Results for 'Decision tree'

980 found
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  1.  56
    Application of Image Analytics for Tree enumeration for diversion of Forest Land.Shailaja Dr K. - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (4):1-12.
    The diversion of forest land for development requires accurate tree enumeration to assess environmental impact. Traditional methods, like manual counting and sampling, are labor-intensive, time-consuming, and prone to error. This project leverages high-resolution satellite and drone imagery, combined with advanced image processing and machine learning, to automate tree counting. Our system includes analytical tools, and provides authorities with historical environmental data (like rainfall, temperature, humidity) for informed decision-making. With a userfriendly interface and appealing data visualizations, it also (...)
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  2. CSsEv: Modelling QoS Metrics in Tree Soft Toward Cloud Services Evaluator based on Uncertainty Environment.Mona Gharib, Florentin Smarandache & Mona Mohamed - 2024 - International Journal of Neutrosophic Science 23 (2):32-41.
    Cloud computing (ClC) has become a more popular computer paradigm in the preceding few years. Quality of Service (QoS) is becoming a crucial issue in service alteration because of the rapid growth in the number of cloud services. When evaluating cloud service functioning using several performance measures, the issue becomes more complex and non-trivial. It is therefore quite difficult and crucial for consumers to choose the best cloud service. The user's choices are provided in a quantifiable manner in the current (...)
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  3.  14
    Revolutionizing Chronic Kidney Disease Prediction with Machine Learning Approaches.P. Meenalochini - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-16.
    Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision (...)
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  4. Personalized Medicine Recommendation System Using Machine Learning.P. Lavanya - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (2):1-12.
    Personalized medicine recommendation systems are increasing in popularity to predict diseases and provide customized health advice on diet, workout plans and medication. The medical suggestion system can be valuable when pandemics, floods, or cyclones hit. In the age of Machine Learning (ML), recommender systems give more accurate, precise, and reliable clinical predictions while using less resources. Through the use of machine learning algorithms like Decision Tree, Random Forest, K-Means Clustering, and Hierarchical Clustering, these systems analyze patient inputs such (...)
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  5. Towards Knowledge-driven Distillation and Explanation of Black-box Models.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello - 2021 - In Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello (eds.), Proceedings of the Workshop on Data meets Applied Ontologies in Explainable {AI} {(DAO-XAI} 2021) part of Bratislava Knowledge September {(BAKS} 2021), Bratislava, Slovakia, September 18th to 19th, 2021. CEUR 2998.
    We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to (...)
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  6.  67
    Heart Disease Prediction Using Machine Learning Techniques.D. Devendran - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-17.
    Heart disease remains one of the leading causes of mortality worldwide. Early prediction and diagnosis are critical in preventing severe outcomes and improving the quality of life for patients. This project focuses on developing a robust heart disease prediction system using machine learning techniques. By analyzing a comprehensive dataset consisting of various patient attributes such as age, sex, blood pressure, cholesterol levels, and other medical parameters, the system aims to predict the likelihood of a patient having heart disease. The project (...)
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  7. On Regret.David R. Charles - 2022 - Philosophy Now 153:30-31.
    The decision tree of life is colossal. While physicists and metaphysicians explore the possibility that the multiverse grows larger at every decision, it is the ethicist’s lot to consider the paths chosen. That is to say, ethics is generally concerned with the build-up to a decision point. But what happens afterwards? And how do our choices influence our future decision-making?
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  8.  12
    Improving Chronic Kidney Disease Diagnosis Using Machine Learning Algorithms.R. Karthick - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-16.
    The application of this system can lead to higher crop yields, sustainable farming practices, and reduced risks associated with poor crop choices. Through rigorous evaluation using standard classification metrics, the model's performance demonstrates its potential to revolutionize farming practices by aiding farmers in making informed decisions. The system has the potential to be an invaluable tool for agricultural consultants, farmers, and policymakers, ensuring long-term sustainability and improved productivity.
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  9. Decisional Value Scores.Gabriella Waters, William Mapp & Phillip Honenberger - 2024 - AI and Ethics 2024.
    Research in ethical AI has made strides in quantitative expression of ethical values such as fairness, transparency, and privacy. Here we contribute to this effort by proposing a new family of metrics called “decisional value scores” (DVS). DVSs are scores assigned to a system based on whether the decisions it makes meet or fail to meet a particular standard (either individually, in total, or as a ratio or average over decisions made). Advantages of DVS include greater discrimination capacity between types (...)
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  10. Fraudulent Financial Transactions Detection Using Machine Learning.Mosa M. M. Megdad, Samy S. Abu-Naser & Bassem S. Abu-Nasser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (3):30-39.
