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共 65488 条记录，以下是4601-4610 订阅

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Chronic Illness Detection using Gradient Boosting Algorithm 15

Chronic Illness Detection using Gradient Boosting Algorithm

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Guhan, T. Bhavishya, S. Kalaiarasan, S. Lalith, K. Dhitchith, O.P. Karpagam College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331300579

The proposed work emphasizes the importance of accurate diagnosis of chronic kidney disease. The research helps refine proactive healthcare strategies and provides valuable insights into the prevention and management of chronic diseases. The goal of this project is to develop a framework based on Big Data analysis that predicts the likelihood of kidney failure, a chronic disease. The framework uses advanced machine learning techniques such as MEG (Mean Decrease Gini), MSE (Mean Square Error), Grid Search, K-fold cross validation to detect risk factors and correlations associated with chronic diseases. The models help healthcare professionals make informed decisions and detect high-risk individuals so that proactive strategies can be implemented to prevent or delay chronic diseases. The framework can be used to predict a wide range of chronic diseases beyond kidney failure. However, the risk factors and correlations can vary and need to be adapted accordingly. Further research and testing are needed to determine the effectiveness of the framework. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Foreign Exchange Market Analysis based on Novel Grey Wolf based Extreme learning machine Predictor 4

Foreign Exchange Market Analysis based on Novel Grey Wolf ba...

引用

4th international conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Dash, Swaty Sahu, Pradip Kumar Mishra, Debahuti Veer Surendra Sai University of Technology Burla Dept. of Information Technology Odisha Sambalpur India University Dept. of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

Foreign exchange trading basically bridges a gap between buyer and seller to transact at a set of prices of the currencies to make profit out of it by the traders and investors. In this paper, foreign exchange predictive models are explored based on basic Extreme learning machine (ELM) along with optimized version of ELM. Initially, this work tried to obtain the best optimized ELM network based upon the four nature-inspired optimization methodologies such as;particle swarm, ant colony, moth flame and grey wolf optimization (GWO). From this phase, it has been observed that the ELM network optimized with GWO outperforms rest of other experimented optimized ELM networks with respect to error graphs and performance metrics. In order to evaluate the performance, the ELM-GWO currency exchange predictor, the recognition performance metrics such as;EVS, MAE, MSE and R2 Score are computed, the error graphs are obtained and the average computational time taken are compared. © 2024 IEEE.

关键词： Ant colony optimization

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Prediction of Diabetes Using machine learning Classifiers 3

Prediction of Diabetes Using Machine Learning Classifiers

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Tanvi Kaur, Swapandeep Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319125

Diabetes is the most prevalent chronic disease and a number of people are suffering from it all over the world. Diabetes could be due to hereditary reasons, obesity, etc. Diabetes can cause other complications like heart disease, so prediction and prevention of diabetes is of utmost importance. In this paper, five machine learning algorithms that are Logistic Regression, Decision Tree, Random Forest, Support Vector machine and K-Nearest Neighbor have been used for the prediction of diabetes. It was found that Logistic Regression performed the best amongst the five classifiers and obtained highest accuracy of 0.7359, precision of 0.7312, recall of 0.7359 and f1-score of 0.7329. © 2023 IEEE.

关键词： Decision Tree Diabetes K-Nearest Neighbor Logistic Regression machine learning Random Forest Support Vector machine

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An Ensemble of machine learning Models Utilizing Deep Convolutional Features for Medical Image Classification 3rd

An Ensemble of Machine Learning Models Utilizing Deep Convol...

