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Journal articles on the topic 'Machine Learning(ML) Techniques'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Jeevana, P., T. Nandini, D. Srilekha, G. Dinesh, and Mrs Archana. "Diabetic Prediction using ML Techniques." YMER Digital 21, no. 04 (2022): 585–93. http://dx.doi.org/10.37896/ymer21.04/59.

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In today's world, diabetes is a huge problem. Diabetes can cause blood sugar levels to rise, which can contribute to strokes and heart attacks. One of the most rapidly spreading diseases is this one. After speaking with a doctor and receiving a diagnosis, patients are normally required to receive their reports. Because this procedure is time-consuming and costly, we were able to fix the problem utilizing machine learning techniques. In medical organizations, many machine learning applications are both exciting and important. Machine learning is being more widely used in the medical field. Our

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Tarika, Verma, and S. Gill Nasib. "Machine Learning Techniques for Better Data Driven Decisions Revisited." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 460–64. https://doi.org/10.35940/ijeat.D6766.049420.

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The main goal of machine learning is to accurately predict the decisions to the problems without human expert intervention. These decisions depend upon patterns found and facts learnt during training tenure. However, prior incorporation of human knowledge is necessary for better prediction of the test data. The main aim is to make machines self-reliant for decision making. Providing machine with this vision makes it useful in every modern field. This makes the stepping stone to make computers behave as the humans do. Enhancing its speed and accuracy are the next step in this field. This paper

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Satarkar, Bhagyashri, Dhavalsigh Vibhute, Vishal Wadgoankar, and Yasar Sayyad. "Phishguard: ML- based phishing detection system." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem02759.

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Spam messages in SMS and email systems pose significant security and productivity risks. Traditional detection methods, such as rule-based filters, struggle to adapt to evolving spam techniques. This paper explores machine learning techniques for spam detection, emphasizing algorithms like Naïve Bayes and Support Vector Machines (SVM), along with their applications, strengths, and limitations. Key challenges, including scalability, dataset diversity, and evolving spam patterns, are identified. The study highlights future directions such as real-time classification, deep learning models, and im

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Mahesh, T. R., and Kumar Vinoth. "Early Detection of Cancer using Machine Learning (ML) Techniques." International Journal of Information Technology, Research and Applications (IJITRA) ISSN: 2583-5343 2, no. 1 (2023): 14–21. https://doi.org/10.5281/zenodo.7780162.

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous l

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M., Alagurajan, and Vijayakumaran C. "ML Methods for Crop Yield Prediction and Estimation: An Exploration." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3506–8. https://doi.org/10.35940/ijeat.C5775.029320.

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Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.

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Jin, Jennifer. "OLAP and machine learning." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630019. http://dx.doi.org/10.1142/s2425038416300196.

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The objective of this tutorial is to present an overview of machine learning (ML) methods. This paper outlines different types of ML as well as techniques for each kind. It covers popular applications for different types of ML. On-Line Analytic Processing (OLAP) enables users of multidimensional databases to create online comparative summaries of data. This paper goes over commercial OLAP software available as well as OLAP techniques such as “slice and dice” and “drill down and roll up.” It discusses various techniques and metrics used to evaluate how accurate a ML algorithm is.

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Hussain, Walayat, Asma Musabah Alkalbani, and Honghao Gao. "Forecasting with Machine Learning Techniques." Forecasting 3, no. 4 (2021): 868–69. http://dx.doi.org/10.3390/forecast3040052.

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Çelik, Rumeysa Hilal, Hacı Aslan Onur İşcil, Ecem Bulut, and Saliha Ece Acuner. "Learning molecular machines by machine learning." Eurasian Journal of Science Engineering and Technology 6, no. 2 (2025): 100–120. https://doi.org/10.55696/ejset.1620495.

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Proteins, often referred to as molecular machines, are essential biomolecules that perform a wide range of cellular functions, typically by forming complexes. Understanding their three-dimendional (3D) structures is key to deciphering their functions. However, a significant gap exists between the vast number of known protein sequences and the relatively limited number of experimentally determined protein structures. Unraveling the mechanisms of protein folding remains a central challenge in understanding the sequence-structure/dynamics-function relationship. In recent years, machine learning (

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Simkina, Polina. "Machine Learning Techniques for Calorimetry." Instruments 6, no. 4 (2022): 47. http://dx.doi.org/10.3390/instruments6040047.

