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检索条件"主题词=Machine learning algorithms"

共 25882 条记录，以下是151-160 订阅

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machine learning algorithms for Robust Software Vulnerability Detection

Machine Learning Algorithms for Robust Software Vulnerabilit...

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Innovation and Novelty in Engineering and Technology (INNOVA), International Conference on

作者： Gayatri Bajantri Noorullah Shariff C Department of Computer Science & Engineering BLDEA's V.P. Dr. P.G. Halakatti College of Engineering Vijayapur Karnataka India Department of AIML Ballari Institute of Technology and Management Bellary Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Software flaws pose a serious risk to the dependability and security of computer systems and applications in a variety of industries. Because they take a long time and are prone to errors, traditional techniques of finding these vulnerabilities-like human code reviews and static analysis tools-are frequently insufficient. In order to improve the precision, effectiveness, and scalability of vulnerability detection, this paper presents a thorough technique for creating a software vulnerability detection tool that uses machine learning (ML) methods. The tool uses machine learning (ML) to automatically evaluate code samples, find possible security holes, and give developers useful information. The suggested approach consists of multiple phases, such as gathering and preprocessing data, extracting features, training and assessing models, and integrating and deploying tools. To maximise detection performance, a variety of machine learning (ML) algorithms are investigated, such as logistic regression, decision trees, random forests, support vector machines, and deep learning models. The tool's ability to adjust to new threats is guaranteed by its feedback mechanisms and ongoing monitoring. With the help of this cutting-edge technique, software engineers and security experts will be able to detect and address flaws more effectively and automatically, strengthening software systems' defences against ever-changing cyberattacks.

关键词： Training Deep learning Logistic regression Codes machine learning algorithms Feature extraction Software systems Computer crime Random forests Monitoring

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machine learning algorithms for Smart Healthcare: Breast Cancer Prediction

Machine Learning Algorithms for Smart Healthcare: Breast Can...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Manoj Kumar Usha Urmila Pilania Narapureddy Durga Prasad Reddy Kodati Sai Teja Computer Science and Engineering Manav Rachna Univeristy Faridabad India Applied Sciences MRIIRS Faridabad India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Background: Breast cancer continues to be a significant concern for women worldwide, emphasizing the crucial role of early detection in improving treatment outcomes. The machine learning techniques play an important role in early detection of breast cancer. Detection of breast cancer with traditional techniques is time-consuming and does not provide accurate results. So to enhance the accuracy of detection and to decrease time taken, an automatic breast cancer technique is required. This work introduced innovative machine-learning algorithms for the detection of breast cancer. machine learning algorithms used in this paper are Neural Networks (NN), Logistic Regression (LR), Support Vector machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) with a widespread set of related parameters. The dataset taken for the work contains many patient demographics, clinical details, and histopathological information to train and validate machine learning algorithms. For the evaluation of the work accuracy and loss are calculated. The Logistic Regression outperformed with remarkable accuracy 97.4%, over the mentioned five other algorithms. All five models are compared to verify and validate the experimental results. The proposed work requires very less human intervention and attained high accuracy in considerable time. In future, for validation and its applications in clinical settings, the author can design a hybrid technique that can handle real-time clinical data.

关键词： Support vector machines Radio frequency Logistic regression Accuracy machine learning algorithms Artificial neural networks Nearest neighbor methods Breast cancer Real-time systems Decision trees

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machine learning algorithms for predicting electrical performance of perovskite solar cells

Machine learning algorithms for predicting electrical perfor...

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Technology and Engineering Management Society (TEMSCON LATAM), IEEE

作者： Carlos Castañeda Camilo Hernández Sergio Castro Byron Medina Oriana López Jorge Gómez Erick Reyes Alexander Sepúlveda Facultad de Ingenieria Universidad Francisco de Paula Santander Cúcuta Colombia Facultad de Ingenieria Universidad del Magdalena Santa Marta Colombia Facultad de Ingenieria Instituto Metropolitano de Medellín Medellín Colombia Facultad de Ingenieria Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350354317

ISBN: (纸本)9798350354324

Perovskite solar cells (PSCs) have undergone significant evolution in a short time, achieving an efficiency increase of approximately 22% in less than 15 years. These advancements have been driven by empirical findings from experimental cells, leading to increased costs and labor in the development of this technology. Therefore, based on these empirical findings with experimental cells, predictions close to reality can be obtained using machine learning regression algorithms. These models seek complex patterns in a series of variables that aim to describe the cell. This paper aims to compare machine learning algorithms to predict the electrical performance of a PSC using variables such as open-circuit voltage (Voc), current density (Jsc), fill factor (FF), and power conversion efficiency (PCE), based on variables classified as molecular structure, cell layers, and manufacturing processes. The methodology comprises variable selection, feature engineering, model selection, and model evaluation. For the electrical variables, a mean absolute percentage error (MAPE) of 9.3% and a mean absolute error (MAE) of 0.05 V are predicted for Voc; a MAPE of 10.4% and a MAE of 1.5 mA/cm 2 for Jsc; a MAPE of 10% and a MAE of 0.05 for FF. It is concluded that it is feasible to implement machine learning models to predict the electrical performance of PSCs. This integration represents an advancement in renewable energy technology by providing a predictive model that reduces reliance on costly and labor-intensive experimental procedures. The results contribute to the fields of renewable energy and machine learning by offering a tool that can lead to improved efficiency and cost-effectiveness in PSC development.

