Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

Recent advances in information technology have induced an explosive growth of data, creating a new era of big data. Unfortunately, traditional machine-learning algorithms cannot cope with the new characteristics of big data. In this paper, we address the problem of breast cancer prediction in the big data context. We considered two varieties of data, namely gene expression (GE) and DNA methylation (DM). The objective of our work is to scale up the machine-learning algorithms which are used for classification by applying each dataset separately and jointly. For this purpose, we chose Apache Spark as a platform. In our study, we selected three different classification algorithms, namely support vector machine (SVM), decision tree, and random forest, to create nine models that help in predicting breast cancer. We conducted a comprehensive comparative study using three scenarios with GE, DM, and GE and DM combined, in order to show which of the three types of data would produce the best result in terms of accuracy and error rate. Moreover, we performed an experimental comparison between two platforms (Spark and Weka), in order to show their behavior when dealing with large sets of data. The experimental results showed that the scaled SVM classifier in the Spark environment outperforms the other classifiers, as it achieved the highest accuracy and the lowest error rate with the GE dataset.

Background: The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text. Objective: This study aims to identify hate speech related to the COVID-19 pandemic posted by Twitter users in the Arab region and to discover the main issues discussed in tweets containing hate speech. Methods: We used the ArCOV-19 dataset, an ongoing collection of Arabic tweets related to COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using a pretrained convolutional neural network (CNN) model; each tweet was given a score between 0 and 1, with 1 being the most hateful text. We also used nonnegative matrix factorization to discover the main issues and topics discussed in hate tweets. Results: The analysis of hate speech in Twitter data in the Arab region identified that the number of non–hate tweets greatly exceeded the number of hate tweets, where the percentage of hate tweets among COVID-19 related tweets was 3.2% (11,743/547,554). The analysis also revealed that the majority of hate tweets (8385/11,743, 71.4%) contained a low level of hate based on the score provided by the CNN. This study identified Saudi Arabia as the Arab country from which the most COVID-19 hate tweets originated during the pandemic. Furthermore, we showed that the largest number of hate tweets appeared during the time period of March 1-30, 2020, representing 51.9% of all hate tweets (6095/11,743). Contrary to what was anticipated, in the Arab region, it was found that the spread of COVID-19–related hate speech on Twitter was weakly related with the dissemination of the pandemic based on the Pearson correlation coefficient (r=0.1982, P=.50). The study also identified the commonly discussed topics in hate tweets during the pandemic. Analysis of the 7 extracted topics showed that 6 of the 7 identified topics were related to hate speech against China and Iran. Arab users also discussed topics related to political conflicts in the Arab region during the COVID-19 pandemic. Conclusions: The COVID-19 pandemic poses serious public health challenges to nations worldwide. During the COVID-19 pandemic, frequent use of social media can contribute to the spread of hate speech. Hate speech on the web can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which increases the threat associated with being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19–related tweets in the Arab region.

Lately, Augmented Analytics (AA) has increasingly been introduced as a tool for transforming data into valuable insights for decision-making, and it has gained attention as one of the most advanced methods to facilitate modern analytics for different types of users. AA can be defined as a combination of Business Intelligence (BI) and the advanced features of Artificial Intelligence (AI). With the massive growth in data diversity, the traditional approach to BI has become less useful and requires additional work to obtain timely results. However, the power of AA that uses AI can be leveraged in BI platforms with the use of Machine Learning (ML) and natural language comprehension to automate the cycle of business analytics. Despite the various benefits for businesses and end users in converting from BI to AA, research on this trend has been limited. This study presents a comparison of the capabilities of the traditional BI and its augmented version in the business analytics cycle. Our findings show that AA enhances analysis, reduces time, and supports data preparation, visualization, modelling, and generation of insights. However, AI-driven analytics cannot fully replace human decision-making, as most business problems cannot be solved purely by machines. Human interaction and perspectives are essential, and decision-makers still play an important role in sharing and operationalizing findings.

Artificial Intelligence (AI) has proliferated in the last few years due to the vast data we pro-duce daily and available computing power. AI can be applied in many different sectors, such as transportation, education, healthcare, banking, and finance, among many others. The financial industry is rapidly embracing AI due to its potential for high-cost savings in financial services. AI could transform the financial sector by creating opportunities for tailored, faster, and more cost-effective services. Saudi Arabia is emerging as a fast-growing market in this industry with a strong commitment to technology-driven institutions. While AI is gaining prominence and receiving government support, it has not yet become a critical component for enhancing the efficiency of financial transactions. Limited published research on AI adoption in the Saudi Arabian financial industry calls for a comprehensive literature review to examine the current state of AI implementation in this sector. Therefore, this study explores the benefits, limitations, and challenges of leveraging AI in finance, highlighting the importance of ethical and regulatory considerations for successful AI adoption in the sector. This study’s findings reveal that research has been conducted on how AI improves processes in the financial sector by integrating critical components and efficient algorithms tailored to the industry’s needs. Based on these findings, this study proposes a sequential framework at the macro and micro levels of management to guide AI’s development and integration into the financial sector. Additionally, the framework draws insights from the existing literature to provide a detailed understanding of opportunities, challenges, and areas for improvement to maximize AI’s potential in the Saudi Arabian financial sector.