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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Ahmed, Zahid Gupta, Anjali Preethi, D. Singh, Akhilendra Pratap Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electrical and Electronic Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Community coding algorithms had been demonstrated to have capability to improve the performance of wireless networks. This paper offers a research, assessment, and implementation of community coding algorithms for Wi-Fi networks. The paper affords an overview of community coding algorithms and discusses the related blessings, drawbacks, and tradeoffs that have to be taken into account while evaluating the performance of these algorithms. Furthermore, simulation outcomes are offered to assess the overall performance of one of a kind network coding algorithms while implemented in a wireless community surroundings. Ultimately, the paper outlines the hardware and software elements required to correctly put into effect and test those algorithms in an operational wireless community. The promising consequences of the experiments imply that these community coding algorithms can be used to efficiently improve the overall performance of Wi-Fi networks within the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions 2

The Symphony of Style: An Extensive Examination of Determina...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Guru Prasad, M.S. Bhosagi, Ravatappa A. Madhusudhana, K. Prithviraj Chandrappa, S. Kumar, Vikash Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India NMAMIT Mangalore India Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (纸本)9798331530389

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends. © 2025 IEEE.

关键词： Convolutional neural networks

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application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

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Multimedia Tools and applications 2025年第7期84卷 3459-3485页

作者： Kumar, S. Aanjan Muchahari, Monoj Kumar Poonkuntran, S. Kumar, L. Sathish Dhanaraj, Rajesh Kumar Karthikeyan, P. Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Electronics & Communication Engineering Velammal College of Engineering & Technology Tamil Nadu Madurai625009 India

Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more than 200,000 cases of malaria will occur every day worldwide, and more than 400,000 people will die from malaria. Early diagnosis of malaria is important to mitigate the morbidity and mortality of the disease. In this paper, a fully trained capsule neural network combined with a convolutional neural network model is proposed for malaria detection from blood smear microscopic images. In malaria diagnosis, the hybrid CapsNet model can detect malaria in blood samples. The hybrid model is optimized based on malaria data in microscopic images of infected and uninfected blood. Images are processed and enhanced through rotation before being fed into the hybrid CapsNet model. The CapsNet hybrid model is examined for malaria data with a learning rate of 0.07 and a batch size of 20. Hybrid CapsNet model shows improved results in detecting malaria from microscopic images measured against the currently available standard malaria test. Constraints like malaria parasite detection rate, accuracy, F1 index and blood smear classification is used to gauge the attainment of the hybrid CapsNet model. Detection rate of 99%, accuracy of 99.08% and FAR of 0.97% is attained by the hybrid CapsNet model as contrary to the traditional state-of-the-art model, the DSCN-Net, where the detection rate was 98%, the accuracy was 97.2%, and the FAR was 0.99%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

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Object Recognition Technique for 3D Random Images

Object Recognition Technique for 3D Random Images

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Prabhjyot Kaur Haryal Punam Rattan Computer Science and Engineering Shri Ram Institute of Technology Jabalpur India School of Computer Application Lovely Professional University Phagwara India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

An established method that has been researched for decades by the domains of computer vision, computer graphics, and machine learning is three-dimensional object recognition. Since 2015, convolutional neural networks (CNNs) have become more and more well-liked and have demonstrated exceptional performance so far. In this paper, the focus is on processing of any random image to recognize different objects contained in the image using coordinate geometry along the X, Y and Z coordinate axes. An innovative and groundbreaking 3D Object Recognition Technique has been proposed using Weiler Atherton Polygon Clipping along with visualization using 3D Image Rendering. Upon successful implementation of this technique, it can be further utilized in various domains of major industrial sectors across the globe.

关键词： Geometry Visualization computer vision Three-dimensional displays Image recognition Machine learning Rendering (computer graphics) Control systems Object recognition Convolutional neural networks

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SVR: Feature Selection Based Support Vector Regression for Humidity Data Prediction

SVR: Feature Selection Based Support Vector Regression for H...

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

作者： Bishnupriya Mallick Jibendu Kumar Mantri Niranjan Kumar Ray Department of Computer Application MSCB University India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Weather data like humidity is a key indicator in meteorology which is influenced by multiple climatic factors. Higher humidity levels become a threat to human health, cause storms and also lead to increase in rainfall, temperature and air borne pollutants. Therefore, prediction of humidity can be helpful in taking necessary action plans for protection of both human life and environment. Weather data can be predicted by multiple regression methods using explanatory variables. However, the effectiveness and prediction accuracy of the multiple regression methods are affected by the irrelevant or non-informative features and the uncertainties lie with the predictions. Feature selection (FS) methods can improve the effectiveness of prediction models and also reduce the uncertainties of predictions by reducing the dimensionality of the dataset by selecting more explanatory variables. On this basis, we have proposed feature selection-based support vector regression (SVR). The proposed hybrid model incorporates Random Forest (RF) model-based FS method in SVR to predict the future humidity data. Performance of RF model-based FS method is compared with two other feature selection methods like correlation-based method and recursive feature elimination (RFE) method. The prediction accuracy of the SVR model is compared with two other multiple regression models: artificial neural network (ANN) regression and decision tree regression (DTR).

