Journal articles on the topic 'Machine learning algorithms'
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Mahesh, Batta. "Machine Learning Algorithms - A Review." International Journal of Science and Research (IJSR) 9, no. 1 (January 5, 2020): 381–86. http://dx.doi.org/10.21275/art20203995.
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TURAN, SELIN CEREN, and MEHMET ALI CENGIZ. "ENSEMBLE LEARNING ALGORITHMS." Journal of Science and Arts 22, no. 2 (June 30, 2022): 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.
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Artificial intelligence is a method that is increasingly becoming widespread in all areas of life and enables machines to imitate human behavior. Machine learning is a subset of artificial intelligence techniques that use statistical methods to enable machines to evolve with experience. As a result of the advancement of technology and developments in the world of science, the interest and need for machine learning is increasing day by day. Human beings use machine learning techniques in their daily life without realizing it. In this study, ensemble learning algorithms, one of the machine learning techniques, are mentioned. The methods used in this study are Bagging and Adaboost algorithms which are from Ensemble Learning Algorithms. The main purpose of this study is to find the best performing classifier with the Classification and Regression Trees (CART) basic classifier on three different data sets taken from the UCI machine learning database and then to obtain the ensemble learning algorithms that can make this performance better and more determined using two different ensemble learning algorithms. For this purpose, the performance measures of the single basic classifier and the ensemble learning algorithms were compared
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Ling, Qingyang. "Machine learning algorithms review." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 91–98. http://dx.doi.org/10.54254/2755-2721/4/20230355.
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Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machine learning and its algorithms. It will examine different types of machine learning models and introduce their most popular algorithms. The methodology of this paper is a literature review, which examines the most commonly used machine learning algorithms in the current field. Such algorithms include Nave Bayes, Decision Tree, KNN, and K-Mean Cluster. Nowadays, machine learning is everywhere and almost everyone using a technology product is enjoying its convenience. Applications like spam mail classification, image recognition, personalized product recommendations, and natural language processing all use machine learning algorithms. The conclusion is that there is no single algorithm that can solve all the problems. The choice of the use of algorithms and models must depend on the specific problem.
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K.M., Umamaheswari. "Road Accident Perusal Using Machine Learning Algorithms." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1676–82. http://dx.doi.org/10.37200/ijpr/v24i5/pr201839.
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Nair, Dr Prabha Shreeraj. "Analyzing Titanic Disaster using Machine Learning Algorithms." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 410–16. http://dx.doi.org/10.31142/ijtsrd7003.
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Mallika, Madasu, and K. Suresh Babu. "Breast Cancer Prediction using Machine Learning Algorithms." International Journal of Science and Research (IJSR) 12, no. 10 (October 5, 2023): 1235–38. http://dx.doi.org/10.21275/sr231015173828.
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Kumar, Nikhil. "Review Paper on Machine Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (June 2, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34900.
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This paper comprehensively reviews widely used machine learning algorithms across supervised, unsupervised, and reinforcement learning paradigms. It covers linear models, decision trees, support vector machines, neural networks, clustering techniques, dimensionality reduction methods, and ensemble approaches. For each algorithm, theoretical foundations, mathematical formulations, practical considerations like parameter tuning and computational complexity, and real-world applications across domains like computer vision and finance are discussed. Challenges and limitations such as overfitting and scalability are explored. Recent advancements like deep learning and transfer learning are highlighted. Finally, a comparative analysis evaluating strengths, weaknesses, and suitable problem domains for the algorithms is provided, serving as a guide for effective utilization of machine learning techniques. Keywords:- Machine learning · Deep learning, Gradient Descent, Logistic Regression, Support Vector Machine, K Nearest Neighbor, Predictive analytics,
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Meena, Munesh, and Ruchi Sehrawat. "Breakdown of Machine Learning Algorithms." Recent Trends in Artificial Intelligence & it's Applications 1, no. 3 (October 16, 2022): 25–29. http://dx.doi.org/10.46610/rtaia.2022.v01i03.005.
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Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. A subset of artificial intelligence is called machine learning (AI). A machine may automatically learn from data and get better at what it does thanks to machine learning. “If additional data can be gathered to help a machine perform better, it can learn. A developing technology called machine learning allows computers to learn from historical data. Machines can predict the outcomes by machine learning. For Nowadays machine learning is very important for us because it makes our work easy. to many companies are using machine learning in their products, like google is using google its google assistant, which takes our voice command and gives what do we want from it, and google is also using its goggle lens form which we can find anything just by clicking a picture, and Netflix is using machine learning for recommendation of any movies or series, Machine learning has a very deep effect on our life, like nowadays we are using selfdriving car’s.
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Yu, Binyan, and Yuanzheng Zheng. "Research on algorithms of machine learning." Applied and Computational Engineering 39, no. 1 (February 21, 2024): 277–81. http://dx.doi.org/10.54254/2755-2721/39/20230614.
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Machine learning has endless application possibilities, with many algorithms worth learning in depth. Different algorithms can be flexibly applied to a variety of vertical fields, such as the most common neural network algorithms for face recognition, garbage classification, picture classification, and other application scenarios image recognition and computer vision, the hottest recent natural language processing and recommendation algorithms for different applications are from it. In the field of financial analysis, the decision tree algorithm and its derivative algorithms such as random forest are the mainstream. As well as support vector machines, naive Bayes, K-nearest neighbor algorithms, and so on. From the traditional regression algorithm to the hottest neural network algorithm. This paper discusses the application principle of the algorithm and lists some corresponding applications. Linear regression, decision trees, supervised learning, etc., while some have been replaced by more powerful and flexible algorithms and methods, by studying and understanding these foundational algorithms in depth, neural network models can be better designed and optimized, and a better understanding of how they work can be obtained.
