Employees' intention to leave a job voluntarily can lead to significant risk and costs for any organisation. Staff turnover can affect the performance and sustainability of organisations. Therefore, it is essential for organisations to reduce risk and cost be by retaining employees, especially the talented employees. This explanatory study investigated the impact of working environment, occupational stress, career growth, and labour turnover intention. In this quantitative study, information was gathered from 195 respondents. The target population was bank employees in Klang Valley, Malaysia. The questionnaire was administered using the convenience sampling technique. The results showed that career growth and job stress had a significant direct effect on turnover intention. However, working environment and compensation did not significantly affect turnover intention based on regression analysis. This indicated that it is essential for human resource managers and organisation leaders to focus on career growth and employee wellbeing to reduce their turnover.

Dishonest academic behavior (DAB) by students in Chinese higher education institutions has become a significant concern. However, the related study of academic dishonesty in mainland China is very limited. This study fills this gap by examining the theory of planned behavior and its three extended versions, validating the effectiveness of predicting DAB among Chinese undergraduates, and testing 11 developed hypotheses. This study uses a quantitative research design, and responses are collected online from 525 undergraduate students from five disciplines in the second to fourth year at a public university in China. The results reveal the proposed models have good fitting indices and support 10 hypothetical relationships. These relationships demonstrate that attitudes, norms and control beliefs significantly impact intentions and justifications. Meanwhile, behavioral control, intentions, and justifications significantly influence DAB. Notably, this study found a direct and significant effect of MO on justifications. Therein, Model four best explains the variance in DAB and provides practical support for the expanded TPB models’ application in China.

This study examines the relationship between extrinsic motivation, intrinsic motivation, and work quality and impact towards customer satisfaction. The study aims to determine the relationship between extrinsic motivation and intrinsic motivation towards work quality among police officers. Contributing to the outcome of the study, the police work quality is analyses the relationship on customer (public) satisfaction towards police officers. This study focuses on police officers who servers the Chinese nation. Results showed the relationship between extrinsic motivation, intrinsic motivation, work quality and customer satisfaction meet criteria of Diffusion of Innovation (DOI) Theory. Based on the findings, there is a need to motivate employees to improve their work quality towards job attitude to improve the customer satisfaction. Most importantly this particular research enhancing the “work quality” of China Police department.

The usefulness of Fractal Analysis (FA) is not limited to a particular area. It is applied in variety of fields and has shown its efficiency towards irregular objects. Fractal dimension is the best measure of the roughness for natural elements and hence, it can be treated as a feature of the natural object. Breast masses are irregular and divers from a malignant tumor to benign; hence breast can be treated as one of the best areas where fractal geometry can be applied. It gives a scope where fractal geometry concept can be used as a feature extraction technique in mammogram. On the other hand, the support vector machine is an emerging technique for classification. The survey shows that few works have done on breast mass classification using support vector machine. In our work two most effective techniques are used in separate operations, FA: Box Count Method (BCM) and Support Vector Machine (SVM) that result well in their fields. Feature extraction is done through Box Count Method. The extracted feature, “fractal dimension”, measures the complexity of the input data set of 42 images. For the next segment, the resulting Fractal Dimensions (FD) are processed under the support vector machine classifier to classify benign and malignant cells. The result analysis shows that the combination of SVM and FD yielded the highest with 98.13% accuracy.

With the explosive growth of the Internet and the desire to harness the value of the information it contains, the prediction of possible links (relationships) between key players in social networks based on graph-theory principles has garnered great attention in recent years. Consequently, many fields of scientific research have converged in the development of graph analysis techniques to examine the structure of social networks with a very large number of users. However, the relationship between persons within the social network may not be evident when the data-capture process is incomplete or a relationship may have not yet developed between participants who will establish some form of actual interaction in the future. As such, the link-prediction metrics for certain social networks such as criminal networks, which tend to have highly inaccurate data records, may need to incorporate additional circumstantial factors ( metadata) to improve their predictive accuracy. One of the key difficulties in link-prediction methods is extracting the structural attributes necessary for the classification of links. In this research, we analysed a few key structural attributes of a network-oriented dataset based on proposed social network analysis (SNA) metrics for the development of link-prediction models. By combining structural features and metadata, the objective of this research was to develop a prediction model that leverages the deep reinforcement learning (DRL) classification technique to predict links/edges even on relatively small-scale datasets, which can constrain the ability to train supervised machine-learning models that have adequate predictive accuracy.

This study investigates the effect of employee productivity among employees in Private Higher Educational Institutions, in Malaysia. The independent variables are job motivation, supervisor support, financial rewards and working hours on employee productivity. 198 employees from 10 different private higher educational institutions have responded to the questionnaire of this study. Based on the results, it shows there is a direct and significant relationship between job motivation and working hours on employee productivity and vice versa towards financial rewards and working hours. The study implies that employee productivity increase with high job motivation and flexible working hours. However, supervisor support and financial rewards are secondary compared to job motivation working hours in relation to employee productivity.

Cyber-security, as an emerging field of research, involves the development and management of techniques and technologies for protection of data, information and devices. Protection of network devices from attacks, threats and vulnerabilities both internally and externally had led to the development of ceaseless research into Network Intrusion Detection System (NIDS). Therefore, an empirical study was conducted on the effectiveness of deep learning and ensemble methods in NIDS, thereby contributing to knowledge by developing a NIDS through the implementation of machine and deep-learning algorithms in various forms on recent network datasets that contains more recent attacks types and attackers’ behaviours (UNSW-NB15 dataset). This research involves the implementation of a deep-learning algorithm–Long Short-Term Memory (LSTM)–and two ensemble methods (a homogeneous method–using optimised bagged Random-Forest algorithm, and a heterogeneous method–an Averaged Probability method of Voting ensemble). The heterogeneous ensemble was based on four (4) standard classifiers with different computational characteristics (Naïve Bayes, kNN, RIPPER and Decision Tree). The respective model implementations were applied on the UNSW_NB15 datasets in two forms: as a two-classed attack dataset and as a multi-attack dataset. LSTM achieved a detection accuracy rate of 80% on the two-classed attack dataset and 72% detection accuracy rate on the multi-attack dataset. The homogeneous method had an accuracy rate of 98% and 87.4% on the two-class attack dataset and the multi-attack dataset, respectively. Moreover, the heterogeneous model had 97% and 85.23% detection accuracy rate on the two-class attack dataset and the multi-attack dataset, respectively. Keywords—Cyber-security; intrusion detection system; deep learning; ensemble methods; network attacks

The social network analysis focuses on investigating the connection of social actors and obtaining useful insights. It involves various techniques and methods, including determining the isomorphism among (sub)graphs. In this study, we have presented a critical analysis of seven different algorithms related to graph analytics, namely, Ullman, VF, VF2, MLC, Schmidt and Druffel, Corneil and Gotlieb and canonical graph labelling, by reviewing different types of isomorphic graphs identification algorithms. This research contributes to the domain of knowledge by providing the working mechanisms, features, different application areas and mechanism states of various algorithms used for isomorphic graph.

espanolEl cambio de trabajo se esta convirtiendo en un fenomeno a lo largo de los anos a medida que los profesores se mueven de un trabajo a otro. El objetivo del estudio es analizar los factores que promueven el cambio de trabajo como son la falta de promocion y crecimiento, el salario y los problemas de beneficios. Se distribuyeron un total de 120 cuestionarios a los participantes que trabajan en una universidad privada, Malasia. Los resultados informaron que los problemas salariales y de beneficios, las inseguridades laborales y el desequilibrio entre la vida laboral y personal tienen una correlacion significativa y positiva con la inestabilidad laboral. EnglishJob-hopping is becoming a phenomenon over the years as lecturers keep moving from one job to another for better opportunities and self-development. The objective of this research is to study the factors that influence job-hopping in Private University in Malaysia. The anteceding factors are lack of promotion and growth, salary and benefits issues, job insecurities and work-life imbalance. A total of 120 questionnaires were distributed to participants who work in private university, Malaysia. The results reported that salary and benefits issues, job insecurities and work-life imbalance have a significant and positive correlation with job-hopping.

