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Journal articles on the topic 'Supervised and unsupervised machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d

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Fong, A. C. M., and G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning." International Journal of Machine Learning and Computing 11, no. 2 (2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.

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Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data. This is especially true for deep learning (DL) neural networks, which need huge annotated data sets for good performance. However, large volumes of annotated data are not always readily available. In addition, some of the best performing ML and DL algorithms lack explainability – it is often difficult even for domain experts to interpret the results. This is an important consideration especially in safety-critical applications, such as AI-assisted medical endeavors, in which a DL’s failure

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to <span lang="EN-US">improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to val

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Amrita, Sadarangani *. Dr. Anjali Jivani. "A SURVEY OF SEMI-SUPERVISED LEARNING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 10 (2016): 138–43. https://doi.org/10.5281/zenodo.159333.

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Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classification, and unsupervised clustering and semi-supervised clustering are also discussed.

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Silva, Hugo, and Jorge Bernardino. "Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems." Algorithms 15, no. 4 (2022): 130. http://dx.doi.org/10.3390/a15040130.

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Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a comparison of the related metrics between them with their application to a public diabetes and human resource datasets. The two mainly used categories that allow the learning process without requiring explicit programming are supervised and unsupervised learning. For that, we use Scikit-learn, the free software machine learning library for Pyt

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Ezadeen Mehyadin, Aska, and Adnan Mohsin Abdulazeez. "CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW." Iraqi Journal for Computers and Informatics 47, no. 1 (2021): 1–11. http://dx.doi.org/10.25195/ijci.v47i1.277.

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Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serv

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Yin, Xinxin, Feng Liu, Run Cai, et al. "Research on Seismic Signal Analysis Based on Machine Learning." Applied Sciences 12, no. 16 (2022): 8389. http://dx.doi.org/10.3390/app12168389.

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In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRo

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Retnoningsih, Endang, and Rully Pramudita. "Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python." BINA INSANI ICT JOURNAL 7, no. 2 (2020): 156. http://dx.doi.org/10.51211/biict.v7i2.1422.

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Abstrak: Machine learning merupakan sistem yang mampu belajar sendiri untuk memutuskan sesuatu tanpa harus berulangkali diprogram oleh manusia sehingga komputer menjadi semakin cerdas berlajar dari pengalaman data yang dimiliki. Berdasarkan teknik pembelajarannya, dapat dibedakan supervised learning menggunakan dataset (data training) yang sudah berlabel, sedangkan unsupervised learning menarik kesimpulan berdasarkan dataset. Input berupa dataset digunakan pembelajaran mesin untuk menghasilkan analisis yang benar. Permasalahan yang akan diselesaikan bunga iris (iris tectorum) yang memiliki bun

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Nurhalizah, Ria Suci, Rian Ardianto, and Purwono Purwono. "Analisis Supervised dan Unsupervised Learning pada Machine Learning: Systematic Literature Review." Jurnal Ilmu Komputer dan Informatika 4, no. 1 (2024): 61–72. http://dx.doi.org/10.54082/jiki.168.

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Artikel ini menyajikan tinjauan sistematis mengenai dua paradigma utama dalam Machine Learning yaitu Supervised Learning dan Unsupervised Learning, dengan tujuan memberikan pemahaman mendalam tentang perbedaan, serta kelebihan dan kekurangan masing-masing metode. Penelitian ini menerapkan metode Literature Review (SLR) berdasarkan pedoman PRISMA untuk menganalisis studi-studi relevan yang dipublikasikan dalam lima tahun terakhir. Dari total 540 artikel yang diperoleh, 10 artikel dipilih untuk ditelaah lebih lanjut, terdiri dari lima mengenai Supervised Learning dan lima mengenai Unsupervised L

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Liu, MengYang, MingJun Li, and XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents." Computational Intelligence and Neuroscience 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no s

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Liu, MengYang, MingJun Li, and XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents." Computational Intelligence and Neuroscience 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no s

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Akshada, Sunil Shitole, and Priyadarshini I. "Survey of Machine Learning Algorithms & its Applications." Journal of Advances in Computational Intelligence Theory 3, no. 2 (2021): 1–5. https://doi.org/10.5281/zenodo.5090570.

