Adopting DevOps practices: an enhanced unified theory of acceptance and use of technology framework

DevOps software development approach is widely used in the software engineering discipline. DevOps eliminates the development and operations department barriers. The paper aims to develop a conceptual model for adopting DevOps practices in software development organizations by extending the unified theory of acceptance and use of technology (UTAUT). The research also aims to determine the influencing factors of DevOps practices’ acceptance and adoption in software organizations, determine gaps in the software development literature, and introduce a clear picture of current technology acceptance and adoption research in the software industry. A comprehensive literature review clarifies how users accept and adopt new technologies and what leads to adopting DevOps practices in the software industry as the starting point for developing a conceptual framework for adopting DevOps in software organizations. The literature results have formulated the conceptual framework for adopting DevOps practices. The resulting model is expected to improve understanding of software organizations’ acceptance and adoption of DevOps practices. The research hypotheses must be tested to validate the model. Future work will include surveys and expert interviews for model enhancement and validation. This research fulfills the necessity to study how software organizations accept and adopt DevOps practices by enhancing UTAUT

A systematic literature review for APT detection and Effective Cyber Situational Awareness (ECSA) conceptual model

Advancements in computing technology and the growing number of devices (e.g., computers, mobile) connected to networks have contributed to an increase in the amount of data transmitted between devices. These data are exposed to various types of cyberattacks, one of which is advanced persistent threats (APTs). APTs are stealthy and focus on sophisticated, specific targets. One reason for the detection failure of APTs is the nature of the attack pattern, which changes rapidly based on advancements in hacking. The need for future researchers to understand the gap in the literature regarding APT detection and to explore improved detection techniques has become crucial. Thus, this systematic literature review (SLR) examines the different approaches used to detect APT attacks directed at the network system in terms of approach and assessment metrics. The SLR includes papers on computer, mobile, and internet of things (IoT) technologies. We performed an SLR by searching six leading scientific databases to identify 75 studies that were published from 2012 to 2022. The findings from the SLR are discussed in terms of the literature's research gaps, and the study provides essential recommendations for designing a model for early APT detection. We propose a conceptual model known as the Effective Cyber Situational Awareness Model to Detect and Predict Mobile APTs (ECSA-tDP-MAPT), designed to effectively detect and predict APT attacks on mobile network traffic

Smart Parking System (SPS) Architecture Using Ultrasonic Detector

With the increase in vehicle production and world population, more and more parking spaces and facilities are required. In this paper a new parking system called Smart Parking System (SPS) is proposed to assist drivers to find vacant spaces in a car park in a shorter time. The new system uses ultrasonic (ultrasound) sensors to detect either car park occupancy or improper parking actions. Different detection technologies are reviewed and compared to determine the best technology for developing SPS. Features of SPS include vacant parking space detection, detection of improper parking, display of available parking spaces, and directional indicators toward vacant parking spaces, payment facilities and different types of parking spaces (vacant, occupied, reserved and handicapped) through the use of specific LEDs. This paper also describes the use of an SPS system from the entrance into a parking lot until the finding of a vacant parking space. The system is designed for a four-level parking lot with 100 parking spaces and five aisles on each floor. The system architecture defines the essential design features such as location of sensors, required number of sensors and LEDs for each level, and indoor and outdoor display boards

Enhancing manufacturing process by predicting component failures using machine learning

Manufacturers customize computer components upon receipt of sale orders. They perform burn-in tests on each unit of product before shipment to ensure a high standard of quality. Burn-in is normally associated with high production costs and slows down manufacturing operations. This study aims to enhance the manufacturing process by predicting test failure patterns using machine learning methods. By identifying the components that are likely to cause failures, manufacturers can accelerate the rectification process and improve delivery time which in turn leads to better customer service. This study hypothesized that the component of concern produces a higher test failure rate. To provide insight into the data and test the hypothesis, descriptive and predictive analytics are used at various stages. Predictive analytics was performed using machine learning via Naïve Bayes since it outperformed SVM and Random Forest classifier. For the descriptive analysis stage, a visual representation revealed many components (81) to be associated with a more than average test failure rate. Fisher’s exact test confirmed that 12 of them are statistically significant and worth studying their behaviour further. Moreover, an association rule mining exercise identified several combinations of modules that have a higher inclination with the test failure. For the predictive analytics stage, the Naïve Bayes classifier predicted test failure with 79% accuracy and 53% recall rate. Another Naïve Bayes classifier predicted error messages associated with a test failure with 68% recall rate over manually labelled error messages. However, a neural network-based automatic text classifier was developed and tested that yielded 66% accuracy. This analysis provides the foundation for a recommendation made that can reduce the burn test failure rate by 25% which is expected to increase further with the improved performance model upon training with a larger data set

Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels

The primary objective of this research is to develop and implement an artificial intelligence (AI) approach for the detection and classification of mental breakdowns in literary texts. The study employs text analytics techniques, utilizing natural language processing (NLP) to extract and analyze data from six novels written by Afghan and Pakistani diasporic writers. The aim is to identify and classify the topics and sentiments related to depression in the selected narratives. To achieve these objectives, four algorithms for topic modelling are utilized, namely Latent Dirichlet Allocation (LDA), Latent Semantic Index (LSI), Hierarchical Dirichlet Process (HDP), and Non-negative Matrix Factorization (NMF). Additionally, a rule-based technique is applied for sentiment analysis using two Python libraries, VADER and TextBlob. For the classification of depression, four machine learning models are employed: Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The results indicate that HDP has the highest score in topic modelling with a score of 0.79. Furthermore, Vader provides more insightful sentiment analysis results. With a classification model accuracy of 68%, Naïve Bayes outperforms the other machine learning models. The findings suggest that the proposed model can efficiently predict all classes of depression, particularly when the dataset is balanced

IoT Adoption and Application for Smart Healthcare: A Systematic Review

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study