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Pandey, Mrs Arjoo. "Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 864–69. http://dx.doi.org/10.22214/ijraset.2023.55224.
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Abstract: Machine learning refers to the study and development of machine learning algorithms and techniques at a conceptual level, focusing on theoretical foundations, algorithmic design, and mathematical analysis rather than specific implementation details or application domains. It aimsto provide a deeper understanding of the fundamental principles and limitations of machine learning, enabling researchers to develop novel algorithms and advance the field. In abstract machine learning, the emphasis is on formalizing and analyzing learning tasks, developing mathematical models for learning processes, and studying the properties and behavior of various learning algorithms. This involves investigating topics such as learning theory, statistical learning, optimization, computational complexity, and generalization. The goalis to develop theoretical frameworks and mathematical tools that help explain why certain algorithms work and how they can be improved. Abstract machine learning also explores fundamental questions related to the theoretical underpinnings of machine learning, such as the trade-offs between bias and variance, the existence of optimal learning algorithms, the sample complexity of learning tasks, and the limits of what can be learned from data. It provides a theoretical foundation for understanding the capabilities and limitations of machine learning algorithms, guiding the development of new algorithms and techniques. Moreover, abstract machine learning serves as a bridge between theory and practice, facilitating the transfer of theoretical insights into practical applications. Theoretical advances in abstract machine learning can inspire new algorithmic approaches and inform the design of real-world machine learning systems. Conversely, practical challenges and observations from realworld applications can motivate and guide theoretical investigations in abstract machine learning. Overall, abstract machine learning plays a crucial role in advancing the field of machine learning by providing rigorous theoretical frameworks, mathematical models, and algorithmic principles that deepen our understanding of learning processes and guide the development of more effectiveand efficient machine learning algorithms.
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A, Ms Vidhya, Dr Parameswari R, and Ms Sathya S. "Brain Tumor Classification Using Various Machine Learning Algorithms." International Journal of Research in Arts and Science 5, Special Issue (August 30, 2019): 258–70. http://dx.doi.org/10.9756/bp2019.1002/25.
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Gupta, Jai. "Credit Card Fraud Detection Using Machine Learning Algorithms." International Journal of Science and Research (IJSR) 12, no. 11 (November 5, 2023): 1774–79. http://dx.doi.org/10.21275/sr231123121203.
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M Paithane, Pradip. "Heart Disease Prediction Using Multiple Machine Learning Algorithms." Advances in Robotic Technology 2, no. 1 (January 19, 2024): 1–5. http://dx.doi.org/10.23880/art-16000114.
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Heart disease is a significant health concern globally, and the ability to predict and diagnose it accurately is crucial for effective treatment and prevention strategies. Machine learning algorithms have shown promise in enhancing the prediction of heart disease by analysing complex medical data. So in this paper, we have analysed and compared different machine learning algorithms like Logistic Regression, SVM and Naive Bayes(Gaussian Naive Bayes) for the prediction of heart disease. In proposed work the data used consist of different medical attributes like age, heart rate, chest pain type, restingBP, max heart rate, etc. To increase the accuracy of the models I used cross validation technique (Kfold). The Support vector machine received highest accuracy as compared to other approaches.
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Kovářová, Marie. "Exploring Zero-Day Attacks on Machine Learning and Deep Learning Algorithms." European Conference on Cyber Warfare and Security 23, no. 1 (June 21, 2024): 241–48. http://dx.doi.org/10.34190/eccws.23.1.2310.
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In the rapidly evolving field of artificial intelligence, machine learning (ML) and deep learning (DL) algorithms have emerged as powerful tools for solving complex problems in various domains, including cyber security. However, as these algorithms become increasingly prevalent, they also face new security challenges. One of the most significant of these challenges is the threat of zero-day attacks, which exploit unknown and unpredictable vulnerabilities in the algorithms or the data they process. This paper provides a comprehensive overview of zero-day attacks on ML/DL algorithms, exploring their types, causes, effects, and potential countermeasures. The paper begins by introducing the concept and definition of zero-day attacks, providing a clear understanding of this emerging threat. It then reviews the existing research on zero-day attacks on ML/DL algorithms, focusing on three main categories: data poisoning attacks, adversarial input attacks, and model stealing attacks. Each of these attack types poses unique challenges and requires specific countermeasures. The paper also discusses the potential impacts and risks of these attacks on various application domains. For instance, in facial expression recognition, an adversarial input attack could lead to misclassification of emotions, with serious implications for user experience and system integrity. In object classification, a data poisoning attack could cause the algorithm to misidentify critical objects, potentially endangering human lives in applications like autonomous driving. In satellite intersection recognition, a model stealing attack could compromise national security by revealing sensitive information. Finally, the paper presents some possible protection methods against zero-day attacks on ML/DL algorithms. These include anomaly detection techniques to identify unusual patterns in the data or the algorithm’s behaviour, model verification and validation methods to ensure the algorithm’s correctness and robustness, federated learning approaches to protect the privacy of the training data, and differential privacy techniques to add noise to the data or the algorithm’s outputs to prevent information leakage. The paper concludes by highlighting some open issues and future directions for research in this area, emphasizing the need for ongoing efforts to secure ML/DL algorithms against zero-day attacks.
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Luan, Yuxuan, Junjiang He, Jingmin Yang, Xiaolong Lan, and Geying Yang. "Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning." International Journal of Intelligent Systems 2023 (November 10, 2023): 1–21. http://dx.doi.org/10.1155/2023/1666735.