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Machine Learning is a subset of Artificial Intelligence. Machine learning is one of the latest technologies which has brings new innovations in various fields. Machine learning refers to the concept of train the machine in such a way it can learns from a past experiences or it can learn from a data provided to it. The concept machine learning can be implemented in various fields using its various algorithms. The machine learning contains various algorithms like KNN, K means, decision tree, random forest, support vector machine etc. Machine Learning can be further classified into Supervised Lea

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Sharma, Swapnil. "Supervised Learning: An InDepth Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35414.

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Supervised learning pivotal machine learning paradigm wherein models are trained on labeled datasets. They predict outcomes or classify data. It includes methodologies and diverse applications of supervised learning. Emphasizing significance in modern technology. Key methodologies encompass linear regression logistic regression. Also decision trees, support vector machines neural networks. Each with unique advantages for specific tasks. Versatility is demonstrated through applications in image and speech recognition. Natural language processing, medical diagnosis and financial forecasting also

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Dhamelia, Hemin, and Riti Moradiya. "Unlocking Hidden Insights: Unleashing the Strength of Semi-Supervised Learning in Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 2049–57. http://dx.doi.org/10.22214/ijraset.2023.55468.

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Abstract: Semi-supervised learning bridges supervised and unsupervised methods, utilizing limited labeled data alongside vast unlabeled data. This paper explores its foundations, algorithms, applications, challenges, and trends. It covers co-training, selftraining, multi-view learning, and generative approaches, addressing label scarcity, noisy data, and model robustness. The research offers insights into semi-supervised learning's transformative role in machine learning

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Sarma, Abhijat, Rupak Chatterjee, Kaitlin Gili, and Ting Yu. "Quantum unsupervised and supervised learning on superconducting processors." Quantum Information and Computation 20, no. 7&8 (2020): 541–52. http://dx.doi.org/10.26421/qic20.7-8-1.

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Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the data, the computation time for training and using these statistical models grows quickly. Here, we propose and implement on the IBMQ a quantum analogue to K-means clustering, and compare it to a previously developed quantum support vector machine. We find the algorithm's accuracy comparable to the classical K-means algorithm for clustering and classification problems, and find that it becomes less computationally expensive to implement

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Hsu, Chia-Yi, Pin-Yu Chen, Songtao Lu, Sijia Liu, and Chia-Mu Yu. "Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6926–34. http://dx.doi.org/10.1609/aaai.v36i6.20650.

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Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies focus on supervised learning tasks, relying on the ground truth data label, a targeted objective, or supervision from a trained classifier. In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation. Our framework exploits a mutual information neural estimator as an information theoretic similarity measure to generate adversarial examples without

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Khalaf Hamoud, Alaa, Mohammed Baqr Mohammed Kamel, Alaa Sahl Gaafar, et al. "A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 1105. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp1105-1116.

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The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling Technique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' per

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J., Dr SIRISHA. "Assessing DDoS Detection Accuracy through Semi-Supervised Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29861.

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Despite the proliferation of advanced Machine Learning (ML) techniques in DDoS detection, this pervasive attack remains a significant menace to the Internet's integrity. Existing ML based DDoS detection methods fall into two categories: supervised and unsupervised approaches. This paper synthesizes insights from existing research endeavors, and enhance DDoS detection through machine learning methodologies, specifically focusing on semi-supervised techniques for analysis purposes. By harnessing the power of semi-supervised ML, we employ a succession of algorithms including Naive Bayes, Support

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Almuqati, Mohammed Tuays, Fatimah Sidi, Siti Nurulain Mohd Rum, Maslina Zolkepli, and Iskandar Ishak. "Challenges in Supervised and Unsupervised Learning: A Comprehensive Overview." International Journal on Advanced Science, Engineering and Information Technology 14, no. 4 (2024): 1449–55. http://dx.doi.org/10.18517/ijaseit.14.4.20191.

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Data science and machine learning are at the forefront of modern technological advancements, promising automated insights, predictions, and decision-making. Supervised and unsupervised learning are pivotal paradigms within this dynamic landscape, each presenting its unique challenges. This article provides a comprehensive overview of the multifaceted challenges inherent to both supervised and unsupervised learning. This article reviews research studies published between 2019 and 2023. This article discusses the challenges of supervised and unsupervised learning. In supervised learning, challen

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Mamun, Abdullah Al, Md Shakhaowat Hossain, S. M. Shadul Islam Rishad, et al. "MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS." American Journal of Engineering and Technology 06, no. 11 (2024): 63–76. https://doi.org/10.37547/tajet/volume06issue11-08.

