Automated Early Detection of Astrocytomas Tumor Based on Optimized Adaptive Cluster with Super Pixel Model

In the medical image-processing field brain tumor segmentation is aquintessential task. Thereby early diagnosis gives us a chance of increasing survival rate. It will be way much complex and time consuming when comes to processing large amount of MRI images manually, so for that we need an automatic way of brain tumor image segmentation process. This paper aims to gives a comparative study of brain tumor segmentation, which are MRI-based. So recent methods of automatic segmentation along with advanced techniques gives us an improved result and can solve issue better than any other methods. Therefore, this paper brings comparative analysis of three models such as Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP) and Grey Wolf Optimization based ACSP (GWO_ACSP) and these are tested on CANCER IMAGE ACHRCHIEVE which is a preparation information base containing High Grade and Low-Grade astrocytoma tumors. Here boundaries including Accuracy, Dice coefficient, Jaccard score and MCC are assessed and along these lines produce the outcomes. From this examination the test consequences of Grey Wolf Optimization based ACSP (GWO_ACSP) gives better answer for mind tumor division issue.

Deep Learning-based Mental Health Monitoring Scheme for College Students Using Convolutional Neural Network

Artificial intelligence (AI) in healthcare has recently been promising using deep neural networks. It is indeed even been in clinical trials more and more, with positive outcomes. Deep learning is the process of using algorithms to train a neural network model using huge quantities of data to learn how to execute a given task and then make an accurate classification or prediction. Apart from physical health monitoring, such deep learning models can be used for the mental health evaluation of individuals. This study thus designs a deep learning-based mental health monitoring scheme (DL-MHMS) for college students. This model uses the most efficient convolutional neural network (CNN) to classify the mental health status as positive, negative, and normal using the EEG signals collected from college students. The simulation analysis achieves the highest classification accuracy and F1 scores of 97.54% and 98.35%, less sleeping disorder rate of 21.19%, low depression level of 18.11%, reduced suicide attention level of 28.14%, increasing personality development ratio of 97.52%, enhance self-esteem ratio of 98.42%, compared to existing models.

Research on Face Recognition Sports Intelligence Training Platform Based on Artificial Intelligence

With the application of artificial intelligence in many social fields, the research of human behavior recognition and non-contact detection of human physiological parameters based on face recognition and other technologies has developed rapidly, and the application of artificial intelligence in culture, sports and entertainment has also begun to rise. How to apply the existing mature technology to the sports intelligence training system taking table tennis as an example is a hot issue worthy of study. In this paper, a comprehensive intelligent table tennis training system and platform based on Convolutional Neural Network face recognition and face heart rate detection is designed, which is mainly used to solve the philosophical training problem in table tennis. In the system place, an identification cameras is set at the entrance of table tennis training places, which is used for table tennis players’ sign-in and training table number allocation, and an intelligent analysis cameras is set above each intelligent training table, which is used for detecting the face and heart rate of table tennis players. Each intelligent training platform consists of intelligent voice control unit, server, camera, industrial control computer, monitor and other terminal modules. The member data center constitutes the platform of intelligent table tennis training system.

Automatic Translation of Medical Information Based on Text Feature Recognition

In order to improve the effect of the medical information automatic translation system, based on the text feature recognition technology of medical image, this paper constructs an automatic translation system that can recognize medical images. On the basis of the medical information image retrieval based on the combination of text information and visual information, this paper uses automatic image annotation technology and semantic similarity calculation method to extract the text and semantic features of medical information images. Then, this paper uses the inherent multi-information fusion capability of the Bayesian inference network to fuse the text features of medical information images and the semantic features of image content together to realize medical information image retrieval. Finally, this paper designs experiments to test the performance of the medical information automatic translation system. The research shows that the system constructed in this paper has certain effects.

Wearable Device Based on IoT in the Healthcare System for Disease Detection and Symptom Recognition

Humans in good shape face many challenges in their lives, such as food habits and climate change. The result must be aware of the health situation to survive. Lack of accurate patient information, preventive errors, data risks, overdiagnosis, and delayed implementation are challenges that health support services face. Wearable sensors that connect extensive data, data mining analysis for healthcare, and the Internet of things (IoT) have been proposed to solve this problem. This research, Disease Prediction and Symptom Recognition Model using IoT (DDSR-IoT) framework, is proposed for reasoning with regression rules to gather patient information. The Boltzmann network to train Artificial Intelligence (AI) feedback is introduced in the end. As a result, the broad interaction analysis of genomes is used to predict conditions. If those infections have affected people, emails are sent to warn them and provide them with prescriptions and medical advice. In the recommended approach, the experimental study resulted in an enhanced forecast rate of 97.4 percent and a precision of 97.42 percent.

Performance Comparison Study of Several Interpolation Methods on Streak Metal Artifacts Reduction of CT Images

In the course of generating the CT images, the streak metal artifacts emerge from the reconstructed images, often degraded the quality of the images and blur the fringe information around the metal implant. Although a number of attempts had been reported, among them, our proposed interpolation-based method is the simplest and most efficient approaches. In this paper, three interpolation approaches are compared with subjective and objective criterion based on both simulation and clinical cases. Our results have shown an improvement from the original images. As for the comparison with NRMSD and MAD. For the execution time, the L-MAR possesses the shortest time with S-MAR time being the slowest among the interpolation-based methods. For NRMSD and MAD, the digits from small to large are P-MAR, S-MAR, L-MAR and original. This shows that among interpolation-based methods the image corrected by P-MAR approach is the closest to the ideal image, followed by S-MAR correction, L-MAR correction, and the gap between the original image and the ideal image is the largest.

Brain-computer Interaction Enabled AAC for Visual Interactive Paradigm

The brain-computer interface (BCI) has recently provided a potential means for individuals with the least movement to control a computer utilizing their brain waves, with no motor output needed. Augmentative and alternative communication (AAC) is generally utilized by individuals with severe physical and speech disabilities and is one of the primary application fields for BCI technologies. The main objective of this study is to examine students’ brain parameters such as attention, concentration, and the energy of several brain waves using Augmentative and Alternative Communication-based Visual Interactive Paradigm (AAC-VIP) based on BCI in education systems. Particular emphasis is placed on integrating AAC into daily school life to foster every student’s access to and participation in the education curriculum. The effectiveness of the support plans has been assessed via behavioral observations and team interviews. The experimental findings demonstrate that the proposed model allows the completion of communication, with the highest interaction rate of 97.66%. It can be utilized in the classrooms to enhance the educative way of people with intellectual disabilities.