Accurate disease classification in plants is important for a profound understanding of their growth and health. Recognizing diseases in plants from images is one of the critical and challenging problem in agriculture. In this research, a deep learning architecture model (CapPlant) is proposed that utilizes plant images to predict whether it is healthy or contain some disease. The prediction process does not require handcrafted features; rather, the representations are automatically extracted from input data sequence by architecture. Several convolutional layers are applied to extract and classify features accordingly. The last convolutional layer in CapPlant is replaced by state-of-the-art capsule layer to incorporate orientational and relative spatial relationship between different entities of a plant in an image to predict diseases more precisely. The proposed architecture is tested on the PlantVillage dataset, which contains more than 50,000 images of infected and healthy plants. Significant improvements in terms of prediction accuracy has been observed using the CapPlant model when compared with other plant disease classification models. The experimental results on the developed model have achieved an overall test accuracy of 93.01%, with F1 score of 93.07%.

With the increasing awareness of health and fitness, the demand for more efficient and personalized fitness solutions is on the rise. This research proposes an innovative system that integrates innovative technologies to streamline gym operations, enhance member experiences, and promote personalized health strategies. The system comprises a website for administration and member registration, coupled with an RFID-based hardware device for member authentication and BMI measurement. Member data, including BMI, is securely stored, and visualized on personalized profiles through line charts. Additionally, machine learning algorithms recommend tailored exercise, diet type, Basal Metabolic Rate (BMR), and daily caloric intake based on individual member data. The study’s method ology includes utilizing a comprehensive dataset from Kaggle, separated into sets for testing and training, to develop and evaluate machine learning models. The Random Forest model demonstrated superior performance in precision, recall, F1-score, and R2 score metrics, making it the optimal choice for the recommender system. In Pakistan, both the fitness and healthcare sectors still rely heavily on manual methods for health assessment and monitoring. In most gyms, trainers manually evaluate members’ fitness levels without leveraging data-driven insights, while in hospitals, BMI calculations are often performed manually by doctors, leading to inefficiencies and potential inaccuracies. Existing automated systems, where available, are either too costly or lack personalization, making them impractical for widespread adoption. Our proposed system addresses these gaps by providing a cost-effective, IoT-integrated solution that automates BMI measurement, gym member authentication, and personalized fitness and dietary recommendations. Our system ensures precision, security, and accessibility, making intelligent health tracking feasible for both gym-goers and healthcare professionals. This research not only enhances automation and efficiency in fitness management but also introduces an affordable technological solution to improve health monitoring in hospitals.

The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies into modern agriculture has profound implications on data collection, management, and decision-making processes. However, ensuring the security of agricultural data has consistently posed a significant challenge. This study presents a novel evaluation metric titled Latency Aware Accuracy Index (LAAI) for the purpose of optimizing data security in the agricultural sector. The LAAI uses the combined capacities of the IoT and AI in addition to the latency aspect. The use of IoT tools for data collection and AI algorithms for analysis makes farming operation more productive. The LAAI metric is a more holistic way to determine data accuracy while considering latency limitations. This ensures that farmers and other end-users are fed trustworthy information in a timely manner. This unified measure not only makes the data more secure but gives farmers the information that helps them to make smart decisions and, thus, drives healthier farming and food security.

The ratio of dementia patients is escalating with time and requires proper attention to help the people suffering from it to continue their activities of daily living (ADL). Such patients suffer from the symptoms like irregular sleep patterns, restlessness, wandering, screaming, falling, sadness and depression. Assistive Technology facilitates caretaker to aid the patient efficiently with minimum effort. Advances in technology have made possible state of the art and innovative methods of health care delivery. Home telecare; in which the patient's health is monitored remotely at home, is one such method. This paper is proposing a cost effective and user friendly wearable product based solution (i.e. HealthBand) that monitors patient's activities (specifically fall and scream) and notifies the caretaker in case of emergency to take appropriate action(s). These notifications are sent to the caretaker on the basis of predefined threshold and time span over Bluetooth and GSM mediums to android based application. The android app also keeps patient's medicines' intake record and reminds caretaker regarding medicine dosage and timings.

Text-to-image synthesis is one of the most critical and challenging problems of generative modeling. It is of substantial importance in the area of automatic learning, especially for image creation, modification, analysis and optimization. A number of works have been proposed in the past to achieve this goal; however, current methods still lack scene understanding, especially when it comes to synthesizing coherent structures in complex scenes. In this work, we propose a model called CapGAN, to synthesize images from a given single text statement to resolve the problem of global coherent structures in complex scenes. For this purpose, skip-thought vectors are used to encode the given text into vector representation. This encoded vector is used as an input for image synthesis using an adversarial process, in which two models are trained simultaneously, namely: generator (G) and discriminator (D). The model G generates fake images, while the model D tries to predict what the sample is from training data rather than generated by G. The conceptual novelty of this work lies in the integrating capsules at the discriminator level to make the model understand the orientational and relative spatial relationship between different entities of an object in an image. The inception score (IS) along with the Fréchet inception distance (FID) are used as quantitative evaluation metrics for CapGAN. IS recorded for images generated using CapGAN is 4.05 ± 0.050, which is around 34% higher than images synthesized using traditional GANs, whereas the FID score calculated for synthesized images using CapGAN is 44.38, which is ab almost 9% improvement from the previous state-of-the-art models. The experimental results clearly demonstrate the effectiveness of the proposed CapGAN model, which is exceptionally proficient in generating images with complex scenes.