The Importance of Employability Skills for Hospitality Graduates toward Employment Status

Every individual strives to achieve their dream profession to lead a comfortable life. Currently, individuals are engaged in intense competition to secure employment, particularly those that offer superior perks and remuneration. In today's competitive job market, hospitality graduates need more than just technical knowledge to secure employment. The purpose of this research is to examine the elements that influence the identification of employable abilities as perceived by hospitality graduates. The research study investigates the importance of employability skills among hospitality graduates and their impact on employment status. The primary research problem addresses how soft and hard skills contribute to graduates' job market readiness. The research is quantitative, gathering data from participants through questionnaires. The total number of hospitality graduates involved in answering the questionnaire is 155 respondents. The data demonstrates that employability skills, both soft and hard, are crucial for securing job offers. Major findings indicate that graduates possessing a balanced combination of soft and hard skills have higher employment rates and better job performance. The outcome of this study is to show that employability skills, like hard and soft skills, are important for future careers after graduating from university.  This study can raise awareness among current students, particularly those in the hospitality field, about the importance of enhancing their skills beyond their university education

Evaluation of Oil Palm Germplasm from Senegal and Gambia Using Chemometric Techniques

Estimation of genetic diversity and determination of the relationships between collections are useful strategies for ensuring efficient germplasm collection and utilization. Oil palm germplasm materials collected from Senegal and Gambia maintained at the Malaysian Palm Oil Board (MPOB) Kluang Station were characterized for genetic diversity. A total of 44 agronomic traits of these oil palm materials was subjected to simple statistics to evaluate the genetic variability; a</jats:p

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapper-approach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques