With the boom in information technology, countries around the world are competing the information the education industry. However, there is not yet a well-established standard to evaluate students’ capabilities in the teaching process based on information techniques. To solve the problem, this paper introduces the latest information techniques, such as big data and cloud computing, into the teaching of international trade, designs an individualized teaching system based on student portrait, and constructs an intelligent evaluation system of the teaching process. On this basis, several suggestions were put forward on the reform of the training model of international trade students. This research provides new insights on the evaluation and improvement of the teaching effect in the department of international trade.

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Individualized Teaching and Teaching Process Evaluation for International Trade Students Based on Information Techniques

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly affects memory, behavior, and cognitive abilities. Early detection is crucial for timely medical intervention and improved patient outcomes. This study presents an automated classification framework using brain MRI scans from the ADNI dataset, leveraging deep learning models such as CNN, VGG19, DenseNet, and EfficientNetB7. Preprocessing steps including normalization, segmentation, and data augmentation were applied to enhance model performance. Among the evaluated models, DenseNet and EfficientNetB7 demonstrated superior accuracy, achieving 96.94% and 96.88%, respectively. These models excel in capturing subtle anatomical variations in early AD stages. Automated feature extraction minimizes human error, enhances diagnostic precision, and reduces processing time. Performance was assessed using accuracy, precision, recall, and F1-score metrics. EfficientNetB7, with its compound scaling technique, proved highly efficient for clinical applicability. While results are promising, future work will focus on improving interpretability through Explainable AI and validating models across diverse datasets for real-world deployment.

Early Detection of Alzheimer's Disease Using Deep Learning Models on MRI Scans

Design and Implementation of Medical Image Fusion of Computer Tomography and Magnetic Resonance Imaging Using the Integrated Technique

For the study of functional aspects of the brain network, hypergraph representation is more powerful than normal graph representation. This paper is a study on the hypergraph representation, based on the functional regions of the brain network. A new parameter that can measure how many multifunctioning regions each function contains and thereby the correlation of other functions with each function. This paper introduces an inequality that can be used to construct a modular brain network using hypergraph representation.

Hypergraph representation in brain network analysis

Synthesizing silicon integrated focal plane plasmonic switch array for beam steering sensors

Alzheimer's Disease is a devastating neurological disorder that is increasingly affecting the elderly population. Early and accurate detection of Alzheimer's is crucial for providing effective treatment and support for patients and their families. In this study, we present a novel approach for detecting four different stages of Alzheimer's disease from MRI scan images based on inertia tensor analysis and machine learning. From each available MRI scan image for different classes of Dementia, we first compute a very simple 2 x 2 matrix, using the techniques of forming a moment of inertia tensor, which is largely used in different physical problems. Using the properties of the obtained inertia tensor and their eigenvalues, along with some other machine learning techniques, we were able to significantly classify the different types of Dementia. This process provides a new and unique approach to identifying and classifying different types of images using machine learning, with a classification accuracy of (90%) achieved. Our proposed method not only has the potential to be more cost-effective than current methods but also provides a new physical insight into the disease by reducing the dimension of the image matrix. The results of our study highlight the potential of this approach for advancing the field of Alzheimer's disease detection and improving patient outcomes.

Detection of Alzheimer's Disease using MRI scans based on Inertia Tensor and Machine Learning

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Papers

Design and Implementation of Medical Image Fusion of Computer Tomography and Magnetic Resonance Imaging Using the Integrated Technique

Hypergraph representation in brain network analysis

Synthesizing silicon integrated focal plane plasmonic switch array for beam steering sensors

Detection of Alzheimer's Disease using MRI scans based on Inertia Tensor and Machine Learning