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检索条件"主题词=Machine Learning Algorithms"

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ESEEG: An Efficient Epileptic Seizure Detection using EEG signals based on machine learning algorithms 2

ESEEG: An Efficient Epileptic Seizure Detection using EEG si...

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2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2022

作者： Abdelminaam, Diaa Salama Fahmy, Andrew Gamal Ali, Youssef Mohamed El-Din, Omar Ahmed Diaa Aly, Ahmed Raouf Heidar, Mahmoud Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9781665466776

Epileptic seizure Disease is a substantial health burden and cause of death worldwide. For epileptic seizure localization and classification,It's critical to be able to detect recorded epileptic seizure activity in EEG segments. Seizure progression is usually a dynamic and non-stationary process involving signals of multiple frequency., therefore visual and traditional frequency-based techniques are restricted in their utility. Researchers are attempting to integrate artificial intelligence (AI) and machine learning (ML) approaches to improve medical practise as a result of advancements in AI and ML methodology. Early disease identification and prediction is a critical goal in healthcare in order to provide timely preventive actions. This is especially true in the case of epilepsy, which is characterised by frequent and unexpected convulsions. We present a comprehensive overview of existing machine learning strategies for early seizure prediction using EEG data. We will highlight the research's shortcomings, difficulties, and pitfalls, as well as make recommendations for future directions. The following findings (accuracy) were obtained: KNN : 94%, Decision Tree : 94%, Random forrest: 96%, and Convolutional Neural Network (CNN) : 96.9% © 2022 IEEE.

关键词： Location awareness Visualization machine learning algorithms Epilepsy machine learning Medical services Ubiquitous computing

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machine learning algorithms for Medicine Text Mining Disease Diagnosis

Machine Learning Algorithms for Medicine Text Mining Disease...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Mridula Gupta Vignesh T Udita Goyal Shakeela Banu C Monali Ravindra Borade D. Anandhasilambarasan Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka AI & Ds Vishwakarma Institute of Technology Pune Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The proliferation of digital devices like smartphones and the Internet of Things has resulted in a flood of data. Every second, millions of bytes of data (including text, music, images, and videos) are produced. Thus, the need for big data analytics has been sparked by the current situation. Healthcare service records are an example of the large amounts of data created every day by hospitals. Information on patient symptoms, drug use, and the discovery of new illnesses, among other things, may be gleaned from this massive dataset. In this investigation, we use text processing methodology to public hospital patient record text data from 2017–2019 to better understand how diseases manifest in their patients. Preprocessing data from medical records involves using the Nave Bayes Classifier & Random Forest algorithms to categorise illnesses. To narrow down the collection of symptom statements, a list of altered Indonesian stop words was employed. Compared to the Naive Bayes classification technique. Experiments validate the effectiveness and efficiency of our suggested technique for illness classification in medical records.

关键词： Text mining machine learning algorithms Accuracy Hospitals Stroke (medical condition) Classification algorithms Medical diagnostic imaging Random forests Videos Text processing

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machine learning algorithms for Crop Yield Prediction in Real-Time Scenarios

Machine Learning Algorithms for Crop Yield Prediction in Rea...

引用

International Conference on Signal Processing and Communication (ICSPC)

作者： N. V Suresh Krishna Pokala Neeraj Bathina Surya B.V. Prudhvi V. Hima Deepthi Department of ECE Kalasalingam Academy of Research and Education India

The main pillar of the Indian Economy is Agriculture as it ensures food security and financial stability for all people in ancient days. However, due to the unnatural weather conditions, the production of yield crops has severely affected the income of farmers. This work helps early-stage farmers by guiding them through the process of sowing the right crops based on soil using the help of machine learning algorithms. The seeds are collected based on the soil parameters such as Nitrogen, phosphorous, potassium, temperature, and humidity. The system is trained using the Random Forest algorithm to suggest and help the farmers to achieve a successful harvest. A web-based application is also being developed which will allow users to get data about nutrients required for the crop. Experiments are conducted on the collected data samples from the web application and predict the crop yield with good accuracy of 91.2 % and in less response time.

