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A Study on the Impact of Financial Incentives and Work Environment on Employee Motivation to Work: A Case Study of Employees of Paradise Estate Management Company: DOI: https://doi.org/10.33093/ijomfa.2024.5.2.1
No full textThe study aims to investigate the impact of financial incentives and workplace environment on employee work motivation. Individually, the study examines the impact of work allowances, employee wages, organisational culture, and internal communication on employee motivation to work in an organisation. The research employed a survey research design. The population of the study focused on the employees of Paradise Estate Management Company. A total of 79 respondents were selected from a pool of 120 employees in the organisation. The questionnaires were administered and distributed to respondents through Google Form. Multiple regression analysis was adopted to test the different hypotheses. The findings indicate that employee motivation is influenced by various factors within the work environment. These include internal communication and organisational culture, as well as financial incentives such as employee wages and work allowances
Binary Particle Swarm Optimization for Fair User Association in Network Slicing-Enabled Heterogeneous O-RANs
No full textThe Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for achieving fair resource allocation in such network scenario. Hence, this study formulates the fairness-aware UA problem as a utility-based combinatorial optimization problem, which is computationally hard to solve. Hence, an efficient Binary Particle Swarm Optimization (BPSO)-based UA scheme is proposed to solve the problem. Through simulations of an O-RAN based HetNet with network slicing-enabled, performance of the proposed BPSO-UA scheme is compared against two other baseline UA schemes. Results demonstrate the effectiveness of the proposed BPSO-UA scheme in achieving high fairness through equitable network slicing resource allocation, thereby leading to higher user connectivity rate and comparable average spectral efficiency. This innovative approach sheds light on the potential of metaheuristic algorithms in tackling intricate UA challenges, offering valuable insights for the future design and optimization of mobile networks.
Manuscript Received: 18 January 2024, Accepted: 21 February 2024, Published: 15 September 2024, ORCiD: 0000-0002-6285-948
Malaysian Banknote Reader Featuring Counterfeit Detection Using Fuzzy Logic Weighted Specific (FLWS) Algorithm
No full textTo identify fake Malaysian banknotes, this research suggested a revolutionary fuzzy logic weighted specific (FLWS) approach in image processing techniques. The FLWS Algorithm has the benefit of a more accurate model because it is a human guidance learning algorithm that demands training to obtain the precise weights for each security feature. The trial outcomes also demonstrated that, for the purpose of detecting counterfeit Malaysian banknotes, the FLWS model outperformed the parallel fuzzy logic weighted averaging (FLWA) algorithm, MobileNet model, and VGG16 model. Its adoption of well-known watermark features, with specific weights assigned, and well-known machine learning techniques to distinguish between genuine Malaysian banknotes and counterfeit Malaysian banknotes gives it a clear advantage over earlier or current banknote counterfeit detection techniques.
Manuscript Received: 25 November 2023, Accepted: 27 December 2023, Published: 15 March 2024, ORCiD:0000-0003-1477-844
Microwave Sensor for Sodium Chloride Density Measurement in Aqueous Solutions
No full textAccurate determination of sodium chloride (NaCl) density in water is vital for assessing environmental impact, preventing soil salinization in agriculture, ensuring quality and consistency in industrial processes, facilitating medical treatments, and maintaining taste and preservation standards in the food and beverage industry. This paper introduces a novel microwave sensor design specifically tailored to accurately assess NaCl density in aqueous solutions. Starting with a standard solution of 10 g of salt dissolved in 100 ml of water, resulting in a molarity of approximately 1.71 M, five distinct samples are meticulously prepared. These samples cover a range of NaCl concentrations, with different ratios of salt solution and drinking water, including pure water, 10 ml of salt solution with 90 ml of water, 20 ml of salt solution with 80 ml of water, 30 ml of salt solution with 70 ml of water, and 40 ml of salt solution with 60 ml of water. Each sample undergoes analysis using the developed microwave sensor to determine its transmission coefficient. The magnitude of the transmission coefficient is closely tied to the density of the salt solution based on molarity. Through a detailed regression analysis, a strong quantitative relationship between the transmission coefficient and salt solution density is revealed. This correlation can be accurately represented by a third-order polynomial equation. This research is significant as it advances microwave sensor technology, allowing for accurate and efficient measurement of NaCl density in water.
