The Internet of Things (IoT) refers to the network of interconnected physical devices that are embedded with software, sensors, etc., allowing them to exchange and collect information. Although IoT devices have several advantages and can improve people’s efficacy, they also pose a security risk. The malicious actor frequently attempts to find a new way to utilize and exploit specific resources, and an IoT device is an ideal candidate for such exploitation owing to the massive number of active devices. Especially, Distributed Denial of Service (DDoS) attacks include the exploitation of a considerable number of devices like IoT devices, which act as bots and transfer fraudulent requests to the services, thereby obstructing them. There needs to be a robust system of detection based on satisfactory methods for detecting and identifying whether these attacks have occurred or not in a network. The most widely used technique for these purposes is artificial intelligence (AI), which includes the usage of Deep Learning (DL) and Machine Learning (ML) to find cyberattacks. The study presents a Piecewise Harris Hawks Optimizer with an Optimal Deep Learning Classifier (PHHO-ODLC) for a secure IoT environment. The fundamental goal of the PHHO-ODLC algorithm is to detect the existence of DDoS attacks in the IoT platform. The PHHO-ODLC method follows a three-stage process. At the initial stage, the PHHO algorithm can be employed to choose relevant features and thereby enhance the classification performance. Next, an attention-based bidirectional long short-term memory (ABiLSTM) network can be applied to the DDoS attack classification process. Finally, the hyperparameter selection of the ABiLSTM network is carried out by the use of a grey wolf optimizer (GWO). A widespread simulation analysis was performed to exhibit the improved detection accuracy of the PHHO-ODLC technique. The extensive outcomes demonstrated the significance of the PHHO-ODLC technique regarding the DDoS attack detection technique in the IoT platform.

The Learning Management System (LMS) is an essential tool for educational institutions that facilitates content delivery, assessments, lecture delivery, and collaboration to enhance the learning experience. This study explores the role of LMS in creating an effective learning environment to improve students’ academic performance. To achieve the main objective of this study, we utilized a dataset [xAPI-Edu-Data] comprising multiple factors, such as academic, psychological, and cognitive engagement. Various machine learning techniques are employed to assess the impact of engagement activities on students’ performance. Initially, a class imbalance issue identified in the dataset and addressed using SMOTE technique. In addition, other resampling strategies applied to compare the effectiveness of proposed work. The model performance evaluated and compared using different evaluation metrics before and after data enrichment. In addition, hyperparameter optimization is conducted using a grid search approach to enhance models’ accuracy. The performance of individual models such as support vector machine (0.81), logistic regression (0.80), and decision tree (0.75) enhanced using the enriched dataset. The integration of multiple base learners into an ensemble model, with random forest as the stacking learner, achieved a weighted precision of 0.83, improving from 0.60 with the original dataset. The implementation of the stacking approach with enriched dataset has identified a better result and improved accuracy by 23%. The key contribution of this study includes identifying the effectiveness of data enrichment in improving prediction accuracy. Moreover, the research highlights the role of student engagement and behavior in measuring academic performance. The proposed model can identify the factors behind low performance, allowing further actions to be taken. Based on the prediction, the educators can work on the associated factors that could be low engagement, participation, or attendance. The findings further indicate that better use of LMS by creating more engagement activities can enhance students’ learning.