关键词： machine learning algorithms Crops Production Soil Signal processing Real-time systems Seeds (agriculture)

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machine learning algorithms in Gene Editing

Machine Learning Algorithms in Gene Editing

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kaluad Sanyour Wooyoung Kim Computing and Software Systems School of STEM University of Washington Bothell Bothell WA USA Computer Science Department Alwajh College University of Tabuk Tabuk Saudi Arabia

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), as an adaptive immunity mechanism in prokaryotes, is a programmable system that can be used for editing genes of various species. Possibly it can be used to edit genes causing some rare diseases of humans, such as Ichthyosis. CRISPR is associated with the Cas9 protein that cuts a DNA sequence. Cas9 binds to a gRNA that guides Cas9 to an editable site. Although versatile and easy to design, a misguiding gRNA could result wrong edition of genes. Hence, the CRISPR/Cas9 process needs to find ideal gRNAs that can guide Cas9 to on-targets, and avoid off-targets. Various machine learning algorithms play important roles in finding effective gRNAs for the CRISPR/Cas9 by predicting the cleavage efficiency of gRNA. Here, we aim to provide an overview and comprehensive analysis of various machine and deep learning algorithms that are effective in predicting CRISPR gRNA sequences’ on-target activities. Comparison results show that the hybrid approaches combining deep learning and other machine learning algorithms present excellent outcomes. We applied the models to predict the cleavage efficiency of gRNAs from the FLG gene causing Ichthyosis, and the results suggest to use multiple models to obtain consistent and trustworthy results. Although our work does not provide final solutions, it will open opportunities for computational tools to diagnose and treat rare diseases. The datasets and codes are available at https://***/ksanyour/MLforGeneEditing.

关键词： Deep learning Proteins machine learning algorithms Computational modeling Biological system modeling Predictive models Prediction algorithms

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machine learning algorithms for document clustering and fraud detection

Machine learning algorithms for document clustering and frau...

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International Conference on Data Science & Engineering (ICDSE)

作者： Suresh Yaram Data Architect CSC India Limited Hyderabad India

ISBN: (纸本)9781509012824

machine learning plays very important role in processing of large amounts of structured and unstructured data. A set of algorithms can be used to get meaningful insights into the data that are helpful in making effective business decisions. Document clustering is one of the popular machine learning technique used to group unstructured data (text documents) based on its content and further analyze the data to understand the patterns in it. The unstructured data gets transformed into semi-structured data and structured data in stages by using text mining and clustering (k-means) techniques. Classification is another machine learning technique that can be implemented for use cases like "fraud detection and cross-sell & up-sell opportunity identification" in banking, financial services and insurance industry. This paper focuses on the implementation of both document clustering algorithm and a set of classification algorithms (Decision Tree, Random Forest and Naïve Bayes), along with appropriate industry use cases. Also, the performance of three classification algorithms will be compared by calculation of "Confusion Matrix" which in turn helps us to calculate performance measures such as, "accuracy", "precision", and "recall".

关键词： Classification algorithms Feature extraction Decision trees Entropy Prediction algorithms Insurance machine learning algorithms

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machine learning algorithms for Detecting Mental Stress in College Students

Machine Learning Algorithms for Detecting Mental Stress in C...

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International Conference for Convergence of Technology (I2CT)

作者： Ashutosh Singh Khushdeep Singh Amit Kumar Abhishek Shrivastava Santosh Kumar Department of Data Science and Artificial Intelligence IIIT Naya Raipur Chhattisgarh India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In today’s world, stress is a big problem that affects people’s health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various machine learning algorithms: Decision Trees, Random Forest, Support Vector machines, AdaBoost, Naive Bayes, Logistic Regression, and K-nearest Neighbors. The primary objective of this work is to leverage a research study to predict and mitigate stress and non-stress based on the collected questionnaire dataset. We conducted a workshop with the primary goal of studying the stress levels found among the students. This workshop was attended by Approximately 843 students aged between 18 to 21 years old. A questionnaire was given to the students validated under the guidance of the experts from the All India Institute of Medical Sciences (AIIMS) Raipur, Chhattisgarh, India, on which our dataset is based. The survey consists of 28 questions, aiming to comprehensively understand the multidimensional aspects of stress, including emotional well-being, physical health, academic performance, relationships, and leisure. This work finds that Support Vector machines have a maximum accuracy for Stress, reaching 95%. The study contributes to a deeper understanding of stress determinants. It aims to improve college student’s overall quality of life and academic success, addressing the multifaceted nature of stress.

