Development Of The Internet Research Articles

Marketplace Data Analysis Using Web Scraping

The rapid development of the internet in fast communication has provided significant benefits for business people, allowing them to offer and sell product information boldly. One of the main advantages is the ability to search and find a variety of products easily through the internet. This convenience has led to the proliferation of many online stores in Indonesia, so that the need for applications that can help users search and combine product data from the internet arises. This study aims to develop a web application for product data analysis by utilizing web scraping techniques on the Tokopedia marketplace website, using the BeautifulSoup4 (Bs4) method. The web application is designed using the Streamlit module from Python. The results of this study are web applications that process data analysis taken from the Tokopedia website, which are classified based on certain criteria such as price range, highest and lowest ratings, highest and lowest sales, highest and lowest prices, percentage of location distribution, percentage of seller stores, and product recommendations.

Multi-objective evolutionary neural architecture search for network intrusion detection

Network Intrusion Detection (NID) becomes significantly important for protecting the security of information systems, as the frequency and complexity of network attacks are increasing with the rapid development of the Internet. Recent research studies have proposed various neural network models for NID, but they need to manually design the network architectures based on expert knowledge, which is very time-consuming. To solve this problem, this paper proposes a Multi-objective Evolutionary Neural Architecture Search (MENAS) method, which can automatically design neural network models for NID. First, a comprehensive search space is designed and then a weight-sharing mechanism is used to construct a supernet for NID, allowing each subnet to inherit weights from the supernet for direct performance evaluation. Subsequently, the subnets are encoded as chromosomes for multi-objective evolutionary search, which simultaneously optimizes two objectives: enhancing the model’s detection performance and reducing its complexity. To improve the search capability, a path-based crossover method is designed, which can iteratively refine the subnets’ architectures by simultaneously optimizing their accuracy and complexity for NID. At last, our MENAS method has been validated through extensive experiments on three well-known NID datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The experiments show that our MENAS method obtains an average 1.45% improvement on accuracy and an average 68.70% reduction on floating-point operations through multi-objective optimization process on six scenarios, which outperforms some state-of-the-art NID methods.

A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks

The exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose a hybrid feature selection approach that combines filter and wrapper methods. This approach is integrated into a two-level anomaly intrusion detection system. At level 1, our approach classifies network packets into normal or attack, with level 2 further classifying the attack to determine its specific category. One critical aspect we consider is the imbalance in these datasets, which is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate how the selected features affect the performance of the machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, and k-Nearest Neighbor, we employ benchmark datasets: BoT-IoT, TON-IoT, and CIC-DDoS2019. Evaluation metrics encompass detection accuracy, precision, recall, and F1-score. Results indicate that the decision tree achieves high detection accuracy, ranging between 99.82 and 100%, with short detection times ranging between 0.02 and 0.15 s, outperforming existing AIDS architectures for IoT networks and establishing its superiority in achieving both accuracy and efficient detection times.

AIoT based sentiment analysis via multi‐scale context perception to enhance linguistic teaching

AbstractWith the rapid development of the Internet of Things (IoT) and artificial intelligence technology, real‐time text sentiment analysis plays an important role in online education. Due to the limited resources of clients, existing deep networks cannot be directly deployed in the edge node. In addition, deep convolutional networks cannot fully utilize contextual information. In order to resolve these issues, this paper proposes a multi‐scale context‐aware text sentiment analysis system based on cloud computing, in which a bidirectional long short‐term memory network (BiLSTM) model is deployed in the cloud server. The BiLSTM model can fully explore the contextual feature information of the text stream in online education. The real‐time text data are collected through terminal nodes, such as pad or computer, to send the cloud server. The experiments utilize three public text datasets to simulate the input of terminal nodes. The results show that the proposed system shows better accuracy than previous models and can return the emotional status in time.

Exploration of Big Data Information Network Based on Artificial Intelligence Technology

The rapid development of the Internet has changed people s work and life. The use of network technology, mobile communication technology and cloud computing technology has greatly improved the efficiency of data collection and transmission. While the rapid development of the network provides convenience for human beings, it also puts forward new requirements on network security, data protection, information overload, information divide and so on. However, while enjoying these conveniences, we should pay more attention to the safety management of AI and make it a research focus. Currently, the degree of completeness of AI needs to be improved. The big data network based on artificial intelligence can better cope with the complex and severe network security problems, enhance the security guarantee ability of multi-dimensional network, and promote the progress of national network science and technology.

