Weed management is becoming increasingly important for sustainable crop production. Weeds cause an average yield loss of 11.5% billion in Pakistan, which is more than PKR 65 billion per year. A real-time laser weeding robot can increase the crop’s yield by efficiently removing weeds. Therefore, it helps decrease the environmental risks associated with traditional weed management approaches. However, to work efficiently and accurately, the weeding robot must have a robust weed detection mechanism to avoid physical damage to the targeted crops. This work focuses on developing a lightweight weed detection mechanism to assist laser weeding robots. The weed images were collected from six different agriculture farms in Pakistan. The dataset consisted of 9000 images of three crops: okra, bitter gourd, sponge gourd, and four weed species (horseweed, herb paris, grasses, and small weeds). We chose a single-shot object detection model, YOLO5. The selected model achieved a mAP of 0.88@IOU 0.5, indicating that the model predicted a large number of true positive (TP) with much less prediction of false positive (FP) and false negative (FN). While SSD-ResNet50 achieved a mAP of 0.53@IOU 0.5, the model predicted fewer TP with significant outcomes as FP or FN. The superior performance of the YOLOv5 model made it suitable for detecting and classifying weeds and crops within fields. Furthermore, the model was ported to an Nvidia Xavier AGX standalone device to make it a high-performance and low-power computation detection system. The model achieved an FPS rate of 27. Therefore, it is highly compatible with the laser weeding robot, which takes approximately 22.04 h at a velocity of 0.25 feet per second to remove weeds from a one-acre plot.

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.

Weed management has become a highly labor-intensive activity, which is the reason for decreased yields and high costs. Moreover, the lack of skilled labor and weed-resistant herbicides severely impact the agriculture sector and food production, hence increasing the need for automation in agriculture. The use of agricultural robots will help in the assurance of higher yields and proactive control of the crops. This study proposes a laser-based weeding vehicle with a unique mechanical body that is adjustable relative to the field structure, called the Robot Operating System (ROS) based robust control system, and is customizable, cost-effective and easily replicable. Hence, an autonomous-mobile-agricultural robot with a 20 watt laser has been developed for the precise removal of weed plants. The assembled robot’s testing was conducted in the agro living lab. The field trials have demonstrated that the robot takes approximately 23.7 h at the linear velocity of 0.07 m/s for the weeding of one acre plot. It includes 5 s of laser to kill one weed plant. Comparatively, the primitive weeding technique is highly labor intensive and takes several days to complete an acre plot area. The data presented herein reflects that implementing this technology could become an excellent approach to removing unwanted plants from agricultural fields. This solution is relatively cost-efficient and provides an alternative to expensive human labor initiatives to deal with the increased labor wages.

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.

Non-volatile nanoscale memory devices (such as memristors) have promised to overcome the challenges of scalability and leakage currents of CMOS based memory devices. These novel memories can be fabricated in back-end-of-the-line of any CMOS process. Currently, a lot of research is focused on investigating the benefits of memristors for associative memories. These are Content-Addressable Memories (CAM) in which search based data access takes place. Searching for a particular bit in memristor is time consuming while search in CMOS CAM zone is efficient. To combine the speed and ease of search of CMOS memory and the scalability of memristor memory, we present a novel multibit hybrid CMOS-Memristor Associative Memory Cell. The benefits of such memory cells manifest in on-chip caches - the instruction and data cache, Branch Target Buffer, and Translation Lookaside Buffer. To exemplify the benefit of the cell further, we also simulate the MemCAM as the TLB of an ARM processor and obtained upto 50% decrease in miss rates of Data TLB and upto 93% in that of Instruction TLB. Average speedup of 1.16 was also achieved on various benchmark applications of PARCSEC and MiBench suites.

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.