Issue 2 (December)

Artificial Intelligence (AI) is playing a transformative role in healthcare by enhancing diagnostic accuracy, treatment planning, and patient care. This paper explores the significance of AI in healthcare, emphasizing its role in medical imaging, predictive analytics, and personalized medicine. It delves into various aspects, including machine learning algorithms, electronic health records integration, and telemedicine applications. The discussion includes the benefits of AI in healthcare, such as early disease detection, reduced healthcare costs, and improved patient outcomes. Moreover, the paper addresses the challenges and ethical considerations in AI adoption, including data privacy and regulatory compliance. Through a review of AI-driven healthcare solutions and clinical trials, the study highlights the positive outcomes associated with the integration of AI into medical practices.

Quantum computing has emerged as a groundbreaking technology that has the potential to revolutionize computation. This paper explores the significance of quantum computing, emphasizing its role in solving complex problems, cryptography, and simulations. It delves into various aspects, including quantum algorithms, quantum bits (qubits), and quantum supremacy. The discussion includes the benefits of quantum computing, such as exponentially faster calculations, enhanced security, and novel scientific discoveries. Moreover, the paper addresses the challenges and considerations in building practical quantum computers, including quantum error correction and scalability. Through a review of quantum computing experiments and research, the study highlights the positive outcomes associated with the development of quantum computational systems.

Augmented Reality (AR) is reshaping the landscape of education by providing immersive and interactive learning experiences. This paper explores the significance of AR in education, emphasizing its role in K-12 classrooms, higher education, and vocational training. It delves into various aspects, including AR applications, wearable devices, and content development. The discussion includes the benefits of AR in education, such as improved engagement, visualization of complex concepts, and personalized learning. Moreover, the paper addresses the challenges and considerations in implementing AR in educational settings, including access to technology and pedagogical integration. Through a review of AR educational projects and research, the study highlights the positive outcomes associated with the integration of AR into the learning environment.

Blockchain technology has disrupted traditional financial systems and is extending its impact to various industries. This paper explores the significance of blockchain, emphasizing its role in secure transactions, supply chain management, and digital identity verification. It delves into various aspects, including blockchain consensus mechanisms, smart contracts, and decentralized applications. The discussion includes the benefits of blockchain technology, such as transparency, reduced fraud, and enhanced data integrity. Moreover, the paper addresses the challenges and considerations in blockchain adoption, including scalability and regulatory compliance. Through a review of blockchain projects and research, the study highlights the positive outcomes associated with the implementation of distributed ledger technology.

5G technology has ushered in a new era of connectivity, enabling faster data speeds, low latency, and transformative applications. This paper explores the significance of 5G and beyond, emphasizing its role in telecommunications, IoT, and smart cities. It delves into various aspects, including 5G network architecture, mmWave spectrum, and edge computing. The discussion includes the benefits of 5G technology, such as enhanced mobile experiences, autonomous vehicles, and real-time data analytics. Moreover, the paper addresses the challenges and considerations in deploying 5G networks, including infrastructure development and security. Through a review of 5G use cases and research, the study highlights the positive outcomes associated with the evolution of wireless connectivity.

Cybersecurity has become a critical concern in the digital age, with the increasing volume of data and online threats. This paper explores the significance of cybersecurity, emphasizing its role in protecting sensitive information, critical infrastructure, and personal privacy. It delves into various aspects, including cybersecurity frameworks, threat detection technologies, and encryption methods. The discussion includes the benefits of robust cybersecurity, such as data integrity, prevention of cyberattacks, and trust in digital systems. Moreover, the paper addresses the challenges and evolving nature of cyber threats, including malware, phishing, and ransomware. Through a review of cybersecurity practices and research, the study highlights the positive outcomes associated with proactive cybersecurity measures.

Sustainable agriculture has gained prominence as a means to meet global food demands while preserving the environment. This paper explores the significance of sustainable farming practices, emphasizing their role in food security, resource management, and climate change mitigation. It delves into various aspects, including organic farming, precision agriculture, and agroforestry. The discussion includes the benefits of sustainable agriculture, such as reduced environmental impact, improved soil health, and diversified crop yields. Moreover, the paper addresses the challenges and considerations in transitioning to sustainable farming, including economic viability and policy support. Through a review of sustainable agriculture initiatives and research, the study highlights the positive outcomes associated with environmentally conscious farming practices.

The collaboration between humans and artificial intelligence (AI) systems is reshaping industries and decision-making processes. This paper explores the significance of human-AI collaboration, emphasizing its role in enhancing productivity, data analysis, and decision support. It delves into various aspects, including AI-assisted workflows, machine learning algorithms, and natural language processing. The discussion includes the benefits of human-AI partnership, such as faster data insights, reduced errors, and improved resource allocation. Moreover, the paper addresses the challenges and ethical considerations in integrating AI into human-centric domains, including bias and accountability. Through a review of human-AI collaboration studies and applications, the study highlights the positive outcomes associated with this transformative synergy.

