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A comparative approach towards dairy heifer weight growth by using Brody growth model, Von Bertalanffy growth model, and logistic growth model / Syed Mohamad Hafiy Syed Ahmad Tarmizi
Full text linkGrowth in animals can be interpreted by mathematical functions. These function can predict the development of live weight, aiding in the assessment of breed's productivity under specific breeding conditions. Generally, growth can be described and predicted using conventional mathematical models, as it does not occur in chaotic way. Demonstrated animal growth using non-linear models enables a thorough analysis of their behavior. The data for weight from 1st to 24th month is collected from PennState Extension website will be analysed by three model; Brody growth model, Von Bertalanffy growth model, and Logistic growth model. First, the growth rate for each growth model is calculated. Then, the average of growth rate from each model is used to predict the growth model weight value. The comparison between each model predicted weight and actual weight is presented on the graph. Error analysis is conducted by getting the mean of submission absolute approximate value subtract exact value divide exact value for each model. The best model with least error is used to find the best model used to find maximum weight for Jersey heifer and its time. On the paper, Logistic predicted weight's curve is the most fitted to actual weight than Brody and Von Bertalanffy growth model curve. Based on the error analysis, can be concluded that Logistic growth model is the best model since it has least error value compared to other two model
Silver nanoparticles synthesis and characterization using Turkevich method / Ahmad Al-Amin Ahamad Husaini
No full textThe synthesis of silver nanoparticles (AgNPs) has been extensively studied due to their unique optical properties and various potential applications. However, issues such as agglomeration and impurities during synthesis can affect the optical properties and hinder their functionality. One commonly used chemical reduction method for synthesizing AgNPs is the Turkevich method. In this study, AgNPs were synthesized from silver nitrate (AgNO3) using the Turkevich method by varying the molar concentration of trisodium citrate (C6ft0?Na3) at 0.5 g. 1.0 g. and 1.5 g in 20 mL of distilled water. The molar concentration of AgNO3 was, kept constant at 0.034g in 100 mL of distilled water. The synthesis process was conducted at reaction temperatures of 70°C, 80°C, and 90°C, with a constant stirring speed of 900 rpm. The synthesized AgNPs were characterized using UV-Vis spectroscopy to assess their optical properties, field emission scanning election microscopy (FESEM) was employed to analyze their surface morphology, shape, and size distribution and energy dispersive x-ray spectroscopy (EDX) used to observe the elemental composition of material. Results shows that synthesizing AgNPs at reaction temperature 90oC resulted in smaller AgNPs with more consistent optical properties. Higher stabilizer concentration while reducing the size of nanoparticles leads to aggregation due to the formation of insufficient silver nuclei. The synthesis process was carefully controlled to ensure the successful formation of AgNPs with the desired properties
Design and performance analysis of ultra-wideband (UWB) antenna for wireless system / Muhammad Irfan Hafiz Mazlan
No full textThis project identifies the signal penetration performance of ultra-wideband (UWB) antenna through different materials, focusing on wood and brick as barriers. The antenna, measuring 35 mm x 45 mm x 1 mm, uses Rogers RT-5880 as the substrate and operates within the 0-12 GHz range. The design process involved selecting microstrip patches, feeding techniques, and ground shapes, followed by simulations using CST Studio Suite software and antenna fabrication. Further analysis involved varying the distance between the antenna and wall materials (wood and brick) and observing the S11 parameters. This study highlights challenges in translating theoretical designs into practical applications, emphasizing the need for precise fabrication, material validation, and experimental optimization. Simulations confirmed the antenna met UWB standards, with a wide bandwidth and reflection coefficient of -10 dB. However, differences between simulated and measured results occurred, with the fabricated antenna exhibiting shifted resonance frequencies, reduced bandwidth, and diminished performance. These issues were attributed to fabrication flaws and environmental factors during measurements. While the simulated antenna performed well under ideal conditions, the fabricated version underperformed in real-world conditions. This project provides understanding on UWB signal penetration, highlighting the need of precise analysis and good antenna design. Future work will be focused on optimising antenna design, performance analysis, and integrating findings into practical applications
Object detection and classification in marine ecosystem using deep learning neural network / Muhammad Afiq Azman