    It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. (...)
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  11.  51
    Website Translation Tool from English to Hindi.K. Madhuri - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (2):1-12.
    In today's digital age, the need for accurate and efficient language translation tools is more crucial than ever, particularly for bridging communication gaps between diverse linguistic communities. The primary purpose of this work is to provide an efficient solution for translating website content from English to Hindi, thereby promoting inclusivity and accessibility in the digital space. The methodology involves using the Google Translate API for accurate translations and employing web scraping techniques to retrieve content from user-specified URLs. The application enables (...)
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  12.  84
    Mechanizing Induction.Ronald Ortner & Hannes Leitgeb - 2009 - In Dov Gabbay (ed.), The Handbook of the History of Logic. Elsevier. pp. 719--772.
    In this chapter we will deal with “mechanizing” induction, i.e. with ways in which theoretical computer science approaches inductive generalization. In the field of Machine Learning, algorithms for induction are developed. Depending on the form of the available data, the nature of these algorithms may be very different. Some of them combine geometric and statistical ideas, while others use classical reasoning based on logical formalism. However, we are not so much interested in the algorithms themselves, but more on the philosophical (...)
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  13. Automated Cyberbullying Detection Framework Using NLP and Supervised Machine Learning Models.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-432.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...)
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  14. Understanding with Toy Surrogate Models in Machine Learning.Andrés Páez - 2024 - Minds and Machines 34 (4):45.
    In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and their effect (...)
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  15. OPTIMIZED CARDIOVASCULAR DISEASE PREDICTION USING MACHINE LEARNING ALGORITHMS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized (...)
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  16. Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...)
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  17. Innovative Approaches in Cardiovascular Disease Prediction Through Machine Learning Optimization.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized (...)
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  18. Efficient Cloud-Enabled Cardiovascular Disease Risk Prediction and Management through Optimized Machine Learning.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):454-475.
    The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, (...)
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  19. Machine Learning-Based Cyberbullying Detection System with Enhanced Accuracy and Speed.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-429.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...)
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  20.  83
    Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms.S. Venkatesh - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-15.
    Stress has become a significant concern in today’s fast-paced world, affecting individuals’ physical and mental well-being. This project, titled Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms, aims to address this issue by leveraging data-driven insights to identify stress levels. The proposed system analyzes sleeping patterns, including sleep duration, interruptions, and quality, to classify stress levels effectively. By utilizing advanced machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine, the (...)
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  21.  70
    Crop Recommender System Using Machine Learning Approach.A. Ravikumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-19.
    Agriculture plays a crucial role in the economic stability of many nations, and optimizing crop selection is essential for enhancing agricultural productivity and sustainability. The "Crop Recommender System Using Machine Learning Approach" aims to leverage machine learning techniques to provide precise crop recommendations based on various environmental and soil conditions. By incorporating factors such as soil composition, pH level, temperature, humidity, rainfall, and geographic location, this system suggests the most suitable crops for a given area. The system utilizes machine learning (...)
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  22.  72
    Crime Type and Occurrence Prediction Using Machine Learning Algorithm.R. Senthilkumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    This project aims to develop a predictive system capable of identifying crime types and predicting their occurrences based on historical crime data. The system uses advanced machine learning techniques to analyze factors such as geographic location, time, and other socio-economic variables, enabling authorities to better understand crime patterns and trends. By training models on vast datasets of past criminal activities, the system predicts not only the likely occurrence of specific crime types but also identifies high-risk locations and times, empowering law (...)
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  23. First Acts, Last Acts, and Abandonment.David O. Brink - 2013 - Legal Theory 19 (2):114-123.
    This contribution reconstructs and assesses Gideon Yaffe’s claims in his book Attempts about what constitutes an attempt, what can count as evidence that an attempt has been made, whether abandonment is a genuine defense, and whether attempts should be punished less severely than completed crimes. I contrast Yaffe’s account of being motivated by an intention and the completion of an attempt in terms of the truth of the completion counterfactual with an alternative picture of attempts as temporally extended decision (...)
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  24.  60
    Drug Recommendation System in Medical Emergencies using Machine Learning.S. Venkatesh - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-21.
    In critical medical emergencies, timely and accurate drug recommendation is essential for saving lives and reducing complications. This project proposes a Drug Recommendation System utilizing Machine Learning (ML) techniques to assist healthcare professionals in making quick and accurate drug selections based on patient symptoms, medical history, and emergency condition. The system integrates data from diverse medical databases, including symptoms, diseases, patient demographics, and prior medical records, to recommend the most appropriate drugs or treatments in real-time. The ML model is trained (...)