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The Compact Muon Solenoid (CMS) is one of the general purpose detectors at the CERN Large Hadron Collider (LHC), where the products of proton–proton collisions at the center of mass energy up to 13.6 TeV are reconstructed. The electromagnetic calorimeter (ECAL) is one of the crucial components of the CMS since it reconstructs the energies and positions of electrons and photons. Even though several Machine Learning (ML) algorithms have been already used for calorimetry, with the constant advancement of the field, more and more sophisticated techniques have become available, which can be benefic

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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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Mahesh T R and Vinoth Kumar. "Early Detection of Cancer using Machine Learning (ML) Techniques." International Journal of Information Technology, Research and Applications 2, no. 1 (2023): 14–21. http://dx.doi.org/10.59461/ijitra.v2i1.24.

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous l

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Santhiya, S., N. Abinaya, P. Jayadharshini, S. Priyanka, S. Keerthika, and C. Sharmila. "Orthopedic patient analysis using machine learning techniques." Journal of Physics: Conference Series 2664, no. 1 (2023): 012004. http://dx.doi.org/10.1088/1742-6596/2664/1/012004.

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Abstract Orthopedic patients have been increasing in hospital because of road traffic accidents, advanced age, a lack of exercise, inadequate nutrition, and other factors. The suggested article uses Machine Learning (ML) techniques to examine the patient reports. The ability to mimic the human actions is called ML. It is a subclass of AI that solves a number of healthcare-related issues. Here ML algorithms are used for health-related data. It solves a number of healthcare-related issues. ML is the process of a machine imitating intelligent human activities. It belongs to the Artificial Intelli

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Padarian, José, Budiman Minasny, and Alex B. McBratney. "Machine learning and soil sciences: a review aided by machine learning tools." SOIL 6, no. 1 (2020): 35–52. http://dx.doi.org/10.5194/soil-6-35-2020.

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Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper a

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Prokash, Sharma. "Apply Machine Learning Techniques to Detect Breast Cancer." International Journal of Thesis Projects and Dissertations (IJTPD) 10, no. 4 (2022): 41–45. https://doi.org/10.5281/zenodo.7230150.

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<strong>Abstract:</strong> Breast Cancer (BC) is one of the most extensive diseases worldwide. Proper and earlier diagnosis is a critical stage in treatment. Moreover, it is not easy to detect mammograms due to different uncertainties. Machine Learning (ML) approaches can produce tools for doctors that can be utilized as a valuable system for early identification and diagnosis of BC that will significantly improve the survival rate of patients. This article compares three of the most famous ML approaches typically utilized for BC detection and diagnosis, namely Bayesian Networks (BN), Support

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Babhulkar, Vaishnavi, Apurva Salphale, Anagha Garkade, Kalyanee Pachghare, Tanvi Sagane, and Prof. Vanisha P. Vaidya. "ML-Driven Technique for Adaptive Email Filtering." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 1 (2024): 50–55. https://doi.org/10.5281/zenodo.10822621.

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<em>Email filtering technology must be developed quickly due to the increase of unsolicited emails, or spam mail. Computer security has struggled with spam emails consistently. They are incredibly expensive economically and exceedingly risky for networks and computers. Spam emails are found and filtered using machine learning techniques. This project mainly focuses on machine learning used to find and remove spam emails. Using the K-nearest neighbour algorithm for email spam detection is one of the simple supervised learning techniques. Initially, the relevant features for filtering the spam m

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Dr.Y., Lalitha Kameswari. "A Review on Multilevel Inverters with Machine Learning Techniques." Advancement of Signal Processing and its Applications 6, no. 2 (2023): 17–29. https://doi.org/10.5281/zenodo.8288515.

