An assessment of machine learning algorithms for healthcare analysis based on improved MapReduce

Applying hybrid machine learning algorithms to assess customer risk-adjusted revenue in the financial industry

The accurate prediction of stock prices has a significant role to play in the present economic world. However, stocks have an ever-changing nature coupled with a risk factor that always seems much of a gamble, making it worthwhile for researchers to analyze them with machine learning algorithms. We thus seek to develop a machine-based model replacing the traditional yet unreliable time-series forecasting to predict the stocks from the past historical data of a firm and to uncover the underlying patterns to improve the accuracy of the prediction of the model. As a beginner, we have decided to use a couple of conventional machine learning algorithms to study the behavior of learning techniques for stock prediction. This paper presents an empirical study to study and analyze the behavior of decision Tree, Linear Regression, K-Nearest Neighbours, and LSTM learning algorithms to bet on the algorithm that best predicts the stock prices.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

Code smells are generally not considered as bugs; instead, they point out certain shortcomings in the software design or code. Identification of code smell is a necessary step for improving the software quality and reducing the maintenance effort. In this study, we introduce a bad smell prediction technique based on object-oriented software metrics that use Decision Tree (DT) and Random Forest (RF) machine learning algorithm. An open-source project, namely JHOTDRAW, was used as our dataset, for which values of object-oriented software metrics were calculated. Two feature selection methods-Random Forest Importance (RFI) and Information Gain (IG) were applied to extract the most relevant attributes for the prediction of code smells, namely, Feature envy, Dispersed coupling, refused parent bequest, and God class. The random-search algorithm was used to tune the parameters of Random Forest and Decision Tree. Results show that the best classification accuracy for Decision Tree was obtained at 99.13% for refused parent bequest code smells. Results also show that after using the Random Forest classifier, refused parent bequest smell was predicted with the best accuracy of 99.14%. Finally, in this research study, a set of code smell prediction rules were extracted using Decision Tree.

This paper proposes a novel approach for achieving sustainable energy systems in unexpected sports event management by integrating machine learning and optimization algorithms. Specifically, we used reinforcement learning for peak load forecasting and bat evolutionary algorithm for optimization, since the energy management problem in sports events is typically non-linear. Machine learning algorithms, specifically reinforcement learning, are used to analyze historical data and provide accurate peak load forecasts. This information can then be used to optimize energy consumption during the event through the use of algorithms such as the bat evolutionary algorithm, which can effectively solve non-linear optimization problems. The integration of these algorithms in unexpected sports event management can lead to significant improvements in sustainability and cost-effectiveness. This paper presents a case study of the implementation of reinforcement learning and bat evolutionary algorithms in an unexpected sports event management scenario, demonstrating the effectiveness of the proposed approach in achieving sustainable energy systems and reducing overall energy consumption. Overall, this paper provides a roadmap for integrating machine learning and optimization algorithms, such as reinforcement learning and bat evolutionary algorithm, in unexpected sports event management to achieve sustainable energy systems, promoting a more sustainable future for the sports event industry and the planet as a whole.

Introduction: Machine learning has emerged as a powerful tool for data analysis, enabling systems to identify patterns and make predictions without explicit programming. Among its various approaches, unsupervised learning plays a crucial role in discovering hidden structures within data, especially in scenarios where labeled examples are scarce or costly to obtain. This study provides a comprehensive analysis of unsupervised learning techniques, with a particular focus on clustering and reinforcement learning. Research significance: This study provides an in-depth exploration of unsupervised learning techniques, emphasizing their ability to identify patterns in data without the need for labeled training examples. This is particularly significant in domains where acquiring labeled data is costly or impractical. By highlighting the role of reinforcement learning in unsupervised systems, the research advances the understanding of how agents improve behavior through rewards and penalties, which has implications for robotics and strategic game-playing applications. Methodology: Other options include K-Nearest Neighbors (KNN), Neural Networks, Support Vector Machines (SVM), and Decision Trees. Assessment Criteria: Memory Usage, Accuracy, Training Speed, and Error Rate. Result: According to the results, K-Nearest Neighbors (KNN) had the lowest quality, while neural networks had the highest quality. Conclusion: According to the GRA approach, neural networks are the most valuable datasets for machine learning algorithms. Key words: Unsupervised Learning, Reinforcement Learning, Clustering, Generalization & Over fitting, Decision Trees & Random Forests, Neural Networks & Deep Learning, Ensemble Methods, and Medical Imaging & Cyber security.

System modeling (identification) in complex systems like kinesiological and biological in general is extremely difficult due to the high dimensions of parameters and usually non-linear functional dependencies. Data Science and especially Machine Learning (Deep Learning) algorithms seem to be quite a good tool for analysis and problem-solving in sports today. Data Science (Machine or Deep Learning) algorithms rely on basic use of statistical algorithms, but extend those with models such as Decision tree, K-means clustering, Neural networks, and Reinforcement learning, creating new algorithms that handle input data by predicting outputs that describe correlation relations or predict future states at time points (regression). This study is an attempt to analyze and research applications of machine learning in Sport science - Kinanthropometry related problem of determining somatotype by using the Microsoft Azure Machine Learning platform and comparing several supervised classifier algorithms (Multiclass Neural Network, Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Logistic Regression) which were compared versus classical somatotype categorization algorithms with dataset based on the Heath-Carter method Somatotype determination to gain experience and expertise.

