Solar irradiance prediction is an essential one in providing renewable energy proficiently. The solar irradiance plays a major role in solar power system, solar thermal system, and photovoltaic grid-connected system, owing to uncertainty and variability. Conventional data analysis approaches are complex for demonstrating superior generalization. Therefore, the resource planners are flexible in accommodating these uncertainties while executing planning. To enhance the performance of solar irradiance forecasting, a new Variant Input Scoring Optimized Recurrent Neural Network (VIS-ORNN) is developed. The suggested approach includes two stages that are data collection and three stage simulation. At first, the data are gathered from the various meteorological standard dataset. Then, the prediction begins with feeding data directly to the ORNN. Here, the parameters of RNN are optimized with the help of Adaptive Escaping Energy-based Harris Hawks Coyote Optimization (AEE-HHCO) algorithm. Thus, the first score prediction is obtained. In the second phase, the first order statistical features act as an input, and it is given to the same ORNN, in which the second score is determined. In the third phase, the deep features are extracted by Convolutional Neural Network (CNN) that is subjected to the same ORNN for attaining the score. Finally, the final simulation is determined by taking the average of three prediction models. From the experimental results, while taking the MAE, the suggested AEE-HHCO-ORNN method has correspondingly secured 34.3% enhanced than PSO-ORNN, 7.7% enhanced than WOA-ORNN, 21.7% enhanced than COA-ORNN and 26.5% enhanced than HHO-ORNN. Thus, the simulation outcomes reveal that the offered method ensures maximum accuracy while validating with other baseline methodologies.

Background: Tuberculosis (TB) is a significant global health issue, particularly in resource-limited regions where radiological expertise is constrained. This project aims to develop a scalable deep learning system that safeguards privacy and achieves high accuracy in the early identification of tuberculosis using chest X-ray images. The objective is to implement federated learning with an adaptive-weighted ensemble optimised by a Genetic Algorithm (GA) to address the challenges of centralised training and single-model approaches. Method: We developed an ensemble learning method that combines multiple locally trained models to improve diagnostic consistency and reduce individual-model bias. An optimisation system that autonomously selected the optimal ensemble weights determined each model’s contribution to the final decision. A controlled augmentation process was employed to enhance the model’s robustness and reduce the likelihood of overfitting by introducing realistic alterations to appearance, geometry, and acquisition conditions. Federated learning facilitated collaboration among universities for training while ensuring data privacy was maintained during the establishment of the optimal ensemble at each location. In this system, just model parameters were transmitted, excluding patient photographs. This enabled the secure amalgamation of global data without revealing sensitive clinical information. Standard diagnostic metrics, including accuracy, sensitivity, precision, F1 score, AUC, and confusion matrices, were employed to evaluate the model’s performance. Results: The proposed federated, GA-optimized ensemble demonstrated superior performance compared with individual models and fixed-weight ensembles. The system achieved 98% accuracy, 97% F1 score, and 0.999 AUC, indicating highly reliable discrimination between TB-positive and typical cases. Federated learning preserved model robustness across heterogeneous data sources, while ensuring complete patient privacy. Conclusions: The proposed federated, GA-optimized ensemble achieves highly accurate and robust early tuberculosis detection while preserving patient privacy across distributed clinical sites. This scalable framework demonstrates strong potential for reliable AI-assisted TB screening in resource-limited healthcare settings.

The performance analysis in the present study examines the OTP in contrast to popular contemporary cryptographic techniques like AES, RSA and so on. Four criteria are to be examined: Their security, their protection against attacks with quantum computers, the required key management and their practical feasibility. OTP is presented as an ideal solution in terms of security theory while being critically described in terms of the application to a large-scale system including the problem of key generation, distribution, and storage. However other contemporary methods like the AES or RSA are relatively more suitable to be used in real life systems because the execution time and space complexities are more bearable. However, as the technology of quantum computers develops, these algorithms become most threatened by quantum attacks, especially through those such as Shor’s and Grover’s. The paper also explores the concept of post-quantum cryptography as a developing field for finding solutions to protect encryption from such threats backed by quantum computing. These alternatives remain still in the early stage of development but appear to have great possibilities to protect digital communications in a world in which quantum computers could crack the more conventional cryptographic protocols. The study reveals that there is a need to achieve proper proportionalities in designing cryptographic processes and the security measures offered to ensure that they are not only strong but also implementable. OTP, being a concept of perfect secrecy, however, is not feasible for use in large applications hence why there is a need to search for algorithms that offer high-security levels while at the same time operational. With the advancement of quantum computing, it is essential that the future work be directed toward the proposition of algorithms and communication paradigms that would be both effect and efficient, and, most importantly, secure from attacks utilizing capabilities of quantum computers. Hence, the findings underscore the need for designs of cryptographic protocols that support a high level of security during deployment while still being practical for future implementation as the technologies change.

The development of communication technologies has been rapid leading to a connector world where information can be produced and passed in record time. But these occurrences have served to increase the potential of unauthorized breach into secure information databases. In elaborating on the framework for the construction of DSTS, this paper presents a hybrid model – namely, the application of One-Time Pad (OTP) cryptography combined with Lempel-Ziv-Welch (LZW) compression. DSTS improves data hiding capability, provides a secure system, and resists being detected. Therefore, compared to the existing methods, this method utilizes the strength of OTP that cannot be cracked along with the method of LZW compression to advance the design of sophisticated secure communication systems.