Blockchain, a breakthrough technology, offers solutions to several healthcare challenges, includes Managing and sharing data securely, Making different systems work together seamlessly, Increasing transparency in the supply chain, Giving patients more control over their health information, and Adhering to regulations and guidelines. This paper offers an in-depth study of current literature on blockchain in healthcare. It covers areas that have been investigated, studied and implemented by previous research, provides valuable insights, and introduces practical suggestions. Research findings indicate that blockchain has the capacity to improve data security, privacy, and immutability. It can also facilitate effortless data sharing among different parties involved, enhance transparency in pharmaceutical supply chains, empower patients in managing their health information, and address regulatory concerns. The results of this study recommends Healthcare organizations to consider implementing secure transaction protocols to ensure the confidentiality and integrity of their data. Also to explore the use of Hyperledger Fabric architecture, a blockchain framework specifically designed for enterprise applications, Implementation of Secure Transaction Protocols, Utilization of Blockchain for Data Sharing, Adoption of Lightweight Consensus Protocols. By leveraging blockchain technology, healthcare businesses may securely share data with other trusted parties, improving interoperability and data exchange. Additionally, adopting lightweight consensus methods can help streamline decision-making processes within the organization. Future research shall involve the integration of blockchain technology with artificial intelligence and data analytics, the establishment of decentralized parallel healthcare organizations, the integration of blockchain with cloud computing, and the resolution of regulatory and standards difficulties

The Long-Term Evolution (LTE) system was a result of the 3rd-Generation Partnership Project (3GPP) to assure Quality-of-Service (QoS) performance pertaining to non-real-time and real-time services. An effective design with regards to resource allocation scheduling involves core challenges to realising a satisfactory service in an LTE system, particularly with the growing demand for network applications. The continuous rise in terms of the number of network users has resulted in impacts on the performance of networks, which also creates resource allocation issues when performing downlink scheduling in an LTE network. This research paper puts forward a review of optimisation pertaining packet scheduling performance through the LTE downlink system by introducing a new downlink-scheduling algorithm for serving video application through LTE culler networks, and also accounts for QoS needs and channel conditions. A comparison of the recommended algorithms’ performances was made with regards to delay, throughput, PLR, and fairness by utilising the LTE-SIM simulator for video flow. On the basis of the outcomes obtained, the algorithms recommended in this research work considerably enhance the efficacy of video streaming compared against familiar LTE algorithms.

Long-Term Evolution (LTE) technology is utilized efficiently for wireless broadband communication for mobile devices. It provides flexible bandwidth and frequency with high speed and peak data rates. Optimizing resource allocation is vital for improving the performance of the Long-Term Evolution (LTE) system and meeting the user’s quality of service (QoS) needs. The resource distribution in video streaming affects the LTE network performance, reducing network fairness and causing increased delay and lower data throughput. This study proposes a novel approach utilizing an artificial neural network (ANN) based on normalized radial basis function NN (RBFNN) and generalized regression NN (GRNN) techniques. The 3rd Generation Partnership Project (3GPP) is proposed to derive accurate and reliable data output using the LTE downlink scheduling algorithms. The performance of the proposed methods is compared based on their packet loss rate, throughput, delay, spectrum efficiency, and fairness factors. The results of the proposed algorithm significantly improve the efficiency of real-time streaming compared to the LTE-DL algorithms. These improvements are also shown in the form of lower computational complexity.

Gearbox is an important component in machine system. Hence, it is important to predict and maintain the performance of the gear system, since any unpredictable failure in this system can seriously threaten human lives and cause significant economic losses. Therefore, it is essential to inspect the gear teeth at regular intervals for identifying the crack propagation or other damages affecting the system in advance at the early stage. In this study, a new method have been proposed by the researchers which utilized artificial intelligence processes for routine maintenance. A new 3rd generation Artificial Neural Network (ANN) has been used for diagnosing and classifying the faults occurring in the spur gear systems based on the Acoustic Emission (AE) signals. Hence, they developed a test rig with various seeded gear faults, which later processed the acquired AE signals by utilizing the pre-processing technique based on the Slantlet Transform (SLT), feature extraction and Information Gain (IG) processes. These processes were used before the use of a feature selection technique which was used for developing the Spiking Neural Network (SNN) diagnosis and classification model. These processes were run using a Graphical User Interface (GUI) for fault diagnosis and classification of spur gears. Results of the study showed that this process could improve the accuracy of the diagnosis system depending on the features and information which was fed to the model. In this study, the researchers investigated the probability of increasing the accuracy better for the spur gear fault diagnosis with the help of the Spiking Neural Network (SNN) process. They achieved an accuracy of ≥95% when using SNN. Finally, it was concluded that the proposed technique was as an effective tool for diagnosing and classifying the faults identified in the spur gears.

Spiking neural networks (SNNs) use individual temporal spikes for computation and communication, simulating the actions of biological neurons. SNN had long been disregarded since it was thought to be intricate and difficult to analyze. We investigate the improvement of SpikeProp, a supervised learning model tailored for SNNs, in this work. Three distinct models are being proposed and investigated, including the proposed model 1, the proposed model 2, and the proposed model 3, each providing unique improvements to the SpikeProp algorithm. To accelerate convergence and adaptive learning rates, particle swarm optimization (PSO) and momentum factors are integrated into the proposed model 1. In proposed model 2, a rate dependency is introduced based on angle-driven learning. By incorporating PSO and learning rates, model 3 combines the strengths of both models 1 and 2. We believe, SNNs can be trained and classified more efficiently and accurately using these models. Furthermore, we examine how large language models (LLMs) might inform the design and interpretability of neural architectures and learning methodologies while also enhancing SNN training. Through the use of LLMs, we seek to enhance model transparency and encourage more Responsible AI (RAI) principles. A thorough evaluation and comparison of proposed models with traditional methods confirms that these models consistently outperform traditional methods for various real datasets. Consequently, they have a high potential for practical applications in neural network training in real-world settings and LLM-informed development, contributing to the advancement of AI systems.

Spatially non-uniform haze, temporal inconsistencies such as frame-to-frame flickering and abrupt airlight variation, and color distortion artifacts including color shift, desaturation, and contrast imbalance make video dehazing a challenging task. While recent deep learning based methods are capable of producing strong spatial restoration, they often suffer adversely from temporal instability in heavy and dynamically varying conditions of haze. In this paper, we present a Spatio-Temporal Adaptive Airlight Estimation (STAAE) framework for video dehazing. The proposed method tackles heterogeneous atmospheric conditions by modeling airlight as a spatio-temporally varying parameter and blends depth-aware transmission estimation with adaptive post-enhancement. Through the use of region based airlight estimation and temporal consistency constraints as well as transmission refinement aimed at preserving features, STAAE can yield stable haze removal results that are visually consistent under the experimental settings evaluated in this work. Experiments on synthetic and real-world video datasets report quantified improvements in spatial quality, structural fidelity, temporal stability, and downstream vision performance compared with the evaluated baselines and the closest rivals. Specifically, our evaluation demonstrates that STAAE can achieve PSNR = 23.67 ± 0.89 dB, SSIM = 0.913 ± 0.021, and reduces the Temporal Flicker Index (TFI) from 0.068 to 0.031 (54.4% reduction) relative to the strongest baseline approach. Furthermore, YOLOv5 mAP@0.5 improves by 42.8%–63.7% across light-to-dense haze levels.