引用

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

作者： Jana, Nanda Dulal Dhar, Sandipan Ghosh, Subhayu Phukan, Sukonya Gogoi, Rajlakshmi Singh, Jyoti Natl Inst Technol Durgapur Durgapur 713209 India Jorhat Engn Coll Jorhat 785007 Assam India

ISBN: (纸本)9783031640698;9783031640704

Medical image classification is a rapidly growing research field that has revolutionised various diseases' traditional diagnosis, treatment planning, and prognosis prediction. Due to the recent advancements in deep learning (DL) algorithms for medical imaging, the ability to identify anomalies and crucial features in medical images has greatly improved, enhancing the precision of diagnosis and treatment. However, the training process of deep learning algorithms is time-consuming and resource-intensive. In contrast, a lightweight model can be easily deployed for real-time implementation. Furthermore, in preceding studies, single models are primarily deployed for performing medical image classification tasks, though an ensemble of various models can enhance overall performance. Therefore, in this work, an ensemble of six machine learning (ML) models is used to reduce the training time compared to a conventional DL model. Moreover, the features considered in this work are deep convolutional features extracted using pre-trained DL models. The dataset considered in this work includes X-rays, CT scans, and MRI images of human body parts associated with several diseases. Our proposed ensemble learning approach is experimentally proven to outperform individual models, considering the deep convolutional features.

关键词： Medical Image Classification Ensemble learning Approach Deep Convolutional Features

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Decoding Heart Health using machine learning 15

Decoding Heart Health using Machine Learning

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sisodia, Yashanjali Kotkar, Mansi Katore, Ashlesha Jha, Kaustubh Shinde, Shreya Department of Computer Engineering Savitribai Phule Pune University Maharashtra Pune India

ISBN: (纸本)9798331300579

In this review, we are going to deal with making a decent and ideal model to foresee diabetes right off the bat. The objective is to prevent the illness from deteriorating and creating some issues. We are utilizing data from various datasets. Our fundamental apparatus for this is something many refer to as strategic relapse. We are attempting two methods for picking the main data from the information to improve our model. We are mostly utilizing a couple of stunts to consolidate various forecasts and make our speculations more exact and precise. We are utilizing a programming instrument called Python. Our discoveries show that strategic relapse is very great at this specific employment. The best precision we got was 78% for one dataset and 93% for the other in the wake of utilizing our stunts to consolidate expectations. We likewise discuss how diabetes is a major issue overall and that it is so vital to think that it is early. Our expectation is that our review assists make with bettering apparatuses for anticipating diabetes early. This could mean specialists can assist with peopling sooner, and that is significant for keeping everybody better. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Traffic Congestion Prediction using machine learning 18

Traffic Congestion Prediction using Machine Learning

引用

18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Kafy, Md. Arafath Faisal, Saimon Islam Rahman, Md. Lutfor Moni, Raka Shanmuganathan, Harinee Raza, Dewan Mamun Daffodil International University Dept. of Cse Dhaka Bangladesh Alliance College of Engineering & Design Aliance University Dept. of Cse Bengaluru India

ISBN: (纸本)9789380544519

Due to the world population growing rapidly over time, the number of personal and local vehicles are increasing which is one of the main causes of high traffic on the roads. For high traffic, the average speed of vehicles is decreasing which is known as traffic congestion. It is a very common and alarming problem in today's world. Due to traffic congestion, civilians are facing different problems in this 21st century. Time is a precious thing and traffic congestion is killing the most precious times of our lives. In this paper, the authors aimed to offer a traffic congestion prediction model that will help to predict the traffic congestion of a particular area in a definite time period. During working with machine learning models or algorithms there is a concern about the accuracy of the result. To overcome this problem, 5 different machine learning models which are used decision tree, random forest, logistic regression, SVM, and MLP to predict the congestion rate. The authors compared those models with each other and calculated the mean absolute error for each of the models so that the prediction can be more accurate. Efforts are made to alleviate the traffic congestion reducing commute times and lower carbon emissions and to enhance the overall quality of life in cities. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： decision tree logistic regression random forest time series analysis traffic congestion

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Real Time Noise Pollution Prediction in a City using machine learning and IoT 15

Real Time Noise Pollution Prediction in a City using Machine...

