bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Defect Detection Method of Painting Parts Based on Machine Vision
Authors: Yi Liang, Hongwang Zhao, Xuebang Tang, Tingpeng Li
Published: April 2024

The technology is used to study and analyze the appearance defect characteristics of sprayed auto parts. The corresponding recognition method is developed by using MATLAB and Halcon software. Firstly, the causes of background noise and uneven brightness are described from the hardware and theory, and the specific treatment methods are given from the two perspectives of eliminating background noise and balancing illumination. A variety of frequency domain low-pass filters are constructed to eliminate background noise. The image preprocessing algorithm based on homomorphic filter is studied to achieve the expected preprocessing effect. The image segmentation method of local features is adopted, and the binary processing is carried out. Corresponding recognition algorithms are designed for different defect features. The on-line monitoring system of spraying quality is established and field test is carried out to verify the effectiveness of the system and realize the rapid and accurate identification of spraying quality. The single recognition time is 320 milliseconds, and the recognition accuracy is 97%. Compared with manual identification, it is improved by about 40% and meets the needs of industry applications
Physicochemical reaction mechanism study and model optimization based on big data analysis
Authors: Xiang Li, Liye Hao, Yaoyu Wang
Published: April 2024

The huge amount of data generated by information technology, intelligence and automation has serious redundancy and duplication, which becomes a hidden cost of consuming resources. In the field of physicochemical analysis, for the explanation of mechanism, the analysis of big data can provide reference and integration. The purpose of big data-based analysis is to remove redundancies and inconsistencies in the given raw characterization data to better improve its completeness and accuracy. The paper takes the density matrix of quantum bits, which is different from the classical real number representation, etc. as the representation of feature data, and investigates the cleaning of feature data based on quantum representation from the perspective of the intersection of theoretical physics and computer science. This paper comprehensively describes the basic theories and methods of data mining. On the basis of understanding and analyzing a variety of data mining techniques, it focuses on the BP neural network-based data mining techniques to provide in-depth analysis and elaboration. Further, this paper proposes an improved algorithm with multi-algorithm advantage integration and joint optimization for the deficiencies in the BP neural network algorithm, which is fully demonstrated in the nonlinear function simulation. In addition, based on the HA-BP algorithm, this paper has designed anomaly data detection model and variable factor analysis model, and their reliability and practicality are also fully demonstrated in the nonlinear function simulation in the analytical study of physicochemical reaction mechanism.
Co-simulation of giant magnetostrictive autonomous emergen-cy braking system for intelligent vehicles based on Prescan/Simulink /CarSim
Authors: Changbao Chu, ZhenhuaLiu ,Xingjian Jia
Published: April 2024

The combination of giant magnetostrictive actuator (GMA) and autonomous Emergency brak-ing system (AEB) can achieve the lightweight body and fast braking of intelligent vehicle. In this essay, simulation scenarios are established in PreScan, AEB algorithm and GMA actuator are combined in simulink, vehicle dynamics model is provided by CarSim, and co-simulation analysis of CCRm, CCRs and CCRb working conditions is carried out. When AEB adopts TTC algorithm only, in the stationary vehicle scenario (CCRs) where the vehicle approaches the front at a certain speed, the relative distance between the two vehicles is between 1.86 and 2.85m until stopping. When the front vehicle is driving slowly and the ego vehicle is faster than the front (CCRm), the relative distance between the two vehicles until stopping is between 2.28 and 2.83m; When the ego vehicle is driving at the same speed as the front, the front vehicle suddenly braking (CCRb), and the relative distance between the two vehicles until stopping is 1.98~2.54m; The above scenarios can achieve effective collision avoidance, and the safety distance is within an effective and reasonable range. In the scenario of two vehicles following at the same speed and close distance, TTC algorithm fails. AEB adopts TTC algorithm and Seungwukmoonalgo-rithm for collaborative control to achieve effective collision avoidance. The co-simulation re-sults show that the AEB system can intervene in time and avoid collisions effectively under the above three working conditions, and the relative distance between the two vehicles until stop-ping is between 1.86 and 2.85m, which verifies the timeness and effectiveness of the braking of the giant magnetostrictive actuator when AEB algorithm intervenes, and significantly improves the active safety performance of the vehicle.
Research on Detection System of Intelligent Computer Optimization Algorithm for Concrete Mix Ratio Based on Big Data
Authors: Juanjuan Wang, Xin'e Yan, Yetao Cong
Published: April 2024

