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检索条件"主题词=Machine Learning Algorithms"

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machine learning algorithms for Sentiment Analysis

Machine Learning Algorithms for Sentiment Analysis

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Madhuri Sharma Rajeev Mathur Ganpat Joshi Damodaran B Komal Malsa Pradeep Kumar Dept. of Life Sciences CHRI Campus Gwalior India Dept. of CSE Madhav University Pindwara India Dept. of Psychology Meenakshi A.H. Edu. & Research Chennai India Dept. of CSE Lingayas Vidyapeeth University Faridabad India Dept. of Computer Science J.S. University Shikohabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Sentiment analysis, a subset of natural language processing, focuses on identifying the emotional tone within text computationally. This study reviews machine learning algorithms used in sentiment analysis, assessing their effectiveness in interpreting sentiment from textual data. algorithms such as Support Vector machines (SVM), Naive Bayes, and Neural Networks are evaluated for their ability to classify sentiment polarity, from positive to negative. The research highlights the influence of feature selection techniques like n-grams and word embeddings on model performance. Additionally, challenges such as sarcasm detection, context dependency, and imbalanced data are explored. These obstacles complicate sentiment analysis but provide opportunities for further advancements. The findings offer valuable insights into the capabilities and limitations of various machine learning methods, supporting the development of more accurate and reliable sentiment analysis systems for diverse real-world applications across industries.

关键词： Support vector machines Sentiment analysis machine learning algorithms Accuracy Social networking (online) Reviews Vectors Bayes methods Classification algorithms Reliability

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Prediction of obesity among school going children using machine learning algorithms

Prediction of obesity among school going children using Mach...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Devi, Seeta Chavan, Ranjana Gupta, Harshita Harikrishnan, R. Pune India Pune Campus India

ISBN: (纸本)9798350319200

Obesity has developed as a major global concern in the modern era due to the continuing trend towards unhealthy lifestyles characterized by excessive junk food consumption, insufficient sleep patterns, and prolonged periods of sedentary behavior. This research aims to predict the possibility of obesity using machine learning algorithms. We collected data from a total of 250 samples, which included moms and school-aged children of varied ages, as well as those affected by obesity and those who were not. Six well-ML learning techniques were used to study this. We used supervised machine learning techniques including RF, DT, GB, adaboost, catboost, and MLP to evaluate the suggested model. The results showed that combining the AdaBoost and MLP algorithms produced the best results, with 96% accuracy, 92% precision, 96% recall, and 94% F1. Using machine learning to analyze public health data can improve forecasts, find complicated patterns, and improve our understanding of issues such as identifying obesity risk factors. © 2023 IEEE.

关键词： Childhood obesity machine learning algorithms overweight Prediction Prevention

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machine learning algorithms for Automated Synthesis of Biocompatible Nanomaterials

Machine Learning Algorithms for Automated Synthesis of Bioco...

引用

Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： M Ayyappa Chakravarthi Sonu Sharma Amit Sahu Tanuja Murthy Arulananth T S Smriti Jain Dept. of CSE – Data Science KKR & KSR Institute of Technology and Sciences Guntur AP India Department of Electronics & Communication Engineering IES College of Technology Bhopal M.P India Dept of IOT Cyber Security & Blockchain GGITS Jabalpur M.P India Department of EEE Dayananda Sagar College of Engineering Bangalore Karnataka Dept. of ECE MLR Institute of Technology Hyderabad Telangana India Department of CSE Indore Institute of Science & Technology Indore M.P India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

This study introduces a novel approach, the “machine learning algorithms for Automated Synthesis of Biocompatible Nanomaterials,” aimed at revolutionizing the synthesis processes in nanotechnology. The proposed method leverages three powerful machine learning algorithms —Random Forest, Bayesian Optimization, and Deep Neural Networks—to optimize reaction conditions and predict the properties of biocompatible nanomaterials. In the pursuit of automated synthesis, the Random Forest algorithm acts as an ensemble learning technique, combining multiple decision trees to make robust predictions. Bayesian Optimization, a sequential model-based optimization approach, efficiently navigates the parameter space to identify optimal synthesis conditions. Deep Neural Networks, capable of capturing intricate relationships within data, are employed to model complex patterns in the synthesis process. To validate the effectiveness of the proposed method, we conducted comprehensive experiments and compared its performance with six traditional methods: Linear Regression, Polynomial Regression, Support Vector machine, Decision Tree, K-Nearest Neighbors, and Principal Component Regression. Key performance metrics, including Mean Squared Error (MSE), R-squared ($\mathbf{R}^learning$), and Mean Absolute Error (MAE), were employed for the evaluation. Our results demonstrate that the proposed method consistently outperforms traditional methods across all metrics. It exhibits lower MSE and MAE values and a higher $\mathbf{R}^learning$ value, indicating superior predictive accuracy

关键词： Measurement machine learning algorithms Accuracy Forestry Nanomaterials Bayes methods Optimization Random forests Regression tree analysis Medical diagnostic imaging

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Intrusion Detection System for Smart Vehicles Using machine learning algorithms 2

Intrusion Detection System for Smart Vehicles Using Machine ...