Intrusion Detection System (IDS) is an important tool use in cyber security to monitor and determine intrusion attacks This study aims to analyse recent researches in IDS using Machine Learning (ML) approach; with specific interest in dataset, ML algorithms and metric. Dataset selection is very important to ensure model build is suitable for IDS use. In addition, dataset structure can affect effectiveness of ML algorithm. Thus, ML algorithm selection is dependent on the structure of the selected dataset. After that, metric will provide a quantitative evaluation of ML algorithms towards specific dataset. This study found that soft computing techniques are getting considerable attention, as many have applied it here. In addition, many researchers are focusing on the classification of IDS, which is beneficial in determining known intrusion attacks. However, it may pose a problem in detecting anomalous intrusion, which may include new or modified intrusion attacks. For dataset, many researchers were still using KDDCup99 and its variant NSL-KDD, although they are almost 20 years old. This continuous trend could result in static progress in IDS, while intrusion attacks continue to evolve together with new technologies and user behaviours. Ultimately, this situation will result in the obsolete use of IDS as part of a cyber security tool. Three most used metrices for performance evaluation for IDS are accuracy, True Positive Rate (TPR) and False Positive Rate (FPR). This is expected, because these metrices provide important indications that are very relevant to IDS functionality.

The productivity levels of lecturers and what causes an increase or decrease in them have been the subjects of debate and concentration among academics, practitioners and researchers for many decades.This makes it more volatile because of the complex nature of the numerous different industries involved.In the education industry itself for example, there have been numerous antecedents which have been found to have an effect on productivity levels.The aim of this research is to identify the factors and assist in closing the gaps of knowledge in increasing lecturer productivity, especially in the private universities.The main purpose of this study was to identify the significant relationship of work-life balance, job responsibility, competency, motivation and professionalism on lecturer productivity in private universities Kazakhstan.Hypotheses created to support these objectives results indicated that all tested independent variables have significant relationship with lecturer productivity except for no significant relationship indicated for competency and professionalism based on correlation analysis.This study considers these antecedents on lecturer productivity in the Private universities being the fastest growing industry in Kazakhstan.This implies that the organizational human resource policies may need a re-look on creating better benefits, lecturer assistance program, job design, training and development and organizational culture.These findings suggest that in this complex and dynamic environment, lecturer productivity is a means of organizational success and sustainability.

Resistance to change is important for the current working environment because universities are faced with constant amounts of changes in the environment, which would force the lecturers in the university to change as well. Therefore, the resistance to change needs to be focused on improving the lecturers' well-being in order to boost the productivity and performance in the workplace. This research focuses on the impact of resistance to change among lecturers in SEGi University. A total of 114 respondents consist of different levels from the lower management, middle management to senior management involved in this study conveniently. Findings revealed that an individual's resistance to change was determined by communication and normative commitment factor. This research would further discuss each of the factors and examine how the factors could impact the resistance to change among lecturers. This study has identified few predictors towards the resistance to change among lecturers in university context.

In this current generation, there is great popularity of student in using internet as well as the web 2.0 for different purposes in their daily life, for instance, the primary uses found are for the email, communication and academic-related work. Nonetheless, according to the past research, there determined there is loose relationship between the technology (such as the Facebook as the Web 2.0 technology)) used by the students for their learning purposes. Also, such studies are usually conducted in the Western countries rather than in the Asian countries such as Malaysia. Thus, the research aim is to determine the usefulness of the social networking sites toward Malaysia tertiary education’s student life of social and learning and to investigate whether Facebook could be a preferred SNS choice by the students in aiding their learning and social life. Self-administered questionnaires would be distributed to 200 students who studying at Malaysia tertiary education institution located across Klang Valley. As a result, this study has determined there is positive relationship between the usefulness of the social networking sites toward Malaysia student life of social and learning.

