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224 articles for “Random Forest”
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A Review Paper on Comparison of Algorithms for Supervised Machine Learning
Abstract: AbstractSupervised learning attempts to create calculations that help deliver general speculations, which is able to do forecasts about future occasions. The objective of supervised learning is to manufacture a model of the segregation of class names in terms of predictor components. The classifier produced accordingly is then used to allocate class marks to the testing data where the estimations of the indicator elements are known, yet the estimation of the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 4, Issue 3, 2017 · pp. 11–16 Read article
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A Technical Review on Intrusion Detection System with Various Machine Learning Algorithms in Data Mining
Abstract: With the progression in information & communication technology (ICT), it has become a vital element of human’s life. But this technology has brought a lot of threats in cyber world. These threats increase the chances of network vulnerabilities to attack the system in the network. To evade these attacks there are distinct ways in which one is Intrusion-Detection System (IDS). IDSs are software or hardware products that automate this monitoring …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 5, Issue 1, 2018 · pp. 1–8 Read article
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Quality Inspection, Maturity Detection, and Size based Grading of Various Types of Mangoes using Machine Learning Methods
Abstract: Mango is the most significant and flavorsome fruit in most continents, especially in Asia. Mango grading is an important task in agro-industry. The grading process creates many problems during harvesting for mango growers. The manual grading process is performed by visual inspection and it is very time consuming and labor intensive. Due to different market prices and different market demand, automation in mango grading plays an important role to achieve …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 7, Issue 1, 2020 · pp. 18–31 Read article
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Knowledge discovery in software defect datasets using learning algorithms
Abstract: In this paper, the learning impact on various classification models were studied which were built using binary class-imbalanced data. Before the learning process, some preprocessing techniques were applied to training datasets for removing the redundancy. Nowadays feature selection and sampling techniques become an essential tool for many data mining task because learning algorithms do not perform well with defective datasets, dimensionality reduction problem arises. Sampling technique is also used to …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 5, Issue 2, 2018 · pp. 18–26 Read article
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A Review of Code Defect Likelihood Using ML Methods
Abstract: AbstractSoftware begets defects and defects are inevitable in software design and coding. This paper focused on five machine learning analysis models (MLAM); Support Vector Machine, Random Forest, K-Nearest Neighbor, Classification and Regression Tree, and Linear Discriminant Analysis. These models are trained to detect software defects using the software defect dataset from Promise data repository. The collected dataset is preprocessed to reduce the amount of redundant features using a dimension reduction …
Published in Journal of Open Source Developments · Vol. 7, Issue 3, 2020 · pp. 1–6 Read article
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Google Play Store Analysis
Abstract: Google play store consists of millions of applications and several thousand apps are added on the play store every day. The competition is so fierce that it is really difficult for the developers to find out whether the app that is the product of his hard work is going to be successful or not. The main goal of the study is to create a tool that helps developers and organizations …
Published in Journal of Open Source Developments · Vol. 8, Issue 1, 2021 · pp. 11–25 Read article
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Bank Loan Prediction by using Logistic Regression
Abstract: In the modern era of science and technology, the banking sector is also growing very fast and a lot of people are applying for bank loans. But the bank has limited assets and can be granted to limited number of people only, so the bank must ensure that the person whom the loan is granted is eligible enough to pay it back. So, in this study, we will try to …
Published in E-Commerce for Future & Trends · Vol. 8, Issue 2, 2021 · pp. 17–21 Read article
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Analyzing Potential Leads Data to Improve Marketing Strategies
Abstract: To develop a lead scoring model using multiple machine learning models and create a user-friendly web application, the first step is to clearly define the problem and collect relevant data from various sources such as CRM, website, social media, email campaigns, etc. After cleaning and pre-processing the data, identify the essential features that can affect the lead score and split the data into training and testing sets. Next, select a …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 18–25 Read article
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Software Approach for Implementing Production Fuzzy Inference Rules for a Hybrid Ensemble Model
Abstract: AbstractThis article provides an overview of the decision tree induction software and random forest ensemble. The general programmatic principles of building a fuzzy decision tree in the Microsoft Visual Studio integrated development environment using the object-oriented C # language on the. Net Framework platform are described. It can be used to develop a hybrid ensemble classifier model.