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This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key

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Hossain, Md Shakhaowat, S. M. Shadul Islam Rishad, Md Mohibur Rahman, et al. "MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS." International journal of networks and security 04, no. 01 (2024): 22–32. http://dx.doi.org/10.55640/ijns-04-01-06.

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This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key

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Kim, Sungil, Byungjoon Yoon, Jung-Tek Lim, and Myungsun Kim. "Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning." Energies 14, no. 5 (2021): 1499. http://dx.doi.org/10.3390/en14051499.

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It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly

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Leroux, Sam, and Pieter Simoens. "Hybrid Edge–Cloud Models for Bearing Failure Detection in a Fleet of Machines." Electronics 13, no. 24 (2024): 5034. https://doi.org/10.3390/electronics13245034.

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Real-time condition monitoring of machinery is increasingly being adopted to minimize costs and enhance operational efficiency. By leveraging large-scale data acquisition and intelligent algorithms, failures can be detected and predicted, thereby reducing machine downtime. In this paper, we present a novel hybrid edge–cloud system for detecting rotational bearing failures using accelerometer data. We evaluate both supervised and unsupervised neural network approaches, highlighting their respective strengths and limitations. Supervised models demonstrate high accuracy but require labeled datase

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Parker, Amanda J., and Amanda S. Barnard. "Machine learning reveals multiple classes of diamond nanoparticles." Nanoscale Horizons 5, no. 10 (2020): 1394–99. http://dx.doi.org/10.1039/d0nh00382d.

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Unsupervised clustering and supervised classification of a diverse set of reconstructed, twinned and passivated diamond nanoparticles predict nine classes that have distinctly different characteristics and electronic properties.

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Singh, Anshita. "Intrusion Detection System: Comparative Analysis of Supervised and Unsupervised Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1214–18. https://doi.org/10.22214/ijraset.2025.68447.

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With advancements in technology, the rapid growth in cybercrimes poses crucial challenges to maintaining the security and integrity of computer networks. Signature-based techniques and predefined rules are the traditional methods for Intrusion Detection Systems, which are inadequate for handling emerging cybercrimes. This paper presents a comparative analysis of supervised and unsupervised machine learning techniques in Intrusion Detection Systems. Various machine learning models, such as supervised and unsupervised learning, are used to review the limitations of traditional IDS approaches. Ov

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Das, Saikat, Mohammad Ashrafuzzaman, Frederick T. Sheldon, and Sajjan Shiva. "Ensembling Supervised and Unsupervised Machine Learning Algorithms for Detecting Distributed Denial of Service Attacks." Algorithms 17, no. 3 (2024): 99. http://dx.doi.org/10.3390/a17030099.

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The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical infrastructure. Machine-learning-based approaches have shown promise in developing intrusion detection systems (IDSs) for detecting cyber-attacks, such as DDoS. Herein, we present a solution to detect DDoS attacks through an ensemble-based machine learning approach that combines supervised and unsupervised machine learning ensemble frameworks. This comb

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Sahu, Ranu, and Khushboo Choubey. "Comparative Analysis of Supervised and Unsupervised Learning Methods for Pattern Classification." International Journal of Innovative Research in Computer and Communication Engineering 12, Special Is (2024): 58–63. http://dx.doi.org/10.15680/ijircce.2024.1203509.

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In the higher learning system, this article compares and contrasts supervised and unsupervised learning approaches to see which is more effective for classifying patterns. Among the most significant uses of machine learning algorithms is classification. Our research shows that, although the supervised learning algorithm, Backpropagation learning with errors, does a great deal of nonlinear real-time assignments, the unsupervised learning algorithm, Kohonen Self-Organizing Map (KSOM), performs very well in our study's classification tasks.

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Alaa, Khalaf Hamoud1, Baqr Mohammed Kamel2 3. 4. Mohammed, Sahl Gaafar5 Alaa, et al. "A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 1105–16. https://doi.org/10.11591/ijeecs.v28.i2.pp1105-1116.