Energy Minimization for IRS-Assisted SWIPT-MEC System.

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies.

Feasibility Analysis of the Integration of Guangdong Online Dispute Resolution (ODR) Mechanism and University Talent Resources

In recent years, online dispute resolution mechanisms have penetrated people's lives with the development of the Internet. Online dispute resolution mechanism not only efficiently solves the disputes of various contradictions of the parties, but also greatly alleviate the pressure of the court due to the lack of judicial resources and high cost, at the same time, the online dispute resolution platform is also faced with the dilemma of the lack of high-quality mediation talents. This paper mainly analyses the feasibility of the introduction of local university human resources into the Guangzhou ODR online dispute resolution platform by the local judicial department according to the development and operation status of the ODR online dispute resolution service platform in Guangzhou, Guangdong Province, and provides a feasible reference plan to alleviate the current situation of the lack of talents in China's online dispute resolution platform.

English grammar intelligent error correction technology based on the n-gram language model

Abstract With the development of the Internet, the number of electronic texts has increased rapidly. Automatic grammar error correction technology is an effective safeguard measure for the quality of electronic texts. To improve the quality of electronic text, this study introduces a moving window algorithm and linear interpolation smoothing algorithm to build a Cn-gram language model. On this basis, a syntactic analysis strategy is introduced to construct a syntactic error correction model integrating Cn-gram and syntactic analysis, and English grammar intelligent error correction is carried out through the model. The results show that compared with the Bi-gram and Tri-gram, the precision of the Cn-gram model is 0.85 and 0.91% higher, and the F1 value is 0.97 and 1.14% higher, respectively. Compared with the results of test set Long, the Cn-gram model has better performance on verb error correction of the Short test set, and the precision rate, recall rate, and F1 value are increased by 0.86, 3.94, and 1.87%, respectively. The comparison of the precision, recall rate, and F1 value of the proposed grammar error correction model on the complete test set shows that the precision of the study is 19.10 and 5.41% higher for subject–verb agreement errors. The recall rate is 9.55 and 10.77% higher, respectively; F1 values are higher by 12.65 and 10.59%, respectively. The above results show that the error-correcting technique of the research design has excellent error-correcting performance. It is hoped that this experiment can provide a reference for the relevant research of automatic error correction technology of electronic text.

The Impact of Digital Transformation on Corporate Financial Performance: A Case Study of Luzhou Laojiao

With the development of the Internet and big data, the global economy is tending to the deep integration of digital economy and real economy. The state attaches great importance to the digital development strategy and has issued a number of policies to support the digital transformation of enterprises. Therefore, digital transformation has become a necessary means for enterprises to cope with internal and external adverse environmental changes and improve their survival and development. This paper selects Luzhou Laojiao, which has great development potential in the liquor industry, as the research object to sort out the background and path of its digital transformation, and then selects relevant indicators from four aspects of profitability, debt paying ability, operating ability and growth ability for analysis. This paper analyzes the impact of Luzhou Laojiao's digital transformation on its financial performance, and provides reference for other enterprises in the same industry undergoing digital transformation.

ROLE OF INTERNET MARKETING IN PROMOTING SALES: AN ANALYSIS

Marketing in olden times meant only traditional marketing which involved the use of radio, television, newspapers, magazines, pamphlets etc. With the rapid growth in technology, the focus shifted from the traditional form to digital form. The newest medium of communication known as the internet was developed in 1969 in the US to facilitate a joint project between the US military and several research organizations. In 1971, network e-mail was invented, even though initially it was very slow but gradually gained momentum and was established for international connections in 1973. The official term ‘Internet’ was for the first time used in 1974 and the first online protocols were established in the 1980s. Early 1990s witnessed a tremendous growth in internet and Tim Berners- Lee who is known as the creator of the internet released the URL protocol, web browser, software and world wide web to the public. The firms took this invention to their advantage and used it to promote and facilitate their businesses across the globe. So traditional marketing became slowly a thing of the past and was replaced by digital marketing/E-marketing/ Internet marketing. The objective of the study is to understand the concept of Internet Marketing and how it helps in boosting sales of a business firm.

