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International Conference on Inventive Computation Technologies (ICICT)

作者： G. Ezhilvani G. Thailambal Department of Computer science VEL'S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Heart disease has remained the leading cause of death globally over the previous two decades. In impoverished nations, inadequate resources and access to skilled medical personnel and diagnostic equipment make it difficult to properly diagnose and treat cardiac disease. One of the most important aspects of treating cardiac disorders is analyzing large datasets and using data to predict, prevent, and treat conditions such as heart attacks. It is typical for medical staff at the hospital to have differing degrees of training and experience when it comes to treating cardiac disease, which can have fatal consequences. The occurrence of heart disease can be predicted with the aid of several health parameter analysis. Various machine learning methods, like Decision Trees (DT), Naive Bayes (NB), K-Nearest Neighbors (KNN), and Logistic Regression (LR), can be utilized for the heart disease prediction. This research delves into cardiac disease prediction by employing different algorithms, parameters, and outcomes.

关键词： Heart Logistic regression Machine learning algorithms Cardiac disease Nearest neighbor methods Bayes methods Decision trees Medical diagnostic imaging Diseases Overfitting

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Heart Disease Prediction Model using Genetic Algorithm and Support Vector Machine

Heart Disease Prediction Model using Genetic Algorithm and S...

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International Conference on ent computing and Control Systems (ICICCS)

作者： A. Lakshmi R. Devi Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu

Heart disease has been one of the leading causes of mortality for individuals all around the world in recent years. The prognosis of heart disease would be difficult due to various risk factors such as diabetes, high blood pressure, improper pulse rate, and high cholesterol. Accurate and prompt identification of heart disease is necessary to prevent and treat heart failure. Machine learning techniques highly assist in the development of prediction models for heart disease and its early identification. Feature selection strategies are also used to identify features that are critical to achieve higher accuracy. The proposed model employs the Genetic algorithm to select suitable characteristics, and it also demonstrated that it outperforms the existing models that employ traditional feature selection methods.

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Using NSCTS and DCNN, Rapid Prediction and Forecasting of Breast Cancer 1

Using NSCTS and DCNN, Rapid Prediction and Forecasting of Br...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Seethalakshmi, K. Bhagyalakshmi, A. Sangeetha, A. Vijitha, S. Palukuri, P. Suhas Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350316469

Utilizing the CAD system, which aids radiologists in various diagnostic procedures, a recent study has been conducted on the early identification of breast cancer. Next to lung cancer, breast cancer ranks among the most deadly diseases. Initially, detection is done based on screening tests done through Magnetic resonance imaging (MRI), subsequently Infrared thermography, and digital mammography (DM). Whenever a suspicion is raised, these tests might be recommended by the specialist. Region-based segmentation is used for the test picture in this work, while NSCTS (Non-subsample Contourlet transform segmentation) is used for preprocessing. ResNet-164 DCNN architecture performs feature extraction and classification between normal and aberrant tumor pictures. The diagnosis system adopts the deep learning technology to increase its performance rate. The approach to prediction starts with screening test photos, then preprocessing to enhance the image quality, ROI detection, ROI segmentation, and classification. The resulting images are examined using a variety of evaluation measures, including performance rate, sensitivity, specificity, and accuracy. When compared to other recent work, the outcome is exceptional. The obtained accuracy, sensitivity of the proposed system is 98.58% and 98.78%. The application takes advantage of the MIAS mammography image dataset. © 2023 IEEE.

关键词： Breast cancer contourlet segmentation DCNN Digital mammograms Image preprocessing MRI ResNet

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network 38

SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Re...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Weiyu Sun, Ying Xu, Yijie Qiao, Ziyue Yang, Yongkui Xiong, Hui Thrust of Artificial Intelligence HKUST Guangzhou China School of Computing and Information Technology Great Bay University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Department of Computer Science and Engineering HKUST Hong Kong

Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) High Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs' ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture's performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines (89.26%). Moreover, the actual deployment on the CPU demonstrated a latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture's potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https://***/guoweiyu/SpGesture/. © 2024 Neural information processing systems foundation. All rights reserved.

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Extraction of Actionable Threat Intelligence from Dark Web Data 5

Extraction of Actionable Threat Intelligence from Dark Web D...

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5th International Conference on Control, Communication and computing, ICCC 2023

作者： Varghese, Varsha Mahalakshmi, S. Senthilkumar, K.B. ER&DCI Institute of Technology Cyber Forensics and Information Security Department of Computer Science & Engineering Thiruvananthapuram India Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9798350334128

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of Cyber Threat Intelligence which is the information about the techniques, tactics, or motives of an emerging threat. The life cycle of a Darknet-based Cyber Threat Intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of Data from the Darknet. The Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. The Analysis part is integral to the Threat Intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. The production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making Cyber Threat Intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. The collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. The experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. This could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities. © 2023 IEEE.

