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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Khader Basha Sk Roja D Sunkara Santhi Priya Lavanya Dalavi Sai Srinivas Vellela Venkateswara Reddy B Dept. of CSE Chalapathi Institute of Technology Guntur AP India

Nowadays, digitalization in the healthcare organizations places great emphasis on technological advances in clinical healthcare providers. Traditional methods for measuring and evaluating outcomes for patients in forecasting and diagnosing chronic diseases are being substituted by techniques that capture the most significant insights from medical information by combining predictive modeling with a highly valuable application of machine learning. Heart disease is nowadays among the worst disorders in the world. Because the death rate from heart disease remained largely significant, more intensive efforts in preventive are required, such as enhancing the accuracy of a heart disease prediction system. Additionally, an early diagnosis supports in the appropriate diagnosis of the condition as well as the management of its symptoms. By creating forecasting analytics, Machine Learning (ML) techniques can be used to anticipate chronic diseases including kidneys and cardiac disorders. Hence, this analysis presents coronary heart disease prediction and classification utilizing Hybrid Machine Learning methods. In this approach the combination of Decision Tree (DT) and Ada Boosting algorithms is used as a hybrid ML algorithm to predict the CHD. This method's performance is determined by the performance metrics such as accuracy, True Positive Rate (TPR), and Specificity.

关键词： Heart Training Machine learning algorithms Medical services Prediction algorithms Boosting classification algorithms

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classification of Breast Cancer Diagnosis Using Machine Learning algorithms

Classification of Breast Cancer Diagnosis Using Machine Lear...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： Raghav Dwivedi Khush Ramdev Er. Meenu Jain Neha Agrawal Information Technology Maharaja Agrasen Institute of Technology Delhi India

In the developing world, cancer is one of the major causes of death. Breast cancer has the largest share among all types of cancer among women in India [1] and worldwide [2]. Early diagnosis of this can help the patient to increase their chances of survival, as it can provide much needed treatment on time. This paper focuses on classification of breast cancer into benign and malignant using various machine learning methods namely Logistic Regression, Decision Trees, Random Forest, K Nearest Neighbor, Extreme Gradient Boosting and Support Vector Classifier on the basis of Breast Cancer Wisconsin (Diagnostic) dataset. We propose a comparison between these implementations and evaluate them on various parameters. This survey also analyses the effects of dimensionality reduction using Principal Component Analysis and Recursive Feature Elimination on a few of these algorithms.

关键词： Surveys Machine learning algorithms Support vector machine classification Sensitivity and specificity Breast cancer classification algorithms Decision trees

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Social Media News classification Using Machine Learning algorithms

Social Media News Classification Using Machine Learning Algo...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： M. Jagadeesan T.M Saravanan P.A. Selvaraj S. Nithya K. Snegha P. Sowmiya Department of Computer Applications Kongu Engineering College Perundurai Erode TamilNadu India

Every day, a great amount of data is generated online in the age of technology. However, an unprecedented amount of material is being disseminated online, most of it false news. Fake news is created to draw in viewers, influence people’s opinions and actions, raise click through rates, and have a significant impact on important events like elections. Fake News incorporates material that steers readers in the wrong directions. It may have unfavorable repercussions in the actual world that seek to deceive people, sway public opinion, or attract attention or damage reputation. It is necessary to detect fake news mainly for media outlets to have the capability to attract viewers to their website to generate online advertising revenue. This project develops a SVM approach and uses this model to detect whether the news are fake or not. It uses a fake news dataset, which contains News text and corresponding labels (REAL or FAKE). The pervasive increase in fake news, whether it is produced by humans or algorithms, has a severe social and political impact on society and individuals. The rapid turnover of news in the age of social networks makes it difficult to assess its veracity in a timely manner. Tools for automatically detecting bogus news become essential. K- Nearest Neighbor and a classification based on a support vector machine are utilized in this case to overcome the aforementioned issue. In contrast to other research that merely took particular sentences or phrases into account, The purpose of this study is to investigate a false news posture detection mechanism based on the title and text of the news. Python 3.7 is used in the project’s development.