PLC is used to complete the control function of the concrete proportioning system. The MCGS (Configuration Software Monitor and Control System) is used to realize the real-time monitoring, data management and fault warning of the whole monitoring system. The material feed and PLC control process in the feed system are emphatically introduced. A concrete proportioning design method based on multiple parameters is proposed. The nonlinear multi-objective optimization model of concrete mix ratio was established by stepwise regression analysis and complex optimization method. The problems of narrow range of variables and single form of objective and constraint in traditional linear optimization methods are solved, which makes the selection of optimal index simpler and more flexible. Through the calculation of examples, it is clear that this method can obtain the mixture that meets the use conditions, and can greatly save the experimental work and construction cost.
The Mode Exploration of Combining Chinese Traditional Culture Content with Computer Aided Instruction in Moral Education of Colleges and Universities
Authors: Dan Xie, Chao Yin
Published: April 2024

According to the characteristics of learning style, this paper classifies the traditional cultural classics according to the school. Audio, video, text, pictures and other forms are collected to match students' learning styles. The system uses the three-layer structure of My Batis+ Spring MVC, and uses Ajax+ Free marker+ JS technology, MySQL database, Apache Tomcat8 manager, Eclipse development platform, WebStorm and other network front-end development tools. In addition, auxiliary tools such as Camtasia Studio and PhotoshopCS5 are used to generate prototype systems for traditional culture learning. Its data processing is fast.
Application of Computer Virtual Reality Technology in Welding Simulation Training of Complex Structures Based on Unity3D
Authors: Renping Wang, Yong Lei
Published: April 2024

A virtual reality welding robot simulation system for practical application is developed based on the training and application of welding robot for practical application, which has important theoretical and practical application value for promoting the development of industrial robots in China. The simulation system is synchronized with the welding simulation training platform to achieve the purpose of VR simulation of the welding robot. By modeling the welding process, the process parameters can be inferred automatically, and the digital twin knowledge base for practical application is built. The OPC UA protocol is used to realize real-time communication with mobile devices. The digital twinning technology of CNC machine tool is studied through the simulation experiment of weld motion. This system lays a foundation for realizing the intelligentization of CNC machine tools.
Numerical Analysis on Diffusion Mechanism of Prefabricated Technology Under Government Intervention Based on Evolutionary Game
Authors: Jianlin Mao, Qianhong Diao, Fanli Kong, Lei Wang
Published: April 2024

The diffusion of prefabricated construction technology is an important guarantee for industrialization of construction industry. To investigate the roles of government, developers and contractors in the diffusion of prefabricated construction technology, a three-way evolutionary game model of prefabricated construction technology diffusion is introduced, and the influence of external factors related to game players on the diffusion of prefabricated construction technology is analyzed based on the evolutionary game model. With the numerical simulations conducted by system dynamics software of Vensim, the evolutionary trends under different initial conditions are dynamically displayed, and the results demonstrate that reducing the cost of diffusion assembly construction technology, is conducive to improving the enthusiasm of developers and contractors to share the prefabricated construction technology. Besides, appropriately increasing government incentives is conducive to promote the diffusion of prefabricated construction technology
Research on the Driving Mechanism of Intelligent Renovation and Digital Transformation in Traditional Enterprises
Authors: Cuizhi Yin, Wei Song
Published: April 2024

Intelligent renovation and digital transformation have become effective paths for traditional enterprises to enhance their core competitiveness. The rapid and extensive application of digital technology has become a crucial catalyst for the transformation and upgrading of the manufacturing industry. This digital transformation not only enhances production efficiency, product quality, and cost reduction but also boosts the competitiveness and innovation capabilities of enterprises. In this study, in-depth interviews were conducted with personnel at various levels of 30 traditional enterprises, utilizing grounded theory to extract significant factors that influence enterprise digital transformation. A theoretical model was developed, identifying environmental changes as external factors impacting enterprise digital transformation, and internal driving forces including enterprise resources, entrepreneurial spirit, employee involvement, enterprise capabilities, and strategic planning. Through comprehensive research on the driving mechanism of digital transformation in traditional enterprises, this study proposes effective strategies and path choices for enterprises. These include “government-enterprise collaboration” and “industry chain collaboration” along with “strengthening the foundation” and “enhancing capabilities.” The aim is to promote successful intelligent renovation and digital transformation among traditional enterprises.
Excellent Case of Building A "School-land" Co-construction and Sharing Pattern Network of School Aesthetic Education and Regional Culture
Authors: Yu Ran
Published: April 2024