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When solving real-world optimization problems, the uniformity of Pareto fronts is an essential strategy in multiobjective optimization problems (MOPs). However, it is a common challenge for many existing multiobjective optimization algorithms due to the skewed distribution of solutions and biases towards specific objective functions. This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address this limitation. Our algorithm utilizes uniform initialization and self-organizing map (SOM) to enhance population diversity and uniformity. We track the IGD value and use K-means and CNN refinement with crossover and mutation techniques during evolutionary stages. Our algorithm’s uniformity and objective function balance superiority were verified through comparative analysis with 13 other algorithms, including eight traditional multiobjective optimization algorithms, three machine learning-based enhanced multiobjective optimization algorithms, and two algorithms with objective initialization improvements. Based on these comprehensive experiments, it has been proven that our algorithm outperforms other existing algorithms in these areas.
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Mishra, Akshansh, and Apoorv Vats. "Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints." Frattura ed Integrità Strutturale 15, no. 58 (September 25, 2021): 242–53. http://dx.doi.org/10.3221/igf-esis.58.18.
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Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classification algorithms. In the present study, four supervised machine learning-based classification models i.e. Decision Trees algorithm, K- Nearest Neighbors (KNN) algorithm, Support Vector Machines (SVM) algorithm, and Ada Boost algorithm were subjected to the given dataset for the determination of fracture location in dissimilar Friction Stir Welded AA6061-T651 and AA7075-T651 alloy. In the given dataset, rotational speed (RPM), welding speed (mm/min), pin profile, and axial force (kN) were the input parameters while Fracture location is the output parameter. The obtained results showed that the Support Vector Machine (SVM) algorithm classified the fracture location with a good accuracy score of 0.889 in comparison to the other algorithms.
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Sameer, S. K. L., and P. Sriramya. "Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease." Alinteri Journal of Agriculture Sciences 36, no. 1 (June 29, 2021): 713–20. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21100.
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Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.
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Coe, James, and Mustafa Atay. "Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms." Computers 10, no. 9 (September 10, 2021): 113. http://dx.doi.org/10.3390/computers10090113.
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The research aims to evaluate the impact of race in facial recognition across two types of algorithms. We give a general insight into facial recognition and discuss four problems related to facial recognition. We review our system design, development, and architectures and give an in-depth evaluation plan for each type of algorithm, dataset, and a look into the software and its architecture. We thoroughly explain the results and findings of our experimentation and provide analysis for the machine learning algorithms and deep learning algorithms. Concluding the investigation, we compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most. We evaluate racial bias across five machine learning algorithms and three deep learning algorithms using racially imbalanced and balanced datasets. We evaluate and compare the accuracy and miss rates between all tested algorithms and report that SVC is the superior machine learning algorithm and VGG16 is the best deep learning algorithm based on our experimental study. Our findings conclude the algorithm that mitigates the bias the most is VGG16, and all our deep learning algorithms outperformed their machine learning counterparts.
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Todorov, Dimitar Georgiev, and Karova Milena. "Appropriate Conversion of Machine Learning Data." ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA 6, no. 2 (December 31, 2022): 63–76. http://dx.doi.org/10.29114/ajtuv.vol6.iss2.262.
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Data is an important part of computer technology and, as such, explains the strong dependence of machine learning algorithms on it. The operation of any corresponding algorithm is directly dependent on the type of data and the proper data representation increases the productivity of these algorithms. Advanced in the present article is an algorithm for data pre-processing in a form that is most suitable for machine learning algorithms, with cryptographic secret keys being used as input data. The experimental results were satisfactory, and with the utilization of secret keys with significant differences, the recognition obtained is about 100%.
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Barbosa, Flávio, Arthur Vidal, and Flávio Mello. "Machine Learning for Cryptographic Algorithm Identification." Journal of Information Security and Cryptography (Enigma) 3, no. 1 (September 3, 2016): 3. http://dx.doi.org/10.17648/enig.v3i1.55.
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This paper aims to study encrypted text files in order to identify their encoding algorithm. Plain texts were encoded with distinct cryptographic algorithms and then some metadata were extracted from these codifications. Afterward, the algorithm identification is obtained by using data mining techniques. Firstly, texts in Portuguese, English and Spanish were encrypted using DES, Blowfish, RSA, and RC4 algorithms. Secondly, the encrypted files were submitted to data mining techniques such as J48, FT, PART, Complement Naive Bayes, and Multilayer Perceptron classifiers. Charts were created using the confusion matrices generated in step two and it was possible to perceive that the percentage of identification for each of the algorithms is greater than a probabilistic bid. There are several scenarios where algorithm identification reaches almost 97, 23% of correctness.
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Preethi, B. Meena, R. Gowtham, S. Aishvarya, S. Karthick, and D. G. Sabareesh. "Rainfall Prediction using Machine Learning and Deep Learning Algorithms." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (November 30, 2021): 251–54. http://dx.doi.org/10.35940/ijrte.d6611.1110421.
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The project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial Neural Network using Forward and Backward Propagation, Ada Boost, Gradient Boosting and XGBoost algorithms are used in this model for predicting the rainfall. There are totally five modules used in this project. The Data Analysis Module will analyse the datasets and finding the missing values in the dataset. The Data Pre-processing includes Data Cleaning which is the process of filling the missing values in the dataset. The Feature Transformation Module is used to modify the features of the dataset. The Data Mining Module is used to train the dataset to models using any algorithm for learning the pattern. The Model Evaluation Module is used to measure the performance of the model and finalize the overall best accuracy for the prediction. Dataset used in this prediction is for the country Australia. This main aim of the project is to compare the various boosting algorithms with the neural network and find the best algorithm among them. This prediction can be major advantage to the farmers in order to plant the types of crops according to the needy of water. Overall, we analyse the algorithm which is feasible for qualitatively predicting the rainfall.
