ELearning as technology becomes more affordable in higher education but having a big barrier in the cost of developing its resources. Deep learning using artificial intelligence continues to become more and more popular and having impacts on many areas of eLearning. It offers online learners of the future with intuitive algorithms and automated delivery of eLearning content through modern LMS platforms. This paper aims to survey various applications of deep learning approaches for developing the resources of the eLearning platform, in which predictions, algorithms, and analytics come together to create more personalized future eLearning experiences. In addition, deep learning models for developing the contents of the eLearning platform, deep learning framework that enable deep learn-ing systems into eLearning and its development, benefits & future trends of deep learning in eLearning, the relevant deep learning-based artificial intelligence tools and a platform enabling the developer and learners to quickly reuse resources are clearly summarized. Thus, deep learning has evolved into developing ways to re-purpose existing resources can mitigate the expense of content development of future eLearning.

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease’s impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) website and consisted of 27,558 photos, including 13,780 parasitized cell images and 13,778 uninfected cell images. In conclusion, the MobileNetV2 model outperformed by achieving an accuracy rate of 97.06% for better disease detection. Also, other metrics like training and testing loss, precision, recall, fi-score, and ROC curve were calculated to validate the considered models.

Cancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection to receive immediate and successful treatment. Lesion detection and classification are more challenging due to many forms of artifacts such as hairs, noise, and irregularity of lesion shape, color, irrelevant features, and textures. In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection. The proposed architecture consists of four core steps: image preprocessing, feature extraction and fusion, feature selection, and classification. A novel contrast enhancement technique is proposed based on the image luminance information. After that, two pre-trained deep models, DarkNet-53 and DensNet-201, are modified in terms of a residual block at the end and trained through transfer learning. In the learning process, the Genetic algorithm is applied to select hyperparameters. The resultant features are fused using a two-step approach named serial-harmonic mean. This step increases the accuracy of the correct classification, but some irrelevant information is also observed. Therefore, an algorithm is developed to select the best features called marine predator optimization (MPA) controlled Reyni Entropy. The selected features are finally classified using machine learning classifiers for the final classification. Two datasets, ISIC2018 and ISIC2019, have been selected for the experimental process. On these datasets, the obtained maximum accuracy of 85.4% and 98.80%, respectively. To prove the effectiveness of the proposed methods, a detailed comparison is conducted with several recent techniques and shows the proposed framework outperforms.

Lane departure warning systems have gained considerable research interest in the past decade for its promising usage in automotive, where lane detection and tracking is applied. However, it is a challenging task to improve the robustness of lane detection due to environmental factors, such as perspective effect, possible low visibility of lanes, and partial occlusions. To deal with these issues, we propose a reliable vision-based real-time lane markings detection and tracking system that can adapt to various environmental conditions. The lane detection is composed of three stages: pre-processing, Adaptive Region of Interest (AROI) setting, and lane marking detection and tracking. In the pre-processing stage, smoothing and edge detection operators are applied on input frames to automatically obtain binary images, then, lane markings segmentation are carried out. After that, An Adaptive Region of Interest is extracted to reduce the computational complexity. In the subsequent detection stages, Kalman filter is employed to track road boundaries detected in the AROI using Progressive Probabilistic Hough Transform (PPHT) in the next frame. Based on road boundaries and the vehicle’s position, the proposed algorithm decides if the vehicle has drifted off the lane. For the performance evaluation of lane detection and tracking, real-life datasets for both urban roads and highways in various lighting conditions are used. Applying our method to Catltech dataset, the average correct detection rate is 93.82%. In addition, the proposed method outperforms that of the state-of-the-art methods in processing time (21.54ms/frame).

Nowadays, VANET (vehicular ad hoc network) is one of the key aspects of developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, the network exposes to link failure that affects the firmness of the network and causes delay and congestion. Additionally, the dynamic change in the network routing affects the network’s security, makes it vulnerable to various attacks, and causes data loss. In order to overcome these drawbacks an efficient and highly secured routing protocol is needed. Subsequently, in this research, a new routing protocol is proposed that has the combination of quality of service (QoS)-aware cluster head (CH) selection and hybrid cryptography, which is named QoS+. The QoS+ protocol is mainly divided into QoS-based CH selection and hybrid cryptography modules. The CH selection module performs based on QoS parameters attempt to provide reliable and stable clusters and improve the firmness and connectivity during the communication process of the network. The hybrid cryptography module contains advanced encryption standard (AES) and elliptic curve cryptosystems (ECC) algorithms. It attempts to improve the security and privacy of the network. The QoS+ protocol is evaluated by a developed VANET simulator using NS2 software. The simulator consists of a network model, a load model, and an attack model. Various speed and transmission ranges and gray hole and wormhole attacks are used in the simulator. The outcome calculated from the performance analysis shows that the proposed QoS+ protocol has 7% to 24% higher message success rate, 500 to 800 higher packets normalized routing load, 350 to 550 Kbps higher throughput, 5% to 17% higher efficiency and 50ms to 12ms lower end to end delay when compared with the earlier works of ECHS and KMSUNET. The proposed QoS+ protocol also achieves superior performance in terms of CH efficiency, cluster member efficiency, and average cluster number with various speeds and transmission ranges.

Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top–bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.