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  25.  59
    Prediction of Used Car Prices Using Artificial Neural Networks and Machine Learning.Sathishkumar A. - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-20.
    This project aims to develop a robust system capable of predicting the prices of used cars based on various factors such as make, model, year, mileage, location, and condition. The rising demand for second-hand vehicles has led to the need for accurate pricing models, and this project utilizes machine learning techniques, particularly Artificial Neural Networks (ANNs), to address this challenge. The system is trained on a comprehensive dataset of used car listings, incorporating key features that impact car prices. Various machine (...)
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  26.  54
    Fake Profile Detection on Social Networking Websites using Machine Learning.R. T. Subhalakshmi - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    With the increasing popularity of social networking websites, the problem of fake profiles has become a significant concern. Fake profiles, often created by malicious actors for fraudulent purposes, pose threats to user privacy, security, and trustworthiness of online platforms. This project proposes a machine learning-based approach to detect fake profiles on social networking websites. By analyzing various features such as user activity patterns, profile attributes, and network connections, the model identifies potential fake profiles with high accuracy. The system employs a (...)
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  27.  51
    A Deep Prediction of Chronic Kidney Disease by Employing Machine Learning Method.R. Senthilkumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-20.
    Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision (...)
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  28.  84
    Crime Prediction Using Machine Learning and Deep Learning.S. Venkatesh - 2024 - Journal of Science Technology and Research (JSTAR) 6 (1):1-13.
    Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. Key parameters such (...)
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  29. Predicting the Number of Calories in a Dish Using Just Neural Network.Sulafa Yhaya Abu Qamar, Shahed Nahed Alajjouri, Shurooq Hesham Abu Okal & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):1-9.
    Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research institutions, (...)
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  30. OPTIMIZED CYBERBULLYING DETECTION IN SOCIAL MEDIA USING SUPERVISED MACHINE LEARNING AND NLP TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-435.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...)
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  31.  75
    Optimized Cloud Computing Solutions for Cardiovascular Disease Prediction Using Advanced Machine Learning.Kannan K. S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):465-480.
    The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, (...)
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  32.  32
    Crop Price Prediction Using Machine Learning.P. Aparna - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (5):1-15.
    Ensuring agricultural profitability is a vital issue in developing countries like India, where over a third of the population earns their income directly or indirectly through agriculture. Estimating and evaluating crop yields is done globally to achieve high yields and appropriate pricing. However, there is no accurate procedure in place to provide farmers with insights on which crops should be grown. This project aims to predict crop prices by analysing historical data, such as precipitation, temperature, market prices, land area, and (...)
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  33.  14
    Harnessing Machine Learning to Predict Chronic Kidney Disease Risk.M. Arulselvan - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-16.
    Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD.
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  34.  15
    Data-Driven Insights into Chronic Kidney Disease Prediction with Machine Learning.P. Deepa - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-15.
    Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision (...)
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  35.  17
    Leveraging Machine Learning for Early Detection of Chronic Kidney Disease.A. Manoj Prabaharan - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization (...)
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  36.  15
    Machine Learning Models for Accurate Prediction of Chronic Kidney Disease.V. Sethupathi - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-15.
    By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. T.
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  37.  14
    Predicting Chronic Kidney Disease Using Advanced Machine Learning Techniques.T. Subhalakshmi - 2025 - Journal of Science Technology and Research (JSTAR) 5 (1):1-15.
    Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision (...)
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  38. Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm.Tosin Ige - 2022 - International Journal of Advanced Computer Science and Applications 13 (5):1 - 4.
    Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over (...)
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  39. ARTIFICIAL INTELLIGENT BASED COMPUTATIONAL MODEL FOR DETECTING CHRONIC-KIDNEY DISEASE.K. Jothimani & S. Thangamani - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):15-27.
    Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a Logistic (...)
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  40. Machine Learning and Job Posting Classification: A Comparative Study.Ibrahim M. Nasser & Amjad H. Alzaanin - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):06-14.
    In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and (...)
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  41. A Comparative Analysis of Data Mining Techniques on Breast Cancer Diagnosis Data using WEKA Toolbox.Majdah Alshammari & Mohammad Mezher - 2020 - (IJACSA) International Journal of Advanced Computer Science and Applications 8:224-229.
    Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a (...)
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  42. Undersampling Aware Learning based Fetal Health Prediction using Cardiotocographic Data.M. Shyamala Devi - 2021 - Turkish Online Journal of Qualitative Inquiry (TOJQI) 12 (6):7730-7749.
    With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can beutilized for earlier (...)