Durable Multilayered Sleeve-Structured Hybrid Nanogenerator for Efficient Water Wave Energy Harvesting.

The state-of-the-art triboelectric nanogenerator (TENG) technology has numerous advantages and creates new prospects for the rapid development of the Internet of Things (IoT) in marine environments. Here, to accelerate the application process of TENG, an elaborately designed multilayered sleeve-structured hybrid nanogenerator (M-HNG) is developed to efficiently and persistently harvest marine energy. The M-HNG integrates an electromagnetic nanogenerator (EMG) with four coils and a multilayered sleeve-structured TENG (MS-TENG) with three freestanding layer units to increase spatial utilization efficiency. Moreover, rabbit fur strips are introduced to enhance the output performance and strengthen the durability of TENG. Therefore, the MS-TENG has high durability due to its soft-contact structure, maintaining its performance even after 240,000 cycles. When a 1000 μF capacitor is charged by M-HNG utilizing a power management circuit (PMC), the stored energy is increased from 2.62 mJ to 140.11 mJ, representing a significant improvement of 52-fold. The M-HNG triggered by water waves has successfully powered various small electronic devices, including 1200 LED lights, nine thermo-hygrometers, a water quality testing pen, and water level alarms. The proposed M-HNG effectively harvests low-frequency water wave energy, introducing an innovative concept for constructing a hybrid TENG with enhanced density and durability.

Fiber-Optic Sensors and Their Practical Research in the Internet of Things

With the rapid development of the Internet of Things (IoT) technology, fiber-optic sensors, as a kind of high-precision and high-sensitivity measurement tool, are increasingly widely used in the field of IoT. This paper outlines the advantages of fiber-optic sensors over traditional sensors, such as high precision, strong resistance to electromagnetic interference, and long transmission distance. On this basis, the paper discusses the application scenarios of fiber-optic sensors in the Internet of Things, including environmental monitoring, intelligent industry, medical and health care, intelligent transportation, and other fields. It is hoped that this study can provide theoretical support and practical guidance for the further development of fiber-optic sensors in the field of the Internet of Things, as well as promote the innovation and application of IoT.

BeHarmony: Blockchain-Enabled Trustworthy Communication and Legitimate Decision Making in Multi-Party Internet of Vehicles Systems

The rapid development of the Internet of Vehicles using centralized systems faces significant challenges, including reliability and security vulnerabilities and high latency. This paper introduces a blockchain-enabled authentication and communication network for scalable IoV to enhance security, reduce latency, and relieve the dependency on centralized infrastructures. The network applies blockchain-enabled domain name services and mutual authentication for fault tolerance consensus, such as PBFT and RAFT, featuring a primary layer of road side units and edge servers for inter-vehicle communication and a sub-layer within each vehicle for intra-vehicle communication. The study evaluates various scenarios and assesses roadside unit availability based on random distribution along vehicle routes. This paper also discusses the legal issues involved in the proposed model, highlighting that the IoV system should be governed by a contract-based decentralized IoV system comprising both smart contracts and traditional contracts. This model offers a novel approach to developing a decentralized, secure, efficient, and ethical IoV ecosystem, advancing autonomous and reliable vehicular networks.

Evaluating factors affecting sexual abuse prevention education for primary school students according to the life skills approach

This comprehensive study delves into the intricate factors influencing sexual abuse prevention education for elementary school students, capturing insights from diverse stakeholders, including 423 students, 115 teachers, and 423 parents. Employing a Likert scale, the research evaluates various dimensions, shedding light on the nuanced dynamics that shape prevention programs. The results reveal that student-related factors, encompassing positive behavior, cognitive capacity, personality traits, and knowledge levels, received moderately positive mean scores. This underscores the significant contribution of inherent qualities and cognitive abilities of primary school students to the effectiveness of sexual abuse prevention education. Moving to family-related factors, parents’ extensive knowledge and understanding garnered a positive mean score of 2.91, signifying a favorable impact, while financial considerations received a slightly lower mean score of 2.92. Notably, parental educational methods and the inclination to delegate educational responsibilities to schools scored higher at 2.99 each, underscoring the influential role parents play in shaping their children's perspectives on sexual abuse prevention. Within the school environment, positive interactions between teachers and students stood out with a mean score of 2.97, emphasizing the pivotal role of supportive teacher-student relationships in facilitating effective sexual abuse prevention education. Societal factors exhibited varying influences, with local customs, culture, and social psychology receiving a mean score of 2.91, indicating a moderately positive impact. However, the development of the internet and information channels scored lower at 2.60, revealing potential challenges in adapting prevention education to the evolving landscape of online information dissemination. These nuanced findings contribute to a deeper understanding of the multifaceted factors shaping sexual abuse prevention education for elementary school students, providing a foundation for evidence-based interventions and policy decisions.

