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3rd international conference on Communication, Networks and Computing, CNC 2022

3rd International Conference on Communication, Networks and ...

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3rd international conference on Communication, Networks and Computing, CNC 2022

ISBN: (纸本)9783031431449

The proceedings contain 53 papers. The special focus in this conference is on international conference on Communication, Networks and Computing. The topics include: Prevention and Detection of Poisoning Attacks in Medical-Based machine learning Web Applications;machine learning-Based Solutions for Securing IoT Systems Against Multilayer Attacks;securing Internet of Things Using machine learning Techniques: A Systematic Review;predicting Students’ Performance Employing Educational data mining Techniques, machine learning, and learning Analytics;COVID-19 Detection Using State-of-the-Art Deep learning Models on X-Ray and CT Images;Edge Discerning Using Improved PSO and Canny Algorithm;sniffit: A Packet Sniffing Tool Using Wireshark;design of a Dielectric Resonator Antenna with Two Drilled Hollow Cylinders for Wide Bandwidth;an Analytic Assessment of Medical Applications of the Internet of Things (IoT) During Covid-19 and Possible Future Pandemics;Triple-Wideband Antenna for RF Energy Harvesting;dual-Band Antenna and Low Pass Filter Design for Wireless Energy Harvesting;performance Analysis of Various Digital Adders;Implementation of Low-Power Full Adder Using GNRFET Technology;Design of Dual-Polarized Square Microstrip Patch Antenna in KU Band for Weather Applications;Tumor Detection Using DWT and SVM in MRI Image;TiGen – Title Generator Based on Deep NLP Transformer Model for Scholarly Literature;Patient Monitoring System for COVID Care Using Biosensor Application;enhance the Tourism of Unpopular Places by Fuzzy Distance Method;analysis and Comparison of Posture Detection Using Deep learning Algorithm;Design and Analysis of 16 Channel WDM-OFC System with Minimum BER for 5G Applications;classification Techniques in Remote Sensing: A Review.

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3rd international conference on Communication, Networks and Computing, CNC 2022

3rd International Conference on Communication, Networks and ...

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3rd international conference on Communication, Networks and Computing, CNC 2022

ISBN: (纸本)9783031431395

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Smart Use of machine learning in Heart Disease Identification 3

Smart Use of Machine Learning in Heart Disease Identificatio...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raman, Ramakrishnan Shintre, Vaijanath Joshi, Kapil Prasanthi, G.S.L.B.V. Kaleem, Afshan Rajput, Kapil Symbiosis Institute of Business Management Maharashtra Pune India Sanjay Ghodawat University Department of Electronics Engg Maharashtra Kolhapur India Uttaranchal University Uttaranchal Institute of Technology India Muffakham Jah College of Engineering and Technology India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Heart patients are increasing in number on a daily basis, and each year in every region of the globe, a significant number of people lose their lives unexpectedly as a result of sudden heart *** a result of this, it is essential to diagnose cardiovascular illness at an early stage in order to avert the death of It is necessary for the area of medicine to make use of some kind of technology-based application in order to identify cardiac patients with greater precision and in much shorter time. It is possible to effectively identify heart illness by making use of data mining methods, and there are a considerable number of people suffering from heart disease who are now being treated in hospitals all over the globe. data mining is the practice of sifting through enormous amounts of raw data in search of knowledge or information that can be put to good use. During the prediction analysis, many machine learning algorithms are put to use in order to locate useful patterns and make accurate projections on forthcoming events or trends. Through the use of a reworked machine learning algorithm, this research study will attempt to forecast the risk that individuals would suffer from coronary heart disease. Before being classified, the incoming data go through a number of processes, including preprocessing, grouping, and the selection of useful features. Four different algorithms, including random forest (RF), K Nearest Neighbor (KNN), Particle swarm optimization (PSO) and logistic regression are combined in order to find out what kind of cardiac disease the patient *** enhance performance and shorten the amount of time needed for training, this method removes features from the cardiac dataset that are not relevant to the problem at hand. The random forest approach is the one that finishes off this procedure. In order to group all of the data points that are considered to be outliers, KNN are optimized using PSO algorithm. In the end, a method known as logistic regres

关键词： Forecasting

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Advancing Medical Predictive Models with Integrated Approaches 3rd

Advancing Medical Predictive Models with Integrated Approach...