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Singh, Neetu Agarwal, Ritu Delhi Technological University Department of Information Technology Delhi India

ISBN: (纸本)9798350369960

As Electric Vehicles (EV s) become more prevalent, the need to protect their complicated and associated systems from cyber threats which vital necessity. It is critical to ensure the safety of an electric vehicle to prevent damage to human life or assets. Controller Area Network (CAN) is a message-broadcast protocol i.e., work on data link layer most deployed on EV s to facilitate communication between different control components called Electronic Control Units or ECUs. An ECU controls the functioning of different electronic or mechanical components and communicates with other ECUs on the network by sending messages over CAN bus. Communication over CAN is unencrypted and unauthenticated and thus prone to malicious attacks. This work presents an Intrusion Detection System (IDS) for electric vehicles. It uses different machine learning algorithms designed specifically for EV s to identify adversary attacks. Thus, through this work, we aim to strengthen the security of an EV by detecting and mitigating potential intrusions or attacks. The proposed system keeps check on network traffic, communication protocols and helps in exchanging data between various parts of the EV such as ECU s, also with charging stations and other attached devices. Key components of the IDS include a data collection mechanism, feature extraction, machine learning algorithms, and a decision making module. The data collection mechanism gathers relevant information from different sensors, control units, and communication channels. We employ feature extraction techniques to transform collected data into meaningful features for analysis. We further compare and contrast the different machine learning techniques to provide useful insights into their functioning. © 2023 IEEE.

关键词： Controller Area Network Electric Vehicles Intrusion Detection System IoT machine learning algorithms

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machine learning algorithms Applied to Post-bac Academic Guidance

Machine Learning Algorithms Applied to Post-bac Academic Gui...

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Mediterranean Smart Cities Conference (MSCC)

作者： Salim Lafdoul Sana Baadou Ahmed Bendahmane Department of Mathematics Computer Science and Physical Sciences Higher Normal School Tetouan Morocco

ISBN: (数字)9798350374001

ISBN: (纸本)9798350374018

Educational guidance is the set of social and individual processes and factors that help students to choose the different courses of study that suit them according to their interests, skills and personality. This is a crucial stage in their educational journey, particularly for post-baccalaureate students, and has a significant influence on their success and personal development. However, traditional approaches to guidance can be limited in their ability to accurately predict students' future academic trajectories. And by introducing AI and machine learning algorithms into school guidance, the traditional school guidance process has been revolutionized. [1] The advanced capabilities of machine learning offer innovative perspectives, with the aim of enhancing the effectiveness of school guidance advice and helping students to successfully navigate their academic and career paths. By analysing students' academic and personal data, machine learning algorithms can detect patterns and trends that are difficult to detect manually. This information is used to offer personalized recommendations on the courses and career paths that best match the student's interests, skills and goals.

关键词： machine learning algorithms Engineering profession Smart cities Navigation machine learning Data collection Market research Trajectory

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machine learning algorithms for Disease Prediction Analysis

Machine Learning Algorithms for Disease Prediction Analysis

引用

International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： M Govindaraj V Asha Binju Saju M Sagar Rahul Master of Computer Application New Horizon College of Engineering Bengaluru India

People suffer from a wide variety of diseases due to their environment and lifestyle factors. As a result, early disease prediction becomes critical. On the other hand, making a diagnosis solely on symptoms is too difficult for a doctor. The most difficult challenge is predicting disease accurately. In order to solve this problem, data mining is considered as an important and effective method for disease prediction. Data mining reveals hidden pattern information from massive amounts of medical data derived from disease data. To aid in early diagnosis and treatment, this research work presents a disease prediction prototype to identify potential illnesses based on their symptoms. It reduces the time required to complete the patient’s full diagnosis; additionally, the system’s recommendations can be used to determine the disease. attempts to accurately predict a disease by using machine learning techniques. The proposed system produces results that are up to 87% accurate. This method has enormous potential for more accurately predicting potential diseases. The primary goal of this study is to help nontechnical people and young doctors to make accurate opinions about the disease.

关键词： machine learning algorithms Prototypes Medical services machine learning Data mining Medical diagnostic imaging Diseases

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machine learning algorithms for Producing Equations in Physics

Machine Learning Algorithms for Producing Equations in Physi...

引用

International Conference on Electrical, Communication, and Computer Engineering (ICECCE)

作者： Huber Nieto-Chaupis Universidad Autónoma del Perú Lima Perú

ISBN: (数字)9798331529437

ISBN: (纸本)9798331529444

Based at the prospective scenario that computers might to replace humans, emerge the idea that them can carry out jobs as a scientist does. Thus, one might to expect that for example machine learning can be a serious competitor of humans as to produce research even of cutting edge quality. In this paper, some algorithms based at artificial neural networks are employed to produce known physics based at coincidences with artificial models generated by machine learning approach.