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The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling TEchnique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the s

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Tulasi, G. Amrutha Swapna. "Machine Learning: An In-Depth Review." IOSR Journal of Computer Engineering 26, no. 6 (2024): 26–40. https://doi.org/10.9790/0661-2606022640.

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A large volume of data, such as IoT, cybersecurity, mobile, and health data, is being generated. Machine learning (ML) is essential for analyzing this data and developing intelligent applications. This paper examines different ML algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning, as well as deep learning methods capable of processing large datasets. It provides an overview of how these algorithms are applied in areas like cybersecurity, smart cities, healthcare, e-commerce, and agriculture. The paper also highlights the challenges and potential future

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Writuraj Sarma, Aakash Srivastava, and Vishal Sresth. "Machine learning-based anomaly detection in IoT Security: A comparative analysis of supervised and unsupervised models." World Journal of Advanced Engineering Technology and Sciences 9, no. 2 (2023): 377–90. https://doi.org/10.30574/wjaets.2023.9.2.0207.

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Massive device networks stemming from the rapid growth of Internet of Things devices became a security threat because they expanded exposure to cyberattacks. Security tools from the past show limited capability to detect abnormalities within IoT systems that grow rapidly, so advanced anomaly detection methods must be created. Identifying and detecting IoT network security breaches and malicious activities use machine learning (ML)-based approaches as powerful analytical tools. The work presents a structured overview of machine learning algorithms that monitor IoT security environments using su

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Liu, Ruhao, Lei Zhang, Xinrui Wang, et al. "Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification." Processes 11, no. 7 (2023): 2042. http://dx.doi.org/10.3390/pr11072042.

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Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This pap

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Reddy, Mr Chittimuru S. "Sentiment Analysis Based on Category Detection Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 6771–77. https://doi.org/10.22214/ijraset.2025.69980.

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In this paper, the online consumer reviews were considered to assist purchase- decision making has become increasingly popular. To process the user reviews and find the useful information for making decision of purchase most of existing systems are presented. But one can hardly read all reviews to obtain a fair evaluation of a product or service. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this project presented two methods. The first method presented is an unsupervised method that applies association rul

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Zhou, Xinyu. "Machine-Learning-Assisted Optical Fiber Communication System." Highlights in Science, Engineering and Technology 27 (December 27, 2022): 630–38. http://dx.doi.org/10.54097/hset.v27i.3826.

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With the development of software-defined networking and coherent transmission, to name only a couple of emerging technical and technological areas, optical networks have rapidly expanded during the past few years. To handle with the enormous increment, several sections of optical transmission networks have been addressed via machine learning. Techniques such as support vector machine and KNN algorithms are widely used in fiber-induced nonlinear mitigation, which can cause enormous financial loses if the problem is not solved properly. Optical performance monitoring is another essential area in

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Abijono, Heri, Puput Santoso, and Novita Lestari Anggreini. "ALGORITMA SUPERVISED LEARNING DAN UNSUPERVISED LEARNING DALAM PENGOLAHAN DATA." Jurnal Teknologi Terapan: G-Tech 4, no. 2 (2021): 315–18. http://dx.doi.org/10.33379/gtech.v4i2.635.

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Seiring dengan zaman yang semakin berkembang seperti saat ini, kini kita berada pada zaman yang mana teknologi menjadi satu hal yang paling penting dan tidak pernah terlepas dari kehidupan manusia. Dengan teknologi informasi dan komunikasi yang ada sekarang ini, semakin memudahkan kita untuk melakukan segala aktifitas. Dengan semakin berkembangnya teknologi informasi juga menghasilkan begitu banyak data yang dapat diolah, sehingga banyak informasi yang tidak terbuang sia-sia. Machine learning dapat digunakan sebagai sistem pengolahan data sehingga dapat mempermudah pengguna dalam mengolah info

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Bakumenko, Alexander, and Ahmed Elragal. "Detecting Anomalies in Financial Data Using Machine Learning Algorithms." Systems 10, no. 5 (2022): 130. http://dx.doi.org/10.3390/systems10050130.

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Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalous in general ledger (GL) data. Currently, widely used techniques such as random sampling and manual asses

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Gurpreet Singh. "Key Features and Techniques of Unsupervised Learning." Tuijin Jishu/Journal of Propulsion Technology 45, no. 02 (2024): 479–82. http://dx.doi.org/10.52783/tjjpt.v45.i02.5825.