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Grzymala-Busse, Jerzy W. "Selected Algorithms of Machine Learning from Examples." Fundamenta Informaticae 18, no. 2-4 (April 1, 1993): 193–207. http://dx.doi.org/10.3233/fi-1993-182-408.
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This paper presents and compares two algorithms of machine learning from examples, ID3 and AQ, and one recent algorithm from the same class, called LEM2. All three algorithms are illustrated using the same example. Production rules induced by these algorithms from the well-known Small Soybean Database are presented. Finally, some advantages and disadvantages of these algorithms are shown.
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Gupta, Monica. "A Comparative Study on Supervised Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1023–28. http://dx.doi.org/10.22214/ijraset.2022.39980.
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Abstract: Machine learning enables computers to act and make data driven decisions rather than being explicitly programmed to carry out a certain task. It is a tool and technology which can answer the question from your data. These programs are designed to learn and improve over time when exposed to new data. ML is a subset or a current application of AI. It is based on an idea that we should be able to give machines access to data and let them learn from themselves. ML deals with extraction of patterns from dataset, this means that machines can not only find the rules for optimal behavior but also can adapt to the changes in the world. Many of the algorithms involved have been known for decades. In this paper various algorithms of machine learning have been discussed. Machine learning algorithms are used for various purposes but we can say that once the machine learning algorithm studies how to manage data, it can do its work accordingly by itself. Keywords: Linear Regression, Logistic Regression, KNN, Naive Bayes, Decision Trees, SVM, Random Forest
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Sauer, Sebastian, Ricardo Buettner, Thomas Heidenreich, Jana Lemke, Christoph Berg, and Christoph Kurz. "Mindful Machine Learning." European Journal of Psychological Assessment 34, no. 1 (January 2018): 6–13. http://dx.doi.org/10.1027/1015-5759/a000312.
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Abstract. Mindfulness refers to a stance of nonjudgmental awareness of present-moment experiences. A growing body of research suggests that mindfulness may increase cognitive resources, thereby buffering stress. However, existing models have not achieved a consensus on how mindfulness should be operationalized. As the sound measurement of mindfulness is the foundation needed before substantial hypotheses can be supported, we propose a novel way of gauging the psychometric quality of a mindfulness measurement instrument (the Freiburg Mindfulness Inventory; FMI). Specifically, we employed 10 predictive algorithms to scrutinize the measurement quality of the FMI. Our criterion of measurement quality was the degree to which an algorithm separated mindfulness practitioner from nonpractitioners in a sample of N = 276. A high predictive accuracy of class membership can be taken as an indicator of the psychometric quality of the instrument. In sum, two findings are of interest. First, over and above some items of the FMI were able to reliably predict class membership. However, some items appeared to be uninformative. Second, from an applied methodological point of view, it appears that machine learning algorithms can outperform traditional predictive methods such as logistic regression. This finding may generalize to other branches of research.
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Chaulwar, Amit. "Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 1–11. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1007.
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The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally intensive modules of these trajectory planning algorithms using different efficient machine learning and analytical methods. The required computational resources are measured by downloading and running the algorithms on various hardware platforms. The results show significant reduction in computational resources and the potential of proposed algorithms to run in real time. Also, alternative architectures for 3D-ConvNet are presented for further reduction of required computational resources.
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Yeshwanth, Mylapalle, Palla Ratna Sai Kumar, and Dr G. Mathivanan M. E. ,. Ph.D. "Comparative Study of Machine Learning Algorithms for Rainfall Prediction." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 677–81. http://dx.doi.org/10.31142/ijtsrd22961.
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Borade, Shwetambari, Parshva Chetan Doshi, and Darsh Bhavesh Patel. "MaliceSpotter: Revolutionizing Cyber Security with Machine Learning for Phishing Resilience." Indian Journal Of Science And Technology 17, no. 10 (March 1, 2024): 870–80. http://dx.doi.org/10.17485/ijst/v17i10.148.
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Objectives: To enhance cyber security by implementing advanced algorithms to swiftly identify and neutralize phishing threats. Also, to bolster user protection, fortify data integrity, and ensure a resilient defense against evolving cyber threats. Methods: MaliceSpotter aims in classifying user-entered URLs by analysing 28 features, using algorithms like Logistic Regression, Random Forest, and KNN, combined via a Voting Classifier. Dataset on Kaggle provides diverse samples for evaluation. This methodology's unique aspects include multiple algorithm integration and the utilization of Kaggle as a data source. Findings: MaliceSpotter demonstrates a commendable accuracy of 95%, effectively classifying input URLs as phishing or legitimate. The system's uniqueness lies in its provision of a detailed report on URL behavior, facilitating informed decision-making. The implementation of ensemble learning is notable, particularly the introduction of the Voting Classifier. This approach leverages various algorithms, successfully incorporating bagging and voting concepts. Through the Voting Classifier, MaliceSpotter gains insights into the working of machine learning algorithms, enhancing the scrutiny of URL behavior. This innovative feature sets MaliceSpotter apart, offering a nuanced perspective on the reliability of URLs through the collective input of diverse algorithms. Novelty: MaliceSpotter uniquely combines diverse algorithms, leveraging a voting classifier for robust results. Continuously updating in real time, it meticulously dissects URLs into 28 parts, ensuring thorough scrutiny and effective detection. Keywords: Phishing, Machine Learning, Web Security, Voting Classifier, Bagging
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Erel, Isil, Léa H. Stern, Chenhao Tan, and Michael S. Weisbach. "Selecting Directors Using Machine Learning." Review of Financial Studies 34, no. 7 (April 20, 2021): 3226–64. http://dx.doi.org/10.1093/rfs/hhab050.
