Medical data classification is an important factor in improving diagnosis and treatment and can assist physicians in making decisions about serious diseases by collecting symptoms and medical analyses. In this work, hybrid classification optimization methods such as Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Fireworks Algorithm (FWA), are proposed for enhancing the classification accuracy of the Artificial Neural Network (ANN). The enhancement process is tested through two experiments. First, the proposed algorithms are applied on five benchmark medical data sets from the repository of the University of California in Irvine (UCI). The model with the best results is then used in the second experiment, which focuses on tuning the parameters of the selected algorithm by choosing a different number of iterations in ANNs with different numbers of hidden layers. Enhanced ANN with the three optimization algorithms are tested on biological gene sequence big dataset obtained from The Cancer Genome Atlas (TCGA) repository. GA and FWA are statistically significant but PSO was statistically not, and GA overcame PSO and FWA in performance. The methodology is successful and registers improvements in every step, as significant results are obtained.

Parkinson's disease (PD) is a movement disorder characterized by motor dysfunction commonly bradyphemia, tremor, rigidity, akinesia, or slowness of movement. Noting that motor states can fluctuate in PD the primary aim of this current paper was to differentiate multiple states using wearable sensors in the patients and detection of this PD based on deep learning (CNN). Methodology: In this paper, the researchers recorded the signals of the accelerometer and gyroscope fixed on the wrist of PD in their regular daily functioning after using this dataset collection. The deep learning architecture developed was to optimize a CNN for analyzing the sensor data for motor status like rest, tremor, and dyskinesia. Results and Conclusion: The availability of high accuracy in defining different degrees of motor state was achieved through the deep learning algorithm hence providing a high option in monitoring pd symptoms. These outcomes also show and suggest the possibility and practicality of the wearable sensor for daily activity monitoring for the identification of PD. It can also provide appropriate information to patients other than doctors regarding their sickness which gives them a platform to effectively handle Parkinson’s disease patients. The error has been determined to be within the vicinity of 98%.

The environment friendly designing and running of electricity distribution structures are becoming vaster with the conjunction of renewable electricity options into power distribution systems. This study presents Moth Flame Optimization (MFO) approach to minimize total losses kW and energy cost and maximize net savings with solving the voltage deviation problem through optimal site and size of capacitor banks in electrical radial distribution networks. Two various load profiles are utilized to take a look at the modified algorithm on IEEE 123-, and 13-bus check networks. The OpenDSS software is utilized to resolve the power flowing via MATLAB programming interface. The results obtained are compared with IEEE normal state and other two heuristic approaches in same study, namely, imperialist competitive algorithm and particle swarm optimization. The proposed algorithm is more effective to minimise the total power losses and total cost and maximise net savings and to enhance the voltage profile for various distribution systems.

In recent years, blockchain technology has gained significant attention due to its ability to revolutionize various industries. One area that has seen interest is database management systems (DBMS). Blockchain technology's ability to be integrated with database management systems could transform healthcare networks. With regard to enhanced privacy, security, scalability, and regulatory compliance, this study examines the impact of blockchain on healthcare data management. Blockchain ensures data integrity and authenticity by utilizing cryptographic techniques such as hashing, encryption, and digital signatures, which mitigate vulnerabilities in traditional systems. In addition to patient-centric care, interoperability, and research advancements, blockchain has an experienced impact on healthcare. Health care systems undergo a revolution as patient data ownership, secure sharing, and compliance management are revolutionized, fostering trust among stakeholders and spurring collaboration. It is undeniable that blockchain will revolutionize healthcare data management, ushering in a new era of patient-centered, transparent healthcare. It will also discuss potential healthcare use cases, implementation strategies, and best practices for hybrid systems, as well as a comparative analysis of blockchain and traditional database systems.

Background/Objectives: Breast cancer continues to be one of the most serious and common afflictions affecting women around the globe. Despite ultrasound imaging being an effective method for the detection of abnormalities in dense breast tissues, there are a number of drawbacks when utilizing this method, including the subjective nature of the imaging and the variant nature of the imaging due to the cognitive biases of the interpreting expert and the experience of the interpreting expert. The above factors are the cause of the increased need in the implementation of AI-driven models for diagnostic analysis. In this research, we provide a hybrid deep learning-based framework for cancer classification of the breast cancer ultrasound image dataset (‘BUSI dataset’). Methods: The contributing models of the proposed architecture involve the combination of a light ViT encoder and an EfficientNetV2-RW-S feature extractor. The combination mentioned leverage the positive sensitivities of the convolutional neural networks (CNNs) and the global reasoning neural networks (i.e., transformers) in the explanation of the architecture. The reason being, EfficientNetV2 diminishes the capture of the fine-grained morphological components of the lesions, edges, and echogenic variances of the tissue, whereas the transformer model diminishes the long-range dependencies of the lesions and other surrounding tissues. Results: The experimental results from the proposed hybrid model of the architecture demonstrates an enhanced classification accuracy of 97.95%, in contrast to the self-standing models of the architecture, the hybrid model supersedes the isolated ViT model (i.e., 89%) and the isolated CNN model (i.e., 80%) frameworks. Furthermore, the proposed model hybrid architecture also diminishes the overall self-attention computational complexity of the proposed model by substantially diminishing the number of tokens reaching an overall count of 10 (from the vast 197 tokens). This further leads to a substantial decrease in the memory and cost expended during the attention processes. Conclusions: Overall, this study proposes a method for the improved diagnostic and computational analysis, suggesting the proposed architecture to be a potential framework for use in the contemporary clinical environments.