This study conducted a thorough analysis of a dataset from a video game to reveal the relationship between game content, interactive features, and classification ratings. The project attempted to convert raw data into meaningful insights by employing Python for data processing and machine learning. The analysis uncovered strong relationships between content descriptors and ESRB ratings, indicating a market that strategically customizes game material to different demographic groupings. Moreover, the inclusion of interactive features such as 'Users Interact' and 'In-Game Purchases' suggests a transition towards gaming experiences that are more immersive and financially interactive. The highlight of this project was the creation of a web-based tool that can accurately forecast game classifications, utilizing advanced models such as XGBoost. The application offers developers and rating organizations a vital tool to achieve accuracy in game classification. The study's conclusions provide a detailed comprehension of console market dynamics, clarifying the present patterns and possible future developments in the gaming industry.

The COVID-19 pandemic has led to different health outcomes, including long COVID (LCo) and mental health (MH) disorders, impacting millions globally. To enable early healthcare diagnosis, including the prediction of MH conditions and LCo, various research studies have utilized machine learning (ML) techniques. However, there is still a gap in understanding the mental health of recovered COVID-19 patients with long COVID using ML techniques. This study aims to bridge this gap by developing and evaluating ML models, including support vector machine, multilayer perceptron (MLP), k-nearest neighbor, gradient boosting, voting classifier, and extreme gradient boosting, tailored for mental health and long COVID datasets from recovered COVID-19 patients. Additionally, feature selection methods, e.g., Recursive Feature Elimination (RFE) and Extra Trees (ET), and optimized models with hyper-parameter tuning will be employed. Our experiments utilize the dataset of recovered COVID-19 patients. Among these ML models, the MLP with ET-based features achieved the highest accuracy and AUC scores in this dataset, with 1.00 and 0.97 ± 0.02, respectively. The research reveals the high prevalence and risk factors of mental health disorders and long COVID from the dataset. These findings will contribute to personalized healthcare strategies for individuals navigating the complexities of post-COVID-19 recovery, integrating machine learning insights into mental health and long COVID support.

The IoT ecosystem faces increasingly complex security challenges due to the rapid growth of global IoT devices. Security risks related to device identification and credential compromise are on the rise, especially with the proliferation of IoT devices in various aspects of life. This research highlights the need to address these vulnerabilities through the development of robust security protocols, aiming to create a more secure IoT ecosystem and enhance user trust in this technology. The objective of the research is the development of an innovative IoT security protocol; High-Accuracy Device Identification and Resilience Against Credential Compromise (HADIRACC). This paper contributes significantly to enhancing the security and reliability of the IoT ecosystem. The research methods employed encompass the development of security protocols, the development of a proximity-based solution, and the classification of IoT devices using data processing techniques and machine learning-based classification. This study involves the collection and pre-processing of datasets, training different classifiers using 70% of the dataset, and testing the classifiers using the remaining 30%. The proposed protocol can effectively enhance the security of IoT devices by addressing various scenario-based attacks. Furthermore, the results of the analysis of the five classifiers used in this study indicate that Random Forest has the highest F1 score accuracy, reaching 88.8%. This suggests that Random Forest, as a classifier, can make the most accurate predictions compared to other classifiers.

Smart contracts offer automation for various decentralized applications but suffer from vulnerabilities that cause financial losses. Detecting vulnerabilities is critical to safeguarding decentralized applications before deployment. Automatic detection is more efficient than manual auditing of large codebases. Machine learning (ML) has emerged as a suitable technique for vulnerability detection. However, a systematic literature review (SLR) of ML models is lacking, making it difficult to identify research gaps. No published systematic review exists for ML approaches to smart contract vulnerability detection. This research focuses on ML-driven detection mechanisms from various databases. 46 studies were selected and reviewed based on keywords. The contributions address three research questions: vulnerability identification, machine learning model approaches, and data sources. In addition to highlighting gaps that require further investigation, the drawbacks of machine learning are discussed. This study lays the groundwork for improving ML solutions by mapping technical challenges and future directions.

