An improved approach to Arabic news classification based on hyperparameter tuning of machine learning algorithms

In the telecommunications industry, predicting customer churn is crucial for maintaining business sustainability. High churn rates can negatively impact profitability, necessitating effective retention strategies. This research aims to enhance the accuracy of telecommunications customer churn prediction by optimizing the C4.5 classification algorithm through feature selection and hyperparameter tuning. The methods used include Information Gain for feature selection and hyperparameter tuning with Random Search and Grid Search. This study utilizes the Telco Customer Churn dataset from Kaggle, split into an 80:20 ratio for training and testing data. Six approaches are applied: (1) the basic C4.5 algorithm, (2) C4.5 with Information Gain, (3) C4.5 with Random Search, (4) C4.5 with Grid Search, (5) C4.5 with a combination of Information Gain and Random Search, and (6) C4.5 with a combination of Information Gain and Grid Search. The results indicate that the C4.5 algorithm alone achieves an accuracy of 74.09%, while applying Information Gain increases accuracy to 78.42%. Hyperparameter tuning with Random Search achieves the highest accuracy of 80.05%, whereas Grid Search reaches 77.71%. Combining Information Gain with Random Search results in an accuracy of 78.99%, while combining Information Gain with Grid Search yields an accuracy of 78.85%. These findings suggest that hyperparameter tuning using Random Search significantly improves accuracy compared to other methods, while Information Gain feature selection does not have a significant impact on performance in this context.

Recent technological advances allowed an exponential expansion of biological sequence data, and the extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge improved the understanding of the mechanisms related to several fatal diseases, e.g., Cancer and COVID-19, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine, and precision medicine. These advances benefit our society and economy, directly impacting peoples lives in various areas, such as health care, drug discovery, forensic analysis, and food analysis. Nevertheless, ML approaches applied to biological data require representative, quantitative, and informative features. Necessarily, as many ML algorithms can handle only numerical data, sequences need to be translated into a feature vector. This process, known as feature extraction, is a fundamental step for the elaboration of high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with the design and selection of suitable features. Feature engineering, ML algorithm selection, and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge, and performed manually by a human expert. To deal with this problem, we developed a new package, BioAutoML, which automatically runs an end-to-end ML pipeline. BioAutoML extracts numerical and informative features from biological sequence databases, automating feature selection, recommendation of ML algorithm(s), and tuning of hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules, (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyperparameter tuning modules). Our experimental results, assessing the relevance of our proposal, indicate robust results for different problem domains, such as SARS-CoV-2, anticancer peptides, HIV sequences, and non-coding RNAs. According to our systematic review, our proposal is innovative compared to available studies in the literature, being the first study to propose automated feature engineering and metalearning for biological sequences. BioAutoML has a high potential to significantly reduce the expertise required to use ML pipelines, aiding researchers in combating diseases, particularly in low- and middle-income countries. This initiative can provide biologists, physicians, epidemiologists, and other stakeholders with an opportunity for widespread use of these techniques to enhance the health and well-being of their communities.

A great diversity comes in the field of medical sciences because of computing capabilities and improvements in techniques, especially in the identification of human heart diseases. Nowadays, it is one of the world’s most dangerous human heart diseases and has very serious effects the human life. Accurate and timely identification of human heart disease can be very helpful in preventing heart failure in its early stages and will improve the patient’s survival. Manual approaches for the identification of heart disease are biased and prone to interexaminer variability. In this regard, machine learning algorithms are efficient and reliable sources to detect and categorize persons suffering from heart disease and those who are healthy. According to the recommended study, we identified and predicted human heart disease using a variety of machine learning algorithms and used the heart disease dataset to evaluate its performance using different metrics for evaluation, such as sensitivity, specificity, F-measure, and classification accuracy. For this purpose, we used nine classifiers of machine learning to the final dataset before and after the hyperparameter tuning of the machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. Furthermore, we check their accuracy on the standard heart disease dataset by performing certain preprocessing, standardization of dataset, and hyperparameter tuning. Additionally, to train and validate the machine learning algorithms, we deployed the standard K-fold cross-validation technique. Finally, the experimental result indicated that the accuracy of the prediction classifiers with hyperparameter tuning improved and achieved notable results with data standardization and the hyperparameter tuning of the machine learning classifiers.

