There is a substantial worldwide effect, both in terms of disease and death, that is caused by pediatric pneumonia, which is a disorder that affects children under the age of five. Even while Streptococcus pneumoniae is the most prevalent agent responsible for this sickness, it may also be brought on by other bacteria, viruses, or fungi. An efficient approach utilizing deep-learning methods to forecast pediatric pneumonia reliably using chest X-ray images has been developed. The current study presents an updated version of the DenseNet-121 deep-learning model developed for identifying scans of pediatric pneumonia. The batch normalization, maximum pooling, and dropout layers introduced into the standard model were done so to improve its accuracy. The activations of the preceding layers are scaled and normalized using batch normalization, leading to a mean value of zero and a variance of one. This helps to decrease internal variability during training, which in turn speeds up the training process, promotes model stability, and improves the model’s overall capacity to generalize. Max pooling is a beneficial technique for cutting down on the number of model parameters, making the model more computationally effective. Meanwhile, dropout is a preventative measure against overfitting by decreasing the co-dependence of neurons. As a result, the network acquires more durable and adaptive features. Classifying instances of pediatric pneumonia with the help of the proposed model resulted in an exceptional accuracy rate of 97.03%.

The marine microalgae Chaetoceros calcitrans were used in the synthesis of silver or argentum nanoparticles. The reduction of silver nitrate to silver nanoparticles is observed in marine microalgae. The silver nanoparticles have a good imperative role in technological fields. The synthesis of silver nanoparticles from marine microalgae is a green nanotechnological method. The silver nanoparticle characterization is then carried out. The presence of silver nanoparticles is confirmed by UV‐Vis spectroscopy. Fourier transform infrared spectroscopy, atomic force microscopy, photoluminescence, and X‐ray diffractometry were used for the characterization analysis of synthesized silver nanoparticles. The research on the antimicrobial activity of silver nanoparticles is made to analyze the antagonistic nature against pathogenic organisms.

Nanotechnology is one of the most common areas for current research and development in almost all technological fields. A significant factor is the synergistic benefit of nanoscale dimensions over larger scale alteration. Polymer nanoscience is the analysis and application of nanotechnology to polymer nanoparticle matrices, with nanoparticles described as those with at least one dimension of less than 100 nm. The use of polymer nanotechnology and nanocomposites in practical applications is a rapidly developing area. For making polymeric nanofibers from polymer melts and solutions, a spinning technique is used known as electrospinning. Electrospinning is an easy way to produce ultrafine fibers, which is nanosize. For its wide range of variety of spinning polymeric fibers, it is recommended, as well as producing fibers in nanosize accurately. The aim of this project is to use electrospinning to make nanoclay integrated polycaprolactone membranes. The effects of the nanoclay on morphology, thermal, and sorption behaviors of the electrospun membrane were further studied. The scope of this project work is that the electrospun nanocomposites are best studied for biomedical applications. Because of their influence over porosity, pore size, and fiber diameter, they make excellent scaffold materials.

The rapid expansion of Internet of Things (IoT) devices has brought out novel prospects and obstacles, namely in the domains of energy management and resource optimisation. Ensuring energy efficiency becomes essential as IoT networks grow in order to promote sustainable deployments, save operating costs, and extend the life of devices. The increasing intricacy and dynamic character of contemporary IoT systems sometimes prove to be too much for traditional resource management techniques to handle. The potential of machine learning (ML) to improve resource management in energy-efficient Internet of Things networks is examined in this research. Through the use of machine learning techniques, such as deep learning, supervised learning, and reinforcement learning, Internet of Things systems are able to forecast network traffic, allocate resources intelligently, and reduce energy usage in real time. The study examines cutting-edge methods, addresses important issues, and indicates areas for further investigation. In the end, this research highlights how machine learning may play a revolutionary role in creating IoT networks that are scalable, energy-efficient, sustainable, and able to satisfy the expectations of a world that is becoming more and more connected.

This research introduces an innovative deep learning framework aimed at improving remote sensing image fusion through the Attention Fusion Residual (AFR) technique. The model employs attention mechanisms to prioritize critical features from the source images, while the residual network facilitates the preservation of intricate details. By merging multi-spectral and panchromatic images, the AFR technique enhances both spatial and spectral quality. Experimental findings indicate that the proposed method surpasses conventional and leading-edge techniques in terms of visual quality and quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), representing a noteworthy progression in the specific field of image processing and remote sensing. Thus the fused images demonstrate enhanced detail and better noise suppression compared to traditional fusion techniques. These improvements are particularly evident in the enhanced visibility of fine details and in maintaining the color fidelity of the landscape, which are critical for applications in environmental monitoring and urban planning.