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Machine learning (ML) has emerged as a transformative technology with profound implications for industrial operations across diverse sectors. This paper provides a comprehensive analysis of the applications and challenges, of machine learning in industrial settings. The paper begins by outlining the foundational concepts of machine learning and its relevance to industrial processes. It explores various ML techniques, including supervised learning, unsupervised learning, and reinforcement learning, and discusses their applicability in optimizing production, enhancing quality control, and predic

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Mujawar, Almas Shamshuddin, and Shubhangi Rajesh Patil. "A REVIEW ON CROP YIELD PREDICTION USING RANDOM FOREST, SVM & KNN." International Journal of Computer Science and Mobile Computing 12, no. 6 (2023): 41–44. http://dx.doi.org/10.47760/ijcsmc.2023.v12i06.004.

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Nowadays we all see machine Learning is famous for data prediction we use Supervised learning techniques & Unsupervised learning techniques. By using this prediction, industries, organizations are improving their performance, productivity & income rate. For supervised learning, we can use algorithms like Random Forests, Support Vector Machines, decision trees & Linear classifiers are used in classification. Linear regression & Logistic regression are used for regression. In unsupervised learning we can use clustering, data mining, etc. in our paper we will collect data on the c

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Chen, Binjie, Fushan Wei, and Chunxiang Gu. "Bitcoin Theft Detection Based on Supervised Machine Learning Algorithms." Security and Communication Networks 2021 (February 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/6643763.

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Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methods—the k-nearest neighbor (KNN), s

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Aversa, Rossella, Piero Coronica, Cristiano De Nobili, and Stefano Cozzini. "Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification." Data Intelligence 2, no. 4 (2020): 513–28. http://dx.doi.org/10.1162/dint_a_00062.

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In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from

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Tewogbade Shakir Adeyemi and Ajasa Muhammed. "Botnet attack detection in IoT using machine learning models." International Journal of Science and Research Archive 12, no. 1 (2024): 2221–29. http://dx.doi.org/10.30574/ijsra.2024.12.1.0936.

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Botnet is of great concern when dealing with security of computer networks globally. Bots readily attack network infrastructure through their malicious activities. It is pertinent to mitigate and control the level of threat posit by Bot and thus the need for advanced technologies in predicting their occurrences. Machine learning offered a greater support in this regard with ability to handle voluminous data from IoT devices and the robustness in predicting the potential attack from trained data. Both supervised (DT and RF) and unsupervised learning (Deep Learning) were used to investigate pred

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Balaji Dhashanamoorthi. "Analyzing detection algorithms for cybersecurity in financial institutions." International Journal of Science and Research Archive 11, no. 2 (2024): 558–68. http://dx.doi.org/10.30574/ijsra.2024.11.2.0478.

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Frauds in financial services are an ever-increasing phenomenon, and cybercrime generates multimillion revenues. Even a small improvement in fraud detection rates would lead to significant savings. Traditional rule-based systems have limitations in blocking potentially fraudulent transactions. This chapter explores how machine learning, specifically supervised and unsupervised learning, can address these limitations more effectively. We present a novel AI-based fraud detection system that combines supervised and unsupervised models. In the batch layer, transaction data undergoes pre-processing

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Antonelli, Laura, and Mario Rosario Guarracino. "Special Issue on Supervised and Unsupervised Classification Algorithms—Foreword from Guest Editors." Algorithms 16, no. 3 (2023): 145. http://dx.doi.org/10.3390/a16030145.

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Wei, Xianglong, Yongjun Lu, Zhili Wang, Xingnian Liu, and Siping Mo. "A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes." Water 10, no. 11 (2018): 1680. http://dx.doi.org/10.3390/w10111680.

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Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures are proposed: a supervised support vector machine (SVM) model and an unsupervised model combining a Kohonen neural network with an SVM model (KNN-SVM). It was found that the supervised SVM model predicted the damage level of the validation samples with high accuracy, and the unsupervised data-mining KNN-SVM model ag

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Zhu, Tianyi. "Machine Learning Models in Quantitative Investment." Applied and Computational Engineering 115, no. 1 (2024): 165–70. https://doi.org/10.54254/2755-2721/2025.18521.

