Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization. This survey aims to examine the different works of literature on RSs that corroborate e-learning and classify and provide statistics of the reviewed articles based on their recommendation goals, recommendation techniques used, the target user, and the application platforms. The survey makes a prominent contribution to the e-learning RSs field by providing an overview of current research and traditional and nontraditional recommendation techniques to provide different recommendations for future e-learning. One of the most significant findings to emerge from this survey is that a substantial number of works followed either deep learning or context-aware recommendation techniques, which are considered more efficient than any traditional methods. Finally, we provided comprehensive observations from the quantitative assessment of publications, which can guide and support researchers in understanding the current development for potential future trends and the direction of deep learning-based RSs in e-learning.

The ELVD (Ensemble-based Lenet VGGNet and DropoutNet) model is used in this paper to examine hypothetical principles and theoretical identification of a real-time image classification and object, tracking, and recognition device running on board a vehicle. Initially, we obtained the dataset from Kaggle. After loading the images, they were converted into 4D tensors and then into a grid. The model has to set the training to 70% training and 30% testing. The ELVD model uses 39,209 32 × 32-pixel color images for preparation, and 12,630 images specifically for research, in the GTSD (German Traffic Sign Detection) dataset. Each picture is a photograph of a traffic sign that corresponds to one of the 43 classes. The picture is a 32 × 32 × 3 sequence of pixel quality values in the RGB color region, defined as pixel values. The image’s class is hidden as a numerical value from 0 to 42. The image collection is somewhat unbalanced, and a few classes are represented significantly better than in the alternative model. The contrast and brightness of the images also differ significantly. The proposed model was created with CNN with Keras and applied with ensemble-based combined LeNet, VGGNet, and DropoutNet pooling layer for tuning the information. The proposed model compares the predicted class with the correct class for all input images and time calculation for predicting different road sign detection images. Underfitting is shown by a standard of low accuracy on the training and testing sets. For a small dataset, the trained model achieved a 98% accuracy level which implied that overfitting the model with the best results on classification accuracy, tested with 15 epochs, resulted in a loss of information of 0.059% and test accuracy of 98%, respectively. Next, the ELVD proposed models trained and validated with different class presents, dataset 2 achieved 93% training accuracy and testing accuracy predicted with 91%. Finally, the ELVD proposed model predicted the test results of unseen class information measured with the 60/km ph, which predicted 99% accuracy. The proposed model predicted noisy as well as unseen multiclass information with fast-moving vehicles. The usage of convolutional layer filter with ensemble-based VGGNet, DropouNet, and LeNet trained and predicted with a high classification accuracy of more than 99% combined ELVD model with fastest time calculation also the high accuracy prediction of selected image dataset labels that enables these models to be used in real-time applications. The ELVD model was also compared with other traditional models of VGGNet, LeNet, and DropoutNet; its detection time outperformed the other models, and it achieved a 98% detection label set of information. In the ELVD model, closure to various human abilities on a related responsibility differs from 97.3% to 99.5%; consequently, the ELVD model performs better than an average human.

Every biological system on the planet is severely impacted by environmental change, and its primary driver is deforestation. Meanwhile, quantitative analysis of changes in Land Use and Land Cover (LULC) is one of the prominent ways to manage and understand land transformation; thus, it is essential to inspect the performance of various techniques for LULC mapping to recognize the better classifier to more applications of earth observation. This article develops a Tunicate Swarm Algorithm with Deep Learning Enabled Land Use and Land Cover Change Detection (TSADL-LULCCD) technique in Nallamalla Forest, India. The presented TSADL-LULCCD technique mainly focuses on the identification and classification of land use in the Nallamalla forest using LANDSAT images. To accomplish this, the presented TSADL-LULCCD technique employs a dense EfficientNet model for feature extraction. In addition, the Adam optimizer is applied for the optimal hyper parameter tuning of the dense EfficientNet approach. For land cover classification, the TSADL-LULCCD technique exploits the Deep Belief Network (DBN) approach. To tune the hyper parameters related to the DBN system, the TSA is used. The experimental validation of the TSADL-LULCCD algorithm is tested on LANDSAT-7-based Nallamalla region images. The experimental results stated that the TSADL-LULCCD technique exhibits better performance over other existing models in terms of different evaluation measures.

Software Documentation Management Issues and Practices: A Survey

The Internet of Medical Things (IoMT) is a version of the Internet of Things. It is getting the attention of researchers because it can be used in a wide range of smart healthcare systems. One of the main advancements employed recently is the IoMT-cloud, which allows users to access cloud services remotely over the internet. These cloud services require an efficient task scheduling approach that satisfies the Quality of Service parameters with a low energy consumption. This paper presents an overview of the integration of IoMT and cloud computing technologies. Besides,this work proposes an efficient Extended Water Wave Optimization (EWWO) task scheduling in the IoMT Cloud for healthcare applications. EWWO algorithm performs based on its operations propagation, refraction and breaking. The proposed EWWO scheduling technique minimizes the energy consumption, makespan time, execution time and increases the resource utilization. Cloudsim simulator is used to simulate the IoMT-Cloud environment to verify the effectiveness of EWWO technique. The performance has been evaluated based on various parameters such as energy consumption, makespan time and execution time.

Human emotions are not necessarily tends to produce right facial expressions as there is no well defined connection between them. Although, human emotions are spontaneous, their facial expressions depend a lot on their mental and psychological capacity to either hide it or show it explicitly. Over a decade, Machine Learning and Neural Networks methodologies are most widely used by the researchers to tackle these challenges, and to deliver an improved performance with accuracy. This paper focuses on analyzing the driver’s facial expressions to determine their mood or emotional state while driving to ensure their safety. we propose a hybrid CNN-LSTM model in which RESNET152 CNN is used along with Multi-Library Support Vector Machine for classification purposes. For the betterment of feature extraction, this study has considered Chebyshev moment which plays an important role as it has a repetition process to gain primary features and K-fold validation helps to evaluate the models performance in terms of both training, validation loss, training, and validation accuracy. This study performance was evaluated and compared with existing hybrid approaches like CNN-SVM and ANN-LSTM where the proposed model delivered better results than other models considered.