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检索条件"任意字段=Conference on Visualization and Optimization Techniques"

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Enhanced Diabetes Prediction Through Systematic Analysis of Machine Learning techniques and Data optimization

Enhanced Diabetes Prediction Through Systematic Analysis of ...

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2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Behera, Saroj Kumar Panda, Kedarnath Krishna, M Vamsi Kishore Kumar Reddy, P Ram Supriya, P Lakshmi Reddy Penubaka, Kiran Kumar Gandhi Institute of Engineering and Technology University Department of Csa Odisha India Gandhi Institute of Engineering and Technology University Department of Cse Odisha India Hyderabad India Mahatma Gandhi Institute of Technology Department of Eee Hyderabad India Department of Eee Hyderabad Gandipet India Mlr Institute of Technology Department of CSE-AIML Hyderabad India

ISBN: (纸本)9798331521394

This research concentrates on applying various machine learning algorithms to develop an effective and efficient predictive model for screening for diabetes prediction. Its primary aim is to enhance the performance of the model and time to learn and generalize by performing feature engineering, feature selection, and hyperparameter tuning all apt. The strategy begins with preprocessing data, where problems of missing or incorrect values are addressed and duplicate records are eliminated on a dataset from Kaggle. SMOTE is employed in the case of class imbalance;new samples for the minority class are created, while outliers in datasets are identified using the DBSCAN algorithm. The required features are achieved through the Wolf Optimized Recursive Elimination (WORE), the union of the Grey Wolf optimization (GWO) algorithm, and Recursive Feature Elimination (RFE). The model is constructed through the Random Forest-Enhanced Logistic Regression (RFELR) model, a hybrid of both Random Forest and Logistic Regression in which Random Forest is employed to obtain a nonlinear relationship, combined with logistic regression for a linear decision boundary. The final adjustment of the models is carried out using Grid Search as the Hyperparameter tuning method. These indicate that the overall performance reaches 91.5 % accuracy while precision, recall, and Fl were at 89.6%, 83.1 %, and 87.5% respectively. The paper has pointed out the appropriateness of the suggested approach in predictive performance improvement for diabetes detection to a significant degree beneficial to clinical use. © 2025 IEEE.

关键词： Logistic regression

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Automated Seizure Detection and Diagnosis using Cascaded Reflection Equivariant Visual Attention Quantum Neural Network with Dung Beetle optimization Algorithm Applied to EEG Signals

Automated Seizure Detection and Diagnosis using Cascaded Ref...

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Electronics and Renewable Systems (ICEARS), International conference on

作者： B. Jagadeeswari Ranjitham. G Prasanta Kumar Parida L. Guganathan Megha D Bengalur Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India School of Rural Management KIIT University Bhubaneswar Odisha India Department of Physics Saveetha School of Engineering Saveetha Insitute of Medical and Technical Science (SIMATS) Thandalam Chennai Tamil Nadu India Department of Computer Science and Engineering Presidency University Bengaluru Karnataka India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Automated seizure detection and diagnosis for EEG signals refers to using deep learning algorithms and computational methods to analyze electroencephalogram data, identifying patterns that indicate epileptic seizures. We can assert that these systems speed up the process of correct diagnosis reducing the differentiation between the seizures and normal brain activity, thus improving the overall clinical work and patients’ outcomes. Conventional techniques fail to give precise and rapid results relating to complicated patterns of seizures. To address these challenges; this study introduces the Cascaded Reflection Equivariant Visual Attention Quantum Neural Network with Dung Beetle optimization Algorithm (CasREVAQNNet+DBOA) framework for automated seizure detection and diagnosis in EEG signals. The proposed method leverages the CHB-MIT dataset, with initial preprocessing performed using Gradient Domain Guided Filtering (GDGF) to enhance critical EEG patterns. Feature extraction is carried out using DenseNet-121, which facilitates the effective representation of significant EEG characteristics. For classification, the Cascaded Reflection Equivariant Visual Attention Quantum Neural Network (CasREVAQNNet) is utilized, with performance further optimized by the Dung Beetle optimization Algorithm (DBOA) to ensure accurate and reliable seizure detection (SD). The CasREVAQNNet+DBOA framework is implemented in Python and evaluated on the CHB-MIT dataset. The proposed approach yields excellent outcomes: accuracy of 99.8% and specificity of 98.6% which are significantly higher in contrast to the current techniques. The outcomes of the suggested method enhance accuracy, reduce false detection rates, and accelerate diagnosis, making it a potentially useful tool for reliable and efficient seizure detection in clinical practice.

