In this paper we address the basic limitation of Siammask - the state of the art single object tracking and segmentation algorithm. SiamMask requires semi-supervision in that it needs a bounding box to be drawn manually around the object that has to be tracked. This is however not always possible or feasible, and slows down the pipeline even in the best case. We overcome this limitation by using state-of-the-art object detection algorithms: Detectron and YOLO to automatically detect the object and then track using Siammask. We note that YOLO gives better and more meaningful detection of objects in the scene. However, Detectron gives a higher detection speed than YOLO, making the overall detection and tracking process faster.

Dental health is integral to overall well-being, with early detection of issues critical for prevention. This research work focuses on utilizing artificial intelligence and deep learning-based object detection techniques for automated detection of common dental issues in orthopantomography x-ray images, including broken roots, periodontally compromised teeth, and the Kennedy classification of partially edentulous arches. An orthopantomography dataset has been used to train several models employing various object detection architectures, hyperparameters, and training techniques. The performance of these models was evaluated to select the one with the highest accuracy. This selected model was subsequently deployed for further testing and validation on unseen data to assess its real-world performance and potential for clinical application. The proposed model not only facilitates the classification of the Kennedy classification but also offers detailed information about the arch (maxillary or mandibular) and specifies the affected side of the arch (right or left). It can diagnose multiple dental issues simultaneously within an image, enhancing diagnostic capabilities for dental practitioners. Despite a small dataset, satisfactory results were achieved through tailored hyperparameters and a piecewise annotation scheme.

The sustainability of the hydrological and ecological ecosystems of any region requires continuous monitoring of the water bodies. Recent advancements in satellite-based remote optical sensors, big data analysis and cloud computing have given new dimensions to the field of water body studies including their detection as well as analysis. The present study extends the existing methods to assess the contemporary surface water detection and monitoring techniques via remote sensing. The proposed technique implies an improved hybrid approach for the purpose along with the calculation of the boundary areas. The study has been carried out on the Manchar Lake, the largest natural freshwater lake in Pakistan as well as in South Asia. The proposed hybrid water index along with the different existing water body detection indices and spectral bands have been worked out on the satellite images retrieved from the Google Earth Engine to detect and analyze the area/flow changes in the water body. Based on the 7 years of data, the proposed algorithm calculates the water body area more precisely. With limited availability of metadata about the study area, the results have been validated both qualitatively through national-met data and statistically. These results help to better preserve and improve the quality of the water resource.

To overcome the scarcity of annotated dental X-ray datasets, this study presents a novel pipeline for generating high-resolution synthetic orthopantomography (OPG) images using customized generative adversarial networks (GANs). A total of 4777 real OPG images were collected from clinical centres in Pakistan, Thailand, and the U.S., covering diverse anatomical features. Twelve GAN models were initially trained, with four top-performing variants selected for further training on both combined and region-specific datasets. Synthetic images were generated at 2048 × 1024 pixels, maintaining fine anatomical detail. The evaluation was conducted using (1) a YOLO-based object detection model trained on real OPGs to assess feature representation via mean average precision, and (2) expert dentist scoring for anatomical and diagnostic realism. All selected models produced realistic synthetic OPGs. The YOLO detector achieved strong performance on these images, indicating accurate structural representation. Expert evaluations confirmed high anatomical plausibility, with models M1 and M3 achieving over 50% of the reference scores assigned to real OPGs. The developed GAN-based pipeline enables the ethical and scalable creation of synthetic OPG images, suitable for augmenting datasets used in artificial intelligence-driven dental diagnostics. This method provides a practical solution to data limitations in dental artificial intelligence, supporting model development in privacy-sensitive or low-resource environments.

This study investigates the role of ChatGPT, a generative artificial intelligence tool, in enhancing learning within computer science and engineering education. Conducted during the Fall 2023 semester, the work utilized ChatGPT based on the GPT-3.5 model to assess its effectiveness across three educational domains: programming, analytical problem-solving, and hardware-related system design. A structured methodology was implemented wherein students from multiple academic levels attempted tasks independently and then used ChatGPT for support. Their interactions and reflections were systematically recorded and analyzed to evaluate the model’s utility and limitations in an educational setting. Results indicated that ChatGPT was particularly effective in programming tasks, where it could generate accurate code and useful explanations. However, its performance declined in more complex areas such as artificial intelligence and computer architecture, where deeper analytical reasoning and domain-specific expertise were required. The study further highlights the value of training students in prompt engineering, which significantly improved their ability to formulate targeted queries, increased metacognitive awareness, and helped calibrate their trust in the tool’s responses. Recommendations include future integration of graphical diagram support to aid comprehension in visually complex subjects, such as system design and architecture. Key challenges identified include limited transparency in training data, the lack of visual support, and the need to assess whether prompt engineering skills and improved AI usage persist and transfer across courses. While newer reasoning-oriented models such as GPT-4, Claude 3, and DeepSeek may offer enhanced capabilities, these were not available during the Fall 2023 study period and are suggested for future evaluation. The study also calls for future research using these newer, more advanced large language models to reassess findings and ensure continued relevance as these tools evolve. Overall, this research provides valuable insights into the educational application of generative AI and informs future strategies for integrating AI tools into engineering pedagogy.