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<em>Multilevel inverters have appeared as a promising technology for high-power and high-voltage applications due to their capability to produce higher-quality output waveforms with reduced harmonic distortion and improved power quality. However, to fully exploit the potential of multilevel inverters, advanced control strategies and intelligent decision-making are required to optimize their performance and efficiency under various operating conditions. Machine learning techniques have proven to be instrumental in achieving these goals. It highlights the various machine learning applications in

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Nanajkar, Jyotsna, Mayuresh Warang, Pratik Suthar, Shivam Shinde, and Atharv Pawar. "DDoS Attack Detection Using ML/DL Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem27967.

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The increasing integration of IoT devices has heightened the vulnerability of networks to sophisticated and evolving cyber threats, particularly DDoS attacks, which can severely disrupt service availability. Leveraging machine learning algorithms, this research aims to enhance the proactive identification of anomalous patterns indicative of DDoS attacks within IoT environments. By employing a combination of feature extraction, classification, and ensemble learning methods, the proposed model demonstrates promising results in distinguishing between normal network behaviour and malicious activit

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Ahmed, Md Parvez, Md Arif, Md Salim Chowdhury, et al. "COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR ACCURATE LUNG CANCER PREDICTION." American Journal of Engineering and Technology 6, no. 9 (2024): 92–103. http://dx.doi.org/10.37547/tajet/volume06issue09-11.

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Lung cancer is a major global health concern, being one of the most common and fatal cancers. Accurate early detection and prediction of lung cancer are crucial for improving patient outcomes, and machine learning (ML) algorithms offer promising solutions for enhancing diagnostic accuracy. This study evaluates the performance of five ML algorithms—XGBoost, LightGBM, AdaBoost, Logistic Regression, and Support Vector Machines (SVM)—for lung cancer prediction. Utilizing a diverse dataset with attributes such as demographic variables, lifestyle factors, clinical features, and environmental exposur

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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 (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 des

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Pandey, Prachi, and Abhijitha Bandaru. "Enhancing predictive accuracy of asset returns by experimenting with ML techniques." SHS Web of Conferences 169 (2023): 01062. http://dx.doi.org/10.1051/shsconf/202316901062.

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The unparalleled success of machine learning is indisputable. It has transformed the world with unimaginable solutions to insistent problems. The remarkable accuracy that machine learning manifests for making estimations is an object of fascination for plenty of researchers all over the world. The financial industry has also benefited from the growth of this electrifying field to predict asset returns, creditworthiness of a customer, and portfolio management, among others. In this research, we spotlight how this accuracy is contingent upon the analysis of various aspects of the data. We also e

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J, Jayashree. "Protecting the Internet of Things (IOT) with Machine Learning and Deep Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–9. http://dx.doi.org/10.55041/ijsrem27782.

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Abstract- Deep learning (DL) and Machine learning (ML) as an IoT paradigm have improved problem-solving, and as a result, their application has expanded to many different fields. This has led to the idea that there are two powerful ways to use data—deep learning (DL) and machine learning (ML)—to solve specific problems. Thus, this article's objective is to provide a thorough analysis of "Scanning Machines and Deep Learning Techniques for Internet of Things (IOT) Security and Privacy," which addresses the current state of IoT research as well as its joint endeavor with DL. This technique stops

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Anupama, Y. K., A. Monisha, Sutar Sahana, N. Kavya, and M. R. Mahananda. "Survey of Diagnosis Cardiovascular Disease using Ml (Machine Learning) Techniques." Journal of Image Processing and Artificial Intelligence 6, no. 1 (2020): 14–18. https://doi.org/10.5281/zenodo.3697362.

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<strong>Heart Disease is one of transience reason in the present age as demonstrated by a particular course of action to diminish the amount of passing’ due to coronary disease, for instance cardiovascular breakdown, hypertension, coronary ailment, Arrhythmia needs to foresee suitably through detection systems. In most recent examination AI strategies has been utilized to enable the wellbeing to mind industry and diagnosis of heart related disease. Numerous amounts of patient’s reports are retained and techniques of machine learning such like K-Nearest Neighbor (KNN), Decision tree

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Reddy, Viswanathan Ramasamy, Sukham Romen Singh, Elangovan Guruva Reddy, E. Punarselvam E. Punarselvam, and T. Vengatesh T. Vengatesh. "Machine Learning based Rainfall." Journal of Neonatal Surgery 14, no. 15S (2025): 1435–46. https://doi.org/10.63682/jns.v14i15s.3861.

