Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.

Climate change and variability, highlighted by increasing temperatures, altered precipitation, and extreme weather, have significant effects on agriculture. A suitable crop assessment at an enhanced level needs observing extensive areas by a powerful method. Remote sensing (RS) skill provides this over non-destructive synoptic screening abilities. The technology of RS has revolutionized the farming sector by providing vital insights into resource allocation, environmental sustainability, and crop management. RS provides a cost-effective and non-invasive means of observing crops throughout their lifecycle. Early recognition allows for targeted interventions, decreasing crop damage and diminishing the requirement for chemical inputs. It also helps in monitoring an environment. In the era of digital data propagation, agriculture is poised for a transformative innovation driven by deep learning, utilizing RS imagery. In this manuscript, a Robust Crop Health Assessment Using Improved Fusion Neural Network with Remote Sensing Imagery (RCHA-IFNNRSI) approach is proposed. The aim is to develop an integrated crop health assessment framework using RS and leaf image analysis for enhancing precision and decision-making in smart agriculture. In the initial image pre-processing stage, the bilateral filter (BF)-based noise elimination and contrast enhancement is performed to enhance the raw images by removing noise. For feature extraction, the RCHA-IFNNRSI approach integrates Swin Transformer (ST) and ResNet50 models to create a fused backbone that effectively captures both local and global features for crop health assessment. Finally, the attentive convolutional recurrent neural network (ACRNN) technique is employed to detect and classify crops into diverse health status classes automatically. Extensive experimentation of the RCHA-IFNNRSI technique on the MH-SoyaHealthVision dataset illustrated superior performance, achieving a high accuracy of 98.97% compared to existing models.

Multimedia Super-Resolution (SR) reconstruction is an essential and mandatory process for different visualization functions. Recently, several procedures have been suggested for single and multi-images SR processes. This work presents an effective SR scheme for visual quality and resolution enhancement of 3D Video (3DV) sequences. The idea behind the proposed 3DV SR reconstruction scheme is the utilization of a recursive Bayesian process for improving the reconstruction resolution of the degraded 3DV sequences with down-sampling, blurring, and noise effects. In addition, a significant stage of histogram matching based on a visual image with a better-distributed histogram is employed. The main aim of employing the histogram matching stage for the improved 3DV stream is to introduce a dynamic range modification of each 3DV frame. It further generates 3DV sequences with an enhanced distribution of intensities. This improves the whole performance efficiency of the suggested scheme. The performance of the proposed SR scheme is contrasted with the conventional Bicubic approach. The comparative analyses with recent and related SR techniques are also performed. Simulation results revealed that the proposed scheme achieves superior outcomes of Structural Similarity (SSIM) index, local contrast, average gradient, Mean Square Error (MSE), edge intensity, average gradient, entropy, and Peak Signal-to-Noise Ratio (PSNR) metrics for the resulted 3DV frames.

Artificial intelligence (AI) is a fundamental part of improving information technology systems. Essential AI techniques have revolutionized communication technology, such as mobility models and machine learning classification. Mobility models use a virtual testing methodology to evaluate new or updated products at a reasonable cost. Classifiers can be used with these models to achieve acceptable predictive accuracy. In this study, we analyzed the behavior of machine learning classification algorithms—more specifically decision tree (DT), logistic regression (LR), k-nearest neighbors (K-NN), latent Dirichlet allocation (LDA), Gaussian naive Bayes (GNB), and support vector machine (SVM)—when using different mobility models, such as random walk, random direction, Gauss–Markov, and recurrent self-similar Gauss–Markov (RSSGM). Subsequently, classifiers were applied in order to detect the most efficient mobility model over wireless nodes. Random mobility models (i.e., random direction and random walk) provided fluctuating accuracy values when machine learning classifiers were applied—resulting values ranged from 39% to 81%. The Gauss–Markov and RSSGM models achieved good prediction accuracy in scenarios using a different number of access points in a defined area. Gauss–Markov reached 89% with the LDA classifier, whereas RSSGM showed the greatest accuracy with all classifiers and through various samples (i.e., 2000, 5000, and 10,000 steps during the whole experiment). Finally, the decision tree classifier obtained better overall results, achieving 98% predictive accuracy for 5000 steps.