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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 783 条记录，以下是61-70 订阅

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Enhancing Disease Prediction through Symptoms Based machine learning 7

Enhancing Disease Prediction Through Symptoms Based Machine ...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kumar, Santosh Padmawar, Saikumar Saiyad, Md Faiz Ramteke, Himanshu Kadam, Shrushti Vishwakarma Institute of Information Technology Pune411048 India

ISBN: (纸本)9798350304268

Traditional approaches to diagnosing and treating illnesses may have limitations, particularly when it comes to serious diseases. therefore, it is essential to have precise and timely analysis of health issues for effective prevention and treatment. machine learning (ML) algorithms offer a valuable tool in the development of a medical diagnosis system that can provide more accurate disease predictions compared to conventional methods. Our team has successfully developed a disease prediction system using a variety of ML algorithms, including Decision trees, Random Forest, Naïve Bayes, and Logistic Regression. this system leverages a dataset containing 4962 entries, encompassing 132 symptoms and over 300 diseases. By employing a rule-based approach, we have created a framework that enables the development and application of predictive models. the system's accuracy is remarkable, empowering clinicians to anticipate and analyze ailments at an early stage, thereby addressing health-related challenges more effectively © 2023 IEEE.

关键词： Classification machine learning Prediction Symptoms of Disease

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Applications of Artificial Intelligence and machine learning in Contemporary Quality Engineering 7

Applications of Artificial Intelligence and Machine Learning...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Pradeep, Aneesh New Uzbekistan University Software Engineering Tashkent Uzbekistan

ISBN: (纸本)9798350304268

the increasing complexity and global competition in various industries have compelled organizations to prioritize quality engineering as a critical factor for success. In recent years, Artificial Intelligence (AI) and machine learning (ML) have emerged as transformative technologies with the potential to revolutionize quality engineering practices. this paper explores the applications of artificial intelligence (AI) and machine learning (ML) in contemporary quality engineering. With the rapid growth of technology and increased demand for quality products, there is a need for efficient and effective methods for quality control. AI and ML provide innovative solutions for improving quality engineering processes' accuracy, speed, and *** discuss the role of AI and ML in data analysis and decision-making processes, enabling quality engineers to identify patterns, anomalies, and correlations within vast datasets. these techniques empower organizations to make data-driven decisions, optimize processes, and detect potential quality issues in realtime. the limitations and difficulties of these technologies are discussed, along with potential future lines of investigation. the research presented in this paper suggests that AI and ML hold great promise for revolutionizing quality engineering and raising standards across the board for goods and services. © 2023 IEEE.

关键词： artificial Intelligence machine learning predictive maintenance process optimization quality engineering

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Enhancing Hand Gesture Recognition with Media Pipe and SVM Model on Custom Dataset 7

Enhancing Hand Gesture Recognition with Media Pipe and SVM M...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bhavatarini, N. Bhuvana, K. Supraja, K. Tella, Harika School of Cse Reva University Bengaluru India

ISBN: (纸本)9798350304268

Gesture recognition is an important area of research with many potential applications such as human-computer interaction, language recognition, and gaming. In this research paper a new approach to motion recognition using the Media Pipe and SVM models has been explained. the approach includes preprocessing the video motion input, using Media Pipe to extract relevant features, and using an SVM model to classify the annotation and to evaluate the proposed methods in public information and demonstrate the effectiveness of the plan by achieving high accuracy. © 2023 IEEE.

关键词： Hand gesture recognition machine learning Media Pipe Sign Language Recognition SVM (Support Vector machine)

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Electricity Billing and Consumption Prediction using machine learning 7

Electricity Billing and Consumption Prediction using Machine...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bhandarkar, Mrunalini Suryawanshi, Ashok Deoghare, Seema Isaac, Nikhil Hegu, Ajinkya Wankhede, Piyush Pimpri Chinchwad College of Engineering Pune Pune411044 India

ISBN: (纸本)9798350304268

this research suggests a AI-learning based method for forecasting electricity usage and bill amounts. the project develops and assesses a number of machine learning models using historical electricity use data and customer billing information. the dataset, which consists of several months' worth of historical data from a residential power client, is used to train and test the models. the study shows that the suggested machine learning models are highly accurate in forecasting electricity consumption and bill amounts. According to the results, machine learning-based methods can greatly increase the precision of predictions of power use and bills, which can be helpful for both consumers and utility providers. the goal of this paper is to create a system that fuses the findings of various machine learning techniques, in order to conduct accurate prediction for a tenant. K nearest neighbour, Support vector machine and Artificial Neural Networks are few of the algorithms that are employed. Each algorithm's accuracy is determined along with the model's accuracy. the model for predicting is then chosen from those with decent accuracy. the dataset used for building the model is Smart metres Database, which was selected from Kaggle website. © 2023 IEEE.

关键词： Artificial Neural Networks Electricity Billing Energy consumption K Nearest Neighbor machine learning Prediction Support Vector machine

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Deep learning-Enabled Prediction of Daily Solar Irradiance from Simulated Climate Data 23

Deep Learning-Enabled Prediction of Daily Solar Irradiance f...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Gerges, Firas Boufadel, Michel C. Bou-Zeid, Elie Nassif, Hani Wang, Jason T. L. New Jersey Institute of Technology United States Princeton University United States Rutgers University - New Brunswick United States

ISBN: (纸本)9781450398633

Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. this energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase of greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte-Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. these locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099. © 2023 ACM.