关键词： Machine learning algorithms Social networking (online) Voting Support vector machine classification classification algorithms Fake news Informatics

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Hand Sign classification Techniques Using Supervised And Unsupervised Learning algorithms

Hand Sign Classification Techniques Using Supervised And Uns...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： J L V Sri Harshitha N Saranya Yekkitilli Sruthi S Srithar J Vijaya Chandra Department of Computer science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

Gesture recognition is becoming a sore subject in computer vision captivating the idea of social interaction not only between humans but also create ergonomic systems that control devices ranging from time-of-flight cameras and controlling vehicles to virtual reality. Even though it is gaining ground recently, fast and robust recognition remains an unsolved problem. To our understanding, we are utilizing the Leap motion sensor captured near-infrared image dataset for gesture identification, which tides over word-level sign recognition encompassing a diverse set of hand gestures. We have suggested the approach of extracting the gesture from the image using PCA and image segmentation followed by the feature extraction stage. In this paper, we have analyzed and differentiated various methods of hand recognition counting Random forests, stochastic gradient descent, Naive Bayes, Decision tree, and Logistic regression algorithms and exploited our results

关键词： Training Image segmentation Image recognition Virtual reality Feature extraction classification algorithms Decision trees

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Disease Prediction and classification using Intelligent algorithms

Disease Prediction and Classification using Intelligent Algo...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： P. Mohana Priya Deparment of IT School of Computing SASTRA Deemed to be University Thanjavur India

Cardio Vascular Diseases are the major risk factors for human survival. Cardio Vascular Diseases are comprised with many forms such as abnormal heart rhythms, congenital heart disease, deep vein thrombosis, heart attack, heart failure etc. These diseases are identified using scanning devices and doctors can analyze the report by observing heart x-ray images. This research work is carried out using various intelligence algorithms for predicting and classifying the type of heart disease. It is observed that Support Vector Classifier predicts with an accuracy of 99%, Logistic Regression predicts with an accuracy of 85%, Multi Layer Perceptron prediction accuracy is 86.4%, Simple Logistic prediction accuracy is 85.8%, J48 prediction accuracy as 88.62% and Random Forest classification as 100% based on the training data.

关键词： Heart Veins Training data Support vector machine classification Prediction algorithms classification algorithms Thrombosis

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On the Performance of classification algorithms for Learning Pareto-Dominance Relations

On the Performance of Classification Algorithms for Learning...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Bandaru, Sunith Ng, Amos H. C. Deb, Kalyanmoy Univ Skovde Virtual Syst Res Ctr Skovde Sweden Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9781479914883

Multi-objective evolutionary algorithms (MOEAs) are often criticized for their high-computational costs. This becomes especially relevant in simulation-based optimization where the objectives lack a closed form and are expensive to evaluate. Over the years, meta-modeling or surrogate modeling techniques have been used to build inexpensive approximations of the objective functions which reduce the overall number of function evaluations (simulations). Some recent studies however, have pointed out that accurate models of the objective functions may not be required at all since evolutionary algorithms only rely on the relative ranking of candidate solutions. Extending this notion to MOEAs, algorithms which can 'learn' Pareto-dominance relations can be used to compare candidate solutions under multiple objectives. With this goal in mind, in this paper, we study the performance of ten different off-the-shelf classification algorithms for learning Pareto-dominance relations in the ZDT test suite of benchmark problems. We consider prediction accuracy and training time as performance measures with respect to dimensionality and skewness of the training data. Being a preliminary study, this paper does not include results of integrating the classifiers into the search process of MOEAs.

关键词： Meta-modeling Multi-objective optimization classification algorithms Pareto-dominance Machine learning

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Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier

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AUTOMATIKA 2023年第4期64卷 783-797页

作者： Ramasamy, Karthikeyan Sundaramurthy, Arivoli Velusamy, Durgadevi M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur India Sri Sivasubramaniya Nadar Coll Engn Dept Informat Technol Kalavakkam India M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur 639113 Tamil Nadu India