Financial technology (Fintech) plays a pivotal role in driving contemporary technology, society, economies, and many other fields. The new-generation Fintech is Smart Fintech, mainly empowered and inspired by data science and artificial intelligence (DSAI) technologies. Smart Fintech combines DSAI and transforms finance and economies for driving automated, intelligent, personalized financial and economic businesses, services and systems, and the whole of business. The strength and growth of the country’s economy were evaluated with the accurate prediction of how many companies will succeed and how many will fail. Financial crisis prediction (FCP) has a considerable effect on the economy. Prior research focuses mainly on deep learning (DL), machine learning (ML), and statistical approaches for forecasting the financial health of a company. Thus, this study presents a hybrid hunter–prey optimization with a deep learning-based FCP (HHPODL-FCP) technique. The objective of the HHPODL-FCP algorithm lies in the effective identification of the financial crisis in enterprises or organizations. To accomplish this, the HHPODL-FCP method makes use of the HHPO algorithm for the feature subset selection process. In addition, the HHPODL-FCP technique employs the gated attention recurrent network (GARN) model for the identification and classification of financial and non-financial crises. The HHPODL-FCP method exploits a sparrow search algorithm (SSA)-based hyperparameter tuning process to enrich the performance of the GARN model. The simulation results of the HHPODL-FCP method are tested on different financial datasets. A wide range of experiments highlighted the remarkable performance of the HHPODL-FCP method over recent techniques under various measures.

Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches.

The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special occasions events such as Hajj or Umrah, when pilgrims travel from one region to another with severe disease conditions. Apart from the EDs, it is critical to monitor the movements of patients from EDs to other wards inside the hospital or in the region. This is to track the spread of viral illnesses that require more attention. In this scenario, Machine Learning (ML) algorithms can be used to classify the data into many classes and track the target audience. The current research article presents a Machine Learning-based Medical Data Monitoring and Classification Model for the EDs of the KSA hospitals and is named MLMDMC-ED technique. The most important aim of the proposed MLMDMC-ED technique is to monitor and track the patient’s visits to the EDs, the treatment given to them based on the Canadian Emergency Department Triage and Acuity Scale (CTAS), and their Length Of Stay (LOS) in the hospital, based on their treatment requirements. A patient’s clinical history is crucial in terms of making decisions during health emergencies or pandemics. So, the data should be processed so that it can be classified and visualized in different formats using the ML technique. The current research work aims at extracting the textual features from the patients’ data using the metaheuristic Non-Defeatable Genetic Algorithm II (NSGA II). The data, collected from the hospitals, are classified using the Graph Convolutional Network (GCN) model. Grey Wolf Optimizer (GWO) is exploited for fine-tuning the parameters to optimize the performance of the GCN model. The proposed MLMDMC-ED technique was experimentally validated on the healthcare data and the outcomes indicated the improvements of the MLMDMC-ED technique over other models with a maximum accuracy of 91.87%.

The latest advances in engineering, science, and technology have contributed to an enormous generation of datasets. This vast dataset contains irrelevant, redundant, and noisy features that adversely impact classification performance in data mining and machine learning (ML) techniques. Feature selection (FS) is a preprocessing stage to minimize the data dimensionality by choosing the most prominent feature while improving the classification performance. Since the size data produced are often extensive in dimension, this enhances the complexity of search space, where the maximal number of potential solutions is 2nd for n feature datasets. As n becomes large, it becomes computationally impossible to compute the feature. Therefore, there is a need for effective FS techniques for large-scale problems of classification. Many metaheuristic approaches were utilized for FS to resolve the challenges of heuristic-based approaches. Recently, the swarm algorithm has been suggested and demonstrated to perform effectively for FS tasks. Therefore, I developed a Hybrid Mutated Tunicate Swarm Algorithm for FS and Global Optimization (HMTSA-FSGO) technique. The proposed HMTSA-FSGO model mainly aims to eradicate unwanted features and choose the relevant ones that highly impact the classifier results. In the HMTSA-FSGO model, the HMTSA is derived by integrating the standard TSA with two concepts: A dynamic s-best mutation operator for an optimal trade-off between exploration and exploitation and a directional mutation rule for enhanced search space exploration. The HMTSA-FSGO model also includes a bidirectional long short-term memory (BiLSTM) classifier to examine the impact of the FS process. The rat swarm optimizer (RSO) model can choose the hyperparameters to boost the BiLSTM network performance. The simulation analysis of the HMTSA-FSGO technique is tested using a series of experiments. The investigational validation of the HMTSA-FSGO technique showed a superior outcome of 93.01%, 97.39%, 61.59%, 99.15%, and 67.81% over diverse datasets.