This paper introduces a novel hybrid approach, termed STO-IWGAN, aimed at enhancing energy optimization in fuel cell electric vehicles (FCEVs). The key to maintaining the system's normal operation is the energy management plan and hybrid system performance optimization. The STO-IWGAN method combines the Siberian Tiger Optimization (STO) and an enhanced Wasserstein Generative Adversarial Network (IWGAN). The primary objectives of this approach include improving energy efficiency, reducing fuel usage, and enhancing overall FCEV performance. The STO method is utilized to optimize the operating parameters of the fuel cell arrangement, while the IWGAN method predicts the vehicle's power demand. The proposed technique is implemented and evaluated using MATLAB, benchmarked against contemporary methods. Comparative analysis reveals that the STO-IWGAN method outperforms existing approaches such as heap-based optimization, particle swarm optimization, and wild horse optimization. This work aims to contribute to research on increasing the hybrid power system's energy utilization efficiency and fuel cell the lifespan, offer recommendations for optimal control strategy and structural design, and offer additional ideas for future energy management optimization. The efficiency value of the proposed method, recorded at 95 %, surpasses that of other existing techniques, indicating its superior performance in energy optimization for FCEVs. The Existing HBO method efficiency value is 85 %, PSO method efficiency value is 75 % and the WHO method efficiency value is 65 %. The proposed method efficiency value is higher than other existing method.

This manuscript proposes a self-attention based progressive generative adversarial network optimized with momentum search optimization algorithm for brain tumor classification on MRI image (SPGAN-MSOA-CBTMRI). Initially, the data are gathered through the Brats 2019 dataset. Afterward, the data are given to preprocessing. In pre-processing, it reduces the noise and maximizes the superiority of input image utilizing anisotropic diffusion Kuwahara filtering (ADKF). Then the pre-processing output is fed to Feature extraction segment. Here, six texture features, like homogeneity (Angular Second Moment), contrast, Inverse difference moment, entropy, correlation and variance are extracted based on ternary pattern and discrete wavelet transform (TP-DWT). The extracted features are given to SPGAN for effectively classify the brain tumor on MRI image. The proposed SPGAN-MSOA-CBT-MRI approach is implemented in MATLAB. The performance metrics is evaluated to check the robustness of the proposed technique. The performance of proposed SPGAN-MSOA-CBT-MRI approach attains 6.45%, 9.45%, and 11.67% high accuracy;7.23%, 10.34%, and 12.56% high F-score compared with existing methods, such as GAM-SpCaNet: Gradient awareness minimization-based spinal convolution attention network for brain tumor classification (GAM-SpCaNet-CBT-MRI), Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel (CNN RCNN-CBT-MRI) and Smart brain tumor detection scheme with the help of deep convolutional neural networks (DCNN-CBT-MRI) respectively.

The occurrence of Chronic Renal Disease (CRD), is also referred to as Chronic Kidney Disease (CKD). It depicts a medical condition that harms the kidneys and has an impact on a person's overall health. End-stage renal disease and the patient's eventual mortality can result from improper disease diagnosis and treatment. In the field of medical science, Machine Learning (ML) techniques have become a valuable tool and play a significant role in disease prediction. The development and validation of a predictive model for the prognosis of chronic renal disease is the aim of the proposed study. A dataset on chronic kidney disease with 400 samples was taken from the UCI Machine Learning Repository. Three machine learning classifiersLogistic Regression (LR), Decision Tree (DT), and Support Vector Machine (SVM)-were used for analysis, and the bagging ensemble method was used to enhance the model's performance. The machine learning classifiers were trained using the clusters of the dataset for chronic renal disease. The Kidney Disease Collection is then compiled using nonlinear features and categories. The decision tree produces the best results, with an accuracy of 95%. Finally, we achieve the greatest accuracy of 97% by using the bagging ensemble approach.

Modern commerce outlets are typically preferred by conglomerates of enterprises or chains of businesses. However, the performance of these company chains is hindered by a variety of issues that together harm them. According to the findings of the current research, the marketing performance of modern retail trade in India is significantly impacted by both the introduction of new services and the management of employees. As a result, the purpose of this study is to examine the impact that service innovation and staff engagement have on the overall marketing effectiveness of modern retail establishments. However, market orientation and consumer service performance act as mediators of the relationship between service innovation, employee engagement, and marketing performance. It has been observed that there is a direct relationship between service innovation, employee engagement, and marketing performance. The results of the present study were obtained by the collection of primary data through the administration of a survey to 370 marketing managers in India who are employed by a variety of brands, organizations, and businesses.

Currently various sectors are growing due to the continuous rise of technological development. Thus the use of machinery has increased and the use of humans has decreased. Machines work and humans oversee it. In this case, artificial intelligence technologies such as Amazon Echo and Google’s Voice Assistant are seen as the most advanced technologies based on human voice comment. As a result most of the time is saved and advanced technologies that kill this kind of artificial intelligence have the power to easily complete even some complex tasks that cannot be done by man. In this paper, a virtual assistant algorithm with such advanced artificial intelligence is proposed. It is designed with the ability to get the required data as input through voice and text, and get the job done. And its potential possibilities have been compared with some of the algorithms currently in practice. Thus the optimal performance of the proposed algorithm is determined based on the results.

This paper presents results of an initial investigation into models and control strategies suitable to predict and prevent vehicle rollover due to untripped driving maneuvers. Outside of industry, the study of vehicle rollover inclusive of experimental validation, model-based predictive algorithms, and practical controller design is limited. The researcher interested in initiating study on rollover dynamics and control is left with the challenging task of identifying suitable vehicle models from the literature, comparing these models in their ability to match experimental results, and determining suitable parameters for the models and controller gains. For vehicles that are deemed to be susceptible to wheel-lift, various open-loop control strategies are implemented in simulation. The primary assumption in their implementation is that the vehicle in question is equipped with a steer-by-wire system. Nomenclature

This paper presents results of an initial investigation into models and control strategies suitable to predict and prevent vehicle rollover due to untripped driving maneuvers. Outside of industry, the study of vehicle rollover inclusive of experimental validation, model-based predictive algorithms, and practical controller design is limited. The researcher interested in initiating study on rollover dynamics and control is left with the challenging task of identifying suitable vehicle models from the literature, comparing these models in their ability to match experimental results, and determining suitable parameters for the models and controller gains. This paper presents results that address these issues via comparisons between simulation and experimental results. For vehicles that are deemed to be susceptible to wheel-lift, various open-loop and closed-loop control strategies are implemented in simulation. The primary assumption in their implementation is that the vehicle in question is equipped with a steer-by-wire system.