No full textThe marine ecosystem is vital for maintaining ecological balance and biodiversity, serving as a habitat for countless species and supporting human livelihoods. This study explores the application of artificial intelligence (AI) and machine learning (ML) for the detection and classification of marine organisms using YOLOv8 and ResNet50 models. The primary objective is to develop and implement artificial intelligence (AI) and machine learning (ML) algorithms tailored to effectively identify within marine ecosystems. A comparative performance evaluation revealed that while YOLOv8 excels in object detection with high precision (0.85) and recall (0.83) due to its multiscale feature extraction capabilities, ResNet50 demonstrated higher overall accuracy (77%) in classification tasks. YOLOv8 outperforms in handling multiple objects in complex backgrounds, whereas ResNet50 struggles with multiple-class detection in single images, attributed to its architecture designed primarily for single-object classification. These findings highlight the complementary strengths of both models in advancing marine ecosystem analysis
Development of classification model based on training time in hyperparameter for Acute Myeloid Leukemia (AML) / Nurzulaikha Zaidi@Eddie
No full textThe classification of Acute Myeloid Leukemia (AML) using machine learning models has demonstrated significant potential in advancing diagnostic accuracy and efficiency, offering critical support in clinical decision-making. This study focuses on strategies to enhance AML classification by optimizing hyperparameters and learning rate schedulers, aiming to reduce training time while maintaining high performance. Several learning rate schedulers, including constant, linear, step, and time-based approaches, were evaluated for their effectiveness. The results reveal that step and time-based schedulers consistently outperformed others, achieving superior accuracy, specificity, and computational efficiency, while significantly reducing training time. In addition to exploring learning rate schedulers, hyperparameter optimization techniques were applied to Convolutional Neural Networks (CNNs) such as AlexNet and ResNet-18. These techniques yielded substantial improvements in model accuracy and efficiency by fine-tuning critical parameters like learning rates and momentum. Furthermore, the study developed strategies for handling variable learning rates and momentum adjustments, with SGDM (Stochastic Gradient Descent with Momentum) showcasing excellent adaptability and convergence. This research emphasizes the importance of hyperparameter tuning and advanced optimization strategies in achieving precise and early AML diagnoses. The insights gained contribute to the development of reliable machine learning models that support personalized and effective treatment regimens, paving the way for improved clinical outcomes
Classification of potential dysgraphia symptoms using CNN model based on handwriting images / Shahnon Hakimi Mohd Ikhtiram
No full textDysgraphia is a subnet of Dyslexia, a learning disorder that significantly impacts learning other subjects like reading and arithmetic making it more difficult to understand. Dysgraphia is difficult to write which can make an individual academic and professional development. This disorder can be cured if detected early, especially for children where parents can give different methods or get specialized education. This study aims to develop a CNN-based classification model for identifying low and high- potential dyslexia symptoms from handwriting images. Secondly, to validate the effectiveness of the CNN model in accurately classifying low and high potential dyslexia symptoms using handwriting images. By enabling early detection and intervention where the study will support dyslexic students and enhance their academic success and overall well-being. The study was conducted using a dataset of 249 handwriting samples from individuals involve varying levels of dyslexia symptoms. The images were pre-processed through normalization and resized to 256x256 pixels. The CNN model was built with six layers, including convolutional, pooling, and fully connected layers, and was optimized using the Adam optimizer. The results showed that the CNN model performed classify low- and high-potential dyslexia symptoms. This highlights the effectiveness of deep learning techniques in identifying dysgraphia and dyslexia early. Such advancements have the potential to pave the way for timely interventions, offering valuable support to students with dyslexia and helping them succeed academically while improving their overall well-being. By identifying dyslexia symptoms early, this approach can help students get the support they in term of education and making learning easier and more effective. The success of this model could even be a stepping stone for recognizing other learning challenges, improving how we support students with real needs. The technology to create better, support educational environments, encourage all students to reach their full potential
UiTM Faculty of Pharmacy presents Pharmacare 2025 to promote health awareness and community engagement / Farhana Fakhira Ismail, Nur Sabiha Md Hussin and Gurmeet Kaur Surindar Singh
Full text linkOn January 11, 2025, the PharmaCare programme took place at UiTM, bringing together students, faculty members, and the public for a day of insightful talks, interactive activities, and health awareness initiatives. The event, organised as part of a Community Pharmacy (PHC670) and Know Your Medicine (UPH654) assessment, was a success in promoting knowledge and awareness of public health. Apart from the booths with several health-related topics, one of the major highlights was the blood donation drive. This initiative encouraged civic responsibility and helped raise awareness about the importance of blood donation
Sejauh mana kepentingan pendidikan percukaian dalam meningkatkan kesedaran cukai di kalangan pembayar cukai / Saifulrizan Norizan ... [et al.]