Internal audit is a bulwark ensuring the integrity of financial statements, a linchpin for stakeholder trust and informed corporate decision-making. With the proliferation of complex financial transactions, audit teams face mounting challenges in deciphering voluminous transactional data to safeguard financial reporting quality. Machine learning has the potential to identify signifiers of financial reporting quality. Within the Design Science Methodology framework, we apply the Random Forest Classifier technique to metrics such as the error rate to enhance financial reporting. We find that the Random Forest Classifier identifies that certain parameters are critical to error detection, which enhance account receivable accuracy, lower receivable account control risk. This research advances the argument that technologically-enhanced internal audit procedures can play a pivotal role in ensuring that financial reporting mirrors the economic reality of the company.

The purpose of this paper is to examine the extent to which the Intelligent Enterprise Resource Planning (I-ERP) System can be used in company operations. Machine learning is embedded in a decision tree algorithm to demonstrate the viability of intelligent technology in an ERP system and to enhance the quality of operations through an I-ERP system. The study consists of two steps. In the first step, the algorithm uses the decision tree algorithm to demonstrate the application of intelligent technology in an ERP system. In the second step, the proposed model analyzes four quality criteria related to company operations through I-ERP system in order to determine whether or not I-ERP has significant improvement on managers’ decisions. As a result, the use of I-EPR may improve the quality of operations, agile respond to market demand, increase the efficiency and the competitiveness in organizations. An illustration example is provided to demonstrate the application of I-ERP.

With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.

Ransomware has evolved into a pervasive and extremely disruptive cybersecurity threat, causing substantial operational and financial damage to individuals and businesses. This article explores the critical domain of Ransomware detection and employs Machine Learning (ML) classifiers, particularly Decision Tree (DT), for Ransomware detection. The article also delves into the usefulness of DT in identifying Ransomware attacks, leveraging the innate ability of DT to recognize complex patterns within datasets. Instead of merely introducing DT as a detection method, we adopt a comprehensive approach, emphasizing the importance of fine-tuning DT hyperparameters. The optimization of these parameters is essential for maximizing the DT capability to identify Ransomware threats accurately. The obfuscated-MalMem2022 dataset, which is well-known for its extensive and challenging Ransomware-related data, was utilized to evaluate the effectiveness of DT in detecting Ransomware. The implementation uses the versatile Python programming language, renowned for its efficiency and adaptability in data analysis and ML tasks. Notably, the DT classifier consistently outperforms other classifiers in Ransomware detection, including K-Nearest Neighbors, Gradient Boosting Tree, Naive Bayes, and Linear Support Vector Classifier. For instance, the DT demonstrated exceptional effectiveness in distinguishing between Ransomware and benign data, as evidenced by its remarkable accuracy of 99.97%.

Operational data is always huge. A preprocessing step is needed to prepare such data for the analytical process so the process will be fast. One way is by choosing the most effective features and removing the others. Feature selection algorithms (FSAs) can do that with a variety of accuracy depending on both the nature of the data and the algorithm itself. This inspires researchers to keep on developing new FSAs to give higher accuracies than the existing ones. Moreover, FSAs are essential for reducing the cost and effort of developing information system applications. Merging multiple methodologies may improve the dimensionality reduction rate retaining sensible accuracy. This research proposed a hybrid feature selection algorithm based on ScC and forward selection methods (ScCFS). ScC is based on stability and correlation while forward selection is based on Random Forest (RF) and Information Gain (IG). A lowered subset generated by ScC is fed to the forward selection method which uses the IG as a decision criterion for selecting the attribute to split the node of the RF to generate the optimal reduct. ScCFS was compared to other known FSAs in terms of accuracy, AUC, and F-score using several classification algorithms and several datasets. Results showed that the ScCFS excels other FSAs employed for all classifiers in terms of accuracy except FLM where it comes in second place. This proves that ScCFS is the pioneer in generating the reduced dataset with remaining high accuracies for the classifiers used.

The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.