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Predicting the amount of rain is important for many industries, including agriculture, water resource management, and disaster relief. The intricate spatiotemporal patterns of rainfall are often difficult for traditional technologies to adequately represent. By utilising historical data and meteorological variables, machine learning (ML) techniques present a viable method for improving rainfall prediction. Rainfall prediction tasks have been subjected to a variety of machine learning techniques, including as decision trees, random forests, support vector machines (SVM), and deep learning model

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Chittibala, Dinesh Reddy, and Srujan Reddy Jabbireddy. "Security in Machine Learning (ML) Workflows." International Journal of Computing and Engineering 5, no. 1 (2024): 52–63. http://dx.doi.org/10.47941/ijce.1714.

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Purpose: This paper addresses the comprehensive security challenges inherent in the lifecycle of machine learning (ML) systems, including data collection, processing, model training, evaluation, and deployment. The imperative for robust security mechanisms within ML workflows has become increasingly paramount in the rapidly advancing field of ML, as these challenges encompass data privacy breaches, unauthorized access, model theft, adversarial attacks, and vulnerabilities within the computational infrastructure. Methodology: To counteract these threats, we propose a holistic suite of str

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Choubey, Shubham. "Diabetes Prediction Using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4209–12. http://dx.doi.org/10.22214/ijraset.2023.54415.

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Abstract: The goal of this research is to create a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy. Machine learning approaches have the potential to develop into trustworthy tools for diabetes diagnosis by utilising data analytics and pattern identification. Utilising feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found. Implemented and assessed using performance metrics including accuracy, recall, precision, and F1 Score are various machine learning algorithms, such as K-Nearest

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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 (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,

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Joseph, Jyothis. "Twitter Bot Detection Using Machine Learning and Deep Learning Techniques." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47401.

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Abstract—The proliferation of Twitter bots poses a serious threat to the reliability of online conversations and results in disinformation, spam, and opinion manipulation. This paper presents a comprehensive examination of Twitter bot detection techniques with traditional machine learning (ML) algorithms contrasted with cutting-edge deep learning (DL) models. Key fea- tures like tweet frequency, follower-following ratios, user behavior patterns, and content features are investigated. We compare algorithms like Random Forest, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbo

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Choudhary, Laxmi, and Jitendra Singh Choudhary. "Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence." Journal of Scientific Research and Reports 30, no. 11 (2024): 865–75. http://dx.doi.org/10.9734/jsrr/2024/v30i112614.

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The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like he

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Petreska, Anita. "Cardiovascular disease prediction with machine learning techniques." Journal of Cardiology & Current Research 17, no. 2 (2024): 41–51. http://dx.doi.org/10.15406/jccr.2024.17.00603.

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Cardiovascular disease (CVD) remains the leading cause of death globally. In search of advanced techniques for early detection of CVD, recent research has increasingly focused on using machine learning (ML) methods to improve the accuracy and timeliness of diagnosis. A multifactorial machine learning approach offers a comprehensive solution for cardiovascular disease detection, using vast and diverse datasets to develop predictive models that outperform traditional methods. This paper provides a comprehensive examination of various machine learning approaches and their application in the early

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Lyu, Zhihuai. "Optimization of Chip Design Using Machine Learning Techniques." Journal of Industrial Engineering and Applied Science 2, no. 5 (2024): 29–32. https://doi.org/10.5281/zenodo.13845111.

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Complex chip designs necessitate innovative strategies in order to accelerate and streamline their design processes. This paper investigates the use of machine learning (ML) techniques in chip design, with emphasis placed on optimizing strategies that increase performance while decreasing power consumption and increasing design efficiency. By reviewing recent advances and case studies, we demonstrate how machine learning (ML) has the power to transform traditional design methodologies. Furthermore, we explore various ML algorithms, their uses at various stages in chip design processes, as well

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Siddique, Sunzida, Mohd Ariful Haque, Roy George, Kishor Datta Gupta, Debashis Gupta, and Md Jobair Hossain Faruk. "Survey on Machine Learning Biases and Mitigation Techniques." Digital 4, no. 1 (2023): 1–68. http://dx.doi.org/10.3390/digital4010001.

