Journals of Universiti Tun Hussein Onn Malaysia (UTHM)
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Seasonal Variation of Irrigation Water Quality in Bachok, Kelantan
Full text linkAgriculture is dependent on an adequate supply of suitable irrigation water quality. Physicochemical analysis of surface water for irrigation was conducted in Bachok, Kelantan during wet and dry period from July 2021 to December 2022. Surface water sampling points consist of primary, secondary and tertiary irrigation canals supplying water for paddy plantation. This study aimed to evaluate water quality trends in irrigation canals based on physicochemical parameters and discuss its pollution level and irrigation suitability using the Water Quality Index (WQI), National Water Quality Standards (NWQS) and irrigation suitability classification. It was observed that water quality was within NWQS Class IV except DO, Ammoniacal Nitrogen and TSS in locations with intensive aquaculture. ANOVA analysis showed a significant difference between water quality parameters and sampling locations (p<0.05). However seasonal variation in all irrigation canal’s water quality was statistically insignificant except for pH. WQI in all sampling points are between 55.86-93.71, which classified into the category of clean and adequate water. Irrigation water suitability index in Bachok are in the range of: SAR (0.31-1.75), MAR (22.75-57.3), SSP (25.9-68.78) and %Na (32.87-69.83). Results showed that half of the water sample in Bachok were slightly polluted and polluted according to WQI. Additionally, MAR and SSP classified 5% and 3% of water samples were unsuitable and unsafe for irrigation. Strategies must be developed to ensure pollution level remains below threshold
The Role of Human Dignity Philosophy in TVET Student Representative Council
No full textThe philosophy of human dignity plays a vital role in shaping leadership within Technical and Vocational Education and Training (TVET) institutions, particularly in the context of Student Representative Councils (SRC). This conceptual paper explores how the integration of human dignity principles, enhances the effectiveness of SRC in TVET settings. Drawing on leadership theories and the challenges faced by young leaders, especially in terms of biases and limited authority, the paper examines how SRCs can foster ethical decision-making and student engagement by upholding human dignity. It also discusses the moderating influence of transformational leadership, emphasizing that leadership practices that inspire, challenge, and include all students can significantly improve the outcomes of human dignity-centered leadership. By embracing these principles, SRC can cultivate an environment where student voices are valued, and future leaders are prepared to engage ethically and inclusively in both academic and societal contexts
Replacement of Sisal Fiber with Jute Fiber in the Production of Plaster of Paris (POP) Ceiling Boards
No full textPlaster of Paris (POP) ceiling boards production is reported to be too expensive, this hinder affordability especially to low-income earners in Nigeria. It high cost is attributed to importation of its constituent materials (Gypsum cement and Sisal fiber). This study assessed the performance of jute fiber as compare to sisal fiber in POP ceiling production. Sisal fiber is imported and sold at exorbitant price within the country. The materials used in this study includes gypsum cement, sisal and jute fibers. The jute fiber was obtained from jute stalks bought from Girei market in Girei LGA Adamawa State, Nigeria. These stalks were retted using tank retting method, and fibers obtained were chemically modified. Specimens were prepared and tested using standard methods outlined in ASTM C 473 – 07 (2009). Flexural strength, nail pull resistance and water absorption of the specimens were determined. Use of jute fiber as reinforcement in POP ceiling boards was found to enhance flexural strength and nail-pull resistance by 4% and 3% respectively. The water absorption of sisal and jute fibers specimens are 6% and 7% which are all within ASTM C1396M-11 (2004) specified maximum limit. Hence jute fiber is considered a suitable alternative to sisal fiber in POP ceiling production Nigeria
Comprehensive Study on Impact of Inserted Nanoparticles with Base Fluid on Heat Transfer Enhancement in Various Configurations
No full textMany industrial and engineering applications have low thermal conductivity that affects heating or cooling processes, so the heat transfer process for these applications will be optimized by using small nanometer-sized particles such as metal, oxide, carbide, etc. dispersed in the basic fluid of the application, these particles are called nanofluids. This paper reviews the varying factors affecting the thermal conductivity of various nanomaterials under different conditions. All the authors focused on the thermal conductivity of nanoparticles to increase the heat transfer process, whereby increasing the percentage of nanoparticles, the thermal conductivity increases, and therefore the performance and efficiency of thermal systems increases. The size, shape, collision, aggregation, porous layer, melting point of nanoparticles, etc. are all parameters that affect the thermal conductivity of the nanomaterial, and their control determines the behavior of its increase or decrease. The use of nanofluid is a new and influential technology to improve heat transfer for the next generation. The results of the study indicated that the nanomaterial has an effect on increasing thermal conductivity by significantly raising the efficiency of thermal conductivity of the liquid and improving thermal convection, where the Brownian motion of nanoparticles contributes to improving thermal convection inside the liquid as well as reducing thermal resistance