No full textDi Malaysia, pendidikan berkualiti seperti yang digariskan dalam Matlamat Pembangunan Mampan (SDG 4) dan pendidikan percukaian di kalangan pembayar cukai saling berkait rapat kerana merupakan komponen terpenting dalam melahirkan rakyat yang mempunyai pengetahuan dan bertanggungjawab. SDG 4 menekankan kepentingan pendidikan berkualiti yang inklusif dan adil kepada semua lapisan masyarakat tanpa mengira perbezaan latar kehidupan ekonomi seseorang untuk membolehkan mereka memperoleh kemahiran dan pengetahuan yang diperlukan untuk menjadi rakyat yang bertanggunjawab. Dalam konteks pendidikan percukaian, memberikan pendidikan yang berkualiti mengenai sistem percukaian membantu meningkatkan kesedaran dan kefahaman pembayar cukai tentang kewajipan cukai mereka. Dengan pendidikan yang baik, rakyat bukan sahaja dapat mematuhi undang-undang percukaian, tetapi juga menyumbang kepada kemajuan ekonomi dan sosial negara, selaras dengan matlamat pembangunan mampan global
Design and analysis of 2 in 1 sand sieving machine
No full textThe sand sieving machine successfully improves the process of separating sand mixtures by ensuring the user safety and efficiency. Its innovative design ensures the sand is filtered without risk to the operator, while it’s rotating the mixture of sand. This machine also provides optimal power and speed for effective filtration with minimal physical effort. The project has achieved a lot of achievements, including enhanced sustainability, efficiency, and safety. By promoting eco-friendly practices, the machine addresses issues related to energy waste, time consumption, and environmental impact. Thus, it will offer a progressive solution to these challenge
Production development of catamaran sport fishing vessel / Amsyar Baihaqi Abu Bakar
No full textThis study focuses on the development of a production of catamaran sport fishing vessel, taking advantage of the stability and advantages of a catamaran design compared to a traditional monohull. Traditional ship modeling faces challenges such as complexity, time inefficiency, human error, and high cost. To address this, this project aims to use 3D printing for accurate and cost-effective model fabrication. Key objectives included developing detailed CAD drawings and construct scaled-down models using PolyCAD and Rhino 8 software. A systematic methodology was used, beginning with problem definition, customer needs identification through surveys, and detailed design specifications. This also was followed by concept generation, evaluation using Pugh charts, and the fabrication process. The model was designed through the puzzle method, splitting the components for 3D printing and manual assembly. The results showed the model was successfully completed but revealed defects such as brittle surfaces and layer lines due to the print resolution and thin hull structure. Post-processing coatings and design adjustments mitigate this issue. This project highlights the potential of 3D printing in maritime prototyping, offering efficiency and design flexibility. Recommendations include refining CAD designs, hybrid manufacturing techniques and exploring advanced materials. This work contributes a practical framework for integrating modern technology into marine engineering, promoting innovation in ship production