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Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The

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Wu, Tsung-Chin, Zhirou Zhou, Hongyue Wang, et al. "Advanced machine learning methods in psychiatry: an introduction." General Psychiatry 33, no. 2 (2020): e100197. http://dx.doi.org/10.1136/gpsych-2020-100197.

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Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods have been employed in mental health, recent research applications in psychiatry were reported.

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Rao, Faizan Ali, Muneer Amgad, Almaghthawi Ahmed, Alghamdi Amal, Mohamed Fati Suliman, and Abdulwasea Abdullah Ghaleb Ebrahim. "BMSP-ML: big mart sales prediction using different machine learning techniques." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 874–83. https://doi.org/10.11591/ijai.v12.i2.pp874-883.

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Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an esse

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sengaliappan, Dr M. "Optimizing Energy Consumption in Smart Homes Using Ml Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem41971.

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Because of growing demand and rising electricity prices, energy consumption in smart homes is a serious concern. Conventional energy management systems frequently lack flexibility, which results in wasteful energy use. In order to improve energy efficiency in smart homes, this study suggests a machine learning-based optimization strategy that combines Reinforcement Learning (RL), Support Vector Machine (SVM), and Genetic Algorithm (GA). Using past consumption trends and environmental variables, SVM is used to forecast energy demand. GA minimizes energy consumption while preserving user comfort

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Parmar, Ajaykumar, Manish Patel, Bhavik Prajapati, and Apexa Patel. "Credit Card Fraud Detection Using AI & ML Ensemble Techniques." SPU Journal of Science, Technology and Management Research 1, no. 2 (2024): 29–39. https://doi.org/10.63766/spujstmr.24.000014.

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Credit card fraud continues to be a pervasive and costly issue in the financial industry, necessitating robust and efficient solutions for detection and prevention. Machine learning has become an effective method for determining fraudulent transactions, offering the potential to save financial institutions and consumers billions of dollars annually.This research paper explores the application of Python, a comprehensive machine learning platform, to deploy a fraud detection system for credit cards. In this research, we initially review the existing literature on credit card fraud detection meth

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Balkrishna, Rasiklal Yadav. "Machine Learning Algorithms: Optimizing Efficiency in AI Applications." International Journal of Engineering and Management Research 14, no. 5 (2024): 49–57. https://doi.org/10.5281/zenodo.14005017.

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Machine learning (ML) is an AI technology that creates programs and data models that can perform tasks without being instructed. It has three major types: guided learning, uncontrolled learning, and reinforcement learning. ML is essential for big projects like real-time decision-making systems and self-driving cars, robots, and drones. It improves AI systems by making it easier to create models, work with data, and run algorithms. ML algorithms have different types of learning, require different amounts of data and training times, and can be improved by tuning hyperparameters. Techniques like

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Shariar Islam Saimon, Intiser Islam, Shake Ibna Abir, Nigar Sultana, Md Sanjit Hossain Roy, and Sarder Abdulla Al Shiam. "Advancing Neurological Disease Prediction through Machine Learning Techniques." Journal of Computer Science and Technology Studies 7, no. 1 (2025): 139–56. https://doi.org/10.32996/jcsts.2025.7.1.11.

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Late prediction is a major health problem for neurological diseases and early prediction is essential to advance patient outcomes and allow timely intervention. Machine learning (ML) advances are enabling doctors to more efficiently and innovatively predict the onset of neurological conditions using complex biomedical data. In this study the assessment of the power of different ML algorithms to predict Parkinson’s disease, epilepsy, and multiple sclerosis is done to evaluate the relative performance and practical applications. In order to determine the effectiveness of ML techniques, a compreh

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Khalid, Aimen, Gran Badshah, Nasir Ayub, Muhammad Shiraz, and Mohamed Ghouse. "Software Defect Prediction Analysis Using Machine Learning Techniques." Sustainability 15, no. 6 (2023): 5517. http://dx.doi.org/10.3390/su15065517.

