Fahim Hafiz

ABSTRACT This paper proposes a novel curriculum for the microprocessors and microcontrollers laboratory course. The proposed curriculum blends structured laboratory experiments with an open-ended project phase, addressing complex engineering problems and activities. Microprocessors and microcontrollers are ubiquitous in modern technology, driving applications across diverse fields. To prepare future engineers for Industry 4.0, effective educational approaches are crucial. The proposed lab enables students to perform hands-on experiments using advanced microprocessors and microcontrollers while leveraging their acquired knowledge by working in teams to tackle self-defined complex engineering problems that utilize these devices and sensors, often used in the industry. Furthermore, this curriculum fosters multidisciplinary learning and equips students with problem-solving skills that can be applied in real-world scenarios. With recent technological advancements, traditional microprocessors and microcontrollers curricula often fail to capture the complexity of real-world applications. This curriculum addresses this critical gap by incorporating insights from experts in both industry and academia. It trains students with the necessary skills and knowledge to thrive in this rapidly evolving technological landscape, preparing them for success upon graduation. The curriculum integrates project-based learning, where students define complex engineering problems for themselves. This approach actively engages students, fostering a deeper understanding and enhancing their learning capabilities. Statistical analysis shows that the proposed curriculum significantly improves student learning outcomes, particularly in their ability to formulate and solve complex engineering problems, as well as engage in complex engineering activities.

Small nucleolar RNAs (snoRNAs) are increasingly recognized for their critical role in the pathogenesis and characterization of various human diseases. Consequently, the precise identification of snoRNA-disease associations (SDAs) is essential for the progression of diseases and the advancement of treatment strategies. However, conventional biological experimental approaches are costly, time-consuming, and resource-intensive; therefore, machine learning-based computational methods offer a promising solution to mitigate these limitations. This paper proposes a model called ‘GBDTSVM’, representing a novel and efficient machine learning approach for predicting snoRNA-disease associations by leveraging a Gradient Boosting Decision Tree (GBDT) and Support Vector Machine (SVM). ‘GBDTSVM’ effectively extracts integrated snoRNA-disease feature representations utilizing GBDT, and SVM is subsequently utilized to classify and identify potential associations. Furthermore, the method enhances the accuracy of these predictions by incorporating Gaussian integrated profile kernel similarity for both snoRNAs and diseases. Experimental evaluation of the GBDTSVM model demonstrates superior performance compared to state-of-the-art methods in the field, achieving an AUROC of 0.96 and an AUPRC of 0.95 on the ‘MDRF’ dataset. Moreover, our model shows superior performance on two more datasets named ‘LSGT’ and ‘PsnoD’. Additionally, a case study conducted on the predicted snoRNA-disease associations verified the top-ranked snoRNAs across twelve prevalent diseases, further validating the efficacy of the GBDTSVM approach. These results underscore the model’s potential as a robust tool for advancing snoRNA-related disease research. Source codes and datasets for our proposed framework can be obtained from: https://github.com/mariamuna04/gbdtsvm.

In this paper, we present an integrated approach to real-time mosquito detection using our multiclass dataset (MosquitoFusion) containing 1204 diverse images and leverage cutting-edge technologies, specifically computer vision, to automate the identification of Mosquitoes, Swarms, and Breeding Sites. The pre-trained YOLOv8 model, trained on this dataset, achieved a mean Average Precision (mAP@50) of 57.1%, with precision at 73.4% and recall at 50.5%. The integration of Geographic Information Systems (GIS) further enriches the depth of our analysis, providing valuable insights into spatial patterns. The dataset and code are available at https://github.com/faiyazabdullah/MosquitoFusion.

Rapid diagnostic tests are crucial for timely disease detection and management, yet accurate interpretation of test results remains challenging. In this study, we propose a novel approach to enhance the accuracy and reliability of rapid diagnostic test result interpretation by integrating artificial intelligence (AI) algorithms, including convolutional neural networks (CNN), within a smartphone-based application. The app enables users to take pictures of their test kits, which YOLOv8 then processes to precisely crop and extract the membrane region, even if the test kit is not centered in the frame or is positioned at the very edge of the image. This capability offers greater accessibility, allowing even visually impaired individuals to capture test images without needing perfect alignment, thus promoting user independence and inclusivity. The extracted image is analyzed by an additional CNN classifier that determines if the results are positive, negative, or invalid, providing users with the results and a confidence level. Through validation experiments with commonly used rapid test kits across various diagnostic applications, our results demonstrate that the synergistic integration of AI significantly improves sensitivity and specificity in test result interpretation. This improvement can be attributed to the extraction of the membrane zones from the test kit images using the state-of-the-art YOLO algorithm. Additionally, we performed SHapley Additive exPlanations (SHAP) analysis to investigate the factors influencing the model’s decisions, identifying reasons behind both correct and incorrect classifications. By facilitating the differentiation of genuine test lines from background noise and providing valuable insights into test line intensity and uniformity, our approach offers a robust solution to challenges in rapid test interpretation.