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Recent Progress in Floating Solar Photovoltaic Systems: A Review from Malaysia’s Perspective
No full textFloating solar photovoltaic (FPV) technology has been gaining popularity in various parts of the world to address the growing clean energy demand and land scarcity challenges posed by conventional solar PV installations. This paper presents a comprehensive review of the recent progress in FPV research and actual implementations, highlighting advancements, challenges, and future prospects in this dynamic and promising field in Malaysia.
Manuscript Received: 26 July 2023, Accepted: 6 August 2023, Published: 15 March 2024, ORCiD: 0000-0003-2353-010
Investigation of Flexible Job Shop with Machine Availability Constraints
No full textIn real-world scheduling applications, machines may be unavailable during certain time periods for deterministic and stochastic reasons. This situation does not pose any problems if the jobs always have more than one machine available for processing. However, it becomes an issue if the only available machine is the one which more than one job needs for processing. Thus, the investigation of limited machine availability, along with the practical requirement to handle this feature of scheduling problems, are of huge significance. This paper examined a flexible job shop environment of a manufacturing firm to optimize different performance criteria related to makespan, due dates, priorities and penalties by using a metaheuristic approach, taking into account the precedence constraints. The work investigated the case in which all machines are available for processing and another case in which some of the machines are known in advance to be unavailable. From the results, the best schedules are analysed and the perspectives of the findings to decision-makers are discussed with the purpose of achieving high machine utilization, cost reduction and customer satisfaction.
Manuscript Received: 11 August 2023, Accepted: 27 September 2023, Published: 15 March 2024, ORCiD: 0000-0002-0168-096
Experimental Characterization of Process Pressure Variations on The Accuracy and Performance of Liquid Ultrasonic Flow Meters
No full textThis paper investigated the influence of process pressure variations on the accuracy and performance of ultrasonic flow meters. Process measurement technology provides a tool for optimizing production processes and dosing operations. Accurate measurement is key and primary to profitability in the business of supply and purchase of liquids like petroleum, gas and chemical products. Three 6” size ultrasonic flow meters were mounted on a skid and used to carry out the experiment parallel in connections each other to take flows from a common header, measure and discharge their individual flows into a common discharge header. The three meters were designate 1, 2 and 3 respectively. Meters 1 and 2 being service meters while Meter 3 is the calibrated master meter. The experiment was carried ten times to increase reliability of results. Experimental data were collected and analyzed using computational formulae technique. Results showed that; Meter 1 had an optimum process pressure of 12.38 and 9.43 bar with respect to flow rate and meter factor respectively as performance indicator. While Meter 2 had an optimum process pressure of 12.4 and 12.41 bar with respect to flow rate and meter factor respectively as performance indicator. Findings indicated significant relationship between process pressure, flow rate and meter factor using ultrasonic flow meter. The outcome of this study will be a useful guide to users of ultrasonic flow meters to maintain optimum process pressures of each meter during fluid supply.
Manuscript Received: 22 December 2023, Accepted: 21 February 2024, Published: 15 September 2024, ORCiD: 0000-0002-2367-598
Sine Cosine Algorithm for Enhancing Convergence Rates of Artificial Neural Network: A Comparative Study
No full textArtificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back Propagation Neural Networks (SCA_BP), Elman Neural Networks (SCA_ElmanNN), and Elman Neural Networks (ElmanNN). The evaluation employs two key performance metrics: Accuracy (ACC) and Mean Squared Error (MSE). The training dataset, representing 70% of the data, is used for algorithm training, and the testing dataset, constituting the remaining 30 %, assesses the algorithms' ability to generalize to new, unseen data. Results indicate that SCA_ElmanNN in both training and testing datasets, achieving high accuracy and minimal MSE, showcasing its proficiency in classification and prediction precision. SCA_BP and SCA_ANN also demonstrate robust performance. Conversely, ElmanNN, while relatively accurate, exhibits a slightly higher MSE on the testing data, indicating some variability in its predictions. These findings offer valuable insights for researchers in selecting the most appropriate algorithm for specific classification tasks.
Manuscript Received: 26 December 2023, Accepted: 24 January 2024, Published: 15 September 2024, ORCiD: 0000-0003-1718-703
Mind Care Solution Through Human Facial Expression
No full textUsing proposed system psychologists can use technology to make decisions which can provide ease for both patients and psychologists. Psychologists can check the progress of patients by analysing emotions reports of patient over time. Using historical data and emotion detection technology psychologists can make more accurate decisions. Using proposed system patient and psychologists don’t have to go to anywhere they only need a device and internet. Based on the characteristics of patient emotion psychologist only need report generated by system and prescribe medicine in emergency situation. Proposed system improves consultancy method by using machine learning emotion detection algorithm. Proposed system detects facial emotion of patient by using CNN with HAAR cascade classifier. We use FER 2013 dataset to train our model. We use VGG 19 architecture to train our model for optimization function to enhance the accuracy of model. We use RELU. We use DJANGO framework for integration with frontend. Result of our model on dataset 82.3 % after find tuning the accuracy goes to 82.3 % to 92 %. We use recall and F1 method to check the performance of model. We trained model on the testing dataset which have gray scale images and 48*48 pixel images to achieve his performance. To achieve our accuracy goal, we split dataset into trainee validation and testing dataset. We use CNN and achieve 93 % accuracy in our system which help patient to get feedback only selected question and psychologist. Patients select psychologist to answer questions of psychologist system stores emotions of patient against every question to generate emotion report. Psychologist can analyze emotion report to provide better prescription to patient.
