Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.

Poultry production is a pivotal contributor to global economic growth, playing a central role in promoting human ecosystem sustainability. It offers affordable and readily accessible protein sources, encompassing meat, eggs, and other by-products. Beyond its direct nutritional benefits, poultry production enhances household income, bolsters food security, and aids in poverty reduction, making it integral to worldwide economic advancement. However, as the global population surges, so does the demand for poultry meat and eggs. Concurrently, poultry disease management emerges as a paramount challenge, leading to significant threats to food security and economic stability. Leveraging cutting-edge technology offers promising avenues to devise strategies that not only bolster farm profitability but also mitigate environmental impacts and foster the well-being of both animals and humans. This study systematically reviews the latest literature concerning poultry disease diagnosis based on deep learning techniques, elucidating the clinical manifestations associated with various ailments. The analysis indicates that emerging technological solutions, especially image processing and deep learning (DL), substantially outperform conventional manual inspection methods in early disease detection and warning in the poultry sector. Such innovations underscore their potential for revolutionizing poultry health management and disease mitigation.

Purpose: This paper aims to identify and review the critical success factors (CSFs) for successful Six Sigma implementation in service and manufacturing companies given in published literature. Design/methodology/approach: A descriptive evaluation of the literature body is followed by future research opportunities. Studies published on the topic of Six Sigma during 2005-2016 are reviewed to retrieve identified CSFs. Findings: From published literature, 48 CSFs vital for Six Sigma implementation were identified. The identified CSFs were classified under eight core categories. As per the categorization, 35 out of 48 CSFs identified are either Company Strategy related, Six Sigma Project related or Human Resources related. Only a limited number of studies are carried out mainly focusing on the stated three core areas. Hence, it is vital that these three core areas are further explored in future research. Originality/value: With the outcome of this paper, the identified CSFs for Six Sigma will be taken for further studies, where they will be applied to service and manufacturing companies based in Sri Lanka. Hence, it would provide Sri Lankan service and manufacturing companies guidelines for successful implementation of Six Sigma for future endeavors.

This paper aims to present a comprehensive review of advanced techniques and models with a specific focus on deep neural network (DNN) for resource-constrained environments (RCE). The paper contributes by highlighting the RCE devices, analyzing challenges, reviewing a broad range of optimization techniques and DNN models, and offering a comparative assessment. The findings provide potential optimization techniques and recommend a baseline model for future development. It encompasses a broad range of DNN optimization techniques, including network pruning, weight quantization, knowledge distillation, depthwise separable convolution, residual connections, factorization, dense connections, and compound scaling. Moreover, the review analyzes the established optimization models which utilizes the above optimization techniques. A comprehensive analysis is conducted for each technique and model, considering its specific attributes, usability, strengths, and limitations in the context of effective deployment in RCEs. The review also presents a comparative assessment of advanced DNN models’ deployment for image classification, employing key evaluation metrics such as accuracy and efficiency factors like memory and inference time. The article concludes with the finding that combining depthwise separable convolution, weight quantization, and pruning represents potential optimization techniques, while also recommending EfficientNetB1 as a baseline model for the future development of optimization models in RCE image classification.

Studies show that there are many benefits of using Web 2.0 and social media in governments. However, the factors influencing government employees‘ adoption of these technologies are not well known. In this paper, the Unified Theory of Acceptance and Use of Technology (UTAUT) was integrated with two constructs, namely, perceived confidentiality risks (PCR) and attitude (AT) in order to investigate the factors influencing adoption of these technologies among government employees. The sample consisted of 600 usable responses collected from Tanzania Government Employees. To analyze the data, Structural Equation Modeling was used. Both PCR and AT were found to be influential.