The deep learning models are being used in the agricultural sector, ushering in a new age of decision support for crop pest and disease control. In light of the difficulties faced by farmers, this literature study seeks to identify the critical components of deep learning models that affect decision support. All the way from model design to data input to training approaches and their effects on efficient decision-making. Examining the deep learning model factors influencing decision support in crop diseases and pest management was the primary goal. The researcher looked at articles and journals published by IEEE access, ACM, Springer, Google Scholar, Wiley online library, Taylor and Francis, and Springer from 2014 to 2024. From the search results, sixty-three publications were selected according to their titles. The paper provides a synopsis of deep learning models used for crop health management based on a careful evaluation of scholarly literature. In order to shed light on the merits and shortcomings of different models, the article conducts a thorough literature review and literature synthesis. Future studies might be guided by the identification of methodological issues and gaps in present research. Applying deep learning to the problem of agricultural diseases and pest control has real-world consequences, as shown by several case studies and applications. Insightful for academics, practitioners, and legislators in the field of precision agriculture, this extensive study adds to our knowledge of the complex relationship between elements in deep learning models and their impact on decision support.

Dimensionality reduction is an essential ingredient of machine learning modelling that seeks to improve the performance of such models by extracting better quality features from data while removing irrelevant and redundant ones. The technique aids reduce computational load, avoiding data over-fitting, and increasing model interpretability. Recent studies have revealed that dimensionality reduction can benefit from labeled information, through joint approximation of predictors and target variables from a low-rank representation. A multiplicity of linear and non-linear dimensionality reduction techniques are proposed in the literature contingent on the nature of the domain of interest. This paper presents an evaluation of the performance of a hybrid deep learning model using feature extraction techniques while being applied to a benchmark network intrusion detection dataset. We compare the performance of linear and non-linear feature extraction methods namely, the Principal Component Analysis and Isometric Feature Mapping respectively. The Principal Component Analysis is a non-parametric classical method normally used to extract a smaller representative dataset from high-dimensional data and classifies data that is linear in nature while preserving spatial characteristics. In contrast, Isometric Feature Mapping is a representative method in manifold learning that maps high-dimensional information into a lower feature space while endeavouring to maintain the neighborhood for each data point as well as the geodesic distances present among all pairs of data points. These two approaches were applied to the CICIDS 2017 network intrusion detection benchmark dataset to extract features. The extracted features were then utilized in the training of a hybrid deep learning-based intrusion detection model based on convolutional and a bi-direction long short term memory architecture and the model performance results were compared. The empirical results demonstrated the dominance of the Principal Component Analysis as compared to Isometric Feature Mapping in improving the performance of the hybrid deep learning model in classifying network intrusions. The suggested model attained 96.97% and 96.81% in overall accuracy and F1-score, respectively, when the PCA method was used for dimensionality reduction. The hybrid model further achieved a detection rate of 97.91% whereas the false alarm rate was reduced to 0.012 with the discriminative features reduced to 48. Thus the model based on the principal component analysis extracted salient features that improved detection rate and reduced the false alarm rate.

Regression testing is carried out to ensure that software modifications do not introduce new potential bugs to the existing software. Existing test cases are applied in the testing, such test cases can run into thousands, and there is not much time to execute all of them. Test Case Prioritization (TCP) is a technique to order test cases so that the test cases potentially revealing more faults are performed first. With TCP being deemed an optimization problem, several metaheuristic nature-inspired algorithms such as Bat, Genetic, Ant colony, and Firefly algorithms have been proposed for TCP. These algorithms have been compared theoretically or based on a single metric. This study employed an experimental design to offer an in-depth comparison of bat and genetic algorithms for TCP. Unprioritized test cases and a brute-force approach were used for comparison. Average Percentage Fault Detection (APFD)- a popular metric, execution time and memory usage were used to evaluate the algorithms’ performance. The study underscored the importance of test case prioritization and established the superiority of the Genetic algorithm over the bat algorithm for TCP in APFD. No stark differences were recorded regarding memory usage and execution time for the two algorithms. Both algorithms seemed to scale well with the growth of test cases.