关键词： Renewable energy sources Manufacturing processes machine learning algorithms Photovoltaic cells Voltage Predictive models Prediction algorithms Approximation algorithms Perovskites Random forests

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machine learning algorithms Application for Fixed-Income Market Analysis: Cross Countries Comparisons

Machine Learning Algorithms Application for Fixed-Income Mar...

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International Conference on Advanced Computer Information Technologies (ACIT)

作者： Natalia Chernova Olena Serhiienko Maryna Mashchenko Iryna Lisna Olha Haponenko Department of Software Engineering and Management Intelligent Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Business Trade and Logistics Department National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Business School University of Southampton Southampton United Kingdom

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

The research aim is to distinguish the global fixed income market structure and dynamics basing on such core indicator as yields to maturity of government bonds. The processes taken part within or linked with fixed income market are of core importance for the global world economic system as a whole, thus have to be analyzed and researched constantly to ensure decision-making processes on all levels of hierarchy. The following tasks have been implemented to achieve the aim: initial observation set of yields data for world government bonds of different maturities has been formed; initial dataset dimension has been reduced based on principal components analysis technique; fixed-income market structure has been analyzed in the obtained three-dimensional level-slope-curvature space for each researched country; set of countries has been classified onto homogenous groups in multi-dimensional space basing on k-means algorithm; the resulting groupings composition and structure have been analyzed.

关键词： machine learning algorithms Heuristic algorithms Government Decision making Benchmark testing Prediction algorithms Trajectory Information technology Elbow Principal component analysis

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machine learning algorithms for Emotion Detection using EEG Signals

Machine Learning Algorithms for Emotion Detection using EEG ...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Md Sajjad Hossain Saroj Kr. Biswas Dalton Meitei Thounaojam Atiya Khan Kazy Noor e Alam Siddiquee CSE Dept. NIT Slichar Assam India Faculty of Engineering Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Assessment of clinical subjects’ cognitive functions and state is essential to e-healthcare delivery and developing novel human-machine interfaces. With the advancements in brain physiology research, electroencephalogram (EEG)-based emotion detection has emerged as an intriguing and popular study area. The emotion detection model can be implemented in human mental health monitoring, therapeutic support, law enforcement agencies, driver monitoring, etc. Numerous studies have been conducted in the last few decades to use EEG waves to detect emotions. In these research articles, an exhaustive study was not shown to find the best machine learning (ML) algorithms for multi-class emotion detection. Moreover, most of these researchers used a large number of electrodes in the experiment, which creates complexity in real-time setup. Therefore, the primary objective of this study is to identify the most effective ML algorithm that gives the expected accuracy using a single electrode. The proposed research has been done on the SEED-IV dataset to classify four common types of emotions: Happy, Fear, Sad, and Neutral. Different types of standard ML classifiers, including ensemble ML classifiers, have been used in this experiment. The performance of this study has been assessed by splitting and 10-fold cross-validation. Linear Discriminant Analysis (LDA) shows a better classification rate of 72.22% and 72.31% among all the classifiers according to Accuracy and F1 score. This study’s results anticipate opening up new possibilities for neuroscience by demonstrating that single-channel EEG data alone is sufficient for categorizing emotions.

关键词： Electrodes Emotion recognition machine learning algorithms Accuracy machine learning Electroencephalography Real-time systems Object recognition Monitoring Standards

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machine learning algorithms for Data Mining and Predictive Analytics in Precision Agriculture

Machine Learning Algorithms for Data Mining and Predictive A...

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Proceedings of the International Convention MIPRO

作者： Diyana Kinaneva Georgi Hristov Georgi Georgiev Plamen Zahariev Department of Telecommunications University of Ruse “Angel Kanchev” Ruse Bulgaria

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

Artificial intelligence is a global topic that holds significant importance in recent years. It sparks debates about its usage, but despite the contradictions that arise, there are many benefits to applying it in various areas. In this paper, the authors investigate the possibilities of implementing machine learning algorithms to help optimize crop management for precision agriculture. Meeting current food demands becomes an increasingly challenging task as the population grows. Applying machine learning using IoT data analytics in the agricultural sector will bring forth additional advantages, increasing not only the quantity but also the quality of production from crop fields to meet the rising food demands. The main objective of the paper is to employ various machine learning algorithms for predictive modeling and subsequently develop a robust model capable of making accurate predictions on unseen agricultural data. The research methodology involves preprocessing the acquired dataset, including data cleaning and feature engineering to enhance model performance. The authors systematically apply a range of machine learning algorithms, such as Logistic regression, Decision trees, Naïve Bayes, and k-nearest Neighbor, to identify the most effective approach for crop yield prediction. For better model performance, boosting techniques of machine learning are also implemented. Cross-validation and performance metrics such as accuracy, confusion matrix, precision, recall and F1 score, are included to evaluate the accuracy of the models, aiming to highlight the strengths and weaknesses of each algorithm in the context of precision agriculture.

关键词： Logistic regression machine learning algorithms Accuracy Computational modeling Predictive models Data models Bayes methods

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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 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 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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