关键词： Solar energy

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'Comparative Study of Classification Algorithms on Sonar Rock and Mine Dataset' 7

'Comparative Study of Classification Algorithms on Sonar Roc...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Gobade, Sahil Deshpande, Prathmesh Bhaldar, Md Zaid Ghorpade, Apoorva Bagade, Jayashree Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350304268

Accurate classification is essential for the safety of naval operations and is a difficult problem in marine mine removal. In this research, this study compares and contrasts six popular machine learning algorithms for classifying rocks and mines: K-Nearest Neighbors (KNN), Support Vector machine (SVM), and Logistic Regression (LR), Linear Discriminant Analysis, Gaussian Naive Bayes, Decision tree. the models are trained and evaluated using different performance metrics, such as accuracy, precision, recall, and F1 score. the data were carefully analyzed and compared, and it was discovered that SVM performed better than the other algorithms, obtaining an accuracy of 94.23%. the creation of more precise and effective classification models in numerous real-world applications, such as naval operations and other undersea pursuits, will be significantly impacted by these discoveries. the study emphasizes the significance of choosing the right method for classification tasks. © 2023 IEEE.

关键词： Accuracy Metric Hyperparameters machine learning classifiers Support Vector machine Tunning

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A Review of machine learning Algorithms and Feature Selection Techniques for Cardiovascular Disease Prediction: Insights and Implications 7

A Review of Machine Learning Algorithms and Feature Selectio...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mahajan, Abhigya Kaushik, Baijnath School of Cse Shri Mata Vaishno Devi University Katra India

ISBN: (纸本)9798350304268

Cardiovascular disease (CVD) is a formidable public health challenge across the globe and is the most prevalent cause of mortality. Early detection and accurate prediction of CVD can help prevent disease progression and reduce the risk of complications. machine learning (ML) techniques show promising results in improving the accuracy and efficiency of CVD prediction to precision. However, the effectiveness of machine learning algorithms in CVD prediction largely depends on the selection of relevant features from complex datasets. the performance and interpretability of ML models are improved by feature selection strategies, which attempt to identify significant attributes while eliminating duplicate or irrelevant features. the feature selection and ML algorithms for CVD are thoroughly reviewed in this publication. the review provides insight into the selection of appropriate feature selection techniques and machine learning algorithms for accurate CVD prediction and evaluates the effectiveness and performance of these methods on cardiovascular datasets. Insights from the findings of this study can be used for interpreting the selection of optimal feature selection methods and ML algorithms for the precise prediction of cardiovascular disease, thereby improving patient outcomes and reducing healthcare costs. © 2023 IEEE.

关键词： chi-square feature selection filter method machine learning wrapper method

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Detection of Cervical Cancer using Colposcopy: An Image Processing Approach 7

Detection of Cervical Cancer using Colposcopy: An Image Proc...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Gaikwad, Aaishwarya Ashish Shinde, Swati Bhuta, Nishi Jadhav, Tejas Jangale, Param Kalbhor, Madhura Pimpri Chinchwad College of Engineering Dept. of Computer Science Pune India

ISBN: (纸本)9798350304268

the fourth most frequent disease in women globally is cervical cancer, and effective treatment depends on early detection. Colposcopy, which involves visually inspecting the cervix under a specialized microscope, is a frequently employed technique for identifying cervical cancer. In this study, we suggest a colposcopy image-based computer-aided diagnostic (CAD) system for the quick and painless identification of cervical cancer. machine learning techniques are used by the system to extract features from the photographs and categorize them as normal or abnormal. the suggested method Naïve Bayes and Logistic Regression had an overall accuracy of 73 % after being trained and tested on a dataset of 1313 colposcopy images. © 2023 IEEE.

关键词： Biomedical Cervical Cancer Colposcopy Image Processing machine learning

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GemInsight: Unleashing Random Forest for Diamond Quality Forecasting 7

GemInsight: Unleashing Random Forest for Diamond Quality For...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Swain, Debabrata Chaturvedi, Avani Rathi, Vishesh Pandit Deendayal Energy University Computer Science and Engineering Department Gujarat India

ISBN: (纸本)9798350304268

Diamonds are considered as one of the rarest and most expensive gemstones in the world. the quality of any diamond always determines its market price. Diamond quality generally depends upon several characteristics. the total internal reflection and refractive indices of the diamond, which depend on its cut, are used in the conventional diamond industry to determine the quality of the diamond. As the cost of this gemstone is very high, hence accurate grading of diamond is quintessential. In this work an automated diamond grading solution is proposed using machine learning. For better identification of the diamond cut, the recommended system uses its different qualitative parameters. For model training and evaluation, diamond dataset residing in the Kaggle repository has been utilised. Random Forest Model has shown promising accuracy of 79%. © 2023 IEEE.

关键词： Accuracy Carat Weight Clarity Color Cut Encoding machine learning Precision Random Forest Recall

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Easy Job Eligibility Checking System using machine learning 7

Easy Job Eligibility Checking System using Machine Learning

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Balpande, Mangesh Mali, Sakshi Dagadu Chaudhari, Durgesh Sunil Mali, Nivedita Bapu Chaudhari, Ketaki Pradip SVKM's Institute of Technology Department of Information Technology Dhule India

ISBN: (纸本)9798350397253

this study presents a novel approach to automate the process of job eligibility verification. the suggested system examines the candidate's qualifications and the job criteria using machine learning and natural language processing methods. the algorithm generates an eligibility score by comparing a candidate's qualifications to the job's criteria. Both recruiters and job seekers will have a smooth experience because to the suggested system's powerful, effective, and user-friendly architecture. the system also gives suggestion of skills and courses required for the eligibility job. the system's effectiveness was evaluated through extensive experimentation on a large dataset of job requirements and candidate qualifications, and the results demonstrated its high accuracy and effectiveness. the proposed system has the potential to revolutionize the job application process, making it easier and more efficient for both recruiters and job applicants. © 2023 IEEE.

关键词： machine learning

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