关键词： Support vector machines Radio frequency Accuracy Uncertainty Humidity Predictive models Feature extraction Mathematical models Data models Regression tree analysis

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Lalini Chandra Tej Bahadur Chandra Anirud Rathy Anushka Rai Department of Computer Application National Institute of Technology Raipur Chhattisgarh India School of Computer Science Engineering and Technology Bennett University Greater Noida UP India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model’s efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Brain modeling Transformers Robustness Long short term memory Tuning Testing

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Chandra, Lalini Chandra, Tej Bahadur Rathy, Anirud Rai, Anushka National Institute of Technology Department of Computer Application Chhattisgarh Raipur India Bennett University School of Computer Science Engineering and Technology UP Greater Noida India

ISBN: (纸本)9798331530259

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model's efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images. © 2025 IEEE.

关键词： Diagnostic radiography

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RTAD-HIS: Regulated transformer architecture based anomaly detection framework towards security in healthcare IoT systems

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Applied Soft Computing 2025年 177卷

作者： Rai, Arun Kumar Verma, Deepak Kumar Dwivedi, Rajendra Kumar Department of Computer Science and Engineering University Institute of Engineering & Technology Chhatrapati Shahu Ji Maharaj University Kanpur India Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

The rapid expansion of Healthcare Internet of Things (HIS) devices has transformed modern medical systems by enabling real-time patient monitoring, seamless data exchange, and automated diagnostics. However, existing methods struggle with accurate anomaly detection, often facing overfitting and false positives/negatives due to vast and complex datasets. Reliable solutions are essential to ensure precise and timely anomaly identification of security threats and system vulnerabilities. This paper introduces the Regulated Transformer-Based Anomaly Detection (RTAD) model, which employs a Self-Regulated Transformer-Convolutional Residual Neural Network (SRTrans-ConvRNN) for anomaly detection in HIS data streams. The RTAD model utilizes temporal pooling layers to enable feature extraction, Stateful Long Short-Term Memory (SLSTM) layers to enable sequential learning, Multi-Head Self-Attention (MHSA) to enable parallel processing, and position-wise feed-forward neural networks to boost data processing and feature extraction. Additionally, an Anomaly Scoring Module (ASM) evaluates and ranks deviations from normal behavior patterns, thus ensuring efficient and effective anomaly detection. Experimental findings show that the RTAD realizes precision of 98.8 %, recall of 99.2 %, F1-score of 99.3 %, and overall accuracy of 99.6 % tested on three publicly available datasets such as Edith Cowan University - Internet of Health Things (ECU-IoHT) dataset, the Washington University in St. Louis - Enhanced Healthcare Monitoring System 2020 (WUSTL-EHMS 2020), and the Canadian institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) dataset. From these fact, it is a reliable real-time solution for anomaly detection in HIS environments. © 2025 Elsevier B.V.

关键词： Electrotherapeutics

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FRANN: Fuzzy Rough Set Based Artificial Neural Network for Prediction of Rainfall Status

FRANN: Fuzzy Rough Set Based Artificial Neural Network for P...

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

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Rainfall status prediction is a process of forecasting the future status or condition of rainfall for a particular region. Prediction of rainfall status is essential because heavy or low rainfall may have many adverse effects on agriculture, transportation, environment, living conditions and economic strength. So, accurate prediction of rainfall status is helpful in designing better action plans for water resource management, disaster risk reduction, sustainable development, emergency management and improving living conditions. Computational intelligence and approximate reasoning of conventional machine learning models are limited by incomplete or imperfect knowledge. However, the soft computing techniques have the capabilities to handle uncertainty, imprecision, partial truth and approximate reasoning to extract information in a number of ambiguous situations. In this aspect, a fuzzy rough set (FRST) based artificial neural network (ANN) model is proposed to predict the future rainfall status. Incorporating FRST method in ANN model can improve the prediction accuracy and reduce the overheads associated with both memory as well as computation time by selecting the dominant attributes and handling inconsistent data present in the information system. Rainfall status prediction accuracy of the proposed hybrid model is compared with two other hybrid models namely FRST based decision tree (DCST), FRST based support vector machine (SVM) and three other existing classification based prediction models namely DCST, SVM and ANN. The findings of the result suggest that the proposed model outperform its counterparts.

关键词： Support vector machines Rain Accuracy Computational modeling Biological system modeling Rough sets Artificial neural networks Predictive models Data models Numerical models

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