This paper explores the feasibility of machine learning algorithms on nonlinear ultrasonics for classification of the austenitic stainless-steel material subjected to different annealing conditions. The material that is isothermally annealed at 1323 K for different soaking times showed a marginal variation in its nonlinearity parameter at larger mean grain sizes. The grain growth during annealing followed the Arrhenius type equation fairly well, which has been verified using a genetic algorithm approach. The machine learning algorithms are trained using features such as the ratio of the harmonic amplitudes, root-mean-square value, and the phase difference between the fundamental and second harmonic components derived from the nonlinear ultrasonic response. Upon evaluating the performance of decision tree and ensemble learning algorithms in the classification of annealed materials, it was observed that the LPBoost classifier has the highest accuracy of 97%. According to the results, it is concluded that a machine learning strategy based on a minimal number of features can effectively classify specimens that are otherwise indistinguishable in their nonlinear response. This research takes a step forward to the automation of non-destructive testing toward Industrial Revolution 4.0. The results also pointed out the necessity of parameter fusion in non-destructive decision making.

This study aims to examine the computational thinking skills (CTS) of engineering students through classification and regression trees (CART) analysis and to develop a predictive model using machine learning (ML) algorithms. The sample consists of 435 engineering students from the engineering faculties of three state universities located in Southeast and East Anatolia. A relational survey model is utilized, and data are collected via a personal information form and the CTS scale. Data analyses include two-stage cluster analysis, CART analysis and ML algorithms. Findings reveal that 71.43% of students possess high CTS, 12.70% are at a medium level and 15.87% are at a low level. The majority of engineering students exhibit moderate to high CTS. The variable department is identified as the most significant predictor of students’ CTS. Dimensions of computational thinking (CT) are analyzed using CART, identifying the most predictive variables for creativity, algorithmic thinking, cooperativity, critical thinking and problem-solving. ML algorithms, including AdaBoost, decision tree, multi-layer perceptron, naive Bayes and random forest, are employed to predict the CTS. The study’s findings and data patterns underscore the importance of targeted planning in engineering education to foster the effective development of CTS.

Dengue fever is widespread disease in the tropical areas caused by bite of female Eddie mosquito. Pakistan has been victim of this rapidly growing disease since last few years. The world health organization identified the four types of dengue fever. The experts are facing the problem of misdiagnosis of dengue fever. The Tests needed for empirical Classification of Dengue Fever takes a lot of time and money especiallyin epidemic situation in a country with limited resources. Therefore, we have used data mining techniques for the efficient classification of the dengue fever Type. The decision tree learning algorithm has been used as a classification Model. We performed two experiments using decision tree. The first general experiment shows the accuracy of 99.44%. It prunes the attributes which classify the dengue fever on the basis of the values in the dataset. The Second experiment classifies the dengue fever on the basis of expert weighted attributes, which are used in the classification on the basis of Minimum Cost and resource availability. The accuracy of this Model is still high 98.62%. We compared the performance in term of Type II error. We found that the Type II error is very low in second experiment

We apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning algorithm similar to the C4.5 or CART algorithms. Previous MPC-based work only focused on decision tree learning with discrete attributes (De Hoogh et al. 2014). Our starting point is to apply an existing generic MPC protocol to a standard decision tree learning algorithm, which we then optimize in several ways. We exploit the fact that even if we allow the data to have continuous values, which a priori might require fixed or floating point representations, the output of the tree learning algorithm only depends on the relative ordering of the data. By obliviously sorting the data we reduce the number of comparisons needed per node to O(N log2 N) from the naive O(N 2), where N is the number of training records in the dataset, thus making the algorithm feasible for larger datasets. This does however introduce a problem when duplicate values occur in the dataset, but we manage to overcome this problem with a relatively cheap subprotocol. We show a procedure to convert a sorting network into a permutation network of smaller complexity, resulting in a round complexity of O(log N) per layer in the tree. We implement our algorithm in the MP-SPDZ framework and benchmark our implementation for both passive and active three-party computation using arithmetic modulo 264. We apply our implementation to a large scale medical dataset of ≈ 290 000 rows using random forests, and thus demonstrate practical feasibility of using MPC for privacy-preserving machine learning based on decision trees for large datasets.

In this paper is presented an experimental study with Data Mining (DM) tools, such as, CMSR Data Miner, KnowledgeSTUDIO and RapidMiner. It is goal to discover at that problems (classification, regression, clustering, association, among others) may be used, including, to verify if can treat Big Data, these tools were applied to three databases: Breast Cancer, Iris and Wine (obtained from the Machine Learning repository from University of California). In the experiments with the CMSR Data Miner tool, the Decision Tree and SOM (Self Organizing Maps) Neural Network algorithms of Data Mining were used. After that, with the KnowledgeSTUDIO tool, Logistic Regression, Deep Learning and K-Means algorithms were used. And finally, with the RapidMiner tool, Decision Tree, Naive Bayes and Deep Learning algorithms of Data Mining were applied. Additionally, it is also with this research aim to decision making by organizational managers, helping them to choose tools from DM more appropriate to their daily work.

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.

This paper analyses the recent evolution of the electricity price of one of the EastEuropean countries’ Balancing Markets (BM) – Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants’ trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players.