Smart Grid is an intelligent power grid with a bidirectional flow of electricity and information, that applies intelligent techniques to operate the grid autonomously near the stability limit. An intelligent technique is developed to identify and predict the abnormalities due to changes in customer behaviour and the unexpected disruption in the grid. A cost-sensitive stacked ensemble classifier (CS-SEC) is proposed for predicting the operations in smart grid that combines four cost-sensitive base classifiers, namely Extreme gradient boosting, Naive Bayes, Nu-support vector machine and Random forest at level-1 and the support vector machine as the meta classifier in level-2. The meta classifier uses the probability of prediction of the first-level classifiers with stratified 5-fold cross-validation to predict the decentralized smart grid stability. The proposed stacked ensemble classifier achieved an accuracy of 98.6% with specificity, recall and precision of 98.34%, 99.0% and 99.06%, respectively. Extensive experimental evaluation and results show that the proposed CS-SEC provides an accurate prediction of grid stability compared with other state-of-the-art models. The results reveal the robustness and competency of the proposed CS-SECs with optimized parameters.

关键词： classification algorithms machine learning Smart Grids stability analysis support vector machines

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Investigation of classification algorithms for a Prototype Microwave Breast Cancer Monitor 8

Investigation of Classification Algorithms for a Prototype M...

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8th European Conference on Antennas and Propagation (EuCAP)

作者： Santorelli, Adam Li, Yunpeng Porter, Emily Popovic, Milica Coates, Mark McGill Univ Dept Elect Engn Montreal PQ Canada

ISBN: (纸本)9788890701849

In this paper we investigate the use of differential signals to monitor changes within the breast. Specifically, we focus on the use of machine learning classification algorithms to determine whether any malignant tissues are developing. Experimental data is obtained from a 16-element antenna array that transmits a 2 - 4 GHz broadband pulse. We implement both the Linear Discriminant Analysis and Support Vector Machine (SVM) detection algorithms to analyze the experimentally obtained data.

关键词： cancer detection classification algorithms microwave sensors

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Machine Learning algorithms for Flow Pattern classification in Pulsating Heat Pipes

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ENERGIES 2022年第6期15卷 1970页

作者： Loyola-Fuentes, Jose Pietrasanta, Luca Marengo, Marco Coletti, Francesco Hexxcell Ltd Foundry Bldg77 Fulham Palace Rd London W6 8AF England Univ Brighton Adv Engn Ctr Sch Architecture Technol & Engn Lewes Rd Brighton BN2 4AT E Sussex England Brunel Univ London Dept Chem Engn Kingston Lane Uxbridge UB8 3PH Middx England

Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the pr

关键词： two-phase flow pulsating heat pipes flow pattern maps machine learning classification algorithms

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Research on Object Detection and Combination Clustering for Railway Switch Machine Gap Detection

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Chinese Journal of Electronics 2025年第1期34卷 186-203页

作者： Qingsheng Feng Shuai Xiao Wangyang Liu Hong Li School of Automation and Electrical Engineering Dalian Jiaotong University School of Software Dalian Jiaotong University

Turnouts and switch machines play a crucial role in facilitating train line operations and establishing routes, making them vital for ensuring the safety and efficiency of railway transportation. Through the gap detection system of switch machines, the real-time working status of turnouts and switch machines on railway sites can be quickly known. However, due to the challenging working environment and demanding conversion tasks of switch machines, the current gap detection system has often experienced the issues of fault detection. To address this, this study proposes an automatic gap detection method for railway switch machines based on object detection and combination clustering. Firstly, a lightweight object detection network, specifically the MobileNetV3-YOLOv5s model, is used to accurately locate and extract the focal area. Subsequently, the extracted image undergoes preprocessing and is then fed into a combination clustering algorithm to achieve precise segmentation of the gap area and background,the algorithm consists of simple linear iterative clustering, Canopy and kernel fuzzy c-means clustering. Finally, the Fisher optimal segmentation criterion is utilized to divide the data sequence of pixel values, determine the classification nodes and calculate the gap size. The experimental results obtained from switch machine gap images captured in various scenes demonstrate that the proposed method is capable of accurately locating focal areas, efficiently completing gap image segmentation with a segmentation accuracy of 93.55%, and swiftly calculating the gap size with a correct rate of 98.57%. Notably, the method achieves precise detection of gap sizes even after slight deflection of the acquisition camera, aligning it more closely with the actual conditions encountered on railway sites.

关键词： Image segmentation Accuracy Clustering algorithms Transportation Switches Object detection Cameras Rail transportation classification algorithms Safety

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