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Kumar, Aman Singh, Randeep IEC Univ Dept Comp Sci & Technol Solan Himachal Prades India

ISBN: (纸本)9783031640667;9783031640674

This study proposes a thorough methodology for improving the precision and dependability of liver disease prediction utilising cutting-edge methods. The study uses demographic information, blood test results, and machine learning to distinguish between healthy blood donors and people who have liver disorders including cirrhosis, hepatitis C, and fibrosis. To create a reliable prediction model, the study integrates PCA, PSO, & the XGBoost algorithm. Age, sex, and test results of patients are among the information gathered from the University of California Irvine machine learning Repository. Categorical variables are numerically encoded while missing values are substituted using column means to maintain data integrity. By using PCA to keep considerable data variance in a lower-dimensional space, dimensionality reduction is achieved. The suggested feature selection strategy combines PSO and Random Forest, with the latter being used to optimise the feature subset that has the greatest impact on prediction accuracy. The population size, iterations, cognitive and social components (C1 and C2), as well as inertia weight (w), are simulation parameters used by the PSO method. The database is categorized into training & testing sets in way to assess the performance of the design. With feature-selected data, the XGBoost algorithm known for its powerful prediction abilities-is trained. Grid search is used to fine-tune hyperparameters such as maximum depth, learning rate, and number of estimators. The conductance of the design is analyzed by a variety of assessment criteria, like accuracy, precision, sensitivity, specificity, F1 Score. The design's predictions are further illustrated by a confusion matrix and ROC curve, & the AUC measures the model's discriminating capability. In order to help doctors, diagnose liver illness early and administer efficient therapy, this research attempts to generate precise and trustworthy forecasts for the condition. The combination of PCA, PSO,

关键词： Liver Disease Prediction data mining machine learning Textual data PCA PSO Random Forest XGBoost

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A GAN-Based Anti-obfuscation Detection Method for Malicious Code 3

A GAN-Based Anti-obfuscation Detection Method for Malicious ...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Hu, Wei Cheng, Jie Chong, Xulei Zhang, Ru Lin, Bingjie Xia, Ang State Grid Information & Telecommunication Branch Beijing100761 China Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing100876 China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

To avoid detection, malicious code developers often use various obfuscation methods to evade detection by malicious code detection systems. In this paper, we propose a GAN-based anti-obfuscation training method for malicious code detection models. The method addresses the problem that the obfuscated malicious code can easily evade the malicious code detector. Based on the idea of adversarial training, the method proposes to use GAN to enhance the data of small samples of obfuscated malicious code samples and use the data-enhanced samples to train the malicious code for anti-obfuscation to improve the detection performance of the malicious code detector against the obfuscated malicious code. Finally, experiments are conducted on three different malicious code detectors, and the experiments show that the anti-obfuscation training method proposed in this paper helps to improve the anti-obfuscation capability of the malicious code detector and enhance its detection performance against the obfuscated malicious code. © 2022 IEEE.

关键词： Deep learning

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A Review on framework to Detect and Diagnose Body System based Cancer using deep learning Techniques 3

A Review on framework to Detect and Diagnose Body System bas...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Gandikota, Hari Prasad Abirami, S. Annamalai University Department of Computer Science & Engineering Tamilnadu Chidambaram India

ISBN: (纸本)9798350399264

Cancer imaging examination is based mainly on doctors' manual interpretation, which requires professional abilities, clinical experience, and attentiveness. However, as the volume of medical imaging data grows, radiologists face increasing challenges. Artificial Intelligence (AI) detection of Digestive System Cancer (DSC) can provide a solution for automatic picture analysis and assist doctors in achieving high-precision intelligent cancer diagnosis. The primary purpose of this study is to explain the critical research methodologies for AI-based DSC detection, as well as to give meaningful learning and reference for relevant researchers. Meanwhile, it outlines the essential issues with these methodologies and provides better direction for future research. DSC automated categorization, recognition, and segmentation can be improved by utilizing machine learning and deep learning detection techniques, which reduce internal image information that humans find difficult to uncover. Using AI to assist imaging surgeons in diagnosing DSC for multiple organs and high incidence can achieve rapid and effective cancer identification while saving doctors' diagnosis time. These can be the foundation for better clinical diagnosis, therapeutic planning, and quantitative DSC evaluation. © 2023 IEEE.

关键词： Digestive system

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Predictive Analysis of Road Accidents Using data mining and machine learning 3rd

Predictive Analysis of Road Accidents Using Data Mining and ...

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3rd international conference on Information Technology, InCITe 2023

作者： Kumar, Abhinav Arora, Harshit Jain, Prasuk Department of Information Technology ASET Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819959938

Today, one of the top concerns for governments is road safety. Given the significance of the subject, reducing the harm caused by traffic accidents has shifted its focus to finding the root causes of incidents on the road. On the other hand, it is impractical to analyze the components in this regard due to the exponential growth of data volume. Despite everything that has been done so far to increase road safety in India, many areas continue to be vulnerable to these flaws. Due to the diversity of these vulnerability-causing factors, a thorough analysis is required in order to significantly reduce the worrying numbers. The aim of this paper is to use data mining and machine learning approaches to help develop a model using LR and RF that not only smooths out the heterogeneity of the data by grouping like objects together to identify the regions of the nation that are most likely to experience accidents in relation to various accident-factors, but also assists in establishing the relationship between these parameters, factors, and casualties. The outcomes of data mining will assist businesses like transportation in exploring the police information system’s accident data, finding trends, forecasting future behavior, and making wise judgments to reduce accidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： data mining

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Vehicle Industry Big data Analysis Using Clustering Approaches 3rd

Vehicle Industry Big Data Analysis Using Clustering Approach...