Bibliometric analysis for artificial intelligence in the internet of medical things: mapping and performance analysis.

The development of computer technology has revolutionized how people live and interact in society. The Internet of Things (IoT) has enabled the development of the Internet of Medical Things (IoMT) to transform healthcare delivery. Artificial intelligence has been used to improve the IoMT. Despite the significance of bibliometric analysis in a research area, to the best of the authors' knowledge, based on searches conducted in academic databases, no bibliometric analysis on artificial intelligence (AI) for the IoMT has been conducted. To address this gap, this study proposes performing a comprehensive bibliometric analysis of AI applications in the IoMT. A bibliometric analysis of top literature sources, main disciplines, countries, prolific authors, trending topics, authorship, citations, author-keywords, and co-keywords was conducted. In addition, the structural development of AI in the IoMT highlights its growing popularity. This study found that security and privacy issues are serious concerns hindering the massive adoption of the IoMT. Future research directions on the IoMT, including perspectives on artificial general intelligence, generative artificial intelligence, and explainable artificial intelligence, have been outlined and discussed.

The future of medicine: an outline attempt using state-of-the-art business and scientific trends.

Currently, there is a lot of discussion about the future of medicine. From research and development to regulatory approval and access to patients until the withdrawal of a medicinal product from the market, there have been many challenges and a lot of barriers to overcome. In parallel, the business environment changes rapidly. So, the big question is how the pharma ecosystem will evolve in the future. The current literature about the latest business and scientific evolutions and trends was reviewed. In the business environment, vast changes have taken place via the development of the internet as well as the Internet of Things. A new approach to production has emerged in a frame called Creative Commons; producer and consumer may be gradually identified in the context of the same process. As technology rapidly evolves, it is dominated by Artificial Intelligence (AI), its subset, Machine Learning, and the use of Big Data and Real-World Data (RWD) to produce Real-World Evidence (RWE). Nanotechnology is an inter-science field that gives new opportunities for the manufacturing of devices and products that have dimensions of a billionth of a meter. Artificial Neural Networks and Deep Learning (DL) are mimicking the use of the human brain, combining computer science with new theoretical foundations for complex systems. The implementation of these evolutions has already been initiated in the medicinal products' lifecycle, including screening of drug candidates, clinical trials, pharmacovigilance (PV), marketing authorization, manufacturing, and the supply chain. This has emerged as a new ecosystem which features characteristics such as free online tools and free data available online. Personalized medicine is a breakthrough field where tailor-made therapeutic solutions can be provided customized to the genome of each patient. Various interactions take place as the pharma ecosystem and technology rapidly evolve. This can lead to better, safer, and more effective treatments that are developed faster and with a more solid, data-driven and evidence-concrete approach, which will drive the benefit for the patient.

Text Classification Based on Natural Language Processing and Machine Learning in Multi-Label Corpus

The rapid development of the Internet has led to a geometric expansion of text information resources online. Among them, corpus, as the basic data source of natural language processing based on statistical language model, its construction and application have become a hot issue in current language processing research. After consulting a large number of relevant literature and materials, it was found that many researchers have provided new ideas for multi label corpus text classification methods. However, this article adds its own understanding and takes this as the direction and basis. In the introduction, the research significance of text classification was introduced, and then academic research and analysis were carried out on the two key sentences of corpus text classification and natural language processing in multi-tag corpus text classification. This article then utilizes an algorithm model to provide a theoretical basis for the study of multi-label corpus text classification methods; At the end of this article, a simulation comparative experiment is conducted, and the experiment is summarized and discussed; In the Enterprise L corpus, the difference in recall rates before and after the use of Entrance 1 was 5.5%, the difference in recall rates before and after the use of Entrance 2 was 7.8%, the difference in recall rates before and after the use of Entrance 3 was 3.3%, and the difference in recall rates before and after the use of Entrance 4 was 4.5%. At the same time, with the continuous research of natural language processing and machine learning, the research on text classification methods of multi tag corpus is also facing new opportunities and challenges.

Carbon‐Based Textile‐Structured Triboelectric Nanogenerators for Smart Wearables

Recent advances in wearable electronics have been propelled by the rapid growth of microelectronics and Internet of Things. The proliferation of electronic devices and sensors relies heavily on power sources, predominantly batteries, with significant implications for the environment. To address this concern and to reduce carbon emissions, there is a growing emphasis on renewable energy harvesting technologies, among which textile‐based triboelectric nanogenerators (T‐TENGs) stand out as an innovative and sustainable solution due to having the interesting characteristics like large contact area, lightweight design, flexibility, comfort, scalability, and breathability. T‐TENGs can harness mechanical energy from human movement and convert it into electric energy. However, one of the challenges is low electric power output, which can be addressed by meticulous selection of material pairs with significant differences in work function and optimizing contact areas. The incorporation of carbon‐based nanomaterials, such as carbon nanotubes and graphene, emerges as a key strategy to enhance output. This review delineates recent progress in T‐TENGs incorporating carbonaceous nanofillers, comprehensively addressing fundamental classification, operational mode, structural design, working performance, and potential challenges that are hindering commercialization. By doing this, this review aims to stimulate future investigations into sustainable, high‐performance smart wearables integrated with T‐TENGs.

Understanding farmed salmon imports and e-commerce consumer satisfaction in China: A text mining approach

Salmon, a luxury aquatic species, has seen a steady increase in imports in China. The rapid development of the Internet, the transformation of consumption behaviour, and the impact of the COVID-19 pandemic have prompted consumers to purchase salmon products from e-commerce platforms in China. In this context, understanding salmon import trends and consumer satisfaction with salmon e-commerce is critical to promoting and sustaining sales of salmon products through e-commerce platforms. Therefore, this study aims to investigate the import trend analysis of salmon in China as well as understand factors influencing consumer satisfaction with salmon e-commerce. We analysed 10,064 consumer comments out of 25,763 for this study after resampling the proportion of positive/negative comments. Sentiment analyser, Textblob, classified 65.2 % of consumer comments as positive, 21.7 % as negative, and 13.1 % as neutral. Additionally, Latent Dirichlet Allocation (LDA) topic modelling identified five areas critical for satisfaction with salmon e-commerce: purchase decision (19.5 % of the comments), eating experience (22.3 %), service quality and delivery (20.6 %), purchase history (17.5 %), and general product quality (20 %). Our findings indicate that service quality and delivery are the most critical factors affecting consumer satisfaction, whereas purchase history has the least impact. Consumers’ PLUS membership profile, salmon product style, and preservation form had a significant influence on consumer satisfaction, while the country of origin, weight and price of the product had no significant influence. Salmon e-commerce platforms need to improve logistic service and delivery quality to influence consumer satisfaction.

Triboelectric Nanogenerators with Machine Learning for Internet of Things

AbstractThe development of the Internet of Things (IoT) indicates that humankind has entered a new intelligent era of the “Internet of Everything”. Thanks to the characteristics of low‐cost, diverse structure, and high energy conversion efficiency, the self‐powered sensing systems, which are based on the Triboelectric Nanogenerator (TENG), demonstrate great potential in the field of IoT. In order to solve the challenges of TENG in sensing signal processing, such as signal noise and nonlinear relations, Machine Learning (ML), which is an efficient and mature data processing tool, is widely applied for efficiently processing the large and complex output signal data generated by TENG intelligent sensing system. This review summarizes and analyzes the adaptation of different algorithms in TENG and their advantages and disadvantages at the beginning, which provides a reference for the selection of algorithms for TENG. More importantly, the application of TENG is introduced in multiple scenarios, including health monitoring, fault detection, and human‐computer interaction. Finally, the limitations and development trend of the integration of TENG and ML are proposed by classification to promote the future development of the intelligent IoT era.