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This paper explores the application of machine learning models in the realm of quantitative investment, emphasizing their potential to enhance decision-making processes and predictive accuracy in financial markets. Beginning with an overview of machine learning typessupervised, unsupervised, and semi-supervisedthe paper delves into specific models commonly employed in investment strategies. These include supervised models such as Random Forests and Support Vector Machines, as well as unsupervised models like K-Means Clustering and Bayesian Networks. The practical applications and advantages of

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Wang, Yanyan, Qun Chen, Murtadha H. M. Ahmed, et al. "Supervised Gradual Machine Learning for Aspect-Term Sentiment Analysis." Transactions of the Association for Computational Linguistics 11 (2023): 723–39. http://dx.doi.org/10.1162/tacl_a_00571.

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Abstract Recent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be effectively performed by Gradual Machine Learning (GML). However, the performance of the current unsupervised solution is limited by inaccurate and insufficient knowledge conveyance. In this paper, we propose a supervised GML approach for ATSA, which can effectively exploit labeled training data to improve knowledge conveyance. It leverages binary polarity relations between instances, which can be either similar or opposite, to enable supervised knowledge conveyance. Besides the explicit polarity relations indicat

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Aiyanyo, Imatitikua D., Hamman Samuel, and Heuiseok Lim. "A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning." Applied Sciences 10, no. 17 (2020): 5811. http://dx.doi.org/10.3390/app10175811.

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This is a systematic review of over one hundred research papers about machine learning methods applied to defensive and offensive cybersecurity. In contrast to previous reviews, which focused on several fragments of research topics in this area, this paper systematically and comprehensively combines domain knowledge into a single review. Ultimately, this paper seeks to provide a base for researchers that wish to delve into the field of machine learning for cybersecurity. Our findings identify the frequently used machine learning methods within supervised, unsupervised, and semi-supervised mach

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Jain, Anusha. "A Review on Leveraging Machine Learning for Anomaly Detection in Cloud Cost Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35775.

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Cloud computing has revolutionized the way organizations manage and scale their IT infrastructure. However, with the increased reliance on cloud services, the need for effective cost management has become paramount. Anomalies in cloud cost data can indicate potential issues such as resource misconfigurations, security breaches, or inefficiencies, leading to unexpected financial burdens. This paper explores the application of machine learning (ML) techniques for anomaly detection in multi-cloud cost management. By leveraging supervised, unsupervised, and semi-supervised learning methods, this s

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Iqbal, Zafar, Ahthasham Sajid, Muhammad Nauman Zakki, Adeel Zafar, and Arshad Mehmood. "Role of Machine and Deep Learning Algorithms in Secure Intrusion Detection Systems (IDS) for IOT & Smart Cities." International Journal of Information Technology, Research and Applications 3, no. 4 (2024): 1–16. http://dx.doi.org/10.59461/ijitra.v3i4.111.

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In this study the authors have examines various machine learning algorithms that could be used in IDS for making secure IoT and Smart Cities. The study examines various deep learning architectures of supervised, unsupervised, and semi-supervised learning methods to improve security and resource usage. Federated learning, edge computing, explainable AI, adversarial machine learning defense, and transfer learning are also explored for smart farming and IoT challenges. Machine learning has the potential to improve security and agricultural sustainability, but it must be researched and developed.

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Rashidi, Hooman H., Nam K. Tran, Elham Vali Betts, Lydia P. Howell, and Ralph Green. "Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods." Academic Pathology 6 (January 1, 2019): 237428951987308. http://dx.doi.org/10.1177/2374289519873088.

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Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (

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Song, Yide. "Weakly-Supervised and Unsupervised Video Anomaly Detection." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 160–70. http://dx.doi.org/10.54097/hset.v12i.1444.

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As surveillance technology is continuously improving, an ever-increasing number of cameras are being deployed everywhere. Relying on manual detection of anomalies through cameras may be unreliable and untimely. Therefore, the application of deep learning in video anomaly detection is being extensively studied. Anomaly Detection (AD) refers to identifying events that deviate from the desired actions. This article discusses representative unsupervised and weakly-supervised learning methods applied to various data types. In these machine learning methods, Generative Adversarial Network, Auto Enco

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