With the development of computer science, some of the excellence artworks of Henan Provence are very popular through network dissemination. To achieve the transformation of Henan cultural brand from "parts" to "whole" and from "internet celebrity" to "long-lasting", it is necessary to establish an active inheritance mechanism network for Henan regional cultural resources and enhance the endogenous and innovative capabilities of traditional regional culture. The modern school aesthetic education system that runs through all stages has a natural advantage in incubating regional cultural communities. However, in reality, the aesthetic and spiritual attributes shared by school aesthetic education and regional culture have not been sufficiently valued, and the connection between the two lacks active policy design and mechanism innovation. Through Literature, field research and interviewing professors, artists and persons in charge of regional culture, the Department of Public Art Education at Zhengzhou University of Aeronautics has recognized the value of integrating school aesthetic education with regional culture in terms of cultivating students and building a rich cultural life. Leveraging their connections with social artist groups and individuals online and offline, who have conducted research on the ecological conditions of Zhengzhou's art districts, intangible cultural heritage, and the current status and issues of various school aesthetic education programs in the province through various ways. The working mechanisms and platforms have been establishment online and offline. The establishment of a pattern of co-construction and sharing of art education and regional culture network in the school is essentially a practical and methodological issue. Some plans have been made for the future. Research has found that for school aesthetic education, curriculum, teaching staff, and internal and external collaborative mechanisms are key points for the reform and development of aesthetic education. Schools aesthetic education curriculum system should integrate regional culture from multiple dimensions with network. A collaborative art education framework should be formed which integrates social and cultural resources at the regional level.
The Evaluation Method of Xinjiang Bazaar Cultural Service Function Based on Data Information Processing
Authors: Ade·Muhuyati, Tao Fan
Published: April 2024

Based on the processing of data information, the paper formulates the evaluation criteria and weights of each index according to the results of questionnaire survey, market statistics and quantitative calculation, and constructs the evaluation index system of Xinjiang Bazaar from three dimensions: economy, environment and society. Taking into account the influence of surrounding factors on the evaluation model, the environmental conditions are quantified as correction coefficient to correct the evaluation model, and the evaluation model of Xinjiang Bazaar cultural service function is constructed by mathematical model. Three typical cases of Xinjiang Bazaar were selected for value evaluation, and the final result was the score of Xinjiang International Bazaar was 1.53, the score of Tianyu City was 0.43, and the score of Yotegan Old City was 0.47. It can be seen that the scores obtained by using data information processing were consistent with the actual operation of the three projects
The Application of 3D Printing Formwork Technology in Concrete Building Materials
Authors: Yiquan Zou, Zexu Wang
Published: April 2024

Concrete 3D printing technology is a prime example of contemporary digital manufacturing, showcasing its efficiency and automation in the realm of construction. Compared to traditional construction methods, the characteristics of labor-saving and formwork-free in 3D printing technology have brought changes to the construction personnel structure and forming process. Enhancing the flexibility of concrete materials and 3D printing technology are two major areas of concentration, along with promoting the wider application of 3D printing in the construction industry. An extensive review of the advancements in concrete 3D printing technology is given in this paper. The mechanical, printable, buildable, and rheological properties of concrete materials that are suitable for 3D printing are also discussed. The study also shows how 3D printing formwork technology is frequently used in decorative elements and atypically shaped projects. The goal is to offer informative analysis and practical tools for further research and advancements in 3D printed concrete technology. The study's overall findings emphasize how important it is to improve 3D printing technology's compatibility with concrete materials. By tackling essential performance criteria and exploring creative uses, we can unlock the complete potential of concrete 3D printing, which will result in notable progress within the construction sector.
Evaluating the Impact of Internet Users' Emotional Tendencies on Opinion Dynamics through Computer-Assisted Sentiment Analysis of Texts
Authors: Qi Liu Chao Yang,
Published: April 2024

