolsters their overall navigational safety. Through this research, we introduce a formidable tool for enhancing AUV capabilities in practical applications, ultimately facilitating more efficient underwater exploration and intervention. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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A Hybrid APF Approach for Obstacle Avoidance in a 3D Environment 3

A Hybrid APF Approach for Obstacle Avoidance in a 3D Environ...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Saini, Amrita Talukdar, Bhaskar Jyoti Dash, Rudra Narayan Rao, D Chandrasekhar Das, Bikramaditya BPUT Dept. of Computer Science and Engineering Odisha Rourkela India VSSUT Dept. of Electronics and Telecommunication Engineering Odisha Burla India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India BPUT Dept. of Electronics and Communication Engineering Odisha Rourkela India

ISBN: (纸本)9798331533205

This study's main objective is to look at strategies, for avoiding underwater collisions and planning trajectories while navigating around static obstacles. Traditional Artificial Potential Fields (APF) face challenges such as getting stuck in local minima, which diminishes the path's optimality. Additionally, conventional APF often results in trajectories characterized by sharp turns or sudden changes in direction, particularly in environments populated with numerous obstacles. These obstacles can significantly hinder Autonomous Underwater Vehicles (AUVs) that depend on smooth trajectories for maintaining stability and control. To address these drawbacks, we present the Hybrid A∗ - Artificial Potential Field (HA-APF) algorithm. When using AUVs for collision avoidance duties, adherence to safety procedures is crucial. We propose an enhanced HA-APF algorithm to address this issue for efficient path planning and collision avoidance. Additionally, the A∗ algorithm, which has been altered to mitigate the innate problems with local optimality in the conventional APF approach, is used in conjunction with the improved APF technique. Finally, validations are conducted in three different regions with different beginning and finishing positions. These tests show how feasible and successful the suggested algorithm is in assisting AUVs in avoiding collisions while also carrying out path-planning duties. Through this work, we provide a robust tool for advancing AUV capabilities in real-world applications, ultimately allowing for more effective underwater exploration and intervention. © 2025 IEEE.

关键词： Motion planning

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Efficient Parallel Algorithm for Detecting Longest Flow Paths in Flow Direction Grids 3

Efficient Parallel Algorithm for Detecting Longest Flow Path...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Jayarukshi, Karunamuni Agarwal, Shivali Girish, K.K. Goudar, Santosh Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

High-performance computing (HPC) has transformed the capacity to address complex computational tasks across various scientific fields by enabling the efficient processing of large datasets and intricate simulations. In hydrological modeling, a critical task is identifying the longest flow channel within a catchment, which is essential for understanding water flow patterns and managing resources. However, existing geographic information system (GIS) algorithms for flow path identification often suffer from inefficiencies and inaccuracies. To address these challenges, this paper introduces innovative parallel methods utilizing Open Multi-Processing (OpenMP), a widely-used API that supports multi-platform shared-memory parallel programming. This approach optimizes the analysis of flow direction data, resulting in faster and more accurate identification of flow channels. The results demonstrate that the proposed method outperforms current approaches, offering substantial improvements in both performance and precision. These advancements have the potential to significantly enhance hydrological modeling practices and water resource management. © 2025 IEEE.

关键词： Flow direction GIS Hydrology Longest flow paths OpenMP Parallel Computing

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum Machine Learning 3

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Manideep Raya V S R S Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum Machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.

关键词： Artificial Intelligence Biopsy Breast Cancer Mammography Quantum Machine Learning

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Optimizing Data Movement in Heterogeneous Computing: A LASSA-based Approach for Efficient Nucleation List Precomputation 3

Optimizing Data Movement in Heterogeneous Computing: A LASSA...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Bhowmik, Biswajit Girish, K.K. Pandey, Himanshu Prabhanjans, Piyus National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly evolving landscape of heterogeneous computing, the efficiency of data movement between CPUs and GPUs can make or break system performance. Despite advancements in parallel processing, existing methods for managing data transfers - particularly in GPU offloading scenarios - suffer from significant inefficiencies. These inefficiencies are particularly evident in nucleation list precomputation for non-equilibrium solidification models, where redundant data movements and complex dynamic work-sharing in OpenMP lead to significant performance overhead. To tackle this issue, this paper proposes a novel solution that integrates the Location-Aware Heap Static Single Assignment (LASSA) algorithm into the compilation process. This approach identifies and eliminates redundant memory copy operations, optimizing data transfers and reducing overhead. The findings reveal a dramatic performance boost, with up to a 9.6-fold increase in efficiency. By addressing the specific challenges of nucleation list precomputation, this work provides valuable insights into optimizing data movement in heterogeneous computing environments, paving the way for enhanced performance in parallel programming models. © 2025 IEEE.

