This study focuses on leveraging data-driven techniques to diagnose brain tumors through magnetic resonance imaging (MRI) images. Utilizing the rule of deep learning (DL), we introduce and fine-tune two robust frameworks, ResNet 50 and Inception V3, specifically designed for the classification of brain MRI images. Building upon the previous success of ResNet 50 and Inception V3 in classifying other medical imaging datasets, our investigation encompasses datasets with distinct characteristics, including one with four classes and another with two. The primary contribution of our research lies in the meticulous curation of these paired datasets. We have also integrated essential techniques, including Early Stopping and ReduceLROnPlateau, to refine the model through hyperparameter optimization. This involved adding extra layers, experimenting with various loss functions and learning rates, and incorporating dropout layers and regularization to ensure model convergence in predictions. Furthermore, strategic enhancements, such as customized pooling and regularization layers, have significantly elevated the accuracy of our models, resulting in remarkable classification accuracy. Notably, the pairing of ResNet 50 with the Nadam optimizer yields extraordinary accuracy rates, reaching 99.34% for gliomas, 93.52% for meningiomas, 98.68% for non-tumorous images, and 97.70% for pituitary tumors. These results underscore the transformative potential of our custom-made approach, achieving an aggregate testing accuracy of 97.68% for these four distinct classes. In a two-class dataset, Resnet 50 with the Adam optimizer excels, demonstrating better precision, recall, F1 score, and an overall accuracy of 99.84%. Moreover, it attains perfect per-class accuracy of 99.62% for ‘Tumor Positive’ and 100% for ‘Tumor Negative’, underscoring a remarkable advancement in the realm of brain tumor categorization. This research underscores the innovative possibilities of DL models and our specialized optimization methods in the domain of diagnosing brain cancer from MRI images.

The management of the production line is a challenging task due to the high level of uncertainty in demand, which can lead to unbalanced utilization of resources. This may result in a potential deterioration of management satisfaction in terms of cost-effectiveness. Therefore, it requires efficient tools to optimize resource utilization. With such inherent needs, this paper presents a simulation-based decision support framework for garments industries. The Discrete Event Simulation (DES) is used to model different scenarios for the operational processes. The procedure focuses on the line balancing technique, which aims to eliminate bottlenecks and optimize the production process by balancing the workload. The results of this study demonstrate the effectiveness of the line balancing technique in improving line efficiency, reducing the idle time of the operators, and increasing productivity. The simulation was developed using AnyLogic simulation software. The outcome of the process is thoroughly evaluated and justified using a case study.

While travelling with speedy vehicles like motorbikes, cars, buses, and trucks, a huge wind flow is seen to be flowing against the travel direction which inhibits huge renewable energy. In this study, the author constructed a portable wind generator through which this huge renewable energy/wind energy can be converted into effective and usable electric energy. As this is a light weighted, small and portable generator, it can be carried easily while travelling and can be installed/ fixed at suitable places of any speedy vehicles. And can get necessary electric power at the time of need. The electricity generated against the vehicle’s speeds of 20 kph, 30 kph, 40 kph, 50 kph and 60 kph is measured with the help of a multimeter, and an average 20v, 35v, 50v, 70v and 105v AC voltages are found respectively. This generated electric power can be stored in a battery or power bank through which various electronic apparatus like mobile phones, torch light etc. can be charged simultaneously or later.