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Symptoms based disease prediction using machine learning techniques 3

Symptoms based disease prediction using machine learning tec...

引用

3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Hamsagayathri, P. Vigneshwaran, S. Bannari Amman Institute of Technology Sathyamangalam Dept. of ECE Erode Tamilnadu India

ISBN: (纸本)9780738111834

Computer Aided Diagnosis (CAD) is quickly evolving, diverse field of study in medical analysis. Significant efforts have been made in recent years to develop computer-aided diagnostic applications, as failures in medical diagnosing processes can result in medical therapies that are severely deceptive. machine learning (ML) is important in Computer Aided Diagnostic test. Object such as body-organs cannot be identified correctly after using an easy equation. Therefore, pattern recognition essentially requires training from instances. In the bio medical area, pattern detection and ML promises to improve the reliability of disease approach and detection. They also respect the dispassion of the method of decisions making. ML provides a respectable approach to make superior and automated algorithm for the study of high dimension and multi - modal bio medicals data. The relative study of various ML algorithm for the detection of various disease such as heart disease, diabetes disease is given in this survey paper. It calls focus on the collection of algorithms and techniques for ML used for disease detection and decision making processes. © 2021 IEEE.

关键词： machine learning

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Academic Performance Prediction of At-Risk Students using machine learning Techniques

Academic Performance Prediction of At-Risk Students using Ma...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Shoukath TK Midhunchakkravarthy Faculty of Computer Science and Multimedia Lincoln University College Malaysia

Imparting quality education is a top priority of higher education institutions. Leaving at risk students unattended would adversely impact the quality of education. Predicting the academic performance of students is one of the basis for improving the quality of education, as it would help the institution to take early and timely measures to support the low-performing students and improve their performance. At the same time, it has identified that the prediction of student performance considered as challenging one due to large dataset in educational environment. However, Educational data mining (EDM) using machine learning techniques is an emerging research area, which allows the researchers to effectively process and evaluate the educational data collected from different sources. In this paper, we propose the use of support vector machine (SVM) on an imbalanced dataset to predict the academic performance of students.

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HRPA: Human Resource Prediction Analytics 3

HRPA: Human Resource Prediction Analytics

引用

3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Pandey, Shweta Mehta, Jalpa Shah and Anchor Kutchhi Engineering College Information Technology Mumbai India Shah and Anchor Kutchhi Engineering College Department of Information Technology Mumbai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Each company tries its hardest to make the best use of its employees in order to accomplish the business's profitability. However, they encounter a number of issues unique to their best employees, which is frequently where people analytics comes into play. Day-to-day operations, procedural efficiencies, and other strategic operational challenges are handled by HR analytics. As a result, HR analytics considers all components of an organization at a high level, whereas work force analytics concentrates on personnel data such as engagement, job satisfaction, and success. The wide category of HR analytics includes labor force analytics. Employees leave for a number of reasons, including dissatisfaction with their pay, stagnant career advancement, and so on. A great defeat is possible for a firm to be profitable not just in terms of money, but also in terms of losing valuable employees. If the company determines whether or not that employee should be promoted, predicts what proportion of earnings that employee should receive, and determines whether or not that employee will leave the company in the near future, the company will work on employee retention in advance to keep their valuable and hardworking employees. machine learning approaches might be used to forecast staff turnover and retention. Each modern organization accumulates a wide range of employee data;We'll utilize this data, analyze it, and extract insights from it so that the firm can make better decisions about how to conduct employees job. © 2022 IEEE.

关键词： data mining

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recognition of Bean Leaf Diseases Using Neural Network and machine learning Techniques

Recognition of Bean Leaf Diseases Using Neural Network and M...