关键词： visualization Accuracy Filtering Brain modeling Electroencephalography Reflection Classification algorithms Reliability optimization Biological neural networks

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Case Study on the Effectiveness Feature Engineering techniques for Performance Improvement of Data Mining Applications

Case Study on the Effectiveness Feature Engineering Techniqu...

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Intelligent Control, Computing and Communications (IC3), International conference on

作者： Shruti Mehra B S Shajee Mohan Neha Agarwal M.Tech CSE Data Science Sharda University Gr.Noida Sharda University Gr.Noida

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

In this paper we try to demonstrate the effectiveness of different techniques for feature selection and optimization strategies that are used for data mining applications. Feature extraction is an important step in every data mining application, which aims to find discriminative patterns, interconnection, and structures within large datasets. In this paper we try to illustrate the effective use of the classical methods such as Principal Component Analysis (PCA), Kernel principal component analysis (KPCA), as well as more innovatory methods to better represent the data so as to achieve a better performance of the underlying pattern analysis, datamining tasks. We also consider effective representation of the data items in a discriminative way. We demonstrate this by making use of synthetic data so that a visual analysis can be made. We also use 3 real world datasets to demonstrate the effectiveness of PCA and KPCA based representations for obtaining improved performance for an SVM based multi-class classifier.

关键词： Wavelet transforms Text mining Support vector machines visualization Feature extraction Transformers Data mining Kernel Principal component analysis Synthetic data

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Web application for real-time enhancement of low-light images using Zero-DCE and hybrid optimization

Web application for real-time enhancement of low-light image...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： U Kumaran Ashwin ER Naveenkumar V Department of AIML Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This paper presents the development of a web application designed for the real-time enhancement of low-light images using advanced deep learning techniques, specifically Zero-DCE (Zero-Reference Deep Curve Estimation) and hybrid optimization methods. Low-light images often suffer from noise, poor visibility, and detail loss, making it challenging to interpret them effectively. Traditional image enhancement methods do not perform well in real-time applications. To address this, the proposed web application leverages the Zero-DCE model, which requires no reference image, making it suitable for diverse real-world conditions. Further, a hybrid optimization approach, combining techniques such as adaptive learning rate adjustments, GPU acceleration, and a Generative Adversarial Network (GAN) for fine-tuning, is employed to enhance computational efficiency, output quality, and perceptual realism. The application processes images in real-time, restoring detail, reducing noise, and improving contrast, thereby significantly enhancing the visibility of objects in low-light conditions. The system’s user-friendly interface allows for easy uploading and real-time processing of images, providing an accessible solution for various fields, including security, medical imaging, and satellite imagery, where clear visual interpretation is critical. We also evaluate the model’s performance on a diverse set of low-light images and compare it against state-of-the-art methods. An analysis of the computational requirements and user experience is also provided.

关键词： Deep learning visualization Computational modeling Generative adversarial networks Real-time systems User experience Image restoration Satellite images Computational efficiency Biomedical imaging

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Spatially Nonuniform Filtering and Encoding of Visual Information in a Retinomorphic Hardware System

Spatially Nonuniform Filtering and Encoding of Visual Inform...

引用

International conference on Autonomous Robots and Agents, ICARA

作者： Tatsuya Yamada Hiroyuki Yokota Yuki Hayashida Shinsuke Yasuakwa Dept. Human Intelligence Syst. Kyushu Institute of Technology Kitakyushu Fukuoka JAPAN Dept. Inform. Eng. Grad. Eng. Mie University Tsu Mie JAPAN

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Efficient information processing in biological early-vision systems is thought to rely on spatially nonuniform computations in the central and peripheral visual fields. Inspired by this functional segregation, we developed a digital hardware vision system that integrates two distinct spatiotemporal filters and spike encoders for the central and peripheral regions on a single image sensor plane. The system employs digital processing techniques to emulate graded potential signals and point-process spike outputs, mimicking the functionality of vertebrate retinas. System-level testing on actual hardware equipment verified region-specific response characteristics for the central and peripheral regions. This proposed approach advances the realization of retinomorphic efficient computations in future robotics and embedded vision systems.

