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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Suresh Kumar Natarajan G S Pradeep Ghantasala S. Anthoniraj Pellakuri Vidyullatha R Rajesh Sharma U Ananthanagu School of Computer Science and Engineering Jain (Deemed-to-be-University) Ramanagara India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

In this research, a explore the realm of medical imaging, specifically focusing on early brain pathology detection through the lens of deep learning. The primary objective is to optimize SqueezeNet, lightweight convolutional neural network architecture, for brain pathology detection using CT imaging. The motivation stems from the critical need for accurate and swift identification of brain abnormalities, with potential implications for patient outcomes. The meticulously preprocess a dataset of CT images of the human brain to enhance the model’s ability to discern subtle pathology patterns. SqueezeNet, known for its efficiency, is employed, and optimization techniques during training enhance its performance. These findings suggest SqueezeNet’s potential as a valuable tool in the medical field, offering a balance between efficiency and diagnostic accuracy. Throughout the discussion, A critically analyzes the implications of results, drawing comparisons with existing literature and addressing any encountered limitations. In this research contributes to the discourse on leveraging deep learning for brain pathology detection and the experimental results showcase promising outcomes, with a test accuracy of 78%. Precision, recall, and F1-score metrics indicate the model’s proficiency in distinguishing between different pathologies: 87% for Hemorrhagic, 70% for Ischemic, and 79% for Normal cases. Confusion matrices and ROC curves provide comprehensive insights into the model’s classification capabilities. The optimized SqueezeNet application demonstrates promise in enhancing diagnostic capabilities, with the obtained results providing valuable insights for future advancements in the intersection of deep learning and medical imaging, potentially impacting patient care and outcomes positively.

关键词： Deep learning Training Measurement Pathology Accuracy Computed tomography Signal processing Brain modeling Optimization Medical diagnostic imaging

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Improved YOLOv5 based on CBAM and BiFPN for Rice Pest and Disease Detection

Improved YOLOv5 based on CBAM and BiFPN for Rice Pest and Di...

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2023 International Conference on computer, Internet of Things and Smart City, CIOTSC 2023

作者： Zhao, Yanqin Cui, Yichao Wang, Ziyi Heilongjiang University of Science and Technology Computer and Information Engineering Harbin150022 China Heilongjiang University of Science and Technology Graduate School Harbin150022 China Jiangxi Institute of Fashion Technology Fashion Design School 330201 China

ISBN: (纸本)9798350306569

Addressing the issue where traditional YOLOv5 algorithm struggles in accurately detecting pests and diseases of small to medium scale with dense leaf distribution in rice, this study introduces an enhanced YOLOv5-CW algorithm model, derived from the original YOLOv5, specifically for rice pest and disease detection. Firstly, the feature fusion is improved by introducing the improved three-layer BiFPN (Bidirectional Feature Pyramid Network) weighted bidirectional feature pyramid network BiFPN-W into the original YOLOv5 algorithm to replace the original Feature Pyramid Network (FPN) structure and Path Aggregation Network (PAN) structure in the Neck part, which can fuse more features without increasing the cost. Second, the introduction of Convolutional Block Attention Module (CBAM) attention mechanism will significantly improve the detection of small targets for the identification of small-scale pests and dis-eases in rice plants. Finally, experiments are conducted on the dataset. The experimental results showed that compared with the detection of rice pests using the traditional YOLOv5 algorithm, the precision (P) and recall (R) rates of the improved YOLOv5-CW algorithm model detection increased from 86.05% and 77.46% to 90.65% and 85.63%, respectively, and the mean Average Precision (mAP) also increased from 83.52% to 87.40%, while the detection speed reached 22.38ms, which is faster than the detection speed of the original YOLOv5 algorithm. Meanwhile, compared with target detection algorithms such as SSD, Faster R-CNN, and YOLOv4, this improved algorithm has certain superiority and is an ideal model in rice pests and diseases. © 2023 IEEE.