Studies in software effort estimation (SEE) have explored the use of hyper-parameter tuning for machine learning algorithms (MLA) to improve the accuracy of effort estimates. In other contexts random search (RS) has shown similar results to grid search, while being less computationally-expensive. In this paper, we investigate to what extent the random search hyper-parameter tuning approach affects the accuracy and stability of support vector regression (SVR) in SEE. Results were compared to those obtained from ridge regression models and grid search-tuned models. A case study with four data sets extracted from the ISBSG 2018 repository shows that random search exhibits similar performance to grid search, rendering it an attractive alternative technique for hyper-parameter tuning. RS-tuned SVR achieved an increase of 0.227 standardized accuracy (SA) with respect to default hyper-parameters. In addition, random search improved prediction stability of SVR models to a minimum ratio of 0.840. The analysis showed that RS-tuned SVR attained performance equivalent to GS-tuned SVR. Future work includes extending this research to cover other hyper-parameter tuning approaches and machine learning algorithms, as well as using additional data sets.

Mental health is an essential component of human life and maintaining a healthy state is often overlooked in today's world. While playing games online is a fantastic method to reduce stress, it imposes a negative impact on people's mental health. For instance, anxiety disorders are a group of mental illnesses marked by intense emotions of fear and anxiety which are witnessed in online gamers to a greater extent. To aid the identification process of anxiety levels, machine learning algorithms have emerged as a handy tool. In this paper, the anxiety levels of online gamers are detected by utilizing a dataset from Kaggle by nine machine learning algorithms. The performances of the ML models have been observed through Python simulation, and comprehensive comparative analysis has been shown for both hyperparameter tuning and without hyperparameter tuning. Random search cross-validation has been brought into action for tuning the hyper parameters. In terms of several performance measures such as accuracy, precision, recall, F1-Score, and ROC-AUC, satisfactory results have been observed. It is observed that Multilayer perceptron (MLP) outperformed the other classifiers with an accuracy of 99.96%. However, Support Vector Machine (SVM) depicted promising accuracy of 99.43% whereas Gradient Boosting (GB) and XGBoost (XGB) depicted 98.54% and 98.04% accuracy respectively. Therefore, it can be concluded that with proper implementation of the ML-based diagnosis system, it is possible to detect the anxiety level of online gamers which can assist in having a deeper understanding of behaviors and impact of online gaming in daily life.

Gesture recognition using myoelectric signals (sEMG) is a powerful tool for Human-Machine Interfaces (HMIs). This study investigates the classification of sEMG signals with varying sets of gestures and predictive features using Bayesian Optimization for hyperparameter tuning of Machine Learning (ML) algorithms employed for this task. Experiments were conducted on the Ninapro DB2 and DB3 datasets, covering data from both intact and amputated individuals and exploring different ML algorithms. Even though Support Vector Machines (SVMs) showed the most notable improvement through hyperparameter tuning, the best experimental results were achieved by the Random Forest (RF) classifier on both datasets, with an average F-Score of 0.853 in DB2 and 0.720 in DB3, using only five gestures and nine features. Overall, a larger feature set enhanced signal representation, particularly in reduced gesture sets, while data from amputated individuals posed greater classification challenges.

Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

Abstract. The information available on the internet nowadays is diverse and moves very quickly. Information is becoming easier to obtain by the general public with the numerous online media outlets, including news portals that provide up-to-date information insights. Various news portals earn revenue from advertising using pay-per-click methods that encourage article writers to use clickbait techniques to attract visitors. However, the negative effects of clickbait include a decrease in journalism quality and the spread of hoaxes. This problem can be prevented by using text classification to classify clickbait in news titles. One method that can be used for text classification is a neural network. Artificial neural networks use algorithms that can independently adjust input coefficient weights. This makes this algorithm highly effective for modeling non-linear statistical data. The artificial neural network algorithm, especially the Long Short-Term Memory (LSTM), has been widely used in various natural language processing fields with satisfying results, including text classification. To improve the performance of the neural network model, adjustments can be made to the model's hyperparameters. Hyperparameters are parameters that cannot be obtained through data and must be defined before the training process. In this research, the Long Short-Term Memory (LSTM) model was used in clickbait classification in news titles. Sixteen neural network models were trained with different hyperparameter configurations for each model. Hyperparameter tuning was carried out using the random search algorithm. The dataset used was the CLICK-ID dataset published by William & Sari, 2020[1], with a total of 15,000 annotated data. The research results show that the developed LSTM model has a validation accuracy of 0.8030, higher than William & Sari's research, and a validation loss of 0.4876. Using this model, researchers were able to classify clickbait in news titles with fairly good accuracy. Purpose: The study was to develop and evaluate a LSTM model with hyperparameter tuning for clickbait classification on news headlines. The thesis also aims to compare the performance of simple LSTM and bidirectional LSTM for this task. Methods: This study uses CLICK-ID dataset and applies different text preprocessing techniques. The dataset later was used to build and train 16 LSTM models with different hyperparameters and evaluates them using validation accuracy and loss. This study uses random search for hyperparameter tuning. Result: The results of the study show that the best model for clickbait classification on news headlines is a bidirectional LSTM model with one layer, 64 units, 0.2 dropout rate, and 0.001 learning rate. This model achieves a validation accuracy of 0.8030 and a validation loss of 0.4876. The results also show that hyperparameter tuning using random search can improve the performance of the LSTM models by avoiding zero probabilities and finding the optimal values for the hyperparameters. Novelty: This study compares and analyzes the different preprocessing methods on text and the different configurations of the models to find the best model for clickbait classification on news headlines. The study also uses hyperparameter tuning to tune the model into the best model and finding the optimal values for the hyperparameters.

Hyperparameter tuning is crucial for optimising deep learning models. Traditional methods like grid search and random search can be time consuming and computationally expensive. This study explores space filling methodology for hyperparameter tuning, aiming to explore the hyperparameter space for optimal configurations. Hyperparameters are tuned for ResNet models using CIFAR-10 dataset and compared MaxPro space filling methodology with randomised search and grid search in terms of accuracy and computational costs. A greater accuracy was observed for the hyperparameters selected based on space filling methodology as compared to the randomised search while the runtime is similar for both. By systematically exploring the hyperparameter space, space filling offers an efficient approach to hyperparameter tuning, enabling faster model development. The findings underscore space filling methodology's potential for effective hyperparameter tuning, especially in resource constrained scenarios.

Empirical studies on software effort estimation have employed hyper-parameter tuning algorithms to improve model accuracy and stability. While these tuners can improve model performance, some might be overly complex or costly for low dimensionality datasets used in SEE. In such cases a method like random search can potentially provide similar benefits as some of existing tuners, with advantage of using low amounts of resources and being simple to implement. In this study we evaluate impact on model accuracy and stability of 12 state-of-the-art hyper-parameter tuning algorithms against random search, on 9 datasets of PROMISE repository and 4 sub-datasets from ISBSG R18 dataset. This study covers 2 traditional exhaustive tuners (grid and random searches), 6 bio-inspired algorithms, 2 heuristic tuners, and 3 model-based algorithms. The tuners are used to configure support vector regression, classification and regression trees, and ridge regression models. We aim to determine techniques and datasets for which certain tuners were 1) more effective than default hyper-parameters, 2) more effective than random search, 3) which models(s) can be considered the best for which datasets. The results of this study show that hyper-parameter tuning was effective (increased accuracy and stability) in 862 (51%) of 1,690 studied scenarios. The 12 state-of-the-art tuners were more effective than random search in 95 (6%) of 1,560 studied (non-random search) scenarios. Although not effective in every dataset, combination of flash tuning, logarithm transformation and support vector regression obtained top ranking in accuracy on highest amount (8 out of 13) of datasets. Hyperband tuned ridge regression with logarithm transformation obtained top ranking in accuracy on highest amount (10 out of 13) of datasets. We endorse use of random search as a baseline for comparison for future studies that consider hyper-parameter tuning.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Heart disease (HD) remains one of the leading causes of mortality worldwide, emphasizing the urgent need for accurate and timely diagnostic methods. Recent advances in machine learning (ML) have shown promising potential in improving clinical decision-making through predictive modeling. This study aimed to enhance HD prediction by applying selected ML algorithms with hyperparameter optimization to improve their accuracy and reliability. Four classifiers: Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) were implemented, and missing data were handled using the SimpleImputer technique to ensure robust preprocessing. Model performance was evaluated using standard metrics, including accuracy, precision, recall, F1-score, and the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrated that all classifiers improved after hyperparameter tuning, with KNN achieving the highest accuracy of 96.0%, precision of 0.99, recall of 0.93, F1-score of 0.96, and ROC of 0.98. Comparative analysis revealed that the adopted approach outperformed existing methods reported in related literature, thereby confirming the effectiveness of hyperparameter optimization combined with preprocessing techniques in HD prediction. The study contributes to knowledge by highlighting the significance of optimized ML models in healthcare applications and recommends further validation with larger, diverse datasets for clinical integration.

The advancement in the field of computer science, especially in machine learning (ML), represents a flourishing innovation that carries great importance in the domain of education. The beneficial impact of ML can also be observed in the realm of Qur’anic studies, particularly in Arabic text recognition and recitation analysis. This paper presents a comprehensive analysis of 34+ published scholarly articles devoted to Qur’anic studies. This work explores the convergence of machine learning methodologies and Qur’anic studies, examining the innovative applications and methodologies for Arabic text and voice classification. The fusion of ML algorithms makes the work easy and accurate to analyze, interpret, and extract valuable insights from the sacred text. Subsequently, we delve deeper into the emergent field of ML algorithms like k-NN, ANN, BLSTM, MFCC, SVM, NB and DL approaches have been adapted for Qur’anic texts classification, recitation and recitation analysis on accuracy, speed, class recognition, response rate and biasness benchmark. This work covers a diverse range of applications, including automated Qur’anic exegesis and analysis of usage of Ahkam Al-Tajweed. The main contribution of the work is to provide insight into how ML facilitates in Arabic and Kufic textual analysis, linguistic subtleties, and thematic structures of the Qur’anic text. Using the deep learning approaches, the reciters, recitation style and of the Quranic text has also explained in the work.

P125. Development of a novel ensemble machine learning algorithm for prediction of complications and readmission after anterior cervical spinal fusion

Empirical software measurement is a dynamic research domain within software engineering. Measuring the software attributes during software development is crucial to ensure the successful delivery of a desired software product. It involves the measurement of multiple dynamic metrics based on software process and software product. The traditional approaches to measure the current software practices are not so appropriate due to the changing development process and increasing complexity. The changing environment requires techniques to make measurements that are adaptable to the changes. Machine learning (ML)-based techniques are prominently being deployed for measuring the software empirically. Past three decades witness the successful application of ML-based techniques for empirical measurements of software. ML is powerful enough to accurately measure the software attributes. But the synergy of ML algorithms and metaheuristic algorithm is apparently more promising in the domain of software measurement. Since 2001, metaheuristics are being fused with ML-based techniques to optimize the accuracy of the predictor based on ML. The estimation of software cost (or effort), estimation of development time (or schedule), prediction of software quality, and early detection of software anomalies are key tasks in the most popular research sector of entire field of software measurement. The vital role of metaheuristics along with ML in empirical software measurements is as follows. First, metaheuristics are used to optimize the hyperparameters of ML algorithms; second, to mitigate the problem of the curse of dimensionality of data; and third, to select the best ML model for the candidate problem. This study proposes a full-fledged description to the application of metaheuristic algorithms to empirical software measurements. The chapter begins with the introduction to the metaheuristic approach and its application to measure software empirically in conjunction with ML algorithms. Then, the state of the art of deployment of metaheuristics in the domain of software measurements is elaborated. Later, the study discusses the three major application areas of metaheuristics: hyperparameter tuning, dimensionality reduction, and model selection in detail. For each of these application areas, case studies are quoted for clearer insight into the techniques and their applications. The author proposes a novel metaheuristic technique in this chapter for feature selection. It brings more clarity for devising new methods. Then the chapter is concluded with special remarks on future scope and references made.