- Internet communication is characterised by "non-linear flow" and "unorganised aggregation,"  and computer-assisted text sentiment analysis is an important means of evaluating the impact of netizens' emotions on the dynamics of public opinion. The uncertainty, anonymity, disorder, and blindness inherent in fast-gathering and fast-dispersing Internet communication drive the continuous generation of texts, which has an important impact on public opinion. In order to study this issue in depth, this paper chooses two text types of "popular Weibo" and "popular topics" in Sina Weibo as the object of study and analyses the issue in detail from the two dimensions of agenda construction and agenda setting. In the process of the study, the required text data were collected through Python, and the theoretical assumptions were verified with text analysis methods to explore the influence of text sentiment bias on opinion diffusion, with a view to providing new insights and references for the application of the theory of opinion diffusion and the field of computational diffusion.
Impact of Heterogeneous Environmental Regulation on Ecological Industrialisation in the Upper Reaches of the Yangtze River in the Context of "Internet + Big Data"
Authors: Wenyu Li, Chuanhao Wen Wangying Xie, Yun Qing,
Published: April 2024

The solution to the ecological and protection conundrum in the upper reaches of the Yangtze River lies in ecological industrialization, which is predicated on ecological preservation and has the capacity to convert local ecological advantages into economic advantages. Environmental regulations are essential to the ecological industrialization process since they help to solidify the base of resource endowment and guarantee the industrialization process's steady progression. There may be a relationship between environmental laws and eco-industrialization, according to the research. With the rapid development of new-generation information technology such as the Internet, big data, cloud computing and the Internet of Things, it is of great significance to strengthen the Internet and big data to empower eco-industrialisation development. Using a panel regression model, the influence and moderating function of environmental regulations on eco-industrialization were examined. The findings demonstrate that environmental rules with a focus on public engagement and command-and-control encourage area eco-industrialization. Nonetheless, the degree of regional eco-industrialization was lowered by the market incentive form of environmental control. It was also determined whether environmental laws had a moderating effect on eco-industrialization. The findings of the mediating impact demonstrate how, through the mediating function of upgrading the industrial structure, all three environmental regulations influence the evolution of eco-industrialization. In conclusion, this study makes the case that environmental legislation has a significant impact on eco-industrialization. Simultaneously, consideration needs to be given to how various forms of environmental control affect eco-industrialization. To this purpose, upgrading the eco-industrialization level of the upper reaches of the Yangtze River requires a reasonable regulation of the strength of environmental regulations. This justifies accelerating the industrial structure's upgrade.
Ensemble Learning-Based Browser Extension for Mitigating Cyber Attacks Carried out using Malicious Short URLs
Authors: Pradeep K V, Rajarajeshwari S, Sujay Doshi, D. Yuvaraj Nachiyappan S,
Published: April 2024

As digitization continues to expand and cybercrimes become more prevalent, making it is crucial to prioritize the implementation of robust security measures. Malicious short URLs are frequently utilized as a vector for cyber-attacks on online forums and social media platforms. To address this issue, a plugin-based solution that uses ensemble learning to combine random forest, k-neighbors classifier, and logistic regression into a stacked model, was developed. The model was trained over the combination of three most popular kaggle datasets, with over 1081195 URLs. Additionally, gradient boosting was applied to further enhance the model's performance, resulting in a 92% accuracy in the detection. We developed the browser extension with Flask and JavaScript that identifies URLs as malicious or safe, for facilitation of the proposed solution. The work emphasizes the need for effective measures to mitigate cyber-attack risks, particularly those involving malicious short URLs.
Analytical Modelling and Performance Characterization of Single Electron Transistor
Authors: Shruti Suman Ashok D. Vidhate,
Published: April 2024

One important component of the present nanotechnology research field is single electron transistor (SET) which has unrealized quality to operate at breakneck speeds with ultralow power consumption. The single electron transistor is a novel nanoscale switching device that can regulate the motion of a single electron in addition to maintaining its scalability down to the atomic level. In this