Identifying factors that affect the acceptance and use of E-assessment by academics in Saudi Universities

As assessment is one of the important pillars of the learning process, and E-assessment has become an essential part of education systems. E-assessment has developed to address some of the limitations and problems of a paper-test. In last the10 years, E-assessment has improved in developed countries such as the UK. In contrast, in Saudi Arabia, one of the developing countries, less attention has been paid to the usage of E-assessment and research which discusses E-assessment issues in Saudi Arabia is limited. Consequently, we investigate the factors that impact on academic’s use of E-assessment in Saudi universities. In order to examine these factors, the Decomposed Theory of Planned Behavior model (DTPB) is adopted with slight modification. Age and gender are added to the proposed model as moderating factors that affect attitude, subjective norms and perceived behavioral control. IT support is also added as a sub-factor under perceived behavioral control and technology facilitating conditions are included under resources facilitating conditions

Modified arithmetic optimization algorithm with Deep Learning based data analytics for depression detection

Depression detection is the procedure of recognizing the individuals exhibiting depression symptoms, which is a mental illness that is characterized by hopelessness, feelings of sadness, persistence and loss of interest in day-to-day activities. Depression detection in Social Networking Sites (SNS) is a challenging task due to the huge volume of data and its complicated variations. However, it is feasible to detect the depression of the individuals by examining the user-generated content utilizing Deep Learning (DL), Machine Learning (ML) and Natural Language Processing (NLP) approaches. These techniques demonstrate optimum outcomes in early and accurate detection of depression, which in turn can support in enhancing the treatment outcomes and avoid more complications related to depression. In order to provide more insights, both ML and DL approaches possibly offer unique features. These features support the evaluation of unique patterns that are hidden in online interactions and address them to expose the mental state amongst the SNS users. In the current study, we develop the Modified Arithmetic Optimization Algorithm with Deep Learning for Depression Detection in Twitter Data (MAOADL-DDTD) technique. The presented MAOADL-DDTD technique focuses on identification and classification of the depression sentiments in Twitter data. In the presented MAOADL-DDTD technique, the noise in the tweets is pre-processed in different ways. In addition to this, the Glove word embedding technique is used to extract the features from the preprocessed data. For depression detection, the Sparse Autoencoder (SAE) model is applied. The MAOA is used for optimum hyperparameter tuning of the SAE approach so as to optimize the performance of the SAE model, which helps in accomplishing better detection performance. The MAOADL-DDTD algorithm is simulated using the benchmark database and experimentally validated. The experimental values of the MAOADL-DDTD methodology establish its promising performance over another recent state-of-the-art approaches

Deep FM-Based Predictive Model for Student Dropout in Online Classes

The student’s high dropout rate is a severe issue in online learning courses. As a result, it is creating concerns for academics and administrators in the field of education. A practical method of preventing dropouts is predicting students’ likelihood of dropping out. This study uses an explainable factorization machine and deep-learning approach to predict students’ dropouts with two datasets, namely HarvardX Person-Course Academic Year 2013 De-Identified and MOOC datasets. With the solvable approach, the aim is to enable the interpretation of the predictive models to produce actionable insights for related online educational interventions. This approach creates a DeepFM-based prediction model for student dropout, which entails multiple processes, including data preparation, feature engineering, model construction, training, assessment, and deployment. Moreover, the DeepFM design combines a factorization machine with DNN models to forecast student dropouts. It examines performance metrics, including recall, F1 score, accuracy, precision, and AUC-ROC. After ten iterations and 64 batches, the DeepFM model accurately predicted student dropout from online courses with a 99% accuracy rate on validation data. It also outperformed other techniques because of its capacity to capture complicated non-linear connections between features, combine dense and sparse information, and consider the unique properties of online learning. This study illustrated using an explainable factorization machine learning and DNN approach called DeepFM to interpret the underlying reasons for predicting students’ dropout from online classes. Moreover, this approach has the potential to be extended to additional Massively open online courses (MOOC) datasets to assist educators and institutions in identifying at-risk students and providing targeted interventions to enhance their learning results

Factors that impact the acceptance and usage of e-assessment by academics in Saudi universities

As assessment is one of the important pillars of the learning process, and E-assessment has become an essential part of education systems. E-assessment developed to address some of the limitations and problems of a paper-test. In last 10 years, E-assessment has improved in developed countries such as UK. In contrast, in Saudi Arabia, one of the developing countries, less attention is still paid to the usage of E-assessment and research which discusses E-assessment issues in Saudi Arabia is limited. Consequently, this paper will investigate the factors that impact on academic’s use of E-assessment in Saudi universities. In order to examine these factors, this paper adopts the Decomposed Theory of Planned Behaviour model (DTPB) with slight modification. Age and gender are added to the proposed model as moderating factors that affect attitude, subjective norms and perceived behavioural control. IT support is also added as a factor under perceived behavioural control and technology facilitating conditions are included under resources facilitating condition

