The rapid development of artificial intelligence (AI) has opened up new avenues for improving the efficiency and quality of academic writing. This paper presents ChatGPT, an advanced model based on the GPT-4 (Generative Pre-trained Transformer 4) architecture. Traditional academic writing faces challenges such as time constraints, language barriers, and content creation difficulties. AI-driven natural language processing (NLP) tools can effectively alleviate these challenges. This paper employs a transformer-based machine learning framework, combining bidirectional encoder representation (BERT) with GPT-4 to improve the syntactic and semantic quality of generated text. Empirical analysis of academic writing samples shows that ChatGPT-assisted writing reduces grammatical errors in the evaluation samples by 2.00% and 1.92%, respectively. This research further explores the cognitive advantages of AI-assisted writing tools, proposing that AI can not only enhance the writing process but also has the potential to reshape traditional academic writing practices by improving innovation, efficiency, and academic productivity.

As the telecommunications industry gears up for the development of 6G mobile networks, the transition from the current 5G infrastructure requires careful and strategic management. This article examines the evolution from 4G to 5G, drawing valuable analysis on lessons learned and proposes strategies for the forthcoming 5G to 6G migration. We analyse standardized solutions from previous generational shifts, identifying their applicability and limitations in the context of emerging 6G technologies. By emphasizing cost-effective and strategic approaches, we provide insights to interested partners for navigating the complexities of this transition while leveraging emerging advancements for the next era of mobile communications.

With the increasing number of web texts, the classification of web texts has become an important task. In this paper, the text word vector representation method is first analyzed, and bidirectional encoder representations from transformers (BERT) are selected to extract the word vector. The bidirectional gated recurrent unit (BiGRU), convolutional neural network (CNN), and attention mechanism are combined to obtain the context and local features of the text, respectively. Experiments were carried out using the THUCNews dataset. The results showed that in the comparison between word-to-vector (Word2vec), Glove, and BERT, the BERT obtained the best classification result. In the classification of different types of text, the average accuracy and F1 value of the BERT-BGCA method reached 0.9521 and 0.9436, respectively, which were superior to other deep learning methods such as TextCNN. The results suggest that the BERT-BGCA method is effective in classifying web texts and can be applied in practice.

This study aims to analyze the application of information security (IS) technology and blockchain in the media economy, and the impact of cross-border cooperation and value chain reconstruction combined with blockchain technology (BCT) on promoting innovation and digital transformation in the media economy. This study delves into the interplay between cross-border collaboration, value chain reconstruction, and BCT. It constructs a media economic digital platform by amalgamating blockchain and cross-border value chain principles, subsequently evaluating and analyzing its performance. Findings indicate that the proposed model exhibits a reduced block propagation time and enhanced security performance, boasting a 5% improvement. The platform’s latency remains stable at approximately 245 ms. Survey analysis reveals an agreement rate exceeding 57.94% across dimensions such as data mining, customer relationship management, and classification effectiveness, contributing to heightened user satisfaction. Consequently, this study offers valuable insights into user relationship security management within cross-border collaborations, shedding light on novel avenues for sustainable development and digital transformation in the realm of the media economy.

Impulsive purchasing is one of many fascinating subjects to investigate in e-commerce research. Online buying facilitates purchases and provides impulsive buyers with a venue to meet their demands. Impulse buying occurs when a client feels a sudden, overpowering need to make a purchase, which is a common practice among online shoppers. Therefore, the study’s purpose is to apply the partial least square (PLS) algorithm to investigate the factors driving online impulsive buying behavior (OIBB). Besides, in this research, the stimulus organism response (SOR) model was used as the research’s guiding theory; with the stimulus such as visual appeal and price attributes, the organism comprising perceived enjoyment and perceived usefulness, and response as OIBB. In addition, a non-probability sampling method was employed to collect data from 313 customers who shared their online impulsive purchase experiences through a Google Forms online survey. The collected data was analyzed using the PLS technique to assess the reliability, convergent validity, and discriminant validity of the variables, as well as test the proposed hypotheses. The findings reveal that visual appeal and price attributes were positive connections to perceived enjoyment and perceived usefulness. Likewise, perceived enjoyment and perceived usefulness also impacted positively OIBB. Additionally, the findings disclosed that visual appeal and price attributes also were associated positively with OIBB.