Android Ransomware Detection by Deep Learning
No full textThis research proposes a novel deep learning-based detection model to combat the growing menace of Android ransomware. Deep learning models can learn complex features and training models with many convolutional layers and millions of parameters, leading to overfitting in a few numbers of epochs. As shown by previous works, current methods for Android malware detection are constrained by insufficient feature sets and preprocessing methods. Combining static and dynamic information for a more thorough analysis is essential to improve detection accuracy. While Recurrent Neural Network (RNN) has effectively solved temporal problems, it has limitations such as gradient dispersion and high calculation costs. The goal is to develop Android ransomware detection by deep learning with optimal epochs and test the model using parameter evaluation of accuracy, precision, recall and F-1 score. The methodology comprises of five phases: dataset, data preprocessing, Deep Learning model (CNN and LSTM), 10-fold cross-validation and result. The model is tested on an Android dataset, CICAndMal2019, which includes Permission and Intent as static features and API calls as dynamic features. For this research, only the sample of ransomware from Jisut, RansomBO, Charger, Lockerpin, Koler, Pletor, PornDroid, Simplocker, SVpeng, WannaLocker family and other benign samples will be used. The CNN model obtained its best performance at 40 epochs, with the result of 97.93% accuracy, 98.00% precision, 99.93% recall and 98.95% F-1 score. The LSTM model performed best at 10 epochs, with a result of 97.74% accuracy, 97.74 % precision, 100% recall, and 98.86% F-1 score. This research highlights that the CNN model obtains accuracy slightly higher than the LSTM model, improving the Android ransomware detection by deep learning
Efficient Kidney Cancer Classification from CT Images Using a Lightweight Convolutional Neural Network Optimized with an Enhanced Crow Swarm Optimization Algorithm
Full text linkKidney cancer is among the fifty most common cancers in the global statistics, therefore, early and accurate classification could enhance the prognosis. However, present classification models are more of a challenge to handle with data obtained from CT imaging. Our study proposes a lightweight and automated classification for kidney cancer detection using a hybrid feature extraction approach and a novel lightweight convolutional neural network improved by a hybrid Crow Swam Optimization (CSO) algorithm. Two datasets were used to develop and validate the model: the CT Normal – Kidney dataset containing 6,101 CT images and the CT Cyst, Tumor & Stone Kidney – Normal dataset comprising 6,345 CT images together and the Kidney Cancer dataset with 8,400 images. The technique used for feature extraction involved the use of multiple descriptors where useful image features were obtained. This was followed by optimising the Hybrid CSO algorithm with better results observed on augmented feature selection for better classification. The experiments’ outcomes were an accuracy of 100%, an F1-score of 97.49%, a Precision of 97.97%, a recall of 98.28% fast processing and the model’s successful differentiation of kidney pathologies. This more efficient and accurate framework, based on the application of both deep learning and conventional methods depending on levels of accuracy, opens up a valuable window on real-time kidney cancer classification that should directly assist radiologists in clinical diagnosis and raise detection reliability
Employing Hybrid Watermarking to Improve Email Security Against Cyber Attacks
Full text linkEmail security is increasingly essential due to the growing number of IT security threats. This paper aims to assess the risks associated with emailing as a communication medium, including message interception, message loss, message content tampering, and intellectual property theft. These threats can be mitigated by employing Watermarking (WM) techniques, which have become a highly effective data protection tool. Information placed in digital content can be hidden, which can be used for document authentication and should not be exposed to unauthorized users. Hybrid watermarking is an advanced approach that can be exploited to enhance the security of an email, where both visible and invisible marks are strategically placed to protect the integrity, authenticity, and confidentiality of email content. This integration provides a robust security