Internet Technology Research Articles

Research on cargo volume prediction and personnel scheduling optimization of sorting center based on BP neural network and improved genetic algorithm

With the development of Internet technology, e-commerce has become the primary choice for consumers to shop, and the huge order transaction volume has generated logistics problems. As an important part of the logistics network, the sorting platform is essential to improve its management efficiency. To enhance the efficiency of the sorting center, the topological relationship diagram is used to show the logistics network, and the cargo volume of the sorting center in the next 30 days and every hour is predicted based on the BP neural network model. Based on the mixed integer linear programming model and the improved genetic algorithm SCAGA model, the total man-days are minimized to ensure that the cargo volume is processed. At the same time, to ensure that the actual hourly efficiency is as balanced as possible, the optimization problem of personnel scheduling is studied. To maintain the efficient operation of the logistics industry, the accurate prediction of the number of goods and the reasonable scheduling of personnel, to provide a set of efficient and economical personnel scheduling programs for the logistics company, thus improving the overall operational efficiency and service quality.

Improving Text Classification in Agricultural Expert Systems with a Bidirectional Encoder Recurrent Convolutional Neural Network

With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing methods often struggle with specialized terminology and context, and they lack the adaptability to handle complex text classifications. The diversity and evolving nature of agricultural texts make deep semantic understanding and integration of contextual knowledge especially challenging. To tackle these challenges, this paper introduces a Bidirectional Encoder Recurrent Convolutional Neural Network (AES-BERCNN) tailored for short-text classification in agricultural expert systems. We designed an Agricultural Text Encoder (ATE) with a six-layer transformer architecture to capture both preceding and following word information. A recursive convolutional neural network based on Gated Recurrent Units (GRUs) was also developed to merge contextual information and learn complex semantic features, which are then combined with the ATE output and refined through max-pooling to form the final feature representation. The AES-BERCNN model was tested on a self-constructed agricultural dataset, achieving an accuracy of 99.63% in text classification. Its generalization ability was further verified on the Tsinghua News dataset. Compared to other models such as TextCNN, DPCNN, BiLSTM, and BERT-based models, the AES-BERCNN shows clear advantages in agricultural text classification. This work provides precise and timely technical support for intelligent agricultural expert systems.

APPLICATION OF COMPUTERISED TECHNOLOGIES IN THE REHABILITATION OF PATIENTS WITH STROKE CONSEQUENCES

A stroke is a sudden onset of focal or generalized impairment of brain function due to vascular causes only, which is related to cerebral blood flow and lasts for more than 24 hours. Cerebral stroke remains the second leading cause of death and the third most significant cause of disability in the world. Over the past 30 years, the absolute number of cases of this disease has increased by 70%, the prevalence by 85%, with a 43% increase in mortality rates. According to the latest Global Burden of Disease (GBD) analysis, in 2019, there were about 12.2 million stroke cases, 143 million disability-adjusted life years and 6.6 million deaths worldwide, 86% of which occurred in low- and lower-middle-income countries. Purpose to analyse and systematise the use of computerised technologies in the rehabilitation of patients with stroke consequences. Materials and methods. To achieve the objectives of the research, the researchers analysed scientific literature and systematised information using electronic databases such as PubMed, Google Scholar and others. Research results. Traditional methods have proven their effectiveness and are widely used in the practice of rehabilitating patients after stroke. However, with the development of science and technology, there is a need to find new approaches to therapy that can provide a more comprehensive and effective treatment of this pathology. Today, computerized technologies deserve special attention in the rehabilitation of patients after stroke, namely robotic systems, specially designed video games and virtual reality, and TV rehabilitation. Robotic systems are devices that automate various tasks and processes. They can be programmed to perform certain functions with high accuracy and repeatability. In rehabilitation, such systems help to restore motor functions, support physical activity, and facilitate recovery from injuries or illnesses, including stroke. Video games and virtual reality technologies are examples of exciting and fun rehabilitation for stroke patients. Traditional therapies can be complex and tedious to repeat, making it less likely that patients will do them at home. Physical therapists are finding that video games are more engaging and easier to incorporate into home treatment regimens. Tele-rehabilitation after stroke is a form of remote medical care that allows patients to receive rehabilitation services via the Internet or other telecommunication technologies. This provides access to the necessary therapy regardless of the patient's place of residence. Conclusions. Computerized technology significantly improves the rehabilitation of patients with stroke. Robotic systems provide high precision and control during movements, which avoids incorrect exercise and reduces the risk of re-injury, regularity of training, and patient comfort. In addition, many robotic systems have built-in sensors that allow you to track the patient's progress and provide feedback to both the patient and the doctor. This helps to adjust the rehabilitation programme according to the patient's needs. The use of video games and virtual reality in stroke rehabilitation offers numerous benefits, including increased motivation, improved motor and cognitive function, psychological support, and social interaction. Tele-rehabilitation after stroke is an innovative approach to patient recovery, providing access to rehabilitation services regardless of where they live.

Exploring The Celebrity Effect on Live-Streaming E-Commerce: A Comprehensive SWOT Analysis of Influencer Marketing Strategies

In recent years, with the rapid development of the Internet and mobile technology, the landscape of business and marketing has changed dramatically, and particularly notable is the rise of live-streaming e-commerce. This new e-commerce model combines e-commerce with live video streaming to enhance the shopping experience through product display, promotion and sales by anchors. In this model, the celebrity effect, which takes advantage of celebrities' large fan bases and strong influence, plays an important role in driving consumer behaviour. This paper explores the impact of the celebrity effect on live-streaming e-commerce and analyses its strengths, weaknesses, opportunities and threats through SWOT analysis with typical case studies in China. The findings highlight the key role of celebrity endorsement in increasing consumer engagement and purchase intention, while also exploring the potential risks and challenges. The paper concludes with suggestions for optimisation and development to maximise the role of the celebrity effects in live-streaming e-commerce.

Internet Technology for Efficiency and Resilience in Ocean Transportation Supply Chain Management: A Case Study of China Ocean Shipping Group

With the global maritime transport industry has faced many challenges in recent years, such as market volatility, environmental requirements and technological upgrading. The wide application of Internet technology has changed the management mode of the marine shipping industry and brought new opportunities for its development. Taking China Ocean Shipping Group as a case study, this study aims to explore the use of Internet technology to solve the problems faced by the ocean shipping supply chain and to discuss the importance of Internet technology in the ocean shipping supply chain, its areas of application, and the changes it may bring. Currently, the main problems faced by the marine transport supply chain include human resource costs, logistics information technology level, port and route selection, and shipping market uncertainty, which seriously affect the efficiency and flexibility of marine transport and operating costs. This study discusses Internet technologies to solve practical problems and specific supply chain management measures, including supply chain efficiency, flexibility and operating costs.

A Review on the Impact of Social Media and the Internet on Consumer Purchasing Decisions

This essay's goal is to examine the nuanced effects of social media and the internet on consumers' decision-making processes while making purchases. Due to the quick advancement of Internet technology and the enormous popularity of social media platforms, social media serves as a vital forum for customer interaction and communication as well as the formation of opinions that have a significant impact on consumers' decision-making processes when making purchases. A significant quantity of information about how social media and the Internet affect consumer buying decisions has been gathered through sample analysis and literature reviews. The research framework is established in the literature review portion. The data collection and analysis of the earlier samples was utilized in the sample analysis phase to validate the research findings and identify the precise mechanisms by which social media and networks influence consumer purchase decisions. The following factors primarily illustrate how social media and the Internet affect consumer purchasing decisions: First, social media offers a wealth of product information via e-live broadcasting and other channels. Users may explore, search, and compare products to gain a more thorough understanding of them and to assess them more critically, enabling them to make better purchasing decisions. Second, social media advertising, word-of-mouth marketing, and user reviews have a big influence on consumers' purchasing decisions, which are frequently impacted by other people's assessments and opinions. Furthermore, social media influences consumers' psychological views and emotional reactions, which in turn influences their purchasing decisions.

Optimizing the Agricultural Supply Chain Through the Internet + Model: Enhancing Efficiency with a Case Study of TikTok

This study uses the TikTok platform as a case study to explore the application of the Internet+ model in the agricultural supply chain and analyses the impact of Internet+ model on improving supply chain efficiency and reducing costs. With the development of science and technology, Internet+ has become an important trend in modern industry, which deeply integrates traditional agriculture with information technology and promotes the innovation and efficiency of supply chain management. This study not only discusses the practical application of the Internet + model in agriculture but also proposes to solve the bottlenecks in the traditional agricultural supply chain using logistics informatization, intelligent scheduling system, cold chain monitoring and intelligent warehouse management. Through case studies, the effectiveness of Internet technology in optimizing the supply chain, improving the circulation efficiency of agricultural products and reducing costs are verified, providing theoretical and case study support for promoting the development of agricultural modernization.

Legal Consequences for Businesses which include Exoneration Clauses in Online Sale and Purchase Agreements

With the rapid development of internet technology, products offered by business actors can now be marketed and distributed quickly and globally through websites. This allows anyone, regardless of location, to directly access these sites and make online transactions. However, in some cases, business actors include terms such as "goods cannot be exchanged or returned" or "No Complain," which fall under what is known as an exoneration clause. The problems formulated for discussion are: 1) How can consumers anticipate and respond to the inclusion of exoneration clauses in online sales and purchase agreements? and 2) What are the legal consequences for business actors who include exoneration clauses in online agreements? The research method used is normative legal research, which analyzes legal problems, facts, and related legal phenomena through a legal approach to obtain a comprehensive understanding of the issue being studied. This descriptive analytical research aims to describe the situation or problem without drawing universally applicable conclusions regarding the legal consequences for business actors who apply exoneration clauses in online sales and purchase agreements. Based on the research results, the author concludes that business actors who include exoneration clauses for food products in online agreements are absolutely liable for any losses suffered by consumers. In such cases, the business actors are directly responsible for product defects caused by their own negligence.

Prevalence of sleep disturbances in endometriosis patients: a systematic review and meta-analysis.

This study systematically analyzes the prevalence of sleep disturbance in patients with endometriosis. The PubMed, Web of Science, Embase, Wanfang, China National Knowledge Internet Database (CNKI), China Science and Technology Journal Database were searched from their establishment to January 2024, using the search terms endometriosis and sleep disturbance to collect relevant literature on the prevalence of sleep disturbance in patients with endometriosis. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias. The prevalence of sleep disorders in patients with endometriosis was systematically analyzed using Stata17.0 software. Sixteen studies with 2573 participants were included. The prevalence of sleep disturbance in patients with endometriosis was 70.8% (95% confidence interval: 60.7%~80.9%). The said prevalence was higher in China than in Iran and the European countries (78.2 vs. 57.6 vs. 64.4, Q=9.27, P=0.010) and increased significantly since 2018 (79.0 vs. 61.3, Q=3.97, P=0.046). This prevalence was significantly higher in the cohort study than that in cross-sectional and case-control studies (84.0 vs. 74.0 vs. 59.5, Q=7.16, P=0.028). The prevalence of sleep disturbance is high in patients with endometriosis, particularly in China and its prevalence has increased significantly in recent years. Appropriate interventions are recommended to effectively prevent or minimize sleep disturbances in patients with endometriosis.

DELM: Deep Ensemble Learning Model for Anomaly Detection in Malicious Network Traffic-based Adaptive Feature Aggregation and Network Optimization

With the rapid advancements in internet technology, the complexity and sophistication of network traffic attacks are increasing, making it challenging for traditional anomaly detection systems to analyze and detect malicious network attacks. The increasing advancedness of cyber threats calls for innovative approaches to identify malicious patterns within network traffic precisely. The primary issue lies in the fact that these approaches do not focus on the essential adaptive features of network traffic. We proposed an effective anomaly detection system for malicious network traffic attacks called the Deep Ensemble Learning Model (DELM). We leverage the structure of the Feedforward Deep Neural Network (FDNN), and Deep Belief Network (DBN), incorporating multiple hidden layers with non-linear activation functions. Integrating Adaptive Feature Aggregation (AFA) with the FDNN algorithm dynamically adjusts the feature aggregation process based on incoming traffic characteristics to improve adaptability. The Conditional Generative Network was employed to enhance DELM for generating data for minority classes. To improve the model’s accuracy, we applied batch normalization and data augmentation techniques for preprocessing, utilized n-gram, one-hot encoding, and feature aggregation methods for effective feature extraction. This study significantly contributes to network security by enhancing systems for detecting malicious network traffic. With its interpretability and adaptability, our proposed model shows promise in addressing the evolving cyber threat and fortifying critical network infrastructure. The experimental results demonstrate that our model performs with higher stability than the existing state-of-the-art detection approaches, as reflected by its higher accuracy, precision, recall, F1-score, and AUC-ROC.

Consensus Control of Leader–Follower Multi-Agent Systems with Unknown Parameters and Its Circuit Implementation

With the development and progress of Internet and data technology, the consensus control of multi-agent systems has been an important topic in nonlinear science. How to effectively achieve the consensus of leader–follower multi-agent systems at a low cost is a difficult problem. This paper analyzes the consensus control of complex financial systems. Firstly, the dynamic characteristics of the financial system are analyzed by the equilibrium points, bifurcation diagrams, and Lyapunov exponent spectra. The behavior of the financial system is discussed by different parameter values. Secondly, according to the Lyapunov stability theorem, the consensus of master–slave systems is proposed by linear feedback control, wherein the controllers are simple and low cost. And an adaptive control method for the consensus of master–slave systems is investigated based on financial systems with unknown parameters. In theory, the consensus of the leader–follower multi-agent systems is proved by the parameter identification laws and linear feedback control method. Finally, the effectiveness and reliability of the consensus of leader–follower multi-agent systems are verified through the experimental simulation results and circuit implementation.

From Virtual Trees to Real Forests: Public Participation in Virtual Forest Realization Projects in China

With the rapid development of Internet technology, the channels for the public to participate in forest protection and ecological construction have been expanded. In China, virtual forest realization projects have successfully heightened environmental awareness among the public. However, these projects have also faced a “green gap”, characterized by a substantial disconnect between environmental intentions and actual participation behaviors. Based on 69 articles about virtual forest realization projects in China, this paper aims to reveal the roots of the intention–behavior gap, influencing factors, and potential mitigation strategies. Our findings indicate the following: (1) While virtual forest realization initiatives have made progress, they still face multiple challenges such as governmental inefficiencies and platform traffic bottlenecks. (2) The factors influencing public participation in virtual forest realization projects can generally be categorized into three types: external situational factors, internal psychological factors, and demographic variables. Initially, external influences like online word-of-mouth and gamified platforms played a crucial role in attracting the public. Over time, internal factors such as public trust in the platform and perceived enjoyment have become more prominent. Inconsistent findings regarding demographic variables suggest a need for further research. (3) Current solutions primarily focus on macro policies and have not fully addressed existing challenges. This study reveals the reasons and influencing factors of the intention–behavior gap, with the goal of offering valuable insights for future public engagement and project optimization.

Research on Marketing Strategy based on Social Media Big Data

With the rapid advancement of internet technologies, social media platforms have emerged as pivotal channels for information exchange and dissemination. The vast amounts of user-generated data provide businesses with unprecedented insights. The analysis of big data from social media allows for more precise and effective marketing strategies. This paper aims to explore the characteristics of big data from social media and analyze its applications in marketing strategies. By delving into data types, volumes, structures, and user behavior, it reveals the main challenges businesses face in big data applications and proposes corresponding solutions. Precision marketing, brand management, market trend forecasting, and crisis management are significant areas of application for big data in marketing. This paper, through a detailed examination of these strategies, highlights the potential and challenges of big data in contemporary marketing. Ultimately, it seeks to offer theoretical support and practical guidance for businesses on how to develop and implement effective marketing strategies in a big data-driven market environment.

Network Data Intrusion Detection and Data Feature Extraction of Electromechanical Facilities from Machine Learning

With the rapid development of Internet technology, network security issues have become more complex and changeable. Various intrusion methods threaten the information network environment in the Electromechanical Facility (EF) system. This paper focuses on EF to study the relevant detection methods and data feature extraction in complex network intrusions. Firstly, four common machine learning algorithms are used to calculate the data set. The advantages and disadvantages of each algorithm are analyzed after tuning and comparison. Secondly, a network intrusion detection algorithm is proposed based on Recursive Feature Elimination (RFE) principal component analysis. It uses RFE to reduce the number of features and improve the elimination judgment index to align with the detection requirements of information network datasets. Finally, a fault diagnosis method is proposed based on empirical pattern decomposition and support vector machine under Renyi entropy complexity measurement. This method trains and identifies the Renyi entropy of several basic pattern components obtained by decomposing empirical patterns as feature vectors. The results show that the RFE method judged by random forest removes irrelevant features, and the evaluation index is improved to align with the network dataset’s detection requirements. It reduces the data dimension, reduces the operation time, and improves the accuracy of a few attack types. The comprehensive final detection effect is better than other algorithms. Additionally, the embedded operating system construction method based on the protection mechanism realizes the separate storage of the operating system and key data. Also, it can prevent the network system from being maliciously invaded, ensuring the stability of the instrument operation under harsh working conditions.

IEC104 protocol-based substation data upload IoT cloud platform implementation

Abstract The Industrial Internet of Things (IoT) has been developing rapidly under the background of Internet technology, and the most crucial part of its realization is the unified collection and access of heterogeneous data from multiple sources across networks and regions. For the access of substation system data, this paper designs a scheme to read data from IEC 60870-5-104 protocol and upload the data to IOT cloud platform by converting the data to data format supported by Kafka protocol, solving the problem of incompatibility between communication protocols, obtaining data from 104 secondary stations by connecting to them, parsing and processing the data in different formats, and converting the data to JSON format by processing and sending it to the IOT cloud platform. The data is converted into JSON format through processing and sent to the Kafka server, and the cloud platform obtains its data by subscribing to the corresponding topics. This approach makes substation data access more convenient, and efficiently and effectively saves the cost of hardware gateway, solves the incompatibility and interoperability between the underlying equipment access protocol and the cloud platform communication protocol, and builds a bridge for the remote transmission of the underlying equipment data of the Industrial Internet of Things.

Research on Chinas Economic and Trade Pattern under the Internet Technology Era

In the era of the Internet, Chinas economic and trade models have undergone profound and transformative changes, reshaping the landscape of global commerce. This paper aims to explore the critical role that internet technology has played in driving Chinas economic development while also delving into the future directions and possibilities for further growth. By focusing on Alibaba and JD.com as primary case studies, the paper examines how these industry leaders have leveraged advanced technologies such as cloud computing, cross-border e-commerce, and electronic logistics to revolutionize their operations and expand their global reach. The study not only highlights their innovative approaches and development experiences but also addresses the societal challenges that have emerged or been exacerbated by the rapid expansion of internet technology. Understanding these challenges is essential for formulating strategies to mitigate their impact and ensure long-term, sustainable growth. The significance of this research lies in its potential to provide valuable insights and practical references for small and medium-sized enterprises (SMEs) in China. By learning from the successes and challenges of leading enterprises, SMEs can navigate the complexities of the Internet era more effectively, contributing to the sustainable and healthy development of Chinas economy in an increasingly digital world.

Intellectual Property Management and Legal Protection Mechanism of Blockchain-based Crowdsourced Testing

While promoting the vigorous development of crowdsourced testing services, Internet technology has also generated problems such as non-standard intellectual property management, unclear ownership of rights, and lack of trustworthiness. Based on the characteristics of being decentralized, traceable and tamper-proof of blockchain, IPFS, SHA256 algorithm, and digital certificate technology, this paper puts forward a new intellectual property management framework for crowdsourced testing to meet the requirements of intellectual property traceability of crowdsourced testing, right confirmation and query, and proposes the idea of applying on the chain and off chain decomposition and integration technology to address the storage performance bottleneck problem caused by data expansion on the chain, thus realizing a trusted intellectual property management system of crowdsourced testing. Meanwhile, this paper puts that the intellectual property of crowdsourced testing cannot be fully maintained only by technical means, and corresponding legal support means should be supplemented, including improving the laws and regulations on judicial and administrative protection of intellectual property, and properly handling the legal risk control of intellectual property of crowdsourced testing.

Exploring User Experiences with E-wallet DANA among Indonesian Millennials: E-satisfaction and E-loyalty Analysis

Millennials have been significantly shaped by advancements in internet technology, affecting their preferences and behaviors. This generation is characterized by comfort with digital tools, multitasking abilities, and a preference for flexibility. As technology evolves, millennials will also encounter challenges like the Fear of Missing Out (FOMO), which is particularly evident in the financial sector, which drives the adoption of digital payment systems and fintech solutions. This study analyzes millennial satisfaction with DANA, a prominent digital payment system in Indonesia, and investigate how satisfaction predicts loyalty. Using a quantitative approach, data were collected from 150 respondents, all of whom were millennials, using DANA via Google Forms on a 5-point Likert scale. This study employed Partial Least Squares (PLS) statistics to examine the correlation between customer satisfaction and loyalty. The results reveal a strong positive correlation, underscoring the importance of user satisfaction as a key driver of loyalty. These findings provide valuable insights for developing marketing strategies and enhancing customer retention in the digital payment industry.

CYBERCRIME CHALLENGES: EXPERIENCE OF INTERNATIONAL COOPERATION

The study focuses on the problem of countering cybercrime in the context of the experience of international cooperation. The trend in the process of ongoing transformation from a post-industrial to an information society stands out: global informatization and digitalization of processes lead to the threat of interference in personal life, data theft, and violation of privacy. The analysis of international legal acts made it possible to conclude the need to expand cooperation between states in the fight against cybercrime under the auspices of the UN based on programmatic, strategic acts of international legal regulation of the relevant range of relations. The importance of creating and developing organizational and legislative measures to combat cybercrime, improving interaction between the competent authorities of various states, as well as the need to develop and adopt universal conventions to combat cybercrime, is noted. The authors of the article state that the large-scale growth in the volume of information transfer causes the need to ensure the maximum level of data protection, both by the interested ICT users themselves and by governments - guarantors of state information security and stability. The international legal framework provides a solution to the problem in three areas: reducing discrepancies between national laws, introducing new powers of bodies, and promoting international cooperation. It is stated that in the Republic of Kazakhstan, there are no comprehensive studies on combating cyberterrorism and cybercrime as phenomena that include the entire range of crimes that are committed today on the Internet. The study fills this gap: the activities of the legislative and law enforcement bodies of the Republic of Kazakhstan are analyzed. The authors focus on such positive aspects as the creation of the Ministry of Internal Affairs of the Republic of Kazakhstan in April 2003 of the "K" department to intensify the fight against a wide range of crimes related to computer and Internet technologies, including cyberbullying, the production of counterfeit video discs, the dissemination of information that contributes to extremism, terrorism, cruelty, and violence. Chapter 7 of the Criminal Code of the Republic of Kazakhstan, devoted to criminal offenses in the field of informatization and communications, was subjected to scientific turnover; Cybersecurity Concept ("Cyber Shield of Kazakhstan"); a pilot project of 2023, which launched a special cyber police unit "Cyberpol Strike Task Force" to protect citizens and businesses of the country from cyber threats.

MANAGING INDONESIAN DATA BREACH NOTIFICATION IN THE FINANCIAL SERVICES SECTOR: A CASE FOR ONE-STOP NOTIFICATION MODEL

As a business of trust, the banking and financial services industry must protect its reputation to ensure consumer’s confidence. However, recent adoption of emerging internet communication technologies (ICT) have introduced new risks and challenges, such as safeguarding systems from cyberattacks and protecting consumer’s personal data. Cyberattacks, especially ransomware have shed new light on the importance of privacy and security throughout the banking and financial industry’s digitization efforts. Any organisation affected by cybersecurity attacks must face a twofold legal question. First, whether or not there has been a violation of the legal security requirements? Second, is to determine whether the attack triggers Data Breach Notification to the Data Protection Authority and/or Data Owners. This paper examines the complexity of maintaining security obligations under Indonesian Law (UU ITE, UU PDP, RPP PDP, and other relevant regulations) while also highlighting the common challenges in steering Data Breach Notification, with an enhanced perspective of the European General Data Protection Regulation (EU GDPR) practices. To address the challenges of patchwork data breach notification requirements in Indonesia, this paper proposes a proactive approach by Indonesia’s future Personal Data Protection Authority in creating a one-stop notification model to enable effective data breach incident management and notification.