Categorizing brain cancers, including glioma, meningioma, and pituitary tumors, based on magnetic resonance imaging (MRI) images presents a significant challenge. Deep learning and machine learning techniques have shown promise in enhancing image categorization. To address this challenge, we leverage the power of the optimized ResNet50 model. Our approach involves classifying medical images using Convolutional Neural Network (CNN) features, which are then compared with the ResNet50 model. The primary goal is to detect brain tumors at an early stage using an advanced deep-learning model. We utilize an accessible dataset from Figshare, containing MRI images of the three distinct categories of brain tumors. Existing brain tumor models face limitations in handling multi-class problems and early-stage diagnosis. Therefore, we propose a fully automated approach employing Convolutional Neural Networks (CNN) to extract diverse properties from brain MRI scans. This method aims to provide accurate tumor diagnosis, even with a high number of classes and limited information in MRI data. Our proposed model involves the creation of identification blocks within a four-layered primary architecture, followed by testing and assessment of the interconnected layers. The results demonstrate that our model outperforms existing methods, achieving an impressive overall classification accuracy of 99.03%,

The major objective of this research is to design sensitive components, conversion components, and various sensor circuits to achieve the miniaturization design for more accurate measurements. This article conducts performance testing on the designed miniaturized pressure sensor to determine whether it meets the qualified standards. The response time of designed sensor results increases with the increase of pressure under experimental conditions of different pressure application values (5MPa to 50MPa). The detection accuracy of the micro automatic pressure measurement sensor designed in this paper can reach 0.0452\%; the average pressure is 0.00364%, and the insulation resistance is 68.44 megohms, which meets reliability requirements. The sensitivity is 0.0582%; the nonlinearity is 0.0741%; the hysteresis is 0.0266%; the repeatability of 0.0625% meets the qualification standard for this instrument. Still, compared with traditional sensors, the sensor reduces the response time of results by about 60%. However, the author conducted the detection in an ideal environment. The actual working environment of sensors is relatively good. Therefore, the detection results obtained in this article may have some errors compared to the actual situation, and further analysis and testing are needed to optimize the performance of the designed sensor.

In dictionary-based single-frame image reconstruction algorithms, dictionaries rely on the design of artificial shallow features and are limited in their ability to represent image features. Therefore, this paper proposes a high-accuracy reconstruction method based on deep learning feature dictionary. This algorithm first uses a deep network to learn high-resolution and low-resolution training example images with deep features; Then co-train the feature dictionary under the super dense framework of the sparse dictionary; Finally, a single low-resolution image can be input and a super-resolution reconstruction can be performed using a dictionary. From the theoretical analysis, the introduction of deep network to extract the deep-level features of the image and its use in dictionary training is more beneficial to complement the high-frequency information in the low-resolution image. Experiments show that the proposed method achieves the best results in terms of both the peak signal-to-noise ratio and the gradient energy function of the reconstructed images. This shows that compared with traditional interpolation methods and some deep learning methods, the proposed method can recover image details to a high degree while preserving the original image damage information. This proves that the subjective visual and objective evaluation indicators of the algorithm presented in this article are higher than those of the comparative algorithm.

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

Developing, implementing, and maintaining a multi-intelligent body collaboration system necessitates significant investments in finances, time, and expertise. While multi-intelligent body collaboration has the potential to enhance power system stability significantly, it also comes with challenges related to interoperability, security, system complexity, and resource allocation. Resource allocation and training costs can be substantial. Addressing these challenges is crucial to harnessing the full benefits of this approach and ensuring the reliable and efficient operation of power systems. Effective communication and coordination strategies among intelligent agents are integral to maintaining power system stability. Timely information exchange, load balancing, disturbance management, and the integration of AI contribute to a more resilient and adaptive energy grid. As technology advances, refining these strategies will be essential to meet the growing demands of an ever-evolving power landscape. As technology marches forward, it becomes increasingly evident that the refinement of these strategies is paramount. The dynamism of the power landscape, driven by technological advancements and evolving needs, necessitates an agile and adaptable power system. The fusion of multi-intelligent bodies and modern technology stands as a testament to our collective pursuit of a more reliable, efficient, and sustainable energy future. In this ever-evolving landscape, the innovation and enhancement of these strategies are our compass, guiding us toward a brighter and more efficient future.