Manuscript Received: 17 January 2024, Accepted: 16 February 2024, Published: 15 September 2024, ORCiD: 0009-0006-3101-524
Assessing Malaysia’s Readiness for the Beijing Treaty on Audiovisual Performances through Copyright Act 1987
No full textThe Beijing Treaty on Audiovisual Performances known as the 'Beijing Treaty' represents a crucial international treaty designed to enhance the protection of audiovisual performers and their rights in audiovisual performances. To successfully implement the treaty, addressing issues related to defining the scope of protection, transfer of rights and establishing mechanisms for collective rights management for audiovisual performers is important. Given the dynamic evolution of the global audiovisual entertainment industry, it is paramount for countries like Malaysia to thoroughly assess their readiness to effectively implement the Beijing Treaty in the copyright legal framework. Malaysia's commitment and policy decision to evaluate crucial aspects of the issues will ultimately determine the success of its involvement in the broader global initiative to strengthen copyright protection for audiovisual performers within the copyright law spheres. Hence, this article will comprehensively examine the provisions related to the Beijing Treaty and determine whether Malaysia is prepared for the implications and requirements set forth by the Beijing Treaty to be incorporated in the Copyright Act 1987. This article also aims to guide the policymakers in crafting strategies for effective integration of the treaty into the national legal framework particularly in the Copyright Act 1987
Onomastics of Age-Grade Names in the Igbo Community
No full textOnomastics of Igbo age-grade names: a fundamental sub-category of African names is the act of naming a group of people that are born in a particular period of time. It is a cultural practice that has survived civilisation and urbanisation, as the Igbo worldwide now join age-grade activities of their hometown via various social media platforms. No previous study has investigated how age-grade names are acquired and classified. Therefore, this study aims to provide a better understanding of how names given to age grades in Igbo communities are created and classified. Qualitative data elicited through one-on-one interviews with 18 members of different age groups in Anam, an Igbo community, were subjected to descriptive and thematic analysis. Findings reveal that age-grade names are acquired during the teenage period mainly by boys (for both males and females) and maintained till death. The different categories of names acquired by members of age grades include testimonial, ideational, monumental, war historical, descendant, praise, and lexical matching names. The age-grade names, like Igbo personal names, reflect the distinctive experience of the bearers. They also serve as a collective identity for all members during social interactions and community engagements
Streamlining Dental Clinic Management for Effective Digitisation Productivity and Usability
No full textOral health is an integral part of overall health, and poor oral hygiene can lead to a variety of health problems. Modern oral care has greatly improved our quality of life, but the increasing demand for routine dental checkups and treatments calls for improved systems for managing patient records and appointments. While technology has significantly enhanced the efficacy and experience of dental care, many dental clinics still rely on paper records to record the patient’s oral condition, but these are not easily accessible to the patients for viewing. This study aims to address the issue by developing a Dental Clinic Management System to manage patient appointments and records. This system will allow patients to manage their appointments, view their dental history, and receive comments from dentists. Dentists will be able to view appointments, perform treatments, and provide feedback to patients, while the administrator or receptionist will be able to manage appointments, view records, and create invoices. By streamlining dental clinic management, this system aims to improve the overall quality of oral healthcare
Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study
No full textThe presence issue of inaccurate plant disease detection persists under real field conditions and most deep learning (DL) techniques still struggle to achieve real-time performance. Hence, challenges in choosing a suitable deep-learning technique to tackle the problem should be addressed. Plant diseases have a detrimental effect on agricultural yield, hence early detection is crucial to prevent food insecurity. To identify and categorise the indications of plant diseases, numerous developed or modified DL architectures are utilised. This paper aims to observe the performance of the YOLOv8 model, which has better performance than its predecessors, on a small-scale plant disease dataset. This paper also aims to improve the accuracy and efficiency of plant disease detection and classification methods by proposing an optimised and lightweight YOLOv8 architecture model. It trains the YOLOv8 model on a public dataset and optimises the YOLOv8 algorithm with the integration of the GhostNet module into the backbone architecture to cut down the number of parameters for a faster computational algorithm. In addition, the architecture incorporates a Coordinate Attention (CA) mechanism module, which further enhances the accuracy of the proposed algorithm. Our results demonstrate that the combination of YOLOv8s with CA mechanism and transfer learning obtained the best result, yielding score of 72.2% which surpassed the studies that utilised the same dataset. Without transfer learning, our best result is demonstrated by YOLOv8s with GhostNet and CA mechanism yielding a score of 69.3%
Temporal Climatic Shifts in Henan Province: A 16-decades Perspective Through Regression, SARIMA, and NAR Modeling
No full textGlobal warming is having a significant impact on all aspects of human production and life. This study employs a cross-sectional analysis to investigate the temporal dynamics of average temperature changes in Henan Province, China, from 1851 to 2012. Utilizing the Berkeley Earth Surface Temperature Data and the Daily Meteorological Dataset of China National Surface Weather Station v3.0, we applied regression analysis, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Nonlinear Autoregressive Network (NAR) models to predict temperature trends. Results indicate a significant warming trend over the 160-year period, with the models demonstrating strong predictive performance, albeit with some variability. The study underscores the increasing temperatures' implications for the province's agricultural sustainability and ecological balance. This study highlights the urgency of understanding and mitigating climate change's impacts, particularly in Henan Province, China, for the sake of agricultural sustainability, water resources, and public health. The research findings contribute valuable insights and methodologies to climate data analysis, aiding future predictions and policy-making efforts