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Smart data Intelligence (ICSMDI), international conference on

作者： L. Rahunathan D. Sivabalaselvamani E.S. Elakkiya M. Madhumitha K. Kumaresh Department of Computer Applications Kongu Engineering College Perundurai Erode Tamil Nadu India

Plant leaf diseases have increased in prevalence recently, making it necessary to conduct accurate research. Bean leaf diseases can be prevented from spreading by earlier detection and accurate identification. Diseases of bean leaves have negatively affected bean yield and quality. There are two types of diseases predicted in bean leaves: angular leaf spots and rust. This work includes a contrast between deep learning algorithms and machine learning algorithms-based approaches such as CNN (Convolutional Neural Networks/ConvNet) and its predefined models, K-Nearest Neighbor in short KNN, Support Vector machine can also have written as SVM, Multinomial Logistic Regression that automate the identification of leaf diseases in bean plant species. As far as known, no one has offered a comparison study for identifying bean leaf disease.

关键词： Support vector machines Deep learning machine learning algorithms Neural networks Predictive models Prediction algorithms Classification algorithms

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Enhancing Bharatanatyam Face Emotion recognition Using machine learning Techniques

Enhancing Bharatanatyam Face Emotion Recognition Using Machi...

引用

data, Decision and Systems (ICDDS), IEEE international conference on

ISBN: (数字)9798350363890

ISBN: (纸本)9798350363906

Facial Expression Analysis of Bharatanatyam a Classical South Indian Dance Using machine learning Techniques The research explores how emotion alters facial expressions, particularly the areas of expression in the eyebrows and surrounding structures, lips, and mouth region. Local binary patterns (LBP), grey-level cooccurrence matrix (GLCM), Histogram of Oriented Gradients (HOG) and invariants descriptors such as Discrete cosine transforms (DCT) or oriented fast and rotated brief (ORB). Finally, using SVM (Support Vector machine), KNN (K-Nearest Neighbours), DT (Decision Tree), and RF (Random Forest) classifiers the deafest features are tested separately. Experiments are carried out via a Bharatanatyam face image database, and it is inferred that the combination of DCT with RF gives the best performance in classification. From the evaluations for colour-scale and grey-scale images, we can see that DCT with RF has an accuracy of 82.14%, and 80.40% is colour scale and greyscale respectively According to the above results, it is concluded that DCT along with RF has successfully categorized emotional facial expression of Bharatanatyam.

关键词： Support vector machines Histograms Accuracy Face recognition Mouth Nearest neighbor methods Feature extraction Discrete cosine transforms Random forests Classification tree analysis

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Research on the Application of machine learning in Photovoltaic Power Station 3

Research on the Application of Machine Learning in Photovolt...

引用

3rd international Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Wang, Jiapeng Xi'An Jiaotong-liverpool University International Business School Suzhou Suzhou215000 China

ISBN: (纸本)9781665402675

With the increasingly prominent environmental problems, clean energy is gradually replacing fossil energy and changing the existing energy structure is the only way to achieve sustainable development in the future. This paper mainly studies the application of machine learning in photovoltaic power stations from three aspects of photovoltaic forecasting, data monitoring and fault detection, and aims to explore the development direction of machine learning in the next stage. Though study these parts, the paper discusses the current monitoring methods of photovoltaic forecasting data, and comes to the development path of in-depth data mining, multi-source data acquisition and multi-system integration. It is concluded that the comprehensive application of multiple prediction methods and monitoring methods will be the key to improve the accuracy of prediction in the future, including the intelligent detection method, unintelligent detection method and UAV (unmanned aerial vehicle) monitor method. © 2021 IEEE.

关键词： machine learning

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Energy-Efficient Monitoring of Industrial Processes Using IoT Sensors and Predictive Algorithms

Energy-Efficient Monitoring of Industrial Processes Using Io...

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Disruptive Technologies (ICDT), international conference on

作者： Gunjan Agarwal Arnav Kaushik Manoj Kumar Singh Vaishali Bhargava Saloni Bansal Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad UP India Computer Science & Engineering Galgotia University Greater Noida UP India Computer Science & Engineering IMS Engineering College Ghaziabad UP India Department of Electrical Engineering GLA University Mathura UP India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Industrial processes are becoming increasingly complex and larger in scale, which has resulted in a constantly growing demand for robust, scalable monitoring systems. Conventional monitoring strategies entail a huge quantity of sensors and steep infrastructure charges, main to excessive electricity intake and operational costs. Burkhard Theen, vice president at weather analytics company Iteris describes a smart monitoring device using IoT sensors and predictive algorithms for accurately tracking industrial processes on this track. The system is made using a low-voltage IoT sensor community at essential points within the process, which constantly collects and transmits statistics in real time. This data is then fed into the predictive algorithms that use machine learning techniques to analyze and determine any anomalies in the system. The system helps to improve process performance and reduce power consumption by identifying & addressing potential issues at their early stages.

