A set of rules for constructing gender-based personality types’ composition for software programmer

The current era has been declared as technological era where both profit and no-profit organisations rely solely on software to cope with myriad issues they typically face.The growing demand for software has equally placed challenging tasks on workplaces to produce quality and reliable software.Unfortunately, software development industries have drastically failed to produce software in due time or even if software is produced in time but it fails to yield the desired results.Keeping this problem in view, this study tried to address this problem by offering team composition model lucrative for software development. For instance, Personality types, especially Introvert (I) and Extrovert (E) traits, of team members of software development are explored with gender diversity with a key focus on the programmer role.Moreover, descriptive and predictive approaches were applied to gain the hidden facts from data.The data of this study was taken from both academia and industry to establish the generalizability in the findings.Additionally, different personality traits composition was set based on gender which was not studied in previous studies.The findings of this research suggest that male-programmer should be composed of E trait of personality and, whereas female-programmer should be I.The overall findings contribute to serve the cause of software development team and also contribute to the existing literature on software development and its team composition

Balancing the personality of programmer: software development team composition

The production of software and their effectiveness have become the prerequisite for the development of various sectors of the world.Persistent demand for the software, feasible and effective in nature to address the clients’ demand have levitated the interest amongst researchers to determine the factors that idealize the software development team since an adept and compatible team members, in terms of personality, are likely to ensure the success of software.In this regard, personality clashes have been attributed as the prominent factors of all to the failure of the software. Although copious research studies have been carried out in the past to suggest ideal and compatible personalities for making an ideal software development team, it is regret to add that the findings of these studies have rather enhanced the gravity of the problem for giving different suggestions for composing an ideal team for software development.To lessen such confusion, this study aims to propose solution for personality-based team composition by executing the different ranges of the programmer’s role based on Myer Brig Type Indicator (MBTI) pairs. This method supposedly allows the researchers to reach the suitable conclusion by thorough investigation of all traits of personality for programmer role.In order to attain the best solution, student population was involved to develop the software projects in teams.The experiments were divided into two segments: defining balancing benchmark and validating the benchmark. In outcomes, this study proposed different ranges of personality traits based on gender classification for software programmers

A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection) plays a crucial role in identifying critical events. Because of its widespread use in many applications, it remains an important and extensive research brand in data mining. As a result, numerous techniques for finding anomalies have been developed, and more are still being worked on. Researchers can gain vital knowledge by identifying anomalies, which helps them make better meaningful data analyses. However, anomaly detection is even more challenging when the datasets are high-dimensional and multivariate. In the literature, anomaly detection has received much attention but not as much as anomaly detection, specifically in high dimensional and multivariate conditions. This paper systematically reviews the existing related techniques and presents extensive coverage of challenges and perspectives of anomaly detection within highdimensional and multivariate data. At the same time, it provides a clear insight into the techniques developed for anomaly detection problems. This paper aims to help select the best technique that suits its rightful purpose. It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. Finally, it would be a line of future studies to extend by comparing the methods on other domain-specific datasets and offering a comprehensive anomaly interpretation in describing the truth of anomalies

A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection) plays a crucial role in identifying critical events. Because of its widespread use in many applications, it remains an important and extensive research brand in data mining. As a result, numerous techniques for finding anomalies have been developed, and more are still being worked on. Researchers can gain vital knowledge by identifying anomalies, which helps them make better meaningful data analyses. However, anomaly detection is even more challenging when the datasets are high-dimensional and multivariate. In the literature, anomaly detection has received much attention but not as much as anomaly detection, specifically in high dimensional and multivariate conditions. This paper systematically reviews the existing related techniques and presents extensive coverage of challenges and perspectives of anomaly detection within highdimensional and multivariate data. At the same time, it provides a clear insight into the techniques developed for anomaly detection problems. This paper aims to help select the best technique that suits its rightful purpose. It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. Finally, it would be a line of future studies to extend by comparing the methods on other domain-specific datasets and offering a comprehensive anomaly interpretation in describing the truth of anomalies

A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection) plays a crucial role in identifying critical events. Because of its widespread use in many applications, it remains an important and extensive research brand in data mining. As a result, numerous techniques for finding anomalies have been developed, and more are still being worked on. Researchers can gain vital knowledge by identifying anomalies, which helps them make better meaningful data analyses. However, anomaly detection is even more challenging when the datasets are high-dimensional and multivariate. In the literature, anomaly detection has received much attention but not as much as anomaly detection, specifically in high dimensional and multivariate conditions. This paper systematically reviews the existing related techniques and presents extensive coverage of challenges and perspectives of anomaly detection within highdimensional and multivariate data. At the same time, it provides a clear insight into the techniques developed for anomaly detection problems. This paper aims to help select the best technique that suits its rightful purpose. It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. Finally, it would be a line of future studies to extend by comparing the methods on other domain-specific datasets and offering a comprehensive anomaly interpretation in describing the truth of anomalies

Quality of service-based service level agreement management framework for hydrocarbon exploration and discovery operation

&nbsp;The major contributions presented in this thesis are twofold. Firstly, it presents the research towards a unique services-based Hydrocarbon Exploration and Discovery Model that demonstrates the feasibility of using advanced ICT technologies in the reproduction of stages involved in an oil and gas discovery, processing and analysis process. Secondly, the research demonstrated a solution of the problems in providing agreed level of quality of service (QoS) and formalizing of appropriate Service Level Agreements (SLA) within such complex environment where different services within the model can be delivered by a variety of service providers<br /

Clustering Cum Polar Coordinate Feature Transformation (C-PCFT) Approach to Identify Pores in Carbonate Rocks

Most of the world&#x2019;s oil reserves and natural gas are stored within carbonate rock&#x2019;s pores and fractures. Pores and fractures are quite popular for predicting the amount of petroleum under an adequate trap condition. Hence, their petrophysical properties, such as shape and size, are paramount for accurately predicting the reservoir&#x2019;s state and condition. Current modelling techniques are mostly based on manual and expert judgement which are time-consuming and cost-intensive. In this study, we devised a robust and scalable image processing framework that uses the combination of pixel-based clustering approach with a polar coordinate feature transformation technique to intelligently identify the pores of carbonate rock samples. We reported that such a method can be effective in detecting pores of different shapes and sizes in an automated fashion. We rigorously tested the proposed method on the computed tomography-scanned micro-images of a carbonate rock sample, and the results demonstrate improved identification accuracy of the proposed method than the current deep learning counterparts. Another key advantage compared to deep learning methods, the proposed method does not require extensive training on data, which saves time and effort without being computationally too expensive

A Compromise Programming for Multi-Objective Task Assignment Problem

The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The lecturer’s assigning task is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign constrained tasks (the time and required skills) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that requires the trade-off between two or more conflicting objectives to indicate the optimal solution. We use the compromise programming approach to the multi-objective problem to solve this. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, we tested the model and algorithm with real scheduling data, including 139 sections of 17 subjects to 27 lecturers in 10 timeslots. Finally, a web application supports the decision-maker to visualize and manipulate the obtained results

A Compromise Programming for Multi-Objective Task Assignment Problem

The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The lecturer’s assigning task is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign constrained tasks (the time and required skills) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that requires the trade-off between two or more conflicting objectives to indicate the optimal solution. We use the compromise programming approach to the multi-objective problem to solve this. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, we tested the model and algorithm with real scheduling data, including 139 sections of 17 subjects to 27 lecturers in 10 timeslots. Finally, a web application supports the decision-maker to visualize and manipulate the obtained results