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Relevant bibliographies by topics / ML TECHNIQUES

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Journal articles

Academic literature on the topic 'ML TECHNIQUES'

Author: Grafiati

Published: 19 January 2024

Last updated: 14 June 2025

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Journal articles on the topic "ML TECHNIQUES"

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 study aims to create a system that can better predict a patient's diabetic risk level. The medical data set is put to many different uses. in order to develop an artificial intelligence model for disease prediction The National Institute of Diabetes and Digestive and Kidney Diseases provided the data. Among the items on the list are blood pressure, age, insulin level, BMI, and glucose. Models are created using classification methods such as Ada Boost, Gradient Boost, XG Boost, and Cat Boost. The outcomes reveal that the processes are extremely precise. According to the findings, the prediction made with the use the prediction utilizing the Gradient Boosting model had the highest accuracy, according to the findings. Our investigation covers a wide range of machine learning topics as well as the numerous types of prediction models available. We go over the different sorts of models that can be used to create predictions, as well as the characteristics of machine learning.

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2

Venkata Vara Prasad, D., P. Senthil Kumar, Lokeswari Y. Venkataramana, et al. "Automating water quality analysis using ML and auto ML techniques." Environmental Research 202 (November 2021): 111720. http://dx.doi.org/10.1016/j.envres.2021.111720.

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3

Kosinska, Joanna, and Maciej Tobiasz. "Detection of Cluster Anomalies With ML Techniques." IEEE Access 10 (2022): 110742–53. http://dx.doi.org/10.1109/access.2022.3216080.

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4

Derangula, Sirisha. "Identification of phishing websites using ML techniques." International Journal of Communication and Information Technology 1, no. 2 (2020): 28–32. http://dx.doi.org/10.33545/2707661x.2020.v1.i2a.16.

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5

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 activities associated with DDoS attacks. The study contributes to the advancement of security measures in IoT networks, offering a proactive and adaptive solution to mitigate the impact of DDoS attacks, ultimately bolstering the resilience of interconnected systems in the evolving landscape of cyber threats. This study presents a novel approach for the detection of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks using machine learning techniques.

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Kumar, Sachin, and Subhasree Bhattacharjee. "REVIEW OF AI/ML TECHNIQUES ON COVID-19." International Journal of Engineering Applied Sciences and Technology 7, no. 5 (2022): 116–18. http://dx.doi.org/10.33564/ijeast.2022.v07i05.020.

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COVID-19 has caused severe worldwide threat by taking away over 6 million lives. Artificial intelligence and machine learning methods are used significantly for fighting against this pandemic. For solving different problems of COVID-19, different AI/ML techniques are required. This article provides comprehensive review on different AI/ML applications on COVD-19. For classification, prediction, and diagnosis AI/ML methods have huge contribution. AI/ML methods for risk assessment of COVID-19 have also been narrated.

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7

Abuzaid, Nawal. "Image SPAM Detection Using ML and DL Techniques." International Journal of Advances in Soft Computing and its Applications 14, no. 1 (2022): 227–43. http://dx.doi.org/10.15849/ijasca.220328.15.

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Abstract Since e-mail is one of the most common places to send messages, spammers have, in recent years, targeted it as a preferred way of distributing undesired messages (spam) to several users to spread viruses, cause destruction, and obtain user's information. Spam images are considered one of the known spam types. The spammer processes images and changes their characteristics, especially background colour, font type, or adding artefacts to the images to spread spam. In this paper, we proposed a spam detection model using Several ML (Random-Forest (RF), Decision-Tree (DT), KNearest Neighbor (KNN), Support-Vector Machine (SVM), NaïveBays (NB), and Convolutional Neural Network (CNN)). Several experiments evaluate the efficiency and performance of the (ML) algorithms for spam detection. Using the Image Spam Hunter Dataset extracted from real spam e-mails, the proposed model achieved over 99% accuracy on spam image detection. Keywords: SPAM, Machine Learning, Image Classification, Feature Extraction, Deep Learning.

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8

T., Logeswari. "Performance Analysis of Ml Techniques for Spam Filtering." International Research Journal on Advanced Science Hub 2, Special Issue ICIES 9S (2020): 64–69. http://dx.doi.org/10.47392/irjash.2020.161.

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9

Kore, Rahul C., Prachi Ray, Priyanka Lade, and Amit Nerurkar. "Legal Document Summarization Using Nlp and Ml Techniques." International Journal of Engineering and Computer Science 9, no. 05 (2020): 25039–46. http://dx.doi.org/10.18535/ijecs/v9i05.4488.

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Reading legal documents are tedious and sometimes it requires domain knowledge related to that document. It is hard to read the full legal document without missing the key important sentences. With increasing number of legal documents it would be convenient to get the essential information from the document without having to go through the whole document. The purpose of this study is to understand a large legal document within a short duration of time. Summarization gives flexibility and convenience to the reader. Using vector representation of words, text ranking algorithms, similarity techniques, this study gives a way to produce the highest ranked sentences. Summarization produces the result in such a way that it covers the most vital information of the document in a concise manner. The paper proposes how the different natural language processing concepts can be used to produce the desired result and give readers the relief from going through the whole complex document. This study definitively presents the steps that are required to achieve the aim and elaborates all the algorithms used at each and every step in the process.

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10

Sethuraman, Sriram, V. S. Nithya, and D. Venkata Narayanababu Laveti. "Noniterative Content-Adaptive Distributed Encoding Through ML Techniques." SMPTE Motion Imaging Journal 127, no. 9 (2018): 50–55. http://dx.doi.org/10.5594/jmi.2018.2862647.

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