关键词： Cellular Automata Models Heterogeneous Computing Nucleation List Precomputation OpenMP Performance Issues System Efficiency

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High-Performance Image Restoration with SpectraNet: A Lightweight CNN Architecture

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Procedia computer science 2025年 258卷 2268-2277页

作者： Nitesh Patnaik Rishi Raj Piyush Kumar Singh Shreekara Mahapatra Debashis Pani Ravi Shankar Singh Dept. of Computer Science and Engineering Kalinga Institute Of Industrial Technology Bhubaneswar 751024 India Dept. of Computer Science Central University of South Bihar Bihar 824236 India Dept. of Computer Science and Engineering Indian Institute of Technology BHU Varanasi 221005 India

Deep learning-based image restoration faces significant challenges when deployed on resource-constrained platforms due to the computational demands and large number of parameters in existing models. The high computational load and extensive memory requirements make it difficult to implement these models on devices with limited processing power and storage capacity, such as mobile phones and embedded systems. Additionally, maintaining real-time performance while ensuring high-quality image restoration is a critical challenge, as traditional deep learning models often fail to meet the stringent latency and efficiency requirements of such platforms. This paper introduces a new, lightweight convolutional neural network (CNN) architecture tailored for efficient pixelation detection and restoration. Our approach combines a pre-trained MobileNetV2 with finetuning for pixelation detection, and SpectraNet, a architecture incorporating dept.-wise separable convolutions for image restoration. The proposed architecture was trained on the HQ-50k dataset, with 70% of the data used for training and 30% for testing. It achieved a Peak Signal-to-Noise Ratio (PSNR) of 17-23, outperforming 10 state-of-the-art architectures in both efficiency and image quality. This design addresses the dual problems of high computational load and extensive memory usage, prioritizing real-time performance and resource efficiency. The proposed architecture is ideal for deployment on mobile and embedded devices since it maintains high accuracy while significantly reducing the number of trainable parameters. Our findings advance the feasibility of deploying advanced image restoration techniques in real-world, resource-constrained environments.

关键词： dept.-wise Separable Convolutions Embedded Devices Image Restoration Lightweight Real-Time Performance Resource-Constrained Platforms Deep Learning

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Performance Analysis of Hybrid MPI and OpenMP on Smith-Waterman Algorithm 3

Performance Analysis of Hybrid MPI and OpenMP on Smith-Water...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Ninama, Keyur Patel, Jainamkumar Girish, K.K. Reddy, Manideep Raya V S R S Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly advancing field of bioinformatics, sequence alignment is a pivotal task for elucidating genetic statistics and evolutionary relationships. As the volume and complexity of biological data continue to grow, it becomes imperative to employ effective computational techniques to manage this expansion. The Smith-Waterman algorithm is a key tool for sequence alignment;however, its performance can be constrained by the substantial size of contemporary datasets. To overcome this limitation, this paper explores a hybrid parallelization strategy that integrates message passing interface (MPI) with open multi-processing (OpenMP). This approach aims to significantly enhance the algorithm's efficiency by leveraging the strengths of both parallelization models. By optimizing the scalability and execution speed of the Smith-Waterman algorithm on advanced high-performance computing (HPC) systems, the hybrid technique not only improves performance but also enables more rapid and accurate biological data analysis. © 2025 IEEE.

关键词： High-Performance Computing Message Passing Interface Open Multi-Processing Parallelization Performance Scalability

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Quantum Resistance Blockchain Algorithm using QHF and Novel Consensus Mechanism

Quantum Resistance Blockchain Algorithm using QHF and Novel ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Paul, Soumyadip Mazumdar, Mausumi Maitra Chakraborty, Sarit Government College of Engineering & Ceramic Technology Dept. of Information Technology Kolkata700010 India Government College of Engineering and Leather Technology Department of Computer Science and Engineering Kolkata700106 India

ISBN: (纸本)9798331529833

The emergence of quantum computing poses a significant threat to the security of traditional blockchain systems, which rely heavily on classical cryptographic algorithms. To safeguard the integrity and reliability of blockchain technology in the upcoming era, this work proposes a novel quantum-resistant blockchain architecture. This approach integrates a robust consensus mechanism, with advanced quantum cryptographic primitives, including quantum digital signatures based on a Quantum Hash Function (QHF). Incorporating a quantum hash function enhances the blockchain resistance, providing an additional layer of security against quantum attacks. By combining these elements, our method creates a blockchain system fortified against the potential vulnerabilities introduced by quantum computers. The proposed solution leverages the power of QHFs to safeguard transaction integrity, while the consensus mechanism ensures the reliability and decentralization of the network. This paper also delves into the theoretical underpinnings of quantum blockchains, exploring their design, feasibility, and potential applications, emphasizing their promising future, particularly in areas such as decentralized identity authentication. Finally, it contributes to the development of a secure and resilient blockchain ecosystem capable of thriving in the coming era. © 2025 IEEE.

关键词： Quantum computers

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