Electrospinning has emerged as a simple and cost-effective technique for producing polymer nanofibers, offering a versatile approach for creating nanostructured fibers from a wide range of polymer materials. The pharmaceutical field has particularly welcomed the advent of electrospun nanofibers, as they hold immense potential for revolutionizing drug delivery systems. The recent surge of interest in electrospun nanofibers can be attributed to their unique characteristics, including elasticity and biocompatibility, which make them highly suitable for various biomedical applications. By incorporating functional ingredients into blends of nanostructured fibers, the capabilities and reliability of drug delivery devices have been significantly enhanced. This review aims to provide a comprehensive summary of recent research endeavors focusing on the concept of nanofibrous mesh and its multifaceted applications. With an emphasis on the simplicity of fabrication and the virtually limitless combinations of materials achievable through this approach, nanofibrous meshes hold the promise of transforming specific treatment modalities. By streamlining the delivery of therapeutic agents and offering enhanced control over drug release kinetics, nanofibrous meshes may herald a new era in targeted and personalized medicine.

Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods.

Introduction: Cloud computing is considered a remarkable paradigm shift in Information Technology (IT), offering scalable and virtualized resources to end users at a low cost in terms of infrastructure and maintenance. These resources offer an exceptional degree of flexibility and adhere to established standards, formats, and networking protocols while being managed by several management entities. However, the existence of flaws and vulnerabilities in underlying technology and outdated protocols opens the door for malicious network attacks.Methods: This study addresses these vulnerabilities by introducing a method for classifying attacks in Infrastructure as a Service (IaaS) cloud environments, utilizing machine learning methodologies within a digital forensics framework. Various machine learning algorithms are employed to automatically identify and categorize cyber-attacks based on metrics related to process performance. The dataset is divided into three distinct categories—CPU usage, memory usage, and disk usage—to assess each category’s impact on the detection of attacks within cloud computing systems.Results: Decision Tree and Neural Network models are recommended for analyzing disk-related features due to their superior performance in detecting attacks with an accuracy of 90% and 87.9%, respectively. Neural Network is deemed more suitable for identifying CPU behavior, achieving an accuracy of 86.2%. For memory-related features, K-Nearest Neighbor (KNN) demonstrates the best False Negative Rate (FNR) value of 1.8%.Discussion: Our study highlights the significance of customizing the selection of classifiers based on the specific system feature and the intended focus of detection. By tailoring machine learning models to particular system features, the detection of malicious activities in IaaS cloud environments can be enhanced, offering practical insights into effective attack classification.

Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research was to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aimed to meet three main objectives. These objectives were to identify the best regression model, the best classification model, and the best learning strategy that highly suited sleep disorder datasets. Considering two related datasets and several evaluation metrics that were related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty three regression models. Furthermore, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belonged to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.

A comprehensive analysis of eye diseases and medical data classification

The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision making to categorize documents into groups according to their structure. In this paper, we combined multi-agent systems (MASs), which is one of the IDSS systems, with Bayesian-based classification to filter out the specialization, collaboration, and privacy of distributed business sources to produce an efficient distributed classification system. Bayesian classification made use of MAS to eliminate distributed sources’ specialization and privacy. Therefore, incorporating the probabilities of various sources is a practical and swift solution to such a problem, where this method works the same when all the data are merged into a single source. Each intelligent agent can collaborate and ask for help from other intelligent agents in classifying cases that are difficult to classify locally. The results demonstrate that our proposed technique is more accurate than those of the non-communicated classification, where the results proved the ability of the utilized productive distributed classification system.