Software engineering is the process of developing software by utilizing applications of computer engineering. In the present day, predicting the reliability of the software system become a recent issue and an attractive issue for the research area in the field of software engineering. Different techniques have been applied to estimate and predict the reliability of a system. To make new software from the beginning is a difficult task. Component-Based Software Engineering (CBSE) helps in minimizing these efforts in making new software because it utilizes factors like reusability, component dependency, and component interaction that results in decreasing complexity of the system. Soft computing may be applied to estimate reliability. A new model is proposed to estimate the reliability of Component-based Software (CBS) using series and parallel reliability models and later on, the proposed component-based software reliability model is evaluated using two soft computing techniques- Fuzzy Logic and PSO. The experimental results conclude that the proposed reliability model has a lower error rate in predicting CBSE reliability as compared to reliability prediction utilizing fuzzy logic and PSO.

This research focuses on evaluating whether a website is legitimate or phishing. Our research contributes to improving the accuracy of phishing website detection. Hence, a feature selection algorithm is employed and integrated with an ensemble learning methodology, which is based on majority voting, and compared with different classification models including Random forest, Logistic Regression, Prediction model etc. Our research demonstrates that current phishing detection technologies have an accuracy rate between 70% and 92.52%. The experimental results prove that the accuracy rate of our proposed model can yield up to 95%, which is higher than the current technologies for phishing website detection. Moreover, the learning models used during the experiment indicate that our proposed model has a promising accuracy rate.

The popularity of ransomware has created a unique ecosystem of cybercriminals. Therefore, the objectives of this paper are to provide a thorough understanding of ransomware's threat and discuss recent detection techniques used. Successful ransomware attack has direct financial implication, which is fuelled by several mature enablers, such as encryption technology, cyber currency and accessibility. Encryption is effective and almost unbreakable. Anonymous cyber currency can avoid traceability. Easily obtainable ransomware code enables easy entry. A combination of these provides an attractive avenue for cybercriminals, producing specialist cybercriminals. In terms of detection techniques, it was found that machine learning (ML) via regression algorithms was the most technique adopted by researchers of ransomware. However, none of the researchers have produced any model to protect against ransomware attack. This research highlights the need of a solution using ML algorithm for the detection engine.

This study investigates the determinants of online purchasing intention towards household appliances among Malaysians in the Klang Valley. The study was piloted to test the proposed hypotheses and research question regarding how e-commerce should be developed. A total of 150 questionnaire and online survey responses were analysed for participants’ personal views and online purchasing intention towards household appliances. The results show a positive correlation between social interaction, competition, economic rationale and purchasing intention towards household appliances. It was also found that the online shopping experience has a significant influence on purchasing intention. The study adds value to the existing body of knowledge on online purchasing intention factors towards household appliances.

Swiftness, simplicity, and security is crucial for mobile device authentication. Currently, most mobile devices are protected by a six pin numerical passcode authentication layer which is extremely vulnerable to Shoulder-Surfing attacks and Spyware attacks. This paper proposes a multi-elemental graphical password authentication model for mobile devices that are resistant to shoulder surfing attacks and spyware attacks. The proposed Coin Passcode model simplifies the complex user interface issues that previous graphical password models have, which work as a swift passcode security mechanism for mobile devices. The Coin Passcode model also has a high memorability rate compared to the existing numerical and alphanumerical passwords, as psychology studies suggest that human are better at remembering graphics than words. The results shows that the Coin Passcode is able to overcome the current shoulder-surfing and spyware attack vulnerability that existing mobile application numerical passcode authentication layers suffer from.

Machine learning (ML) is used for network intrusion detection because of its prediction ability after training with relevant data. ML provides a good method to detect new and unknown attacks. There are many types of attacks in network intrusion: however, this paper concentrates on distributed denial of service (DDoS). DDoS is similar to denial of service, the different being that the attack comes from multiple sources instead of only one source. In this paper, various classification algorithms are used and their performances are compared. It was found that random forest (RT) gives the best accuracy at 99.97%, while the least accurate algorithm was support vector machine (SVM) at 63.25%.