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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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Sentiment classification of Cryptocurrency-Related Social Media Posts

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IEEE INTELLIGENT SYSTEMS 2023年第4期38卷 5-9页

作者： Kulakowski, Mikolaj Frasincar, Flavius Erasmus Univ Erasmus Sch Econ NL-3062 PA Rotterdam Netherlands Erasmus Univ NL-3062 PA Rotterdam Netherlands

Many researchers agree that sentiment analysis can improve the performance of quantitative trading models. We develop two off-the-shelf solutions for analyzing the sentiments of cryptocurrency-related social media posts. First, we posttrain and fine-tune a Twitter-oriented model based on the bidirectional encoder representations from transformers (BERT) architecture, BERTweet, on the cryptocurrency domain, resulting in CryptoBERT. Second, we generate the language-universal cryptocurrency emoji (LUKE) sentiment lexicon and prediction pipeline, utilizing the sentiment of emojis prevalent in social media. CryptoBERT is highly accurate, while LUKE is suitable for non-English posts, thus allowing for direct classification and noisy label generation in less popular languages. Our research can help cryptocurrency investors develop trading software supported by sentiments mined from social media.

关键词： Training Sentiment analysis Social networking (online) Predictive models Transformers Cryptocurrency Finance Encoding Natural language processing classification algorithms Investment Bidirectional control

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Coronary Heart Disease Prediction and classification using Hybrid Machine Learning algorithms

Coronary Heart Disease Prediction and Classification using H...

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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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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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An End to End Solution for Object classification Using Artificial Neural Networks and Ultrasonic-Based Sensors

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IEEE ACCESS 2025年 13卷 87254-87267页

作者： Karagoz, Ahmet Dindis, Gokhan Eskisehir Osmangazi Univ Fac Engn & Architecture Dept Elect & Elect Engn TR-26480 Eskisehir Turkiye

With the development of technology, target and object recognition issues are becoming increasingly important day by day. In this article, studies on ultrasonic signal processing and object recognition techniques in the literature were examined. An integrated system design that includes ultrasonic signal data obtained from different objects has been designed to contribute to the literature. With this system design, a new method is proposed that includes signal pre-processing, feature extraction and classification processes. In addition, different machine learning and artificial neural networks algorithms are used to organize and classify the data obtained from ultrasonic signals, to obtain high-performance classification rates and to achieve comprehensive object recognition. As a result of the study carried out in this context, the data set obtained from different types of objects at different distances and angles was arranged and passed through various pre-processing and statistical feature extraction methods. In conclusion, a high-performance classification rate of 97.8% using the random forest algorithm and 99.10% using the artificial neural networks algorithm was obtained during the classification phase.

关键词： Acoustics Sensors Object recognition classification algorithms Feature extraction Signal processing algorithms Convolutional neural networks Cameras Sensor systems Support vector machines Artificial neural networks object classification signal processing ultrasonic sensors

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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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