关键词： visualization Image coding Robot vision systems optimization methods Retina Hardware Spatiotemporal phenomena Computational efficiency Robots Testing

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A Power Efficient Sleepy- Keeper and Lector Leakage Control Technique Based on SRAM Memory

A Power Efficient Sleepy- Keeper and Lector Leakage Control ...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： Sulthana Begam J S. Praveen Kumar T. Aravind S. Ramya Department of Electronics and Communication Engineering M. E. VLSI Design Saveetha Engineering College Chennai Department of Electronics and Communication Engineering Saveetha Engineering College Chennai Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Given the increasing demand for low power in IoT, mobile and embedded systems, leakage power consumption has become a major challenge in modern SRAM (Static Random Access Memory) designs. In this paper, we present a power efficient hybrid technique that combines the Sleepy-Keeper and LECTOR techniques to reduce static power dissipation in SRAM cells. The Sleepy-Keeper technique keeps the data integrity while reducing power consumption in idle states, while the LECTOR consumes power in a dynamic manner by cutting the CMOS structure with high resistance paths to decrease the leakage current. Finally, the proposed approach achieves large leakage reduction by combining these strategies. The proposed hybrid methodology is extensively simulated, and a comparative analysis is carried out between this hybrid methodology and conventional SRAM designs and standalone leakage control technology. Leakage power is reduced significantly, data retention in a standby mode is increased, and there is negligible impact on the read and write performance. It is a power efficient strategy that is highly suitable for energy constrained applications including portable devices, high speed cache memory and IoT based systems.

关键词： Power demand Embedded systems Data integrity Memory management Random access memory Hybrid power systems Power dissipation Delays Leakage currents optimization

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Hyperparameter Tuning Of Machine Learning Models: Analysing Student’s Depression Data

Hyperparameter Tuning Of Machine Learning Models: Analysing ...

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Emerging Systems and Intelligent Computing (ESIC), International conference on

作者： Amiya Ranjan Panda Sattwik Roy Manoj Kumar Mishra Soumyashree Sahoo Subhransu Mohapatra Mahendra Kumar Gaurisaria School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

This research focuses on the tuning of the hyper-parameters of some of the selected machine learning models. Tuning the correct hyper-parameter will use three types of searching techniques for the search space of hyperparameters. The dataset has 502 instances and 11 attributes (1 target attribute). It is a balanced dataset. The target attribute is "Depression" (Yes/No). Our objective is to compare the performance of 10 different supervised machine learning classifiers including Random Forest, KNN, SVM, Logistic Regression, Decision Tree Classifier, Naive Bayes, Gradient Boosting Machine, AdaBoost, Feed Forward Neural Network (MLP) and Extra Trees. Once the comparison report is achieved, we select four different classifiers (KNN, SVM, Logistic Regression and Ada-Boost Classifier), create search space of the hyper-parameters for each classifier algorithm. Every search space is then cross validated through split tests, for three different searching algorithm (grid Search CV, random search CV, Bayesin optimization) to achieve the maximum accuracy and the perfect fit hyper-parameters for the four classifiers. Provided these classifiers are more generalized and less prone to overfitting. There will be visualization charts in the research for visualizing the improving accuracy for each split test.

关键词： Support vector machines Logistic regression Accuracy Computational modeling Nearest neighbor methods Depression Data models Bayes methods Tuning optimization

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Insulator Fault Detection in Aerial Images Using YOLOv9

Insulator Fault Detection in Aerial Images Using YOLOv9

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International conference on

作者： Soham Nilesh Kolambe Arin Johari Soham Dutta Electrical and Electronics Engineering Manipal Institute of Tehnology Manipal india

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Insulator faults are a severe threat to the safety and stability of power transmission lines. Accurate and fast detection of these faults is paramount for preventive maintenance and grid reliability. This paper explores the application of You Only Look Once version 9 (YOLOv9), a state-of-the-art deep learning object detection model, for insulation fault detection in power line imagery. The effectiveness of YOLOv9 in identifying healthy insulators from those exhibiting various fault types is investigated. The paper details the training process, including data preparation, model configuration, and optimization techniques. Standard metrics like mean average precision (mAP) are used to analyze the performance of the YOLOv9 mode and the confusion matrix is used to identify potential areas for improvement. The findings of this research contribute to the development of automated and efficient insulation fault detection systems, enhancing power grid maintenance strategies.