关键词： Extraction

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An Improved Deep Forest Classification and Intelligent Segmentation for skin Cancer Prediction Technique Inspired by Whale Optimization

An Improved Deep Forest Classification and Intelligent Segme...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： Siva Prasad Reddy K.V S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Fast malignant melanoma tumor identification is vital for successful therapy. Melanoma has now been universally acknowledged to be the most lethal form of malignancy over all else since, even if not caught and addressed promptly, it possesses a substantially higher propensity to migrate towards remaining regions of physique. Definitive presentation of various disorders relies largely on quasi therapeutic machine vision / procuring of disorders scanned pics. These methods offer a tool for autonomous image enhancement that allows for a quick and precise appraisal of the abscess. Deficiency of vast samples constitutes one of primary hurdles for designing a trustworthy autonomous classification scheme. This research suggests integrating Deep—learning & Image—processing to develop an algorithm for artificially recognizing melanoma. This survey's phase involves amassing a repository of dermoscopy pics, pre—Processing, separation utilizing hybridized Fuzzy-C-Means, and weighted enhancement employing the whale-optimization-algo. Following the subdivision of the dermoscopic imagery, the chapped skin cellular membranes traits are captured utilizing extraction of features. For the layering of the retrieved attributes, Long -shortterm -memory {LsTm}, bidirectional LsTm && Deep-Forest predictors are deployed. After using freely searchable sample group, the reported classifier reliability was Ninety Seven Percent && the segmentation effectiveness reached ninety three percent.

关键词： Hair Training Object segmentation Melanoma Feature extraction Solids Skin

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Blockchain Ethereum-Based Decentralized Secure Ride-Sharing Framework for Carpooling System

Blockchain Ethereum-Based Decentralized Secure Ride-Sharing ...

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Ubiquitous computing and Intelligent Information Systems (ICUIS), International Conference on

作者： J. Wessly R. Durga Department of Computer science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Carpooling involves sharing a car for a journey, typically with two or more individuals. Car-sharing systems play a crucial role in addressing urban challenges by providing shared vehicles and decreasing the dependence on private cars. Car-sharing systems present security concerns due to transmitting sensitive information through a shared centralized network, including identity, location, and access codes. It is crucial to develop secure authentication methods to prevent unauthorized access and use of this information for illegitimate purposes. To address the problem, this research presents a Blockchain Ethereum-based Decentralized Secure Ride-sharing Framework (BEDSRF) for carpooling systems. The study introduces a Role-based Access Control (RBAC) method to determine user roles for accessing their information. Then, based on these roles, the data is encrypted using the Elliptic Curve Cryptography (ECC) method. The BEDSRF approach verifies each node and transmits the information within the decentralized network. Additionally, the Key Policy Authentication Scheme (KPAS) is utilized to authenticate the user's key and enable role-based decryption for the end user. Furthermore, experiments were carried out to confirm the efficiency and resilience of the proposed approach. The findings indicate that the proposed method outperforms existing carpooling schemes in terms of both security and authentication performance.

关键词： Privacy Shared transport Authentication Elliptic curve cryptography Ubiquitous computing Blockchains Security Automobiles Reliability Resilience

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Optimally Tracking Labels on an Evolving Tree 34

Optimally Tracking Labels on an Evolving Tree

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34th Canadian Conference on Computational Geometry, CCCG 2022

作者： Acharya, Aditya Mount, David M. Department of Computer Science University of Maryland College ParkMD United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

Motivated by the problem of maintaining data structures for a large sets of points that are evolving over the course of time, we consider the problem of maintaining a set of labels assigned to the vertices of a tree. We study the problem in the evolving data framework, where labels continuously change over time due to the action of an agent called the evolver. An algorithm, which can only track these changes by explicitly probing the individual vertices, is tasked with maintaining an approximate sketch of the underlying tree. Such a framework necessitates an algorithm which is fast enough to keep up with the changes, while simultaneously being accurate enough to maintain a close approximation. We present an algorithm that allows for both randomized and adversarial evolution of the data, subject to allowing a constant speedup factor over the evolver. We show that in the limit, it is possible to maintain labels to within an average distance of O(1) of their actual locations. We also present nearly matching lower bounds, both on the distance, and the speed-up factor. © 2022 Yeganeh Bahoo and Konstantinos Georgiou.

关键词： Approximation algorithms

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Preprocessing and feature extraction of MRI Liver Tumour images using A novel multi-class identification (NMCI) framework

Preprocessing and feature extraction of MRI Liver Tumour ima...

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

作者： A. Bhagya S. Perumal Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

One of the leading and most serious causes of death worldwide is liver tumours. Currently, one of the most well-known computer Vision disciplines (CV) is Medical Imaging (MI), which aids radiologists and physicians in the early detection and diagnosis of liver tumours. In order to diagnose patients, radiologists and doctors read hundreds of images using manual or semi-automated technologies, such as Magnetic resonance imaging (MRI). Consequently, a fully automated approach is required to use MRI scans, the most common and extensively used imaging modality, for early tumour detection and diagnosis. Implementing machine learning (ML) techniques in finding the tumour affected parts are the main focus of the proposed work. The multiclass liver tumour classification procedure followed in the tumour identification process follows, Random Forest (RF), Random Tree (RT), Logistic Model Tree (LMT) and J48 with multiple automated Region of Interest (ROI). Hemangioma, cyst, hepatocellular carcinoma, and metastasis are the four basic tumour classes, which are to be classified before severity increases. Transformation procedures followed in image processing are conversion of collected MRI images into grayscale, then boosting the contrast with histogram equations. The Gabor filter was used to minimise noise, and an image sharpening technique was used to enhance image quality. Furthermore, employing texture, binary, histogram, and rotational, scalability, and translational (RST) algorithms, 55 characteristics of various pixel dimensions were obtained for each ROI. Out of these 55 characteristics, 20 optimised features were chosen for classification using the correlated feature selection approach. RF and RT outperformed J48 and LMT, with respective accuracy of 97.48% and 97.08%, according to the collected data. The new framework that has been suggested will aid doctors and radiologists in the diagnosis of liver tumours.

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