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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Aldridge, Rowan Kubánek, David Koton, Jaroslav Freeborn, Todd J. The University of Alabama Dept. Elec. Computer Eng. Tuscaloosa United States Brno University of Technology Dept. Telecommunications Brno Czech Republic

ISBN: (纸本)9798331504847

This research presents a generalized approach to determine the coefficients to realize any (N+α) fractional-order low-pass Butterworth response using transfer function forms based on the Inverse Follow the Leader Filter (IFLF) topology. This approach automates the process to create and assemble the required transfer function (based on user specified values) and then apply a numerical optimization to estimate the necessary coefficients to achieve the target filter magnitude response. MATLAB simulations of (2+α) and (10+α) order filters using the generalized approach validate the process. Further, an experimental 2.5-order low-pass filter is experimentally realized (using an approximated fractional-order capacitor) to validate the characteristics of a low-pass filter designed using this process. © 2025 IEEE.

关键词： Butterworth filters

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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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Implementation of a Word Prediction Model Using LSTM Neural Network with Optimization Strategies

Implementation of a Word Prediction Model Using LSTM Neural ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jagadevi, P. Suresh, Yeresime Ballari Institute of Technology and Management Dept. of Computer Science & Engg. Ballari India Visvesvaraya Technological University Karnataka Belagavi India Ballari Institute of Technology and Management Dept. of Cse - Artificial Intelligence Ballari India

ISBN: (纸本)9798331509828

The development of word predictive models has significantly advanced with the integration of deep learning techniques, particularly in the domain of natural language processing (NLP). Early models like n-grams and basic Recurrent Neural Networks (RNNs), laid the foundation for sequence based text generation by predicting the next word based on a fixed number of preceding words. However such approaches frequently struggled with capturing long range dependencies due to their limited context window and inability to maintain memory over extended sequences. This paper presents the implementation of word prediction model using Long Short-Term Memory (LSTM) neural networks that predicts the next word in a given sequence. The proposed model is trained on a diverse dataset and evaluating performance through several test cases. The model incorporates several optimization strategies to enhance its performance regarding scalability, fault tolerance, and resource allocation. Experimental results demonstrate the effectiveness of these optimizations in improving prediction accuracy and computational efficiency. © 2025 IEEE.

关键词： Prediction models

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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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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

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33rd Southern African Universities Power Engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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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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Electric Vehicle Charging System Using RFID and IoT for Enhanced User Experience 5

Electric Vehicle Charging System Using RFID and IoT for Enha...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Hassain, Md Mehedi Haque Jamil, Injamul Hoque, Md Mahmudul Hasan, Md. Jahid International Islamic University Chittagong Dept. of Electrical and Electronics Engineering Chittagong Bangladesh University of Greenwich Dept. of Computing and Mathematical Sciences London United Kingdom CCN University of Science and Technology Dept. of Computer Science and Engineering Cumilla Bangladesh Comilla University Dept. of Information and Communication Technology Cumilla Bangladesh

ISBN: (纸本)9798331519957

Electric vehicles (EVs) are surging in popularity globally, offering a greener and more energy-efficient alternative to traditional cars. Our research aims to develop an RFID based system designed to automate EV charging with minimal user input. This system integrates RFID readers, tags, a microcontroller, and a relay module to manage and monitor charging sessions efficiently. An IoT platform supports real time monitoring and remote control, while a secure payment mechanism handles transactions seamlessly. RFID tags identify vehicles for automatic charging initiation, and the relay module prevents overcharging by disconnecting power after a set time. The system uses MQTT for data transmission and offers real-Time insights into energy use and account balances. All information is updated and displayed in real-Time on the server. MySQL ensures scalability, enabling effective handling of large volumes of data. The mobile application dashboard provides a view of the charging and discharging processes for user monitoring, and an email notification is sent after the recharge. EV battery charging status is monitored through visual graphs. Key benefits include a streamlined user experience, improved charging management, and support for EV adoption. This innovation advances sustainable mobility with a user friendly, efficient charging solution. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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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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Brain Cancer Detection and Classification using Transfer Learning

Brain Cancer Detection and Classification using Transfer Lea...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Varad Gadaokar Piyush More Madhuri Wakode Geetanjali Kale Dept. of Computer Engineering Pune Institute of Computer Technology Pune India Dept. of Electronics and Telecommunication Engineering Pune Institute of Computer Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Brain tumors are abnormal cells that grow inside the brain. If not diagnosed or treated early, such incidents can be fatal. Types of brain tumors include meningioma, glioma, and pituitary tumors. Magnetic resonance imaging (MRI) is an essential resource in diagnosis due to the clear expression of brain tissues, enabling professionals to analyze abnormal developments in the brain. The detection and classification of brain tumors using MRIs can be automated using machine learning techniques that can help to identify and diagnose brain tumors in their early stages, and subsequent treatment may result in a lower death rate. Transfer Learning allows the transfer of knowledge learned from large-scale datasets to other smaller datasets, thus avoiding wide-ranging training requirements with high accuracy and making the system converge much faster. This substantially minimizes computational resources and time in the training process. Transfer Learning also enables the effective MRI image abstraction of complex features, improved accuracy in tumor detection and classification, minimizes overfitting, especially with small amounts of medical data. In this work, we have used Transfer Learning with two pre-trained deep models, VGG16 and ResNet-50 on 3064 images available in the dataset to classify brain tumors. Fine-tuning for VGG16 and ResNet-50, we achieved validation accuracies of 98.85% and 97.72%, respectively. The results show that transfer learning can be used for the classification of brain tumors to enhance efficiency and precision for early detection and treatment.

关键词： Training Accuracy Magnetic resonance imaging Computational modeling Transfer learning Brain tumors Feature extraction Brain modeling Data models Overfitting

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EXAMSENTRY: Secure, Verify, Excel

EXAMSENTRY: Secure, Verify, Excel

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Kushal Bhalgat Jaee Bawdekar Chirag Siroya Sanjana Mandlecha Shital Girme Dept. of Computer Engineering Pune Institute Of Computer Technology Pune India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

In today’s digital world, online assessments are essential for companies to evaluate candidates effectively. However, they face challenges like malpractice and test integrity. We aim to create a scalable, secure platform that ensures fairness and reliability in online evaluations. The platform monitors the test environment by blocking browser extensions and media interference while verifying participants’ identities using advanced methods. Eye tracking will ensure that candidates stay focused on the screen, while the system checks the stability of their internet connection and browser settings. Artificial intelligence will detect suspicious behavior during the exam, and cloud infrastructure will manage high user traffic for smooth performance under heavy loads. Results will be automatically generated and securely delivered to companies, providing a comprehensive solution for large-scale assessments.

关键词： Cloud computing Scalability Gaze tracking Companies Media Browsers Security Reliability Monitoring Testing

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