This paper presents a literature review for inventory control of deteriorating items since 2018. A classification including 18 classes and 33 subclasses is offered to categorize inventory control models, constraints, and solution methods used in previous studies. Providing standard classes in this field, such as demand, deterioration, shortages, number of warehouses, and time value of money alongside new classes, for example, the type of model costs and supply chain, inventory constraints, number of supply chain levels, time horizon, lead time, considering multi-item models, preservation technology, financial conditions, non-instantaneous deteriorating items, environmental issues, and solution methods made this classification more comprehensive. A brief history and explanation are given to understand each class better, and related articles are grouped in these classes. The research gaps and a crucial aspect that paves the way for future research are presented in each category. A broad view of the future of this topic is provided, and exciting opportunities are highlighted for researchers to contribute to this field and inspire them to explore these potential areas of research. The potential for future research in this subject is vast and promising; this article offers numerous opportunities for researchers to make significant contributions. The results show that the best ways to extend this topic are using variable deterioration rates, costs, and demand functions, considering realistic assumptions, including allowable shortages with partial backlogging, two warehouses, inflation and discounts, preservation technology, uncertain lead time, and environmental issues. Developing cyclic (if possible), multi-item, and production models with financial conditions and various inventory constraints is an excellent way to develop existing models. Finally, solving the proposed models using exact methods to find the global answer is a great effort to contribute to this field.

The vast usage and development of web technology generate numerous types of web pages. Besides, not all these types are legitimate webpages. Phishing sites mislead web page users into taking harmful actions. However, there is a need for a tool to address this type of problem. Deep learning models are used in dealing with web technology to detect whether the webpage is either legitimate or phishing. Herein, a novel Tabular Multi-Head Attention (TabMHA) model is presented to perform a binary classification task. The main task is to classify whether the webpages are phishing or not. The proposed model is trained and tested on a benchmark dataset related to phishing detection. It contains 5000 legitimate web pages and 5000 phishing ones; the overall is 10,000. Also, the feature numbers in the dataset are out of 48 features. As a consequence, the proposed model achieved a powerful performance compared with other models in the literature; the model achieved an accuracy level of 99.6%. This result is considered a promising result and can be integrated into real-world detection models.

Machine vision systems are among the novel tools proven to be useful in different applications, among which monitoring and controlling manufacturing processes is one of the most important ones. However, due to the complexity resulted from high-dimensional image data and their inherent correlations, the acquisition of traditional statistical process control tools seems inapplicable. To overcome the shortcomings of the traditional methods in this regard, a statistical model is proposed in this paper which utilizes the concepts of both the PCA-based T2 control chart and the classification methods to develop a tool capable of controlling an image-based process. By defining the warning zones, collected data taken from an image-based process are classified into more than the two classes related to in-control and out-of-control processes. This helps practitioners to define rules to make it easier to realize when the process is getting out of control. Through simulation, the accuracy performance and the speed of four different types of classifiers including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), kth nearest neighbors (KNN), and support vector machine (SVM) are assessed in different scenarios, based on which the functionality of the proposed approach is evaluated in in-control and out-of-control conditions.

Mobile Applications offer a new style to service sectors, for instance, in higher education, mobile applications are utilized to provide access to academic resources and academic services. Despite the wealth of mobile applications, they encounter various challenges that have attracted the interest of academia and software developers. The usability issues of mobile applications may cause performance degradation, resulting in the company's loss in terms of cost. This study aims to investigate the usability of the Prince Sattam bin Abdulaziz University (PSAU) mobile application by adopting data mining as a descriptive and predictive process. The first step was gathering data of the usability of the PSAU mobile application using the system usability scale. Afterwards, data was preprocessed into a suitable format to apply data mining methods. Specifically, the explanatory model has been employed to describe and investigate insights related to the usability factors and features of the PSAU mobile application. Furthermore, this study adopted the Four Clustering methods to segment the usability levels of the PSAU mobile application into homogenous groups based on user behavior. Additionally, the predictive model was used to build models for predicting the usability level and Grade and five classification algorithms were employed to predict the usability level and Grade. Most algorithms have given positive results in all performance indicators, where the accuracy rate achieved is 98% to 95% for most methods. The results revealed that the PSAU mobile application has an acceptable usability level, and the data mining methods helped to discover hidden patterns. Furthermore, the findings will help the developers and policymakers understand users' and stakeholders' behavior to find the most common usability problems for each group, and customize the PSAU mobile application.