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There is always a desire for defect-free software in order to maintain software quality for customer satisfaction and to save testing expenses. As a result, we examined various known ML techniques and optimized ML techniques on a freely available data set. The purpose of the research was to improve the model performance in terms of accuracy and precision of the dataset compared to previous research. As previous investigations show, the accuracy can be further improved. For this purpose, we employed K-means clustering for the categorization of class labels. Further, we applied classification mo

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Jithendra, V., R. M. Sai Mohit, M. Madhusudhan, B. Jagadeesh, and S. Kusuma. "Diabetes Prediction using Machine Learning Techniques." June 2023 5, no. 2 (2023): 190–206. http://dx.doi.org/10.36548/jaicn.2023.2.008.

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Now a day due to hectic schedules and sedentary lifestyle people do not follow the proper diet. Poor diet may lead to diabetes, and which could result in various health issues such as heart attacks, strokes, renal failure, nerve damage, etc. When diabetes is accurately detected in its early stage , it can be effectively treated. By using Machine Learning methods, the problem can be easily detected and a solution could bearrived. Early diabetes detection and prediction can be greatly improved with machine learning (ML) approaches. When it is detected in an early stage, it can be resolved quickl

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Naresh, Lokiny, and Reddy Pradip. "Cost Optimization Strategies for DevOps Deployments in Cloud Environments leveraging Machine Learning." European Journal of Advances in Engineering and Technology 8, no. 3 (2021): 69–72. https://doi.org/10.5281/zenodo.13325845.

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This paper explores cost optimization strategies for DevOps deployments in cloud environments by leveraging machine learning (ML) techniques. With the growing adoption of cloud services, managing costs while maintaining high performance and reliability has become critical. This study investigates various ML algorithms and their applications in predicting resource needs, automating scaling, and optimizing workloads. The findings demonstrate that integrating ML into DevOps practices can significantly reduce operational costs and improve efficiency.

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Chandra Mouli, K., B. Indupriya, D. Ushasree, Ch V. Raghavendran, Babita Rawat, and Bhukya Madhu. "Network Intrusion Detection using ML Techniques for Sustainable Information System." E3S Web of Conferences 430 (2023): 01064. http://dx.doi.org/10.1051/e3sconf/202343001064.

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Network intrusion detection is a vital element of cybersecurity, focusing on identification of malicious activities within computer networks. With the increasing complexity of cyber-attacks and the vast volume of network data being spawned, traditional intrusion detection methods are becoming less effective. In response, machine learning has emerged as a promising solution to enhance the accuracy and efficiency of intrusion detection. This abstract provides an overview of proper utilization of machine learning techniques in intrusion detection and its associated benefits. The base paper explor

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Naveen Kumar Thawait and Dr. Umakant Shrivastava. "Machine Learning Techniques for Predicting Conductive Properties of New Materials." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 576–85. http://dx.doi.org/10.32628/cseit2410340.

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The study "Machine Learning Techniques for Predicting Conductive Properties of New Materials" explores the application of advanced machine learning (ML) algorithms to predict the conductive properties of novel materials, accelerating the discovery and development process in materials science. Traditional methods for assessing conductive properties are often time-consuming and expensive, necessitating a more efficient approach. This research leverages various ML techniques, including supervised learning algorithms such as support vector machines, decision trees, and neural networks, to analyze

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Abdullah, Abdulhady A., Nergz S. Mohammed, Maryam Khanzadi, Safar M. Asaad, Zrar Kh Abdul, and Halgurd S. Maghdid. "In-depth Analysis on Machine Learning Approaches." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 13, no. 1 (2025): 190–202. https://doi.org/10.14500/aro.12038.