关键词： Support vector machines Surveys Logistic regression machine learning algorithms Conferences Human factors Vectors

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machine learning algorithms for Detecting Phishing Websites

Machine Learning Algorithms for Detecting Phishing Websites

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Shantanu Sudhir Gujar Department of Cyber Security Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Despite the quick growth of the digital world, phishing attempts still constitute a serious threat to the security of online banking transactions. The goal of this research project is to evaluate the usage of machine learning algorithms to detect websites that are used to spread phishing messages. This work specifically considers complex feature engineering and algorithm selection approaches. The detection method is enhanced by the application of optimal model, which is utilized to extract and evaluate text-based elements from phishing websites. It is done with optimal model. DNSPython and Python-Whois are the technologies utilized to finish gathering domain-related data. However, Scikit-learn makes the process of putting machine learning models into practice easier. The objectives that AutoML aids in achieving include efficient model selection and optimization. This has the important advantage of making it possible to automatically identify the algorithms with the highest performance levels. The objective of this research endeavour is to enhance the accuracy and robustness of phishing detection systems through the utilization of diverse methodologies and technologies. Additionally, the study makes an effort to clarify how machine learning would be able to successfully counteract the increasingly complex phishing methods. Through improving the automatic identification of phishing-related websites, our program seeks to contribute to the creation of safer online environments.

关键词： Technological innovation machine learning algorithms Accuracy Phishing Feature extraction Automated machine learning Robustness Real-time systems Security Optimization

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machine learning algorithms for quality control in plastic molding industry

Machine learning algorithms for quality control in plastic m...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Alberto Tellaeche Ramón Arana Tekniker-IK4 Gipuzkoa Spain

Injection molding is a very complicated process to monitor and control. With its high complexity and many process parameters, the optimization of these systems is a very challenging problem. To meet the requirements and costs demanded by the market, there has been an intense development and research with the aim to maintain the process under control. This paper outlines the latest advances in algorithms for plastic injection process and monitoring, and presents a real case of application that verifies their performance.

关键词： Plastics Injection molding Monitoring Process control Optimization machine learning algorithms

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machine learning algorithms for Binary and Multiclass Classification of IoT Network Traffic in Smart Cities

Machine Learning Algorithms for Binary and Multiclass Classi...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： V Santhosh Kumar Dhiraj Sunehra Department of ECE JNTUH Univ. College of Engg. Sc.& Tech. Hyderabad India Department of ECE JNTUH Univ. College of Engg Rajanna SircillaT.S. India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Internet of Things describes variety of embedded devices that are connected to the internet. This network of devices are used to build smart environment for smart city applications and used by many organizations. While data is constantly exchanged among various IoT devices, the detection of malicious attacks is essential for effective quality of service implementation in IoT networks. In this research, the University of New South Wales TON_IOT Train-Test dataset is used to classify network traffic. State of art machine learning methods such as Random Forest (RF), Decision Tree (DT), and Gradient Boosting (GB) are used to detect malicious IoT traffic. There are two classification types are considered in this study-binary and multi-label. When compared to the other two models, the Random Forest model gets the best accuracy of 99% in both binary and multi class classification.

关键词： machine learning algorithms Accuracy Smart cities Databases Telecommunication traffic Boosting Data models

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machine learning algorithms approach for Gastrointestinal Polyps classification

Machine Learning algorithms approach for Gastrointestinal Po...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Kristijan Cincar Ioan Sima Faculty of Mathematics and Informatics West University Timisoara Romania Faculty of Mathematics and Computer Science Babes-Bolyai University Cluj-Napoca Romania

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

In this paper we applied machine learning techniques for Gastrointestinal Polyps classification from colonoscopy video clips and compared our results to other methods and with the results of clinicians with different levels of experience. machine learning technology allows us to classify tissues that can reduce the waiting time for patients' results. We tested four machine learning algorithms (Support Vector machine, Random Forest, Random Subspace and Extra-Trees) for classification of the polyps in hyperplastic, serrated and adenoma lesions. We used a dataset in which there are 152 instances with the three types of lesions, for 76 polyps. The best results were obtained by Random Forest algorithm with the accuracy of 87%, and the worst results were obtained by Support Vector machine with the accuracy of between 63% and 73%.

关键词： Cancer Image color analysis Support vector machines Vegetation machine learning algorithms Lesions Shape

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