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.

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.

Diabetes Mellitus (DM) is a frequent condition in which the body's sugar levels are abnormally high for an extended length of time. It is a major cause of death with high mortality rates and the second leading cause of total years lived with disability worldwide. Its seriousness comes from its long-term complications, including nephropathy, retinopathy, and neuropathy leading to kidney failure, poor vision and blindness, and peripheral sensory loss, respectively. Such conditions are life-threatening and affect patients’ quality of life. Therefore, this paper aims to identify the most relevant features in the diagnosis of DM and identify the best classifier that can efficiently diagnose DM based on a set of relevant features. To achieve this, four different feature selection methods have been utilized. Moreover, twelve different classifiers that belong to six learning strategies have been evaluated using two datasets and several evaluation metrics such as Accuracy, Precision, Recall, F1-measure, and ROC area. The obtained results revealed that the correlation attribute evaluation method would be the best choice to handle the task of feature selection and ranking for the considered datasets, especially when considering the Accuracy metric. Furthermore, MultiClassClassifier would be the best classifier to handle Diabetes datasets, especially when considering True Positive, precision, and Recall metrics.

In the realm of financial security, the detection and prevention of credit card fraud has become paramount. With the ever-increasing reliance on digital transactions, the risk of fraudulent activities targeting credit card systems has grown significantly. To combat this, sophisticated techniques are required to swiftly identify and mitigate potential threats. Machine learning, a cornerstone of modern data analysis, has emerged as a powerful tool in this pursuit. By leveraging vast datasets and employing advanced algorithms, machine learning enables the automated scrutiny of transactions, distinguishing between legitimate and fraudulent activities with remarkable precision. This paper introduces an intelligent method for credit card fraud detection that relies on Competitive Swarm Optimization (CSO) and Random Weight Network (RWN). Additionally, the system includes an automated hybrid feature selection capability to identify the most pertinent features during the detection process. The experimental outcomes validate that this system can attain outstanding results in G-Mean, RUC, and Recall values.

Phishing websites are characterized by distinguished visual, address, domain, and embedded features, which identify and defend such threats. Yet, phishing website detection is challenged by overlapping these features with legitimate websites’ features. As the inter-class variance between legitimate and phishing websites becomes low, commonly utilized machine learning algorithms suffer from low performance in overlapping feature cases. Alternatively, ensemble learning that combines multiple predictions intending to address low inter-class variations in the classified data improves the performance in such cases. Ensemble learning utilizes multiple classifiers of similar or different types with multiple deviations of the training data. This paper develops a framework based on random forest ensemble techniques. The limitations of the random forest are the inability to capture the high correlation between features and their join dependency on the label. The random forest is combined with k-means clustering to capture the feature correlation. The framework is evaluated for phishing detection with a dataset of 5000 samples. The results showed the proposed framework over-performed the random forest classifier, all other ensemble classifiers, and the conventional classification algorithms. The proposed framework achieved an accuracy of 98.64%, precision of 0.986, recall of 0.987, and F-measure of 0.986.

Phishing is an attack by cybercriminals to obtain sensitive information such as account IDs, usernames, and passwords through the use of the anonymous structure of the Internet. Although software companies are launching new anti-phishing tools that use blacklists, heuristics, visual methods, and machine learning-based methods, these products cannot prevent all phishing attacks. This research offers an opportunity to increase accuracy in the detection of phishing sites. This study develops a model using machine learning algorithms, specifically the decision tree and the random forest, due to their outperforming the rest of the classifiers and being accredited by researchers in this field to achieve the highest accuracy. The study is based on two phases: the first phase is to measure the accuracy of classifiers on the dataset in the usual way before and after feature selection. The second phase uses the class decomposition approach and measures the accuracy of classifiers in the dataset before feature selection and after feature selection to detect phishing sites. The class decomposition approach is a technique to improve the performance of classifiers by distributing each class into clusters and renaming the examples of each cluster with a new class. This provides a specific metric that more accurately predicts the level of phishing. Testing on a dataset containing 11,055 instances, 4,898 phishing, and 6,157 legitimate, each instance has 30 features. It achieved the highest accuracy in the first phase through the random forest algorithm by 96.9% before feature selection, and after feature selection, it was by 97.1%. In the second phase, the highest accuracy of both the decision tree and random forest classifiers was achieved by 100% with the two and four classes after feature selection. While before feature selection, the random forest algorithm achieved 100% with only the two classes.