关键词： Attentional Mechanisms BiFPN CBAM component Rice pest and disease detection YOLOv5

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Advances and Predictions in Predictive Auto-Scaling and Maintenance Algorithms for Cloud Computing 2

Advances and Predictions in Predictive Auto-Scaling and Main...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Rathinam, Anantha Raman Vathani, B. Santha Komathi, A. Lenin, J. Bharathi, B. Urugan, S.M. Department of Computer Science and Engineering - Honors KL University Guntur Andhra Pradesh Vijayawada India Department of Management Studies Mahalashmi Women’s College of Arts and Science Tamil Nadu Chennai India Department of Computer Science Nadar Saraswathi College of Arts and Science College Tamilnadu Theni India Department of ComputerScience and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu Chennai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

The use of sophisticated algorithms has radically altered cloud computing predictive auto-scaling and upkeep approaches. Recurrent Neural Networks (RNNs), the Prophet Algorithm, K-Means Clustering, and Seasonal Autoregressive Integrated Moving-Average (SARIMA) all play a role in improving cloud infrastructures, and their interactions are studied here. By capitalizing on their superiority in processing sequential data, RNNs can deduce accurate workload forecasts from past use patterns. Concurrently, the Prophet Algorithm records seasonal and annual patterns, which adds depth to forecasts. By grouping servers into clusters with similar consumption patterns, K-Means Clustering improves resource allocation efficiency and paves the way for precise auto-scaling. SARIMA models capture nuanced seasonal fluctuations, which lead to reliable demand forecasts. This explores the state-of-the-art and future directions of these techniques, illuminating their potential to revolutionize current approaches to cloud management. When these methods are combined, cloud service providers are better able to proactively scale their resources, hence reducing the likelihood of bottlenecks and outages. It foresees the development of these algorithms and their subsequent widespread use in a variety of fields outside of cloud computing, such as Internet of Things (IoT) networks and edge computing infrastructures. © 2023 IEEE.

关键词： Auto-Scaling Cloud Computing Data-driven Insights K-Means Clustering Maintenance Algorithms Predictive Analytics Prophet Algorithm Recurrent Neural Networks Scalability Seasonal Autoregressive Integrated Moving-Average

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Budget-constrained Emission Reduction in Economic and Environmental Dispatch

Budget-constrained Emission Reduction in Economic and Enviro...

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2023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy, PESGRE 2023

作者： Kurte, Kuldeep Ramchandra Raju, Mishma Mariyam Dongritot, Prathamesh Kulkarni, Kedar Jio Platforms Limited Ai Centre of Excellence India Indian Institute of Technology Dept. of Engineering Design Madras Chennai India National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798350310573

To mitigate the impact of climate change, industries worldwide are adopting renewable energy to meet power demand. However, optimizing power generation costs and reducing emissions by integrating renewables with conventional sources presents significant challenges. In this work, we present a multi-objective optimization approach to dynamically optimize both costs and emissions by accounting for fossil-fuel based generators, renewables such as wind and solar, as well as the uncertainty in the demand and renewable energy forecasts. The primary objective of this work is to demonstrate how a multi-objective optimization framework can be used to generate reliable recommendations for emission reduction with minimal impact on cost. Our study reveals that significant emission reduction (≈ 50%) is achievable with a slight increase in the cost budget (≈ 2%). © 2023 IEEE.

关键词： dynamic emission reduction multi-objective Optimal power dispatch renewable energy uncertainty

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Quantum-Enhanced Deep Learning for Predictive Diagnostics and Personalized Treatment in Smart Healthcare Systems

Quantum-Enhanced Deep Learning for Predictive Diagnostics an...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： R. Rajesh P. Hemalatha R. Vijayabharathi S. Bargunan S. R. Ramprasad J. Raja Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Chennai India Department of Computer Science and Design R.M.K. Engineering College Chennai India Department of Computer Science and Design Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

The synergy between quantum computing and deep learning holds revolutionary promise in facilitating smart healthcare by addressing problems in predictive diagnosis, realtime patient tracking, and personalized therapy. Deep learning algorithms such as CNNs and RNNs have scalability and efficiency limitations in processing clinical data with high dimensionality. Their limitations can be addressed by quantum computing through an inbuilt parallelism. This work suggests that Quantum-Enhanced Deep Learning for Smart Healthcare (QEDL-Health) should marry classical deep learning with quantum algorithms to revamp paradigms in healthcare. QEDL-Health applies quantum neural networks (QNNs) with quantum gates (Pauli-X, Hadamard, CNOT) to perform encoding and feature extraction efficiently. Model parameters are trained by quantum variational circuits (QVCs), while quantum support vector machines (QSVMs) diagnose diseases with better accuracy. IoT-generated vitals in real-time tracking are processed by quantum principal component analysis (QPCA), while personalized treatment is created by quantum approximate optimization algorithms (QAOAs). The system applies quantum Fourier transforms (QFTs) and Grover’s search algorithm to process efficiently. Experimental results in MIMIC-III, TCGA, and COVID-19 datasets confirm dominance by improving diagnostic correctness by 15% while computation time is decreased by 40% in genomic data processing compared to classical methods. Experimental results confirm that comparative evaluation establishes that conventional methods in deep learning have scalability limitations in efficiency and performance in intricate tasks in disease detection (such as cancer) and in tracking in real-time. This work bridges quantum computing with AI to lay the foundations for developing efficient, scalable, and intelligent healthcare systems to detect diseases earlier in time to reduce costs and improve patient outcomes.