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Abstract Can algorithms assist firms in their decisions on nominating corporate directors? Directors predicted by algorithms to perform poorly indeed do perform poorly compared to a realistic pool of candidates in out-of-sample tests. Predictably bad directors are more likely to be male, accumulate more directorships, and have larger networks than the directors the algorithm would recommend in their place. Companies with weaker governance structures are more likely to nominate them. Our results suggest that machine learning holds promise for understanding the process by which governance structures are chosen and has potential to help real-world firms improve their governance.
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Golden, Richard M. "Adaptive Learning Algorithm Convergence in Passive and Reactive Environments." Neural Computation 30, no. 10 (October 2018): 2805–32. http://dx.doi.org/10.1162/neco_a_01117.
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Although the number of artificial neural network and machine learning architectures is growing at an exponential pace, more attention needs to be paid to theoretical guarantees of asymptotic convergence for novel, nonlinear, high-dimensional adaptive learning algorithms. When properly understood, such guarantees can guide the algorithm development and evaluation process and provide theoretical validation for a particular algorithm design. For many decades, the machine learning community has widely recognized the importance of stochastic approximation theory as a powerful tool for identifying explicit convergence conditions for adaptive learning machines. However, the verification of such conditions is challenging for multidisciplinary researchers not working in the area of stochastic approximation theory. For this reason, this letter presents a new stochastic approximation theorem for both passive and reactive learning environments with assumptions that are easily verifiable. The theorem is widely applicable to the analysis and design of important machine learning algorithms including deep learning algorithms with multiple strict local minimizers, Monte Carlo expectation-maximization algorithms, contrastive divergence learning in Markov fields, and policy gradient reinforcement learning.
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Yuan, Hongyuan, Jingan Liu, Yu Zhou, and Hailong Pei. "State of Charge Estimation of Lithium Battery Based on Integrated Kalman Filter Framework and Machine Learning Algorithm." Energies 16, no. 5 (February 23, 2023): 2155. http://dx.doi.org/10.3390/en16052155.
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Research on batteries’ State of Charge (SOC) estimation for equivalent circuit models based on the Kalman Filter (KF) framework and machine learning algorithms remains relatively limited. Most studies are focused on a few machine learning algorithms and do not present comprehensive analysis and comparison. Furthermore, most of them focus on obtaining the state space parameters of the Kalman filter frame algorithm models using machine learning algorithms and then substituting the state space parameters into the Kalman filter frame algorithm to estimate the SOC. Such algorithms are highly coupled, and present high complexity and low practicability. This study aims to integrate machine learning with the Kalman filter frame algorithm, and to estimate the final SOC by using different combinations of the input, output, and intermediate variable values of five Kalman filter frame algorithms as the input of the machine learning algorithms of six main streams. These are: linear regression, support vector Regression, XGBoost, AdaBoost, random forest, and LSTM; the algorithm coupling is lower for two-way parameter adjustment and is not applied between the machine learning and Kalman filtering framework algorithms. The results demonstrate that the integrated learning algorithm significantly improves the estimation accuracy when compared to the pure Kalman filter framework or the machine learning algorithms. Among the various integrated algorithms, the random forest and Kalman filter framework presents the highest estimation accuracy along with good real-time performance. Therefore, it can be implemented in various engineering applications.
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Slemenšek, Jan, Iztok Fister, Jelka Geršak, Božidar Bratina, Vesna Marija van Midden, Zvezdan Pirtošek, and Riko Šafarič. "Human Gait Activity Recognition Machine Learning Methods." Sensors 23, no. 2 (January 9, 2023): 745. http://dx.doi.org/10.3390/s23020745.
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Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject’s quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm’s robustness was also verified with the successful detection of freezing gait episodes in a Parkinson’s disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.
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Note, Johan, and Maaruf Ali. "Comparative Analysis of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms." Annals of Emerging Technologies in Computing 6, no. 3 (July 1, 2022): 19–36. http://dx.doi.org/10.33166/aetic.2022.03.003.
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Attacks against computer networks, “cyber-attacks”, are now common place affecting almost every Internet connected device on a daily basis. Organisations are now using machine learning and deep learning to thwart these types of attacks for their effectiveness without the need for human intervention. Machine learning offers the biggest advantage in their ability to detect, curtail, prevent, recover and even deal with untrained types of attacks without being explicitly programmed. This research will show the many different types of algorithms that are employed to fight against the different types of cyber-attacks, which are also explained. The classification algorithms, their implementation, accuracy and testing time are presented. The algorithms employed for this experiment were the Gaussian Naïve-Bayes algorithm, Logistic Regression Algorithm, SVM (Support Vector Machine) Algorithm, Stochastic Gradient Descent Algorithm, Decision Tree Algorithm, Random Forest Algorithm, Gradient Boosting Algorithm, K-Nearest Neighbour Algorithm, ANN (Artificial Neural Network) (here we also employed the Multilevel Perceptron Algorithm), Convolutional Neural Network (CNN) Algorithm and the Recurrent Neural Network (RNN) Algorithm. The study concluded that amongst the various machine learning algorithms, the Logistic Regression and Decision tree classifiers all took a very short time to be implemented giving an accuracy of over 90% for malware detection inside various test datasets. The Gaussian Naïve-Bayes classifier, though fast to implement, only gave an accuracy between 51-88%. The Multilevel Perceptron, non-linear SVM and Gradient Boosting algorithms all took a very long time to be implemented. The algorithm that performed with the greatest accuracy was the Random Forest Classification algorithm.