The concept of a circular economy is gaining traction as a sustainable alternative to linear consumption and waste generation. This paper explores the significance of the circular economy, emphasizing its role in resource efficiency, waste reduction, and environmental conservation. It delves into various aspects, including circular supply chains, product life extension, and recycling innovations. The discussion includes the benefits of adopting circular economy principles, such as reduced environmental impact, cost savings, and job creation. Moreover, the paper addresses the challenges and policy considerations in transitioning to a circular economy, including regulatory frameworks and consumer behavior. Through a review of circular economy initiatives and research, the study highlights the positive outcomes associated with redefining sustainability in resource management.

Space exploration has captivated human imagination for decades, and the prospect of space colonization is becoming increasingly feasible. This paper explores the significance of space exploration and colonization, emphasizing their role in scientific discovery, resource utilization, and the expansion of human presence beyond Earth. It delves into various aspects, including space agencies, interplanetary missions, and habitat technologies. The discussion includes the benefits of space exploration and colonization, such as advancing scientific knowledge, ensuring survival of humanity, and economic opportunities in space. Moreover, the paper addresses the challenges and considerations in these endeavors, including space travel logistics and sustainability. Through a review of space exploration projects and research, the study highlights the promising future prospects associated with humanity's ventures into the cosmos.

This paper focuses on the study of student behavior-based grade prediction. This research investigates grade prediction based on student behavior. After evaluating student behavior data from campus events, an attention-based CNN-LSTM student grade prediction model is developed. Initially, we use Convolutional Neural Network (CNN) to extract deep student behavior features and maximum pooling approach to pick the significant features in student behavior features; the retrieved features are then used as the input of Long and Short-Term Memory networks. A temporal attention mechanism is included at the output of LSTM in order to allocate attention weights for different weekly student behavior characteristics in a reasonable manner, hence improving the model's performance in terms of prediction. Experiments revealed that the student performance prediction model suggested in this paper had excellent performance.

At present, deep learning has been successfully applied in the field of medical diagnosis, and it can effectively improve the diagnostic accuracy by using deep learning to predict brain tumor images. Based on traditional convolution neural networks tend to ignore the problem of key location in brain tumor image information, this paper proposes a door control channel attention conversion unit, door control channel attention switching unit by using the relationship between a small number of parameters to change the channel, under the condition of without any increase in computational cost more significantly improve classification accuracy. Also, the Sequeeze and Excitation block is introduced for the communication between the channels. Finally, a multipath attentional network model (FSnet) for brain tumor image classification is constructed based on ResNeXt network model. Experimental tests on brain MRI data sets show that FSnet has better classification performance than traditional convolutional neural networks.

This paper is mainly based on the prediction of English grades, aiming at the data of students' English grades and personal identity during school, and establishes a fuzzy neural network prediction model based on the spike mechanism and genetic algorithm. First of all, in the aspect of parameter training of neural network optimization design, this paper adopts genetic algorithm as the learning algorithm of fuzzy neural network, so that the network can achieve the global optimum. Then, the spiking mechanism proposed based on the cerebral cortex information transmission mode and the Spiking neuron accumulation trigger (Integrate-and-Fire, IF) model is used to complete the structure growth and pruning of the fuzzy neural network, realize the dynamic adjustment of the network structure during the training process, and improve the performance of fuzzy neural network in English grade prediction application. Finally, after testing, it is found that the fuzzy neural network prediction model based on the spike mechanism and genetic algorithm proposed in this paper has a good performance.

With the rapid development of education for overseas students in China, psychological problems caused by cross-cultural factors have become increasingly prominent. Based on the psychological questionnaire data, in order to make full use of the relationship information between students, this paper proposes a psychological crisis individual identification model based on bipartite graph convolution network model (B-GCN) to make up for the shortcomings of traditional identification methods. Because the GCN model can not realize inductive learning, this paper improves the GCN model based on the structural characteristics of psychological tests, and proposes a bipartite graph neural network model. The model can classify the students who have never appeared in the graph structure, so as to realize the early warning of psychological crisis. Experiments show that the proposed B-GCN model has a good performance.

This report takes learning behavior and psychological support as the factors to predict the students' academic performance. A total of 13 factors were selected at the initial stage. Firstly, PCA and MDT algorithms were used for data preprocessing, and we used CNN to extract deep student behavior features. Then, the maximum pooling approach is used to pick the significant features; next, the selected features are used as the input of LSTM. A temporal attention mechanism is introduced at the output of LSTM to allocate attention weights for different weekly student behavior characteristics. This paper divides students' academic performance into several score intervals and makes a multi-classification prediction. This study uses 39 algorithms based on six types to predict the student's academic performance. The result shows that: (1) The average prediction accuracy based on the deep learning model is the highest (85.46%) in the six types of models; (2) The accuracy of the LSTM algorithm (88.36%) outperformed all the benchmark models. (3) The attention mechanism introduced can effectively improve the model's prediction accuracy; the proposed model in this paper outperformed all the benchmark models. Furthermore, the statistical analysis found that (1) Students who have academic confidence (confidence in teachers, courses, and themselves) may obtain higher academic performance; (2) A clear learning motivation is essential to final academic performance; (3) Lack of confidence in others in the study group may ultimately improve students' academic performance by encouraging them to work harder.