关键词： Deep learning Support vector machines Quantum computing Accuracy Quantum algorithm Scalability Neural networks Approximation algorithms Real-time systems Optimization

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Integrated IoT-Based Smart Home Security System: Real-Time Hazard Detection and Alerts Using Multi-Sensor Fusion

Integrated IoT-Based Smart Home Security System: Real-Time H...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sushma, A.M.S.V. Aswini Priyanka, A. Lakshmi, A.N.G. Khatri, Narendra Sharma, Harish S R K R Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Bhimavaram India S R K R Engineering College Department of Computer Science and Design Engineering Andhra Pradesh Bhimavaram India Sir C.R. Reddy College Engineering Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Manipal Acacdemy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal University Jaipur School of Computer Science and Engineering Department of Artificial Intelligence and Machine Learning Jaipur India

ISBN: (纸本)9798350390049

This paper presents an innovative IoT-based smart home security system that integrates multiple sensors to detect various hazards and provide real-time alerts. Unlike traditional single-function systems, this solution combines motion detection via a PIR sensor, gas leak detection using an MQ-2 sensor, and fire detection through an IR flame sensor into a single, affordable package. The Arduino UNO serves as the central controller, interfaced with a GSM module for instantaneous SMS notifications and supported by an on-site buzzer alarm. Extensive testing in diverse environments demonstrates the system's exceptional performance, detecting intrusions in under 2 seconds and reporting threats with approximately 89% accuracy, while minimizing false alarms. The integration of smart power management techniques reduces energy consumption compared to conventional systems. This approach not only enhances home security but also offers a scalable and easily deployable solution for both residential and commercial settings. This work addresses the urgent need for an affordable, multi-functional smart home security system and provides a robust framework for future IoT-driven safety solutions. © 2024 IEEE.

关键词： Smart homes

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When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study

arXiv

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arXiv 2024年

作者： Xiong, Yang Zhang, Ruichen Liu, Yinqiu Niyato, Dusit Xiong, Zehui Liang, Ying-Chang Mao, Shiwen Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore College of Computing and Data Science Nanyang Technological University Singapore Computer Science and Design Pillar University of Technology and Design Singapore Chengdu611731 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into RAG frameworks further enhanced the performance of RAG in networking applications such as Intent-Driven Networks (IDNs) and spectrum knowledge maps by providing more contextually relevant responses through more accurate retrieval of related network information. This paper introduces the RAG framework that integrates knowledge graphs in its database and explores such framework’s application in networking. We begin by exploring RAG’s applications in networking and the limitations of conventional RAG and present the advantages that knowledge graphs’ structured knowledge representation brings to the retrieval and generation processes. Next, we propose a detailed GraphRAG-based framework for networking, including a step-by-step tutorial on its construction. Our evaluation through a case study on channel gain prediction demonstrates GraphRAG’s enhanced capability in generating accurate, contextually rich responses, surpassing traditional RAG models. Finally, we discuss key future directions for applying knowledge-graphs-empowered RAG frameworks in networking, including robust updates, mitigation of hallucination, and enhanced security measures for networking applications. © 2024, CC BY.