In order to better understand the efficiency optimization problem of radio energy transmission systems, the author proposes a genetic algorithm based research on efficiency optimization of radio energy transmission systems. The author first addresses the issue of improving the efficiency of magnetic coupled radio energy transmission. On the basis of ensuring a certain transmission distance and voltage gain, the system is mathematically modeled using coupling circuit theory, and mathematical expressions such as transmission efficiency, transmission distance, and voltage gain are obtained as the objective functions of the algorithm. Secondly, the impact of metal obstacles on the transmission system was analyzed. Design a radio energy transmission compensation circuit, and through simulation, obtain three transmission system parameter schemes that meet the objective function and constraint conditions. Finally, the multi-objective genetic algorithm is used to optimize the system parameter design and obtain the optimal combination of transmission system parameters, with coupling coefficient k=0.1818 and mutual inductance coefficient M=23.165 × 10^(-5) H. Using multi-objective genetic algorithm, the algorithm has a fast convergence function in terms of the number of iterations, a non dominated function solution, and Pareto graphs have verified that the numerical value (3) in the text is the optimal combination design for the transmission system.

The main application of the informative data in the sector which are related to the energy are defines and explains as one of the crucial elements of Energy Internet. The advancement of the grid system are very vital as well as promising and faces many issues that are connected with the implementation of the renewable energy including solar and wind energy. The capacity of collecting of the data is the main elements of make are easy in taking decisions. The advancement of the technologies and its improvement has many benefits and advantages which was shown by the data analytic of the renewable source of energy in the various power stations. This is the framework which shows the development and growth of the potential establishment of the analyzation of the data and information in the smart grid and the utilities of power by the renewable resources. The seven domains and approaches are used for the purposed of predicting the stability, flexibility and safety from the advancement of the grid system. The secondary qualitative methods are used to define and explain the importance of the grid system in relation with the renewable source of energy that is wing and solar energy.

In order to solve the problem that the channel estimation accuracy of the traditional urban communication system is not high, the author proposes the channel estimation of the urban 5G communication system based on the improved particle optimization algorithm. This method converts the channel estimate into a regression fit and adjusts the fit. Focusing on regression fitting problems, big data models are used to display offline data, study channel nonlinearities, and obtain initial channel prediction models. To solve the adaptive problem, the author collects real-time teaching data in a real online learning mode and integrates blended learning to update the model, to avoid the problem of overspending on offline training. Offline tests show that the performance of the channel estimation model is the best for different channels. As the signal-to-noise ratio increases, the MSE value is stable at around 1200. Conclusion: The channel estimation method can produce different characteristics of channel estimation in different situations and improve the signal recovery function of the communication system.

Due to the interference of false data, there is a large error in the mining results of low voltage loads in the power system. In response to this problem, the author proposes a design of an intelligent mining system for low voltage loads in the power system based on deep learning. Using ARM+DSP dual CPU structure, initializing the adapter agent, and using dual arm spiral antennas, designing a low-voltage load monitor to detect partial discharge signals in the 500-1500 MHz frequency band and suppress noise interference; By transmitting monitoring information to the intelligent switch through CAN bus or 485 bus, remote monitoring can be achieved; Based on the contact points and current characteristics of the circuit breaker, a current transformer has been designed to reduce the range of induced voltage variation; Construct a continuous set of functions MMD in the space, adjust the original network structure, establish a deep learning mining model, initial network parameters, eliminate false data in the network, optimize the network using target domain data, and combine mining engines to achieve intelligent data mining. According to the experimental results, the maximum difference between the load of phase A of the data processing system based on numerical simulation and the actual data is 1000 kVA at a time of 6 seconds; When the load of phase B is 4 seconds, the maximum difference between it and the actual data is 2000 kVA; When the load of phase C is 8 seconds, the maximum difference between it and the actual data is 2000 kVA. It has been proven that the mining error of the system is 0, and it has a precise mining effect.

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. User’s data is stored in large database. The stored data can be accessed and modified by the clients over the Internet. The data is monitored by the Third Party Auditor (TPA) on behalf of the client. Therefore, integrity is lacked by the data stored on the servers. The data integrity is ensured by the cloud services that provide trust to the privacy of users. Aiming at the urgent problem of network data security in cloud computing operations, this paper proposed a security situation assessment system to grasp the security situation in real time. A set of cloud computing network data storage security is proposed. Through this model, the extraction of network data storage security situation elements, the design of network data storage security situation assessment scheme and the calculation method of network data storage security situation value are completed. The experimental results show the error of the predicted value obtained by the network data storage security situation assessment system. The effectiveness of the system model and the superiority of the improved algorithm is verified by the experimental results. This paper uses the cloud model to predict the cloud computing network security situation. On the other hand, the security situation value obtained by the situation assessment process can be used directly without training the original situation value. Performance improvement by the proposed technique over existing technique is seen and it is observe that the proposed technique is 23% and 34% better than the existing techniques.