关键词： Deep learning YOLO visualization Accuracy Transmission line matrix methods Fault detection Inspection Insulators Power grids Maintenance

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Edge Artificial Intelligence for Real-Time Decision Making using NVIDIA Jetson Orin, Google Coral Edge TPU and 6G for Privacy and Scalability

Edge Artificial Intelligence for Real-Time Decision Making u...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： J.V. Anchitaalagammai S. Kavitha R. Buurvidha T. S. Santhiya M. Devi Roopa S. Sri Sankari Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Edge AI or Edge Artificial Intelligence is the provision of AI within edge devices itself without needing a connection to the cloud. AI gets better with more information and time, and so a lot of AI models “live in the cloud.” However, AI made Edge devices is more smarter by deploying models onboard. There are several factors like NVIDIA's Jetson Orin, Google's Coral Edge TPU, and Qualcomm's AI optimized Snapdragon platforms, which has accelerated the adoption of Edge AI and made these onboard Edge devices with modests resources, robust processing capabilities. Experts anticipate that the advent of 6G technology will further expand speeds, making it possible to utilize augmented (AR) and virtual (VR) reality as well as the Internet of Things (IoT). Privacy of the end user is still safeguarded through federated and differential learning which enables intelligent distribution while data is kept close to the source. The concept of optimization techniques such as quantization, pruning, and NAS are resources that help in considerations for deploying AI models at the Edge as highlighted in the literature. Containerization and microservices also provide a great ecosystem for Edge AI deployments supported by Docker, Kubernetes and KubeEdge which provide scalable infrastructure. Evaluations conducted on various use cases rangingfrom predictive maintenance of industrial IoT[l], real- time health monitoring as well as autonomous systems, show the performance of these models to improve in terms of model accuracy, inference computation as well as energy efficiency through the enhanced methodologies. The recent advancements in hardware and software capabilities have placed Edge AI as a preferred option for many applications that have strict latency requirements in addition to needing to make real time decisions. Edge AI is set to revolutionize industries likehealth care, smart cities, industrial automation despite hitches like data unification and barrier from privacylaws. Furthe

关键词： Data privacy Smart cities Computational modeling Biological system modeling Edge AI Medical services Real-time systems Security optimization Interoperability

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Grad-CAM Guided Framework for Quality-Preserving Channel Pruning in GANs

Grad-CAM Guided Framework for Quality-Preserving Channel Pru...

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National conference on Communications (NCC)

作者： Pallav Dwivedi Parimala Kancharla School of Computing and Electrical Engineering IIT Mandi Himachal Pradesh India

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

The remarkable performance of Generative Adversarial Networks (GANs) in tasks such as picture synthesis and editing is well known. However, especially in contexts with limited resources, their massive structures and high parameter counts pose serious computing hurdles. Here, we provide a Grad-CAM Guided technique to prune StyleGan, a unique method that computes channel-wise significance scores by utilizing Grad-CAM, a class-discriminative localization technique. This allows us to efficiently trim channels without sacrificing visual quality. Our methodology prioritizes perceptual quality over standard content-based or diversity-driven pruning techniques, striking a compromise between computational efficiency and output fidelity. Our approach uses Grad-CAM maps to emphasizes important regions in the generated sample and helps in computing the channel sensitivity better and incorporates the results into a channel-wise pruning plan. StyleGAN models using popular datasets such as FFHQ-256, LSUN-HORSE, and LSUN-CHURCH have been used to validate the framework. Our method's robustness is highlighted by experimental results that show large parameter count reductions while maintaining competitive Fréchet Inception Distance (FID) scores. This work shows how Grad-CAM may be used to guide quality-aware pruning techniques and provides a fresh viewpoint on generative model optimisation by fusing interpretability with computational efficiency. This methodology has more applicability in real-world scenarios because it can be tailored to different GAN designs.

关键词： Location awareness visualization Sensitivity Quantization (signal) Computational modeling Generative adversarial networks Robustness Computational efficiency Standards optimization

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