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Machine learning (ML) approaches cover several aspects of daily life tasks, including knowledge representation, data analysis, regression, classification, recognition, clustering, planning, reasoning, text recommendation, and perception. The ML approaches enable applications to learn and adapt with or without being directly programmed from previous data or experience. The ML techniques, coupled with current technologies, provide a range of solutions, starts from vision-based applications to text-generation applications. To this end, this article presents a comprehensive overview of the approac

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Sumathi, P., Arun Kumar S, and Balaji A. "Healthcare - Autism Predicting Tool Using Data Science / AI / ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 440–43. http://dx.doi.org/10.22214/ijraset.2024.60421.

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Abstract: This study presents a comprehensive analysis of the application of machine learning techniques for the prediction of autism spectrum disorder (ASD). The dataset used in this research comprises a range of demographic, behavioral, and diagnostic features. The study focuses on the use of various machine learning algorithms, including limited decision trees, support vector machines, and neural networks, to predict the likelihood of ASD in individuals. In addition, engineering and feature selection strategies are investigated to determine the most pertinent characteristics for precise pre

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Deviatko, Anna. "Evolution of Automated Testing Methods Using Machine Learning." American Journal of Engineering and Technology 07, no. 05 (2025): 88–100. https://doi.org/10.37547/tajet/volume07issue05-07.

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program testing is crucial for guaranteeing program dependability, but it has historically included a lot of manual labor, which restricts coverage and raises expenses. By creating and selecting test cases, anticipating defect-prone locations, and evaluating test results, machine learning (ML)-driven testing approaches automate and improve traditional software testing. This study examines the development of these techniques. Significant enhancements are provided by ML-driven techniques, such as early fault detection, shorter testing times, and increased test coverage. The paper offers a thorou

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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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Ali, Rao Faizan, Amgad Muneer, Ahmed Almaghthawi, Amal Alghamdi, Suliman Mohamed Fati, and Ebrahim Abdulwasea Abdullah Ghaleb. "BMSP-ML: big mart sales prediction using different machine learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 874. http://dx.doi.org/10.11591/ijai.v12.i2.pp874-883.

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<span lang="EN-US">Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the st

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Bavarva, Sneh, Kalpesh Senva, and Priyank Bhojak. "SQLI Attack: An Approach using ML and Hybrid Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1244–49. http://dx.doi.org/10.22214/ijraset.2023.56190.

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Abstract: Web-based systems are significantly at risk from SQL Injection Attacks (SQLIA), particularly in industries that handle sensitive data, like finance and healthcare. During these attacks, hostile actors insert false SQL queries into the database server of a web application in an effort to steal sensitive data. The use of classifiers and techniques like end-to-end deep learning and expanding the Aho-Corasick algorithm to detect SQLIA attacks have been covered in the literature. These researches have shed light on identifying and minimizing SQLIA, but the problem still exists. To detect

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Bhaskar, Lakshmi, Sumana M.N, Varshini S, and Thanushree Anand. "RAINFALL PREDICTION USING MACHINE LEARNING." International Journal of Advanced Research 13, no. 05 (2025): 1211–16. https://doi.org/10.21474/ijar01/21013.

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Accurate rainfall prediction is crucial for sectors like agriculture, disaster management, water resource planning, and climate adaptation. However, forecasting rainfall remains a challenge due to the unpredictable nature of atmospheric conditions. In recent years, machine learning (ML) has proven to be a valuable tool in analyzing complex meteorological data, offering an advanced alternative to traditional statistical models. This study explores the use of machine learning techniques for rainfall prediction through MATLAB, a powerful platform for data analysis, algorithm development, and mode

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Prof., S. P. Veer Harshal Linge Aditya Zade Parikshit Urkande Mrunali Nimbulkar Nutan Sayankar Payal Khedekar. "Machine Learning Techniques in Scrap Management System." International Journal of Advanced Innovative Technology in Engineering 10, no. 2 (2025): 100–107. https://doi.org/10.5281/zenodo.15401161.

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The application of machine learning methods on scrap management systems is discussed in this literature review. ML methods have been used in recycling process optimisation, predictive maintenance, and sorting scrap.  This research looks at several machines learning methodologies, including computer vision, clustering, and classification.  It also discusses practical applications and challenges in implementing machine learning in this domain. The results prove that machine learning is able to enhance sorting accuracy, reduce operational costs, and enable more eco-friendly recycling pr

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