Manuscript Received: 7 March 2024, Accepted: 2 April 2024, Published: 15 September 2024, ORCiD: 0000-0001-7043-649
Writing Anxiety: The Case of Law Students
No full textWriting has been acknowledged as a key skill to law students and lawyers. In English as a Second Language (ESL) learning, law students form part of ESL learners. Multiple studies shed light on the unsatisfactory writing performance among ESL learners and link such poor writing performance to an affective construct, writing anxiety. Nevertheless, research on writing anxiety among law students, whose writing ability is requisite, is scarce. With the objectives of determining anxiety level and identifying anxiety types experienced by law students at a Malaysian private university, this study offers insights into their writing experience as ESL learners. A mixed methods approach, consisting of Cheng’s (2004) Second Language Writing Anxiety Inventory (SLWAI) and the semi-structured interview, is applied to achieve the research objectives. The key findings reveal that the highest percentage of the law students studied in this research encounter a high level of writing anxiety. While cognitive anxiety is the most predominant form of writing anxiety, avoidance behaviour is the least obvious form. Based on these findings and the conclusions drawn, this study draws attention to the necessity of addressing writing anxiety among law students in tandem with building strong basic writing skills
Beyond Motherhood: The Stress Landscape for Working Women in Kuala Lumpur
No full textAmong the roles that women are frequently associated with in society are those of mother, wife, and daughter. However, working women have another role outside the home, and juggling these responsibilities is what working women face. Being a mother is not an easy journey, especially for working mothers. Working mothers experience mental strain and high levels of stress at home and work. A mixed-method approached was used to investigate stress among working mothers at ABC Learning Centre, Kuala Lumpur. Two factors were assessed in a study contributing to stress related to one's job: 1) stress at work and 2) stress at home. To represent the population, 34 working mothers as teachers were selected as respondents. According to the study’s findings, there are no appreciable variations in the stress factors among participants based on the stress level at home and at work, there are appreciable variations in the stress factors among participants based on their full-time and part-time job status, even though the mean is slightly higher for stress at work rather than at home. The qualitative findings show that the respondents were very stressed from the workload like handling students, handling administrative tasks, guidance, counselling, supervision and attending parents. To develop successfully and manage the dual role of working women and mothers, researchers advise management to take a proactive approach by offering exposure through a variety of courses and regular, pertinent training, with a focus on time management skills training. The researcher suggested creating avenues for advancement within every education field as a token of appreciation for the contributions made throughout their tenure. This will foster a never-ending sense of hope and an unwavering desire for personal development among the working mothers who are teachers, leading to promotions which will give them a positive view on handling their stress level
Review on Detecting Pneumonia in Deep Learning
No full textDeep learning is a machine learning technique that has been optimized for image classification and object detection. Deep learning has brought huge advancement to the medical field as it helps to diagnose various diseases through computed tomography (CT) scan or X-ray images. Pneumonia is a respiratory disease, and it is one of the killer diseases that causes numerous death all around the world. In 2019, the outbreak of COVID-19 has increased the number of pneumonia patients tremendously. With the increasing number of patients, the clinical and medical facilities have become insufficient. The lack of doctors and radiologists to diagnose pneumonia has caused a high number of patients to be misdiagnosed. Chest image is one of the most effective methods to diagnose this disease, however, examining the X-ray or CT images requires specialists such as radiologists. Meanwhile, examining chest CT or X-ray images might be subjective as the presence of pneumonia can be unclear in the images. The main objective of this paper is to provide a comprehensive review of recent advancement in the diagnosis of pneumonia with deep learning, including state-of-art methodology, datasets, discussion, challenges, and future improvements.
Manuscript received: 22 Dec 2023 | Revised: 23 Jan 2024 | Accepted: 20 Feb 2024 | Published: : 30 Apr 202
A Review on Sensor Technologies and Control Methods for Mobile Robot with Obstacle Detection System
No full textObstacle detection system is a system that reacts to the object in the path and perform action such as stopping robot movement and collision prevention according to the design of algorithm which enhance the safety level of robot. This paper examines the overview of sensor technology that associates with obstacle detection system and car-like robot. This review summarizes the effectiveness and weakness of common type of sensors such as lidar, radar, ultrasonic sensor, infrared sensor, computer vision, sensor fusion and sensor array. This paper will also discuss on control methods for car-like robot that includes hand gestures, voice control, infrared remote control, Android based Bluetooth mobile control, and Wi-Fi based mobile control, outlining the effectiveness and limitation of each control method.
Manuscript received: 24 Dec 2023 | Revised: 28 Jan 2024 | Accepted: 21 Feb 2024 | Published: : 30 Apr 2024
Susceptibility Inference and Response on Transmission Dynamics of Ebola Virus in Fuzzy Environment
No full textThis article uses fuzzy parameters to develop a susceptibility inference and response (SIR) model for the Ebola virus. The construction of the SIR model involves considering several aspects, including immunization, therapy, compliance with medical protocols, and Ebola virus load. The parameters representing the infection, mortality, and recovery rates caused by the Ebola virus are expressed as fuzzy numbers. These parameters are then employed as fuzzy parameters in the model. The study of the model uses the generation matrix approach to get the fundamental reproduction number and assess the stability of the equilibrium point inside the model. The findings from the simulation indicate that the variation in the Ebola virus load is associated with disparities in the transmission patterns of the Ebola virus. Also, we compare the impact of the variables of vaccination and following the medical guidelines in reducing the spread of the Ebola virus. Using Matlab software, the numerical simulation for this model is carried out, and the analysis of Ebola virus transmission is investigated in the fuzzy environment.
Manuscript received: 14 Apr 2024 | Revised: 15 July 2024 |Accepted: 5 Aug 2024 | Published: : 30 Sep 202
Enhancing Conversions and Lead Scoring in Online Professional Education: DOI: https://doi.org/10.33093/ijomfa.2024.5.1.2
No full textThis study seeks to enhance lead conversion for online professional education providers by using supervised machine learning algorithms for lead conversion targeting and lead scoring, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Random Forst, Bagging, Boosting, and Stacking. A lead dataset was used to train and test the machine-learning models. The Recursive Feature Elimination (RFE) is used to establish a precise lead profile. The performance of the trained lead conversion models was evaluated and compared using the 10-Folds cross-validation method based on accuracy, precision, recall, and F1-score. The results show that Stacking is the best model with an accuracy of 0.9233, precision of 0.9391, and F1-score of 0.8939. Meanwhile, the Logistic Regression-based lead scoring model demonstrated promising potential for automating lead scoring. The results of the Logistic Regression-based lead scoring model achieved an accuracy of 0.9019, recall of 0.9019, precision of 0.9015, and F1-score of 0.9014. The optimal lead scoring threshold is 0.20, which stroked the optimal trade-off balance between accuracy, sensitivity, and specificity