69 research outputs found
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
Deep Reinforcement Learning for Anomaly Detection: A Systematic Review
Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep Reinforcement Learning (DRL) based techniques outperform the existing supervised or unsupervised and other alternative techniques for anomaly detection. This study presents a Systematic Literature Review (SLR), which analyzes DRL models that detect anomalies in their application. This SLR aims to analyze the DRL frameworks for anomaly detection applications, proposed DRL methods, and their performance comparisons against alternative methods. In this review, we have identified 32 research articles published from 2017–2022 that discuss DRL techniques for various anomaly detection applications. After analyzing the selected research articles, this paper presents 13 different applications of anomaly detection found in the selected research articles. We identified 50 different datasets applied in experiments on anomaly detection and demonstrated 17 distinct DRL models used in the selected papers to detect anomalies. Finally, we analyzed the performance of these DRL models and reviewed them. Additionally, we observed that detecting anomalies using DRL frameworks is a promising area of research and showed that DRL had shown better performance for anomaly detection where other models lack. Therefore, we provide researchers with recommendations and guidelines based on this review
Quality of service-based service level agreement management framework for hydrocarbon exploration and discovery operation
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 /
A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
DESIGNING A VEHICLE ALARM NOTIFICATION SYSTEM USING GSM NETWORK
This paper focuses on studying and designing of vehicle alarm by using GSM network as well as to provide the user with information via SMS on whether an intrusion or some other possible factors have triggered the alarm. Nowadays, all vehicles are equipped with an alarm system that will alert the vehicle owner if there is any intrusion detected. However, when the vehicle alarm is triggered, the vehicle owner is unaware of the siren. They might be somewhere far away from their vehicle parking and sometimes there is no intrusion instead there is a false alarm. Alarm trigger for a long time will cause the vehicle’s battery to decrease and will annoy the passers-by. This paper is subdivided into two phases. First, the study on how to integrate GSM devices to send SMS to the vehicle owner's mobile phone. The second phase of this paper is to design and develop a mechanism that can alert the vehicle owner from a remote location by using GSM. As a proof of concept, the proposed mechanism is then validated through a lab scale experiment. This paper is expected to enhance the existing alarm system to benefit all users in order to protect their vehicle.</jats:p
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