Birth marks the evolution from a fluid environment to one in which the baby breathes. Respiration difficulties after birth and in the first hours of life are common. Some babies will experience more severe respiration problems, and some of these will require medical care that only expert physicians can offer. In the first six hours of life, asphyxia is recognized. Neonatal asphyxia is a syndrome categorized by the deprivation of oxygen supply at birth and causes many morbidities and mortalities among newborns. Improved results depend on early detection and action. Using the African Vultures Algorithm that performs hyperparameter optimization expands accuracy and effectiveness in supporting the model. This integration improves the detection and sympathetic of neonatal asphyxia by using African Vultures Algorithm's capability to find fine results in high-dimensional places as well as the capability of LGNN to method structured clinical data. Herein, our model excels over present deep learning models and computer vision techniques for accurate detection of neonatal asphyxia. LGNN model achieved an accuracy of 98% in Dataset 1 and 96.5% in Dataset 2. F1-Score of the proposed model was achieved as 97.6% in Dataset 1 and 96.7% in Dataset 2. In Dataset 1 and Dataset 2, LGNN model as long as 97.9% and 97.1% precision, individually. In Dataset 1 and Dataset 2, the proposed (LGNN) provided 98.8% and 97.7% recall, one-to-one. Efficiently exploit the graph structure for feature aggregation and inference by employing a linear graph neural network architecture. Improved newborn outcomes are made possible by the suggested model, which is a potent diagnostic tool that also helps clinicians comprehend underlying pathophysiological causes.

Virtual reality plays a major role in medicine in the aspect of diagnostics and treatment planning. From the diagnostics perspective, automated methods yields the segmented results into virtual environment which will helps the physician to take accurate decisions on time. Virtual reality of 3D brain tissue segmentation helps to diagnostic the brain related diseases like alzheimer's disease, brain malformations, brain tumors, cerebellar disorders and etc. The work proposed a fully automatic histogram-based self-initializing K-Means (HBSKM) algorithm is performed on compute unified device architecture (CUDA) enabled GPU (QudroK5000) machine to segmenting the human brain tissue. Number of clusters (K) and initial centroids (C) automatically calculated from the mid image from the volume through Gaussian smoothening technique. The experimental dataset was collected from internet brain segmentation repository (IBSR) in segmenting the three major tissues such as grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) to experiment the efficiency of the present parallel K-Means algorithm. Computation time is calculated between the homogenous and heterogeneous environment of CPU and GPU for HBSKM algorithm. This proposed work achieved 6× speedup folds while heterogeneous CPU and GPU implementation and 3.5× speedup folds achieved with homogenous GPU implementation. Finally, volume of segmented brain tissue results was presented in virtual 3D and also compared with ground truth results.

Most digital forensic investigations involve images presented as evidence. One of the common problems of these investigations is to prove the image's originality or, as a matter of fact, its manipulation. One of the guaranteed approaches to prove image forgery is JPEG double compressions. Double compression happens if a JPEG image is manipulated and saved again. Thus, the binaries of the image will be changed based on a “previous” quantization table. This paper presents a practical approach to detecting manipulated images using double JPEG compression analysis, implemented in a newly developed software tool. The method relies on an adaptive database of quantization tables, which stores all possible tables and generates new ones based on varying quality factors of recognized tables. The detection process is conducted through image metadata extraction, allowing analysis without the need for the original non-manipulated image. The tool analyzes the suspected image using chrominance, and luminance quantization tables utilizing the jpegio Python library. The tool recognizes camera sources as well as the programs used for manipulating images with the related compression rate. The tool has demonstrated effectiveness in identifying image manipulation, providing a useful tool for digital forensic investigations. The tool identified 96% of modified images whereas the other 4% identified as false positives. The tool fixes the false positives by extracting the software information from the image metadata. With a rich sources database, forensic examiners can use the proposed tool to detect manipulated evidence images using the evidence image only.  

In Uganda, the efficiency and reliability of solar power plants are often compromised due to inadequate battery management, leading to reduced battery lifespan and suboptimal performance. To address this challenge, this project develops and prototypes a smart Battery Management System (BMS) tailored for solar power plants. The system continuously monitors key battery parameters, including voltage, load current, and temperature, while leveraging Internet of Things (IoT) technology for real-time data transmission and remote monitoring. Intelligent algorithms autonomously regulate charging and discharging cycles to prevent overcharging and deep discharge, optimizing battery performance. Testing demonstrated that the BMS significantly improved battery lifespan and energy efficiency by disconnecting charging at 100% and isolating the load at 10% discharge to prevent battery degradation. Additionally, the system disconnects power when battery temperature exceeds 30°C (ambient temperature: 25°C) and detects abnormal current levels above 0.16A to mitigate faults such as short circuits. These automated protections enhance battery reliability and longevity. By implementing proactive battery management strategies, the developed BMS contributes to more efficient and resilient energy storage systems, promoting sustainable energy development in Uganda.

Nowadays, image synthesis has become essential in the medical field for lever- aging deep learning technique to improve decision- making. Our proposed research work combines Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to synthesize medical im- ages, enhancing diagnostics, medical training, and image analysis. The model presented combines a Discriminator, and a Variational Autoencoder to capitalize on the strengths of both VAEs and GANs. The Decoder is tasked with generating synthetic medical images, the Discriminator evaluates their distinguishing factor, and the VAE learns a probabilistic mapping from input to latent space, ensuring a structured representation of underlying medical features. The training process involves a decoder creating realistic medical images, a discriminator distinguishing real from synthetic ones, and a VAE capturing meaningful data variations in the latent space. Verified on the dataset sourced from the Kaggle. The model refines its parameters iteratively using a training loop, resulting in enhanced quality and variety of generated medical images. The proposed VAE- GAN model demonstrates its efficacy by generating diverse and realistic medical images. The structured latent space contributes to interpretability, making the images suitable for purposes like data augmentation, anomaly detection, and machine learning model training.