Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.

Industrial organizations are turning to recommender systems (RSs) to provide more personalized experiences to customers. This technology provides an efficient solution to the over-choice problem by quickly combing through large amounts of information and supplying recommendations that fit each user’s individual preferences. It is quickly becoming an integral part of operations, as it yields successful and convenient results. This research provides an enhanced integrated fuzzy logic-based deep learning technique (EIFL-DL) for recent industrial challenges. Extracting useful insights and making appropriate suggestions in industrial settings is difficult due to the fast development of data. Traditional RSs often struggle to handle the complexity and uncertainty inherent in industrial data. To address these limitations, we propose an EIFL-DL framework that combines fuzzy logic and deep learning techniques to enhance recommendation accuracy and interpretability. The EIFL-DL framework leverages fuzzy logic to handle uncertainty and vagueness in industrial data. Fuzzy logic enables the modelling of imprecise and uncertain information, and the system is able to capture nuanced relationships and make more accurate recommendations. Deep learning techniques, on the other hand, excel at extracting complex patterns and features from large-scale data. By integrating fuzzy logic with deep learning, the EIFL-DL framework harnesses the strengths of both approaches to overcome the limitations of traditional RSs. The proposed framework consists of three main stages: data preprocessing, feature extraction, and recommendation generation. In the data preprocessing stage, industrial data is cleaned, normalized, and transformed into fuzzy sets to handle uncertainty. The feature extraction stage employs deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to extract meaningful features from the preprocessed data. Finally, the recommendation generation stage utilizes fuzzy logic-based rules and a hybrid recommendation algorithm to generate accurate and interpretable recommendations for industrial applications.

This paper proposes a new controller based on the torque compensation method for the control of a 7-DOF dual-arm of a humanoid robot with input saturation residual vibration of the end-effector. Through theoretical and experimental analysis, an adaptive fuzzy backstepping control strategy was designed for the 7-DOF dual-arm control system. This strategy can be used to suppress the time-varying nonlinear residual vibration of the end-effector caused by inertia variations in the serial robot. Firstly, a boundary feedback controller was designed to asymptotically stabilize the closed-loop system based on the C ₀ -semigroup theory, which also proves the efficacy of the closed-loop system solution. Lyapunov stability analysis proved that the closed-loop demonstrated globally asymptotic stability. Secondly, an adaptive fuzzy backstepping method was designed for the dynamic compensator with a tracking filter for the control system of the 7-DOF dual-arm of the humanoid robot. The primary difference between this new method and conventional methods is that the new method does not require complex computation. In addition, this method improves the limit of computing power and memory space for the controllers. Finally, the effectiveness of the torque compensation strategy and the new method’s ability to suppress the residual vibration are demonstrated via simulation and experiments.