关键词： Knowledge graph

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A Global Visual Information Intervention Model for Medical Visual Question Answering

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computers in Biology and Medicine 2025年第PT A期192卷 110195-110195页

作者： Peng, Peixi Fan, Wanshu Shen, Yue Yang, Xin Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer-Aided Design School of Software Engineering Dalian University Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

Medical Visual Question Answering (Med-VQA) aims to furnish precise responses to clinical queries related to medical imagery. While its transformative potential in healthcare is undeniable, current solutions remain nascent and are yet to see widespread clinical adoption. Med-VQA presents heightened complexities compared to standard visual question answering (VQA) tasks due to the myriad of clinical scenarios and the scarcity of labeled medical imagery. This often culminates in language biases and overfitting vulnerabilities. In light of these challenges, this study introduces Global Visual Information Intervention (GVII), an innovative Med-VQA model designed to mitigate language biases and improve model generalizability. GVII is centered on two key branches: the Global Visual Information Branch (GVIB), which extracts and filters holistic visual data to amplify the image's contribution and reduce question dominance, and the Forward Compensation Branch (FCB), which refines multimodal features to counterbalance disruptions introduced by GVIB. These branches work in tandem to enhance predictive accuracy and robustness. Furthermore, a multi-branch fusion mechanism ensures cohesive integration of features and losses across the model. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art models, achieving a 2.6% improvement in accuracy on the PathVQA dataset. In conclusion, the GVII-based Med-VQA model not only successfully mitigates prevalent language biases and overfitting issues but also significantly improves diagnostic precision, offering a considerable stride toward robust, clinically applicable VQA systems. © 2025

关键词： Question answering

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Establishing a Secure Mobile Online Banking System using AES

Establishing a Secure Mobile Online Banking System using AES

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Delhi Section Conference (DELCON), IEEE

作者： R. C. Karpagalakshmi J. Lenin Robin Rohit V. K. Saravanan Rajesh Sharma R. Sumit Kumar Banshal Department of Computer Science and Information Technology Alliance College of Engineering and Design Alliance University Bangalore India School of Computer Science & Engineering and Information Science Presidency University Bangalore India Department of Information Science Engineering New Horizon College of Engineering Bangalore India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Using a mobile phone or other portable device to conduct financial transactions online is known as “mobile banking.” Activities such as verifying one's account balance, transferring funds to another account, making bill payments, and finding a local automated teller machine (ATM) are included under this classification. Due to the significant expenses associated with implementing this technology, Authentication of users, data privacy, non-repudiation, data integrity, and obtainability of services are all areas where people have voiced concerns. The analysis described in this paper introduces a more advantageous and convenient framework for addressing these issues. The proposed approach effectively accomplishes its intended goals via the use of a widely adopted technique known as salting, which involves the application of the Secure Hash Algorithm (SHA512) to obfuscate sensitive information such as user IDs and passwords. The encoding and decoding procedures used the Advanced Encoding Standard (AES) technique while implementing one-time passwords (OTPs) was implemented to enhance user authentication. The design approach used PHP, JScript, CSS, and a MySQL database. The password recovery technique was used to assess the model's credibility and reliability and acquire the outcomes. This program enhances the process of recovering passwords across various scenarios. It intercepts network traffic using dictionary attacks, brute-force assaults, and cryptanalytics attacks to decrypt encrypted passwords. Additionally, it uncovers password bins, retrieves secreted passwords and analyses routing protocols. The gathered findings support the idea's feasibility since the encrypted and hashed data demonstrates resistance to unauthorized access.

关键词： Online banking Automated teller machine Authentication Passwords Telecommunication traffic Mobile handsets Encoding Routing protocols Internet Standards

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Advancing Alzheimer's Diagnosis: Performance Analysis of Deep Learning Ensembles in Medical Imaging

Advancing Alzheimer's Diagnosis: Performance Analysis of Dee...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Sonali Deshpande Nilima Kulkarni Computer Science & Engineering Department MIT SoC MIT Art Design and Technology University Pune India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Neuroimaging and deep learning have become the focus of much research for diagnosing Alzheimer's disease (AD) in recent years. Nevertheless, the limited availability of neuroimaging training data has resulted in significant overfitting issues for several deep learning models. However, the dataset was very imbalanced. To address this issue, we employed several augmentation techniques such as flipping, rotating, and zooming to balance the dataset. The ADNI dataset consisted of four classes: mild demented, moderate demented, very mild demented, and non-demented. In this work, we have proposed an ensemble of EfficientNetB2, DenseNet121, VGG16, and Xception for the diagnosis of Alzheimer at early stage. Experiment results shows that EfficientNetB2 ensemble with Xception and VGG16 performed extremely well with 99.1% and 99.4% respectively.

关键词： Deep learning Neuroimaging Industries Accuracy Training data Medical services Performance analysis Ensemble learning Alzheimer's disease Overfitting

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