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3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Seixas, Lenon Diniz Correa, Fernanda Cristina Siqueira, Hugo Valadares Trojan, Flavio Afonso, Paulo Fed Univ Technol Parana UTFPR R Doctor Washington Subtil Chueire330 Jardim Car BR-84017220 Ponta Grossa PR Brazil Univ Minho Sch Engn Dept Prod & Syst Guimaraes Portugal

ISBN: (纸本)9783031530357;9783031530364

Considering a globalized world economy and industry, data analysis and visualization offer enlightening information for decision-making and strategic planning. data science provides diverse statistical and scientific methods to extract the most value possible from a data set, covering all data preparation, cleaning, aggregation, and manipulation. machine learning (ML) and Artificial Intelligence (AI) come with it to learn and explore the data, uncovering patterns that cannot be seen with only the analyst experience. This work performs a study exploring clustering methods in a trucks data set of logged inclinations on the roadway, a Big data problem. With a good clustering, the data becomes key to improve product development and fuel efficiency, since different environment of truck usage can be identified. Knowledge discovery and data mining methods were used, namely the K-means and Fuzzy C-means (FCM) algorithms and compared to a rule-based method called GTA. The evaluation metrics addressed are the sum of squares within clusters, the sum of squares between clusters, and the silhouette index. The proposed approach showed satisfactory results and demonstrated how the ML application could benefit this real world problem, especially the FCM.

关键词： Clustering K-means Fuzzy C-means Trucks Slope

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Classification of Mobile Phone Price dataset Using machine learning Algorithms 3

Classification of Mobile Phone Price Dataset Using Machine L...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Hu, Ningyuan University College London Department of Mathematics Jiangxi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the development of technology, mobile phones are an indispensable part of human life. Factors such as brand, internal memory, wifi, battery power, camera and availability of 4G are now modifying consumers' decisions on buying mobile phones. But people fail to link those factors with the price of mobile phones;in this case, this paper is aimed to figure out the problem by using machine learning algorithms like Support Vector machine, Decision Tree, K Nearest Neighbors and Naive Bayes to train the mobile phone dataset before making predictions of the price level. We used appropriate algorithms to predict smartphone prices based on accuracy, precision, recall and F1 score. This not only helps customers have a better choice on the mobile phone but also gives advice to businesses selling mobile phones that the way to set reasonable prices with the different features they offer. This idea of predicting prices level will give support to customers to choose mobile phones wisely in the future. The result illustrates that among the 4 classifiers, SVM returns to the most desirable performance with 94.8% of accuracy, 97.3 of F1 score (without feature selection) and 95.5% of accuracy, 97.7% of F1 score (with feature selection). © 2022 IEEE.

关键词： Classification (of information)

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Breast Cancer Radiogenomics data Generation Using Combined Generative Adversarial Networks GANs 3rd

Breast Cancer Radiogenomics Data Generation Using Combined G...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Anwar, Suzan Ali, Shereen Abdulla, Dalya Omekara, Sam Davis Mendiola, Salavador Wright, Kai Muhammed, Saja Ataallah Philander Smith University Little RockAR72211 United States Polytechnic University Erbil Iraq Salahaddin University Erbil Iraq

ISBN: (纸本)9783031463341

Radiogenomics is a newly emerging field that integrates genomics with medical imaging data. The aim of this field is to elucidate the associations between gene expression data and imaging phenotypes, especially in cancer. However, radiogenomics is hindered by the expensive cost of genetic screening tests which leads to the unavailability of numerous big datasets for paired imaging and genetic data. Big data is crucial for training machine learning-based techniques for analysis relating to radiogenic studies. Currently, fake data is generated on only one of the two radiogenomic types;genomic or medical imaging data are generated separately. In this paper, we propose a deepfake approach implemented by combining two Generative Adversarial Networks (GANs) to create fake image data that are hardly differentiable from the original to improve Breast cancer diagnosis. To evaluate the model, a survey is developed and distributed among the participants to measure their ability to differentiate the original from the Deepfakes images. The results showed that the model-generated fake images cannot be distinguished from the authentic images and are relatively satisfying using the PyTorch framework. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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