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Sarkar, Soumyadip. "Quantum Machine Learning: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 352–54. http://dx.doi.org/10.22214/ijraset.2023.49421.
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Abstract: Quantum machine learning is an emerging field that aims to leverage the unique properties of quantum computing to accelerate machine learning tasks. In this paper, we review recent advances in quantum machine learning and discuss the potential applications and challenges associated with this technology. Specifically, we examine the current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models. We also discuss the challenges associated with practical quantum computing resources, algorithm design, and interdisciplinary collaboration. Furthermore, we highlight the potential applications of quantum machine learning in areas such as drug discovery, speech and image recognition, financial modeling, and many others. We also examine the ethical and societal implications of this technology, including the potential impact on privacy and security. Finally, we discuss future prospects for quantum machine learning, including the potential for quantum-inspired classical algorithms and the development of error correction techniques. We conclude by emphasizing the importance of interdisciplinary collaboration in the continued advancement of this field.
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Baharuddin, Fikri, and Aris Tjahyanto. "Peningkatan Performa Klasifikasi Machine Learning Melalui Perbandingan Metode Machine Learning dan Peningkatan Dataset." Jurnal Sisfokom (Sistem Informasi dan Komputer) 11, no. 1 (March 7, 2022): 25–31. http://dx.doi.org/10.32736/sisfokom.v11i1.1337.
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Classification using machine learning is an alternative that is widely used to classify data. There are various classification methods or also known as machine learning classification algorithms that can be used. However, to get the best classification results, we need a classifier that fits the dataset type to provide the best classification performance. In addition, the quality and quantity of data contained in a dataset also has an influence on the classification performance. In this study, several attempts were made to improve the classification performance of the dataset of Indonesian language exam questions at the elementary school level based on the category of difficulty level. The efforts made consist of improving the quality of the dataset and using the StringToWordVector filter algorithm to manage textual data, as well as the use of several classification algorithms such as the nave Bayes algorithm, Random Forest, and REPTree. Classification is done by using WEKA Tools. The results of the experiments carried out showed the highest performance increase of 15% after improving the quality of the dataset and using the right classification method.
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Zhang, Ruiting, and Zhijian Zhou. "A Fuzzy Least Squares Support Tensor Machines in Machine Learning." International Journal of Emerging Technologies in Learning (iJET) 10, no. 8 (December 14, 2015): 4. http://dx.doi.org/10.3991/ijet.v10i8.5203.
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In the machine learning field, high-dimensional data are often encountered in the real applications. Most of the traditional learning algorithms are based on the vector space model, such as SVM. Tensor representation is useful to the over fitting problem in vector-based learning, and tensor-based algorithm requires a smaller set of decision variables as compared to vector-based approaches. We also would require that the meaningful training points must be classified correctly and would not care about some training points like noises whether or not they are classified correctly. To utilize the structural information present in high dimensional features of an object, a tensor-based learning framework, termed as Fuzzy Least Squares support tensor machine (FLSSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of FLSSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in ORL database and Yale database. The FLSSTM outperforms other tensor-based algorithms, for example, LSSTM, especially when training size is small.
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Idris, Syed Mohammed. "PRACTICAL CLASSIFICATION TEMPLATE FOR DATASETS IN MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 7, no. 10 (February 1, 2023): 110–16. http://dx.doi.org/10.33564/ijeast.2023.v07i10.014.
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In this work, different Machine Learning (ML) algorithms are used and evaluated based on their performance of classifying peer reviewed content of the dataset provided. The ultimate objective is to extract meaningful information from the classification of the given dataset. In pursuing this objective, the ML techniques are utilized to classify different datasets into: Validation Dataset and Test Dataset. The ML techniques applied in this work are Logistic Regression, Support Vector Machines, Naïve Bayes, Linear Discriminant Analysis, KNearest Neighbor, and Decision Tree. In addition to the description of the utilized ML algorithms, the methodology and algorithms for classification using the aforementioned ML techniques are provided. The comparative study based on six different performance measures suggests that - with the exception of Support Vector Machines algorithm - the proposed ML techniques with the detailed pre-processing algorithms may or may not work well for classifying the iris flower dataset.
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Giordana, Attilio, and Filippo Neri. "Genetic algorithms in machine learning." AI Communications 9, no. 1 (1996): 21–26. http://dx.doi.org/10.3233/aic-1996-9103.
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Wang, Bingjie. "Quantum algorithms for machine learning." XRDS: Crossroads, The ACM Magazine for Students 23, no. 1 (September 20, 2016): 20–24. http://dx.doi.org/10.1145/2983535.
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Schwalbe, Ulrich. "ALGORITHMS, MACHINE LEARNING, AND COLLUSION." Journal of Competition Law & Economics 14, no. 4 (2018): 568–607. http://dx.doi.org/10.1093/joclec/nhz004.
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Abstract This paper discusses whether self-learning price-setting algorithms can coordinate their pricing behavior to achieve a collusive outcome that maximizes the joint profits of the firms using them. Although legal scholars have generally assumed that algorithmic collusion is not only possible but also exceptionally easy, computer scientists examining cooperation between algorithms as well as economists investigating collusion in experimental oligopolies have countered that coordinated, tacitly collusive behavior is not as rapid, easy, or even inevitable as often suggested. Research in experimental economics has shown that the exchange of information is vital to collusion when more than two firms operate within a given market. Communication between algorithms is also a topic in research on artificial intelligence, in which some scholars have recently indicated that algorithms can learn to communicate, albeit in somewhat limited ways. Taken together, algorithmic collusion currently seems far more difficult to achieve than legal scholars have often assumed and is thus not a particularly relevant competitive concern at present. Moreover, there are several legal problems associated with algorithmic collusion, including questions of liability, of auditing and monitoring algorithms, and of enforcing competition law.