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  43. (1 other version)Development and Evaluation of an Expert System for Diagnosing Kidney Diseases.Shahd J. Albadrasawi, Mohammed M. Almzainy, Jehad M. Altayeb, Hassam Eleyan & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (6):16-22.
    This research paper presents the development and evaluation of an expert system for diagnosing kidney diseases. The expert system utilizes a decision-making tree approach and is implemented using the CLIPS and Delphi frameworks. The system's accuracy in diagnosing kidney diseases and user satisfaction were evaluated. The results demonstrate the effectiveness of the expert system in providing accurate diagnoses and high user satisfaction.
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  44. Doxastic Voluntarism.Mark Boespflug & Elizabeth Jackson - 2024 - Stanford Encyclopedia of Philosophy.
    Doxastic voluntarism is the thesis that our beliefs are subject to voluntary control. While there’s some controversy as to what “voluntary control” amounts to (see 1.2), it’s often understood as direct control: the ability to bring about a state of affairs “just like that,” without having to do anything else. Most of us have direct control over, for instance, bringing to mind an image of a pine tree. Can one, in like fashion, voluntarily bring it about that one believes (...)
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  45. Scoring Individual Moral Inclination for the CNI Test.Yi Chen, Benjamin Lugu, Wenchao Ma & Hyemin Han - 2024 - Stats 7 (3):894-905.
    Item response theory (IRT) is a modern psychometric framework for estimating respondents’ latent traits (e.g., ability, attitude, and personality) based on their responses to a set of questions in psychological tests. The current study adopted an item response tree (IRTree) method, which combines the tree model with IRT models for handling the sequential process of responding to a test item, to score individual moral inclination for the CNI test—a broadly adopted model for examining humans’ moral decision-making with (...)
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  46. Affine geometry, visual sensation, and preference for symmetry of things in a thing.Birgitta Dresp-Langley - 2016 - Symmetry 127 (8).
    Evolution and geometry generate complexity in similar ways. Evolution drives natural selection while geometry may capture the logic of this selection and express it visually, in terms of specific generic properties representing some kind of advantage. Geometry is ideally suited for expressing the logic of evolutionary selection for symmetry, which is found in the shape curves of vein systems and other natural objects such as leaves, cell membranes, or tunnel systems built by ants. The topology and geometry of symmetry is (...)
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  47. Consciousness as an Adaptation. What animals feel and why.Pouwel Slurink - 2016 - In Andreas Blank (ed.), Animals: New Essays. Munich: Philosophia. pp. 303-332.
    Evolutionary epistemology (Lorenz, Vollmer) and value-driven decision theory (Pugh) are used to explain the fundamental properties of consciousness. It is shown that this approach is compatible with global workspace theory (Baars) and global neuronal workspace theory (De Haene). The emotions are, however, that what drives consciousness. A hypothetical evolutionary tree of the emotions is given – intended to show that consciousness evolves and is probably qualitatively different in different groups of animals.
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  48. Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond. Sixth volume: Various New Uncertain Concepts (Collected Papers).Takaaki Fujita & Florentin Smarandache - 2025 - Gallup, NM, USA: NSIA Publishing House.
    This book is the sixth volume in the series of Collected Papers on Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond. Building upon the foundational contributions of previous volumes, this edition focuses on the exploration and development of Various New Uncertain Concepts, further enriching the study of uncertainty and complexity through innovative theoretical advancements and practical applications. The volume is meticulously organized into 15 chapters, each presenting unique perspectives and contributions to the field. From (...)
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  49. Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond. Third volume.Florentin Smarandache - 2024
    The third volume of “Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond” presents an in-depth exploration of the cutting-edge developments in uncertain combinatorics and set theory. This comprehensive collection highlights innovative methodologies such as graphization, hyperization, and uncertainization, which enhance combinatorics by incorporating foundational concepts from fuzzy, neutrosophic, soft, and rough set theories. These advancements open new mathematical horizons, offering novel approaches to managing uncertainty within complex systems. Combinatorics, a discipline focused on counting, arrangement, (...)
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  50. Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond. Fifth volume: Various SuperHyperConcepts (Collected Papers).Fujita Takaaki & Florentin Smarandache - 2025 - Gallup, NM, USA: NSIA Publishing House.
    This book is the fifth volume in the series of Collected Papers on Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond. This volume specifically delves into the concept of Various SuperHyperConcepts, building on the foundational advancements introduced in previous volumes. The series aims to explore the ongoing evolution of uncertain combinatorics through innovative methodologies such as graphization, hyperization, and uncertainization. These approaches integrate and extend core concepts from fuzzy, neutrosophic, soft, and rough set theories, (...)
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