关键词： Computers Hands Histograms machine learning algorithms Codes Computational modeling machine learning Production Mathematical models Physics

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machine learning algorithms for Land Cover Classification Using Hybrid Remote Sensing and Spectral Indices

Machine Learning Algorithms for Land Cover Classification Us...

引用

International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Desy Ika Puspitasari Edi Noersasongko Moch Arief Soeleman Purwanto Pulung Nurtantio Andono Department of Computer Science Dian Nuswantoro University (UDINUS) Semarang Indonesia Faculty of Information Technology Islamic University of Kalimantan (UNISKA) Banjarmasin Semarang Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

Accurate land cover classification is essential for effective environmental monitoring and resource management. This study aims to evaluate the performance of different machine learning algorithms for land cover classification using hybrid remote sensing data and spectral indices. By integrating data from Landsat 8 and Sentinel-2 satellites, this study utilizes the advantages of both sensors to improve classification accuracy. Spectral indices such as NDVI, MNDWI, NDMI, NDBI, and BSI were computed to enrich the feature set used for classification. A comprehensive dataset of training samples representing different land cover types - water, vegetation, built-up area, and bare land - was used to train and validate the machine learning model. Random Forest, Decision Tree, and Support Vector machine were applied, and their performance was evaluated using metrics such as Overall Accuracy (OA), Kappa coefficient, and F1-score. The results show that integrating hybrid remote sensing data significantly improves the classification performance compared to single source data. Among the evaluated algorithms, Random Forest showed superior accuracy and robustness across different land cover types. This study highlights the importance of hybrid data and the combination of spectral indices in achieving high accuracy land cover classification and provides insights into the use of Random Forest for land cover classification.

关键词： Training Support vector machines Seminars Accuracy machine learning algorithms Land surface Vegetation mapping Sensors Remote sensing Random forests

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The Use of machine learning algorithms in Optimising SGS for Synchronising 4

The Use of Machine Learning Algorithms in Optimising SGS for...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Ramesh, P.S. Naik, Pramod Kumar Afreen Banu, E. Praveenkumar, C. Owaied, Huda Qasim Hassan, Enas Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Computer Science and Engineering India Dayananda Sagar University Department of Cse Bengaluru India Cse Saveetha School of Engineering Chennai India Sri Ramakrishna Engineering College Department of Eee Coimbatore India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

An examination of the relative efficacy of machine learning (ML) algorithms for power supply and demand prediction in smart grid systems is warranted by their growing application. Through meticulous hyperparameter tweaking, we assessed six well-known machine learning algorithms: support vector machine (SVM), random forest, k-nearest neighbor, linear regression, artificial neural network, and Gaussian regression (GR). When the ML algorithms were appropriately calibrated, we found little to no significant difference in performance among them when we used Eskom database data for day-ahead forecasting, including hourly demand and renewable energy generation. On the other hand, wind power forecast proved more difficult because of its unpredictable nature than photovoltaic (PV) electricity. With the exception of GR, statistical analysis showed no discernible changes in timing performance amongst the algorithms, despite SVM's somewhat quicker processing time. The implication is that, with appropriate hyperparameter fine-tuning, using different machine learning algorithms for power prediction - particularly for sources with regular patterns, such as solar PV or daily consumption rates-may not always result in statistically significant variations in prediction quality. © 2024 IEEE.

关键词： Eskom database hyperparameter tuning machine learning algorithms power demand prediction smart grid systems supply prediction

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A Comprehensive Study on machine learning algorithms for Intrusion Detection System 10

A Comprehensive Study on Machine Learning Algorithms for Int...

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10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Mirlekar, Swati Kanojia, Komal Prasad Electronics and Telecommunication Engineering St. Vincent Pallotti College of Engineering & Technology MS Nagpur India Electronics and Communication Engg RKDF INSTITUTE of SCIENCE and TECHNOLOGY BHOPAL SRK University MP Bhopal India

ISBN: (纸本)9781665467414

Rapid advancements in areas of communication and the internet ensued a significant boost in data and capacity of the network. As a consequence, a plethora of novel threats are being built, creating it harder for network security to efficiently identify breaches. Furthermore, intruders also initiate multiple attacks inside this network must be taken into account. An intrusion Detection System (IDS) is a method that checks network activity for availability, consistency, and secrecy to defend the network from potential invasions. Although the greatest efforts of professionals, IDSs continue to struggle with enhancing detecting accurateness whilst lowering false alarm rates and detecting newer intrusions. IDS systems based on ML (machine learning), as well as DL (Deep learning), have recently been deployed as feasible procedures for swiftly identifying network intrusions. The taxonomy presented in this article is based on well-known ML as well as DL approaches for building network-based IDS (NIDS) systems, as well as it first defines the concept of IDS. In this comprehensive review of existing NIDS-based research, the merits and limitations of the suggested solutions are thoroughly explored. This evaluation may act as a standard for researchers and industry in terms of the advancement and development of Network Intrusion Detection Systems in the future. © 2022 IEEE.

关键词： Deep learning Industries machine learning algorithms Taxonomy Network intrusion detection Information processing Network security

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