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hiya, B. Sand, R. P. S. Man ikandan, G. A. nitha, and V. Prasath kumar. "Study on Machine Learning Algorithms." International Journal of Computer Trends and Technology 65, no. 1 (November 25, 2018): 39–43. http://dx.doi.org/10.14445/22312803/ijctt-v65p106.
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P, Sushma, Dr Yogesh Kumar Sharma, and Dr S. Naga Prasad. "Applications of Machine Learning Algorithms." International Journal of Computer Trends and Technology 68, no. 1 (January 25, 2020): 21–25. http://dx.doi.org/10.14445/22312803/ijctt-v68i1p105.
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Burzykowski, Tomasz, Melvin Geubbelmans, Axel-Jan Rousseau, and Dirk Valkenborg. "Validation of machine learning algorithms." American Journal of Orthodontics and Dentofacial Orthopedics 164, no. 2 (August 2023): 295–97. http://dx.doi.org/10.1016/j.ajodo.2023.05.007.
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Sharma, Indrani, and Bhimraj Rathodiya. "Bias in Machine Learning Algorithms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (September 10, 2019): 1158–61. http://dx.doi.org/10.61841/turcomat.v10i2.14387.
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Bias in machine learning algorithms has emerged as a critical concern, casting a shadow on the perceived objectivity and fairness of these systems. This paper delves into the multifaceted landscape of biases inherent in machine learning models, exploring their origins, manifestations, implications, and potential remedies. The investigation begins by elucidating the sources of bias, stemming from various stages of the machine learning pipeline, including data collection, feature selection, algorithmic design, and human interventions. It unravels how biases, whether implicit in historical data or inadvertently introduced, can perpetuate societal inequalities, reinforce stereotypes, and result in discriminatory outcomes. The paper examines the manifestations ofbias in different domains, such as healthcare, criminal justice, finance, and employment, where machine learning algorithms wield substantial influence. It highlights instances where biased models can lead to unequal treatment, exacerbating societal disparities and compromising ethical standards. Moreover, the study explores the challenges associated with detecting, measuring, and mitigating bias in machine learning algorithms. It navigates through various fairness metrics, algorithmic transparency techniques, and debiasing strategies aimed at promoting fairness, accountability,and transparency in algorithmic decision-making. In addition to uncovering the intricacies of bias, this paper underscores the ethical imperatives in mitigating bias, emphasizing the need for interdisciplinary collaboration,ethical guidelines, and regulatory frameworks. It advocates for a holistic approach that amalgamates technical advancements with ethical considerations to steer machine learning algorithms toward equitable and socially responsible outcomes.In conclusion, bias in machine learning algorithms represents a multifaceted challenge, necessitating a concerted effort from researchers, policymakers, and practitioners. Addressing bias requires not only technical innovations but also ethical scrutiny, transparency, and a commitment to promoting fairness and inclusivity in algorithmic systems.This abstract provides an overview of the multifaceted nature of bias in machine learning algorithms, exploring its origins, implications, challenges, and the necessity for a holistic approach encompassing technical and ethical considerations.
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Kumar, Prof K. Senthil. "HEART DISEASE PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27570.
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Heart disease is a major cause of death worldwide, making early diagnosis and prevention essential. Predictive models have gained significant attention in recent years, with several algorithms being employed to develop these models. However, there are challenges in implementing heart disease prediction models, including data quality, model accuracy, ethical concerns, and limited data. Therefore, this project aims to develop a heart disease prediction model and analyse different algorithms used in disease prediction. In order to increase the predictive accuracy of machine learning algorithms, this study compares six algorithms, including KNN (K-Nearest Neighbour), Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and Neural Network. 13 attributes, including age, sex, and cholesterol, are used, and ensemble methods like boosting and bagging are used. The accuracy, recall, f1 score, and precision of each algorithm are calculated to determine the most accurate model. Additionally, this study identifies the limitations of heart disease prediction models and their implications for patient diagnosis and treatment, by developing and analysing heart disease prediction models. In conclusion, while heart disease prediction models have the potential to be financially feasible and be useful in the future, their current limitations and challenges mean that they cannot be relied upon as the sole means of diagnosis or treatment decisions Key Words: Heart Diseases, Machine Learning Algorithms, Logistic Regression, Random Forest, Decision Tree.
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Atiyah, Oqbah Salim, and Saadi Hamad Thalij. "Evaluation of COVID-19 Cases based on Classification Algorithms in Machine Learning." Webology 19, no. 1 (January 20, 2022): 4878–87. http://dx.doi.org/10.14704/web/v19i1/web19326.
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COVID-19 has appeared in china, spread rapidly the world wide and caused with many injuries, deaths between humans. It is possible to avoid the spread of the disease or reduce its spread with the machine learning and the diagnostic techniques, where the use classification algorithms are one of the fundamental issues for prediction and decision-making to help of the early detection, diagnose COVID-19 cases and identify dangerous cases that need admit Intensive Care Unit to provide treatment in a timely manner. In this paper, we use the machine learning algorithms to classify the COVID-19 cases, the dataset got from dataset search on google and used four algorithms, as (Logistic Regression, Naive Bayes, Random Forest, Stochastic Gradient Descent), the result of algorithms accuracy was 94.82%, 96.57%, 98.37%, 99.61% respectively and the execution time of each algorithm were 0.7s, 0.04s, 0.20s,0.02s respectively, and with the mislabeling Stochastic Gradient Descent algorithm was better.