model that can prevent unauthorized access and identify any unauthorized use or tampering with digital content. In this paper, invisible watermarking is employed to enhance email security, complementing visible watermarks. This approach aims to strengthen email protection by increasing the likelihood of easily identifying attack attempts to compromise content or unauthorized retrieval of secure data from received emails. The performance of the selected approaches has been assessed by employing some forms of attacks, including scaling, reformatting, denoising, and noise reduction. Several metrics have been utilized to validate the quality of tested files: the cover image file\u27s PSNR for Peak Signal to Noise Ratio, the invisible watermark file\u27s BER and NC for Normalized Correlation
A Fuzzy-based Cluster Head Selection Technique for Optimizing Communication of VANETs
Full text linkThe continuous developments in vehicular communication technology have brought a significant interest in Vehicular Ad-Hoc Networks (VANETs). VANETs aim to enhance road safety, improve traffic management, and provide a suite of infotainment services to passengers. This type of network is characterized by high-speed, dynamically varying mobility, leading to increased Energy Consumption (EC), End-to-End (E2E) delay, and Routing Overhead (RO) during network communication. Various researchers have developed ways to overcome this drawback through the employment of clustering techniques in VANETs. However, utilizing clustering techniques in VANETs is critical as it requires maintaining robust communication links, optimizing resource allocation, and minimizing E2E delay. Subsequently, this paper proposes an improved Fuzzy-based Cluster Head Selection (FCHS) technique to enhance the overall performance of VANET. In VANET, the clustering is formed from Cluster Head (CH), Cluster Child (CC), and Backup-Cluster Head (BCH) along with the other network nodes. The FCHS optimizes the CH selection using a fuzzy logic algorithm based on various VANET parameters, including average distance, satisfaction degree, EC, Packet Delivery Ratio (PDR), and vehicle connectivity level. The performance of the proposed FCHS technique is simulated utilizing Network Simulator (NS) 2.35 with the Simulation of Urban MObility (SUMO) platform. The performance metrics that are considered for the result evaluation are PDR, EC, E2E delay, and RO. The overall results of the VANET is compared with two recent methods. The results show that the VANET performance with the aid of the proposed FCHS technique achieves the highest PDR, low EC, E2E delay, and RO
Operational Challenges in Maintenance: A Lean and Agile Approach
Full text linkThe Malaysian maintenance industry is vital in sustainable infrastructure and facility management. This paper examines key operational challenges in maintenance and construction, including project delays, workforce shortages, safety risks, miscommunication, and weather disruptions. These inefficiencies can be mitigated by integrating Lean Manufacturing, Agile methodology, Six Sigma, and continuous improvement frameworks. Lean principles and Agile methodology enhance process optimization, reduce delays, and facilitate real-time communication for rapid adjustments. Six Sigma provides data-driven insights to identify sources of operational errors, particularly in safety and communication. Kaizen fosters continuous improvement, addressing emerging issues such as weather disruptions. Research indicates that practitioners and researchers can achieve greater efficiency and cost-effective project outcomes by integrating these methods. Future empirical studies should explore their interrelationships and potential synergy with emerging technologies to further optimize maintenance project success
Evaluating Taxi Passenger Satisfaction Using IPA and CSI: Implications for Competitive Strategy in Indonesia
Full text linkIn the last decade, the taxi industry in Indonesia has undergone significant changes with the arrival of app-based taxis such as Uber, Grab, and Go-Car (2014-2024). Changes in technology and information have driven a shift in people\u27s lifestyles, making online taxi services more desirable due to cheaper fares, efficient services, and ease of access. The purpose of this research is to analyze the level of satisfaction of conventional taxi passengers with online taxis in Indonesia. The number of respondents in this study amounted to 250 taxi passengers across Jakarta, Bogor, Depok, Tangerang, and Bekasi City areas (Indonesia). The sampling method used was convenience sampling, and the questionnaire survey was conducted by benchmarking five large taxi companies in Indonesia, namely PT ABC (company initials), PT Blue Bird Tbk, PT Uber Technology Indonesia, PT GrabBike Indonesia, and PT Gojek Indonesia. The variables in this study were tangible, reliability, assurance, responsiveness, empathy, product quality, and perceived value. The results showed that the IPA analysis variable attributes, placed PT ABC taxis in quadrants I, II, and III. While the CSI analysis found that the online taxi passenger satisfaction index is higher than the conventional taxi