Biomedical Image Analysis for Colon and Lung Cancer Detection Using Tuna Swarm Algorithm With Deep Learning Model

The domain of Artificial Intelligence (AI) is made important strides recently, leading to developments in several domains comprising biomedical diagnostics and research. The procedure of AI-based systems in biomedical analytics takes opened up novel avenues for the progress of disease analysis, drug discovery, and treatment. Cancer is the second major reason of death worldwide; around one in every six people pass away suffering from it. Among several kinds of cancers, the colon and lung variations are the most frequent and deadliest ones. Initial detection of conditions on both fronts significantly reduces the probability of mortality. Deep learning (DL) and Machine learning (ML) systems are exploited to speed up such cancer detection, permitting researchers to analyze a huge count of patients in a lesser time count and at a minimal cost. This study develops a new Biomedical Image Analysis for Colon and Lung Cancer Detection using Tuna Swarm Algorithm with Deep Learning (BICLCD-TSADL) model. The presented BICLCD-TSADL technique examines the biomedical images for the identification and classification of colon and lung cancer. To accomplish this, the BICLCD-TSADL technique applies Gabor filtering (GF) to preprocess the input images. In addition, the BICLCD-TSADL technique employs a GhostNet feature extractor to create a collection of feature vectors. Moreover, AFAO was executed to adjust the hyperparameters of the GhostNet technique. Furthermore, the TSA with echo state network (ESN) classifier is utilized for detecting lung and colon cancer. To demonstrate the more incredible outcome of the BICLCD-TSADL system, an extensive experimental outcome is carried out. The comprehensive comparative analysis highlighted the greater efficiency of the BICLCD-TSADL technique with other approaches with maximum accuracy of 99.33%

Advancing retinoblastoma detection based on binary arithmetic optimization and integrated features

Retinoblastoma, the most prevalent pediatric intraocular malignancy, can cause vision loss in children and adults worldwide. Adults may develop uveal melanoma. It is a hazardous tumor that can expand swiftly and destroy the eye and surrounding tissue. Thus, early retinoblastoma screening in children is essential. This work isolated retinal tumor cells, which is its main contribution. Tumors were also staged and subtyped. The methods let ophthalmologists discover and forecast retinoblastoma malignancy early. The approach may prevent blindness in infants and adults. Experts in ophthalmology now have more tools because of their disposal and the revolution in deep learning techniques. There are three stages to the suggested approach, and they are pre-processing, segmenting, and classification. The tumor is isolated and labeled on the base picture using various image processing techniques in this approach. Median filtering is initially used to smooth the pictures. The suggested method’s unique selling point is the incorporation of fused features, which result from combining those produced using deep learning models (DL) such as EfficientNet and CNN with those obtained by more conventional handmade feature extraction methods. Feature selection (FS) is carried out to enhance the performance of the suggested system further. Here, we present BAOA-S and BAOA-V, two binary variations of the newly introduced Arithmetic Optimization Algorithm (AOA), to perform feature selection. The malignancy and the tumor cells are categorized once they have been segmented. The suggested optimization method enhances the algorithm’s parameters, making it well-suited to multimodal pictures taken with varying illness configurations. The proposed system raises the methods’ accuracy, sensitivity, and specificity to 100, 99, and 99 percent, respectively. The proposed method is the most effective option and a viable alternative to existing solutions in the market

Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning

An IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on IoT healthcare systems is an innovative approach to enhancing the security and privacy of sensitive medical data. By combining the decentralized and immutable nature of blockchain technology with intrusion detection systems (IDS), it is possible to create a more robust and trustworthy security framework for IoT healthcare systems. With this motivation, this study presents Blockchain Assisted IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables IoT devices in the healthcare sector to transmit medical data securely and detects intrusions in the system. To accomplish this, the BHS-ALOHDL technique performs ALO based feature subset selection (ALO-FSS) system to produce a series of feature vectors. The HDL model integrates convolutional neural network (CNN) features and long short-term memory (LSTM) model for intrusion detection. Lastly, the flower pollination algorithm (FPA) is exploited for the optimal hyperparameter tuning of the HDL approach, which results in an enhanced detection rate. The experimental outcome of the BHS-ALOHDL system was tested on two benchmark datasets and the outcomes indicate the promising performance of the BHS-ALOHDL technique over other models