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Keneskyzy, K., and S. B. Yeskermes. "Метод машинного обучения для обратных задач теплопроводности." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 2, no. 1(5) (March 26, 2021): 59–64. http://dx.doi.org/10.54309/ijict.2021.05.1.008.
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Investigated in this work is the potential of carrying out inverse problems with linear and non-linear behavior using machine learning methods and the neural network method. With the advent of ma-chine learning algorithms it is now possible to model inverse problems faster and more accurately. In order to demonstrate the use of machine learning and neural networks in solving inverse problems, we propose a fusion between computational mechanics and machine learning. The forward problems are solved first to create a database. This database is then used to train the machine learning and neural network algorithms. The trained algorithm is then used to determine the boundary conditions of a problem from assumed meas-urements. The proposed method is tested for the linear/non-linear heat conduction problems in which the boundary conditions are determined by providing three, four, and five temperature measurements. This re-search demonstrates that the proposed fusion of computational mechanics and machine learning is an effec-tive way of tackling complex inverse problems.
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Benbouzid, Bilel. "Unfolding Algorithms." Science & Technology Studies 32, no. 4 (December 13, 2019): 119–36. http://dx.doi.org/10.23987/sts.66156.
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Predictive policing is a research field whose principal aim is to develop machines for predicting crimes, drawing on machine learning algorithms and the growing availability of a diversity of data. This paper deals with the case of the algorithm of PredPol, the best-known startup in predictive policing. The mathematicians behind it took their inspiration from an algorithm created by a French seismologist, a professor in earth sciences at the University of Savoie. As the source code of the PredPol platform is kept inaccessible as a trade secret, the author contacted the seismologist directly in order to try to understand the predictions of the company’s algorithm. Using the same method of calculation on the same data, the seismologist arrived at a different, more cautious interpretation of the algorithm's capacity to predict crime. How were these predictive analyses formed on the two sides of the Atlantic? How do predictive algorithms come to exist differently in these different contexts? How and why is it that predictive machines can foretell a crime that is yet to be committed in a California laboratory, and yet no longer work in another laboratory in Chambéry? In answering these questions, I found that machine learning researchers have a moral vision of their own activity that can be understood by analyzing the values and material consequences involved in the evaluation tests that are used to create the predictions.
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Zhong, Yunshun, and Tamer El-Diraby. "Shoreline Recognition Using Machine Learning Techniques." IOP Conference Series: Earth and Environmental Science 1101, no. 2 (November 1, 2022): 022025. http://dx.doi.org/10.1088/1755-1315/1101/2/022025.
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Abstract Coastal areas have emerged to be the most significant and dynamic regions worldwide. Therefore, automating shoreline recognition will aid non-profit conservation authorities to reduce public budget expenditures, relieve erosion damage, and increase the climate resilience of the natural environment. In this paper, advanced ML boosting algorithms including XGBoost, and LGBM are firstly applied into shoreline recognition with aerial images (of Lake Ontario in this study). This paper first discussed the significance and a literature review of recent progress in shoreline detection. Then, this paper adopted semantic segmentation instead of detecting shoreline directly, which enables the (Machine Learning) ML model to achieve relatively high accuracy with a small amount of data. 5 high-resolution images are used for training the model in which shorelines are detected. The work was carried out in four steps: 1) labeling the contents of shoreline images as areas of water and banks; 2) training ML algorithms; 3) using the trained algorithms to classify the image content as either water or land objects; 4) post-processing by de-noising image pixels (applying a Fourier transform algorithm) to obtain a defined shoreline. The averaged training time per image for Random Forest, XGBoost, and LGBM algorithms are 195.2 sec, 71.0 sec, and 8.6 sec, respectively. The averaged accuracy is 95.6%, 96.0%, and 94.8%, respectively; the XGBoost algorithm has slightly higher accuracy, while LGBM has a significantly shorter runtime. Cross-validation of the LGBM algorithm reduced the training time by around 23% (7.0 sec) and increased the accuracy by only 1.1% (to 95.9%).
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Thomas, Philip S., Bruno Castro da Silva, Andrew G. Barto, Stephen Giguere, Yuriy Brun, and Emma Brunskill. "Preventing undesirable behavior of intelligent machines." Science 366, no. 6468 (November 21, 2019): 999–1004. http://dx.doi.org/10.1126/science.aag3311.
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Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior—that they do not, for example, cause harm to humans—is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.
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G, Mr Aniket. "A Comparative Study: Machine Learning Algorithms for Parkinson’s Disease Analysis." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6275–84. http://dx.doi.org/10.22214/ijraset.2023.53175.
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Abstract: Parkinson's disease (PD) is a complex neurodegenerative disorder that affects millions of people worldwide. Accurate diagnosis and monitoring of PD are essential for effective treatment and management of the disease. In recent years, machine learning algorithms have shown great promise in assisting with the analysis of PD data and aiding in diagnosis and prognosis. This study presents a comparative analysis of various machine learning algorithms for PD analysis, with the objective of identifying the most effective approach for detecting and predicting PD progression. Multiple machine learning algorithms, including decision trees, support vector machines, random forests, neural networks, and ensemble methods, are evaluated using a comprehensive dataset of PD patients and healthy individuals. The study in corporates feature selection and dimensionality reduction techniques to enhance the algorithms' performance and reduce computational complexity. The results of the comparative analysis reveal the strengths and weaknesses of each algorithm in PD analysis. In conclusion, this comparative study showcases the effectiveness of machine learning algorithms in the field of PD research. It emphasizes the importance of selecting appropriate algorithms and features for accurate diagnosis and prediction of PD, ultimately leading to improved patient outcomes and better management of the disease