In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals’ sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM.

Nowadays, emails are used across almost every field, spanning from business to education. Broadly, emails can be categorized as either ham or spam. Email spam, also known as junk emails or unwanted emails, can harm users by wasting time and computing resources, along with stealing valuable information. The volume of spam emails is rising rapidly day by day. Detecting and filtering spam presents significant and complex challenges for email systems. Traditional identification techniques like blacklists, real-time blackhole listing, and content-based methods have limitations. These limitations have led to the advancement of more sophisticated machine learning (ML) and deep learning (DL) methods for enhanced spam detection accuracy. In recent years, considerable attention has focused on the potential of ML and DL methods to improve email spam detection. A comprehensive literature review is therefore imperative for developing an updated, evidence-based understanding of contemporary research on employing these methods against this persistent problem. The review aims to systematically identify various ML and DL methods applied for spam detection, evaluate their effectiveness, and highlight promising future research directions considering gaps. By combining and analyzing findings across studies, it will obtain the strengths and weaknesses of existing methods. This review seeks to advance knowledge on reliable and efficient integration of state-of-the-art ML and DL into identifying email spam.

Breast cancer becomes the second major cause of death among women cancer patients worldwide. Based on research conducted in 2019, there are approximately 250,000 women across the United States diagnosed with invasive breast cancer each year. The prevention of breast cancer remains a challenge in the current world as the growth of breast cancer cells is a multistep process that involves multiple cell types. Early diagnosis and detection of breast cancer are among the greatest approaches to preventing cancer from spreading and increasing the survival rate. For more accurate and fast detection of breast cancer disease, automatic diagnostic methods are applied to conduct the breast cancer diagnosis. This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. This study aims to resolve the limitation of an existing method, ID3 algorithm that unable to classify the continuous-valued data and increase the classification accuracy of the decision tree. FID3 algorithm combined the fuzzy system and decision tree techniques with ID3 algorithm as the decision tree learning. FUZZYDBD method, an automatic fuzzy database definition method, would be used to design the fuzzy database for fuzzification of data in the FID3 algorithm. It was used to generate a predefined fuzzy database before the generation of the fuzzy rule base. The fuzzified dataset was applied in FID3 algorithm, which is the fuzzy version of the ID3 algorithm. The inference system of FID3 algorithm is simple with direct extraction of rules from generated tree to determine the classes for the new input instances. This study also analysed the results using three breast cancer datasets: WBCD (Original), WDBC (Diagnostic) and Coimbra. Furthermore, the comparison of FID3 algorithm with the existing methods is conducted to verify the proposed method’s capability and performance. This study identified that the combination of FID3 algorithm with FUZZYDBD method is reliable, robust and managed to perform well in breast cancer classification.

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

New technologies drive educational shifts, transforming offline to online learning. This study investigates e-learning and cloud computing integration to understand synergies and their potential impact. The study addresses two primary research questions: the influence of e-learning on factors like architecture, software, performance, security, hardware, network, and virtual aspects, and the examination of cloud computing services and models such as SaaS, PaaS, IaaS, and S.O.A. The research aims to provide insights into how e-learning is incorporated in a cloud computing environment. The motivation behind this study is to investigate the intricate relationship between e-learning and cloud computing. By analyzing 154 scientific papers, the study delves into the specifics of this integration, highlighting trends and areas that have received more attention. The study examines e-learning in a cloud computing environment, focusing on architecture (27%), general topics (21%), software (19%), and performance (18%). Virtual environments have fewer security issues, while storage and network focus are more prevalent. Cloud computing services are mainly all services, with software as a service (18%), infrastructure as a service (17%), and platforms as a service (10%). Most studies are based on public clouds (74%), all other models (11%), and hybrid clouds (3%). The study examines e-learning integration in cloud computing, highlighting limitations in hybrid and private clouds, specialized infrastructure, and a gap in platforms and infrastructure offerings.