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kumar, A. Manoj Pavan Sai Jagadeesh, S.V.V.D. Siddarth, G. Gagan Sri, K. Manasa Phani Rajesh, V. Lakireddy Bali Reddy College of Engineering Mylavaram India

ISBN: (纸本)9798331300579

This study introduces a novel approach to tackle urban noise pollution in Vijayawada city by leveraging machine learning (ML) and Internet of Things (IoT) technologies. The aim is to predict real-time noise pollution levels, with a particular emphasis on the impact of road traffic. The methodology integrates four regression models (Decision Tree, Gradient Boosting, Support Vector, Linear Regression with Grid Search) and three forecasting models (ARIMA, Holt-Winters Exponential Smoothing, SARIMA), utilizing data collected from IoT acoustic sensors across diverse landscape configurations. This research underscores the importance of ML and IoT in addressing urban noise pollution challenges and implications for urban planning and policy making, acknowledging both strengths and limitations of the proposed approach. In Existing models there is no real time data used for prediction. But this case study includes real time data collected from IOT sound sensor which give better results. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Enhancing False News Detection through Supervised machine learning and NLP Techniques: A Comparative Study of Feature Extraction and Selection Methods Using Python Scikit-Learn 1

Enhancing False News Detection through Supervised Machine Le...

引用

1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sikarwar, Shailendra Singh Kumar Babubhai Patel, Chintan Dhanjibhai, Patel Urvashi Vir Singh, Vijay Ravi, A.T. Mohan Sakya, Lalit Jiwaji University Dept. of Computer Science Gwalior India Madhav University Dept. of Physical Education Pindwara India Lingayas Vidyapeeth University Dept. of Mathematics Faridabad India Dhanalakshmi Srinivasan University Dept. of CSE Coimbatore India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

The spread of false news via social and other media platforms is a major worry because it has the potential to cause significant harm to societies and nations. As a result, many academics are trying hard to detect and counteract the spread of fake news. This study analyses research on false news identification and provides a novel strategy that uses supervised machine learning algorithms, standard machine learning models, and textual analysis tools such as Python, Scikit-Learn, and NLP. The suggested method effectively classifies news articles as true or fake by utilizing techniques such as feature extraction and vectorization. For text data tokenization and feature extraction, the Python Scikit- Learn module, known for useful functions such as Count Vectorizer and Tiff Vectorizer, is used. Furthermore, feature selection methods are used to experiment with and discover the most appropriate features, resulting in maximum precision based on the confusion matrix analysis. The proposed model is trained on a dataset that includes both true and false news articles, allowing it to classify new articles quickly and accurately, assisting users in establishing the legitimacy of online news stories. In summary, this study contributes to continuing efforts to combat false news by reviewing previous research on its detection and providing an approach that combines classical machine learning models, Python Scikit-Learn, and natural language processing (NLP) for effective textual analysis. The suggested model detects false news with high accuracy and precision, providing users with a useful tool for determining the validity of news pieces. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing Operational Efficiency and Security in Multi-Sectioned Businesses with Edge AI through Real-Time Analytics

Enhancing Operational Efficiency and Security in Multi-Secti...

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2024 IEEE international conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Vijay, K. Jeyanth, V. Kamalesh, S. Dheva Prasath, D. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

The goal of this project is to disrupt the way businesses monitor their operations by integrating IoT and machine learning to gain a deeper understanding of customer behavior and improve operational efficiency. Traditional surveillance systems create lots of data to store and analyze, but they do not provide actionable intelligence. CCTV cameras and edge devices strategically placed throughout a facility capture real-time footage, identify visitor movement patterns, and represent critical heat zones on a heat map. This data-driven process enhances resource allocation, enhances customer experience, and enables proactive improvements in operational workflow. Edge devices enable real-time anomaly detection, such as theft or staff sitting, without compromising privacy, as the device retains all information and performs all processing locally. Our next-gen solution provides businesses with an all-inclusive monitoring system that solves today's most pressing security and operational issues, ultimately delivering better business results. © 2024 IEEE.

关键词： Resource allocation

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Classification and Featurization of Motherboard Components Using YOLO Through machine learning

Classification and Featurization of Motherboard Components U...

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2024 IEEE international conference on computing, Applications and Systems, COMPAS 2024

作者： Rahman Fahim, Md. Imadur Mahazabin Senthia, Sabrina Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of Artificial Intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans. © 2024 IEEE.

关键词： Robot vision

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