Published in Recent Trends in Programming languages · Vol. 7, Issue 3, 2020 · pp. 8–16 Read article
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Abusive Language Detection using ML
Abstract: Detection of abusive language in online content has become a serious problem in recent years. Abusive language is an expression that carries grimy phrases each withinside the context of jokes, threat, vulgar intercourse conservation, or to disrespect someone. Nowadays many people post toxic comments in the social media such as Facebook, Twitter, etc. Many researches have tried to do it using the regular expression and blacklists but this is not …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 3, 2021 · pp. 1–7 Read article
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Machine Learning Techniques for Diabetes Prognosis
Abstract: Diabetes is a long-term illness that has the potential to devastate the global health-care system. Diabetes will affect 522 million people worldwide by 2033. Diabetes develops in eagerness, hunger, and the process of excreting due to high blood sugar levels.. Among other problems, diabetes is a primary cause of blindness, renal failure, amputations, heart failure, and stroke. Our bodies convert food into sugars, or glucose, when we consume it. At …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 9, Issue 1, 2022 · pp. 25–32 Read article
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Prediction of Disease by Symptoms Using Machine Learning
Abstract: Machine Learning and data mining, both play the most popular role in analyzing and prediction in the field of health organization. It also helps to uncover different and new patterns in medicinal science. This project is around prediction of disease with help of the symptoms. This project aims to propose a model which will be strongly trained in detecting common diseases so that few of the common diseases can be …
Published in Journal of Advancements in Robotics · Vol. 9, Issue 1, 2022 · pp. 8–11 Read article
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Hybrid Feature Selection Approach for Stability Prediction for Intracerebral Hemorrhage Patients
Abstract: Intra Cerebral Hemorrhage (ICH) stability estimation is useful for enhancing diagnosis accuracy, selecting the best course of treatment, and clinically evaluating variations with healthy individuals. Due to their low predictive value, the clinical application of several ICH progression scoring systems is constrained. The dataset includes clinical parameters including age, Glasgow Coma Scale (GCS), and CT Angiography (CTA) spot that have been retrieved using geometric features from the segmented bleeding volume …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 9, Issue 2, 2022 · pp. 9–17 Read article
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Timely Diabetes Possibility Prediction Using AI Techniques
Abstract: The fastest chronic life-threatening disease affecting more than 422 million people globally is diabetes. The primary causes of type 2 diabetes are lifestyle choices and environmental factors. It is a slowgrowing disease which starts to develop metabolic factors long before they evolve into a disease and is formally diagnosed by a fasting sugar test. There are records of indications related to diabetes from 520 patients in the dataset that was …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 9, Issue 2, 2022 · pp. 37–47 Read article
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A Systematic Review for Heart Disease Prediction Using AIML Algorithms
Abstract: Cardiovascular diseases (CVDs), commonly known as heart diseases, have consistently held the position of being the primary global cause of mortality for many years. They are also the most serious illness in India and the rest of the world. So, a method that is reliable, accurate, and easy to use is needed to find these diseases early and start the right treatment. Using a variety of medical datasets, machine learning …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 53–61 Read article
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Class Imbalance Reduction and Training Data Selection for Cross Project Defect Prediction
Abstract: The research aims to predict errors in a targeted project using data from other projects. This project is named as the Cross-Project Defect Prediction (CPDP). There are a number of ways available to improve the predictable performance of CPDP models. However, there is no comparison of modern methods. Predictability facilitates the rational distribution of testing resources by detecting software modules that may be problematic before releasing products. If a project …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 9, Issue 3, 2022 · pp. 12–20 Read article
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Enhancing Online Security: Machine Learning Methods for Phishing Detection—Literature Survey
Abstract: Phishing attacks represent a substantial online security threat as they mimic authentic websites to trick users into disclosing sensitive information. Detecting these malicious websites is crucial for safeguarding user data and maintaining digital trust. This summary provides insight into the application of machine learning methods in the identification of phishing websites. Machine learning, which falls under the umbrella of artificial intelligence, has proven to be a robust instrument for automating …
Published in Journal of Web Engineering & Technology · Vol. 10, Issue 3, 2023 · pp. 29–34 Read article
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A Novel Technique to Detect Plant Disease
Abstract: One of the important sectors of Indian Economy is Agriculture. Employment to almost 50% of the countries workforce is provided by Indian agriculture sector. India is known to be the world’s largest producer of pulses, rice, wheat, spices and spice products. Therefore, in field of agriculture, detection of disease in plants plays an instrumental role. Plants are highly prone to diseases that affect the growth of the plant, which in …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 7, Issue 2, 2020 · pp. 10–16 Read article
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Investigations on Potential Relationship between Biomass and Surface Temperature using Thermal Remote Sensing over Tropical Deciduous Forests
Abstract: The forest temperature and forest biomass are important parameters in concern to forest ecosystem and related parameters. Satellite remote sensing data is the best possible source for obtaining information on both regional and global scales. Landsat TM is used to retrieve the surface temperature using spectral information and radiation heat flux parameters for a tropical deciduous forest area of Munger forests (Bihar, India). Surface emissivity values derived from spectral band …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 2, Issue 3, 2013 · pp. 13–18 Read article
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Investigating Forest Gap Makers and Seedling Recruitment on Mangrove Forest Ecosystem, Onne, Rivers State, Port Harcourt
Abstract: The present study investigated forest gap makers and seedlings recruitment in mangrove forest, Onne, Rivers state, Nigeria. The aim of the present study was to examine canopy gap characteristics and their influence on mangrove forest regeneration. The specific objectives were to identify gap makers and theagents of canopy gap formation in the three mangrove formations, investigate seedling recruitment and growth in forest gap sizes in the three mangrove formations. The …
Published in Research & Reviews : Journal of Ecology · Vol. 12, Issue 2, 2023 · pp. 47–52 Read article