关键词： machine learning algorithms Power demand Electricity Companies Prediction algorithms Real-time systems Sensor systems pattern recognition Monitoring Intelligent sensors

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Analysis on the Application of data mining Technology in Wisdom Teaching 3

Analysis on the Application of Data Mining Technology in Wis...

引用

3rd IEEE international conference on Computer Science and Educational Informatization, CSEI 2021

作者： Xu, Chuang Wang, Yanqing Sheng, Ruyu Nanjing Xiaozhuang University College of Information Engineering Nanjing China

ISBN: (纸本)9781728184739

With the development of network technology and the encouragement of national policies, education tends to be student-centered wisdom teaching. The use of data mining technology can extract meaningful and valuable teaching and learning information from educational big data resources, and apply the extracted information to education and teaching according to the actual teaching situation, so as to adjust the teaching plan and meet the goal of students' intelligent learning. Based on this, this paper first briefly introduces five kinds of technologies commonly used in education big data mining, and then gives examples of smart education application system cases that have applied data mining and analytics technologies, as well as specific technologies, framework environments, toolkit and platforms, and analyzes the significance and role of data mining technology in wisdom teaching. Finally, it points out the challenges of data security, data collection and collation, data mining, large data need for colleges and universities on scale, and the tendency of big data to mystify. © 2021 IEEE.

关键词： data mining

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Capturing the Essence: Exploring Cutting-Edge Feature Extraction Methods in Digital Image Processing

Capturing the Essence: Exploring Cutting-Edge Feature Extrac...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Prabu S Natarajan B Roshan George S. Sujitha P Divya Bharathi S. Rajeswari School of Computer Science and Engineering VIT University Chennai India Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Roever Engineering College Tamilnadu India Department of Artificial intelligence & Data science Panimalar Engineering College Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Tiruchirappalli Tamilnadu

Feature Extraction (FE) plays a vital role in the fields of image processing, machine learning and pattern recognition. Binarization, resizing, thresholding and normalization are the initial steps applied before extracting features from an image. FE methods are applied to images to extract the most essential features that help in detection, recognition and classification tasks. FE techniques are used in numerous applications, such as object recognition, character recognition and pattern recognition. This research study presents a comprehensive research study of different image FE techniques, including SURF, BRISK, PCA and SIFT, which have been employed for various applications in recent times. This research study explains each method's fundamental concepts and pros and cons and demonstrates in what scenario which feature extraction technique will be better. This research work also presents a summary table of FE techniques used in various applications with their accuracy percentage.

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An Empirical Study on Bankruptcy Prediction using Ensemble learning

An Empirical Study on Bankruptcy Prediction using Ensemble L...

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2022 RIVF international conference on Computing and Communication Technologies, RIVF 2022

作者： Tien, Hoang Luu Quang Tran, Linh Quang Do, Trong Hop University of Information Technology Vietnam National University Faculty of Information Science and Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9781665461665

Bankruptcy prediction helps to assess the financial condition of a company and its future perspectives within the context of long-term operation on the market. Using machine learning to solve this problem can be a time-efficient and cost-effective approach. This paper introduces an approach using Ensemble learning methods to tackle the bankruptcy classification problem, which achieved the fourth position on the leader board of The 3rd Annual international data Science & AI Competition 2022 - Structured data Track. The data set given by the committee of the competition has a lot of challenges so we perform some preprocessing and feature engineering techniques to make the data set become cleaner for modelling. We use three ensemble algorithms, namely Random Forest, Catboost, and LightGBM to compare the performance of three algorithms on the bankruptcy classification problem and find the best result to submit to the competition. After experimenting, we achieve the best result at 98.21% Accuracy on the private leader board of the competition. The result comes from the LightGBM model trained on the data set which is enhanced through feature engineering techniques. © 2022 IEEE.

关键词： data mining

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