With the rapid popularization and development of smart phones and other technological devices, pictures have become the main media for people to record information. However, the traditional mobile photo album has many problems. First of all, with the development of the times, the higher the pixel of the image, the larger the memory required. Obviously, the traditional file storage structure can no longer meet the storage of users’ massive photos. Secondly, people store a large number of face images in mobile phones, so there is a strong demand for face recognition and classification management based on different faces. Third, in the face of the management of massive photos, general image recognition and classification is also a very demanding function. In response to the call of “deeply implementing the digital economy strategy” in today’s era, our team makes full use of the functions of the cloud platform and a large number of industrial resources, and integrates independent optimization algorithms to develop an intelligent cloud album management system that realizes intellectualization and application innovation. SE-ResNeXt algorithm is the core algorithm of this system, which can recognize and extract effective information from massive images in various application scenarios, and help users to intelligently and automatically classify and manage images according to different contents. This paper deeply studies the Intelligent Cloud album management system based on SE-ResNeXt. The system is built by nginx+uwsgi+django+vue as a whole. It has the functions of intelligent classification, face recognition, cloud storage and so on. It aims to provide users with simpler, more intimate and more intelligent album management services.

Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.

Diabetes is a one of the major issue that all people in the world currently face. Diabetes is caused by excessive amounts of sugar in the blood. Once diabetes is diagnosed, it is not completely curable, but it can be controlled with proper medication, exercise and a balanced diet. Diabetes affects the vital organs of the body such as the heart, kidneys, brain and eyes. The diabetes mellitus and its complications can be determined using a variety of pathological tests, such as patients’ symptoms and blood sugar, urine and lipid profile. The use of fuzzy logic in diagnosis is very common and useful because it combines the knowledge and experience of the physician into ambiguous sets and rules. Most of the researchers proposed methods to diagnosis the diabetes mellitus but still it in their infancy level. This work proposed a fuzzy based system for diagnosing diabetes disease. The usage of pesticides in agriculture by farmers is treated as one of the dependent variable for predication. The empirical zif’s law is used to compute the frequency of farmers using pesticides are predicated as diabetic. The output of the proposed system proved that the fuzzy based prediction model diagnosis the disease accurately.

The purpose of this study is to analyse the impact of the risk significance of audit results, the quality of the recommendations given on how easy it is to implement them, and the added benefit to the organization in implementing the recommendations. After a comprehensive literature review, the study provides a statistical analysis through a questionnaire that has been distributed to investigate the effect of Risk Significance, Ease of Implementation, and the Added Value on the implementation of the recommendations within organizations. Regarding the results obtained from the questionnaire, all Cronbach’s Alpha values are within the acceptable level, whereas the first three variables (Implementation of Recommendations, Risk Significance and Ease of Implementation) have a strong positive correlation between each other. There is a weak positive correlation between Added Value of Recommendations with other variables. In the regression analysis was found that all independent variables have a positive effect on the depended variable.

The apparent growth of the internet of things (IoT) has allowed its deployment in many domains. The IoT devices sense their surroundings and transmit the data via the Web. According to statistics, due to the proliferation of smart devices, the number of active IoT devices is expected to exceed 25.4 billion by 2030.1 A large number of IoT objects gather an enormous amount of raw data. The data generated by various IoT objects and sensors are heterogeneous, with varying types and formats. Therefore, it is difficult for IoT systems to share and reuse raw IoT data, which causes the problem of lack of interoperability. The lack of interoperability in IoT systems creates a problematic issue that prevents IoT systems from performing well. To address this issue, data modeling and knowledge representation using semantic web technologies may be an appropriate solution to give meaning to raw IoT data and convert it to an enriched data format. The primary goal of this research section is to highlight the best outcomes for semantic interoperability among IoT systems, which can serve as a guideline for future studies via the presentation of a literature review on semantic interoperability for Internet of Things systems, including challenges, prospects, and recent work. The paper also provides an overview of the application of semantic web technologies in IoT systems, such as specific ontologies, frameworks, and application domains that use semantic technologies in the IoT areas to solve interoperability and heterogeneity problems.