The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity

A comprehensive search for primary research published between 2014 and 2023 was carried across several databases. Studies that describe the application of machine learning (ML) and deep learning techniques for if they was carried out across several databases. Studies that described the application of deep learning (DL) and machine learning (ML) methods for IoT botnet attack detection. Numerous facets of contemporary life have been transformed by the Internet of Things (IoT), including home automation, industrial control systems, healthcare, and transportation. On the other hand, as more devices become connected, security risks have also increased, especially from botnets. IoT Botnet attack detection techniques utilizing ML and DL have been developed in order to reduce these dangers. The best DL and ML techniques for IoT botnet attack detection are identified by a detailed examination of evaluation criteria, and performance measures in this systematic review. Performance metrics from well-known machine learning models are used to illustrate how well these machine learning techniques detect and stop Botnet attacks. When it comes to detecting Botnet assaults, deep learning (DL) and traditional machine learning (ML) methods perform similarly well. Furthermore, traditional machine learning systems still have challenges with real-time monitoring, timely detection and adaptability to novel attack approaches.

With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful for monitoring, diagnosing, and further applications in data analysis and decision-making. Essentially, the data can be divided into three types, namely, statistical, image-based, and sequential data. Each type has a different method of retrieval, processing, and deployment. Additionally, the application of machine learning (ML) and deep learning (DL) in healthcare support systems is growing more rapidly than ever. Numerous high-performance architectures are proposed to optimize decision-making. As reliability and stability are the most important factors in the healthcare support system, enhancing the predicted performance and maintaining the stability of the model are always the top priority. The main idea of our study comes from ensemble techniques. Numerous studies and data science competitions show that by combining several weak models into one, ensemble models can attain outstanding performance and reliability. We propose three deep ensemble learning (DEL) approaches, each with stable and reliable performance, that are workable on the above-mentioned data types. These are deep-stacked generalization ensemble learning, gradient deep learning boosting, and deep aggregation learning. The experiment results show that our proposed approaches achieve more vigorous and reliable performance than traditional ML and DL techniques on statistical, image-based, and sequential benchmark datasets. In particular, on the Heart Disease UCI dataset, representing the statistical type, the gradient deep learning boosting approach dominates the others with accuracy, recall, F1-score, Matthews correlation coefficient, and area under the curve values of 0.87, 0.81, 0.83, 0.73, and 0.91, respectively. On the X-ray dataset, representing the image-based type, the deep aggregation learning approach shows the highest performance with values of 0.91, 0.97, 0.93, 0.80, and 0.94, respectively. On the Depresjon dataset, representing the sequence type, the deep-stacked generalization ensemble learning approach outperforms the others with values of 0.91, 0.84, 0.86, 0.8, and 0.94, respectively. Overall, we conclude that applying DL models using our proposed approaches is a promising method for the healthcare support system to enhance prediction and diagnosis performance. Furthermore, our study reveals that these approaches are flexible and easy to apply to achieve optimal performance.

Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well. There are various techniques devised for the same. Traditional machine learning algorithms have been applied in many application areas. Researchers have put many efforts to improve the accuracy of that machinelearning algorithms. Another dimension was given thought which leads to deep learning concept. Deep learning is a subset of machine learning. So far few applications of deep learning have been explored. This is definitely going to cater to solving issues in several new application domains, sub-domains using deep learning. A review of these past and future application domains, sub-domains, and applications of machine learning and deep learning are illustrated in this paper.

A comprehensive survey on weed and crop classification using machine learning and deep learning

Recent advancements in text classification have focused on the application of machine learn-ing and deep learning techniques. Traditional methods such as Naive Bayes, Logistic Regression, and Support Vector Machines (SVM) have been widely utilized due to their efficiency and simplic-ity. However, the advent of deep learning has introduced more complex models like Artificial Neu-ral Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), which can automatically extract features and detect intricate patterns in textual data. Addi-tionally, transformer-based models such as BERT have set new benchmarks in text classification tasks. Despite their high accuracy, these models require substantial computational resources and are not always practical for every application. The ongoing research aims to balance accuracy and computational efficiency. Purpose of Research. The primary objective of this study is to review and compare various methods for automated text classification based on sentiment analysis. This research aims to evalu-ate the prediction accuracy of different models, including traditional machine learning algorithms and modern deep learning approaches, and to provide insights into their practical applications and limitations. Presentation of the Main Research Material. This study utilizes the “IMDB Dataset of 50K Movie Reviews” to train and test various text classification models. The dataset comprises movie reviews and their associated sentiment labels, either positive or negative. The research employs several preprocessing steps. For feature extraction, methods such as Bag-of-Words (BoW), TF-IDF (Term Frequency-Inverse Document Frequency), and Word2Vec are used. These features are then fed into various classifiers: Naive Bayes, Support Vector Machines (SVM), Logistic Regression, Deep Learning Models. Conclusions. The comparative analysis reveals that while traditional machine learning meth-ods like Naive Bayes, SVM and Logistic Regression are efficient and easy to implement, deep learn-ing models offer superior accuracy by capturing more complex patterns in the data. However, the computational demands of deep learning models, particularly transformers, limit their applicability in resource-constrained environments. Future research should focus on optimizing these models to balance accuracy and computational efficiency, making advanced text classification accessible for a broader range of applications. Recent advancements in text classification have focused on the application of machine learn-ing and deep learning techniques. Traditional methods such as Naive Bayes, Logistic Regression, and Support Vector Machines (SVM) have been widely utilized due to their efficiency and simplic-ity. However, the advent of deep learning has introduced more complex models like Artificial Neu-ral Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), which can automatically extract features and detect intricate patterns in textual data. Addi-tionally, transformer-based models such as BERT have set new benchmarks in text classification tasks. Despite their high accuracy, these models require substantial computational resources and are not always practical for every application. The ongoing research aims to balance accuracy and computational efficiency.

Machine learning in medical applications is one of the focus areas of the researchers these days. Machine Learning with the application of Artificial Intelligence is not only giving solutions to the complex problems but also revolutionised the medical field. The main motive of machine learning is to improve its learning process over time by taking all the relevant data and information in the form of different inputs and observations. This study reviews different medical disease prediction and detection techniques with the help of distinct deep learning & machine learning models. The problems related to medical diseases, like cancer related diseases, heart, lung, thyroid and kidney diseases are being discussed in this article. Detection and analysing of medical diseases is one of the prominent applications of machine and deep learning. Deep learning as a technology offers a huge set of different and innovative tools which are relevant to different issues faced in the field of medical image processing. This study will discuss about the applications of Machine Learning, and then discuss some of the advancements done in different diseases like breast cancer, heart disease, skin disease, kidney disease etc.

This review paper investigates applications of machine learning and deep learning in trading, with a particular emphasis on recent advances in deep learning. It provides an overview of algorithms, including support vector machines (SVMs), random forests, deep neural networks (DNNs), long short-term memory networks (LSTM networks), and deep reinforcement learning (DRL). Findings show that while machine learning and deep learning models were able to surpass traditional strategies in general in terms of profitability, they were also better at risk management. However, despite their performances showing superiority, their performances varied significantly under different market conditions, including markets during periods of high and low volatility. In particular, LSTM networks and random forests can generate substantial returns, higher Sharpe ratios, and lower drawdowns compared to the benchmarks, whereas DNNs struggled during highly volatile periods, as reflected in the returns in the periods. Moreover, the improved DRL agent TradeNet-CR can manage risk significantly better than another, despite not surpassing the original TradeNet-CR model.

The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date. A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa). In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods. The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.

With the development of information technology, thousands of devices are connected to the Internet, various types of data are accessed and transmitted through the network, which pose huge security threats while bringing convenience to people. In order to deal with security issues, many effective solutions have been given based on traditional machine learning. However, due to the characteristics of big data in cyber security, there exists a bottleneck for methods of traditional machine learning in improving security. Owning to the advantages of processing big data and high-dimensional data, new solutions for cyber security are provided based on deep learning. In this paper, the applications of deep learning are classified, analyzed and summarized in the field of cyber security, and the applications are compared between deep learning and traditional machine learning in the security field. The challenges and problems faced by deep learning in cyber security are analyzed and presented. The findings illustrate that deep learning has a better effect on some aspects of cyber security and should be considered as the first option.

Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. Methods: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Conclusions: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Towards precision medicine for otology and neurotology: Machine learning applications and challenges.

Advances and applications of machine learning and deep learning in environmental ecology and health

Recent breakthroughs in Machine Learning (ML) applications, and especially in Deep Learning (DL), have made DL models a key component in almost every modern computing system. The increased popularity of DL applications deployed on a wide-spectrum of platforms (from mobile devices to datacenters) have resulted in a plethora of design challenges related to the constraints introduced by the hardware itself. “What is the latency or energy cost for an inference made by a Deep Neural Network (DNN)?” “Is it possible to predict this latency or energy consumption before a model is even trained?” “If yes, how can machine learners take advantage of these models to design the hardware-optimal DNN for deployment?” From lengthening battery life of mobile devices to reducing the runtime requirements of DL models executing in the cloud, the answers to these questions have drawn significant attention. One cannot optimize what isn't properly modeled. Therefore, it is important to understand the hardware efficiency of DL models during serving for making an inference, before even training the model. This key observation has motivated the use of predictive models to capture the hardware performance or energy efficiency of ML applications. Furthermore, ML practitioners are currently challenged with the task of designing the DNN model, i.e., of tuning the hyper-parameters of the DNN architecture, while optimizing for both accuracy of the DL model and its hardware efficiency. Therefore, state-of-the-art methodologies have proposed hardware-aware hyper-parameter optimization techniques. In this paper, we provide a comprehensive assessment of state-of-the-art work and selected results on the hardware-aware modeling and optimization for ML applications. We also highlight several open questions that are poised to give rise to novel hardware-aware designs in the next few years, as DL applications continue to significantly impact associated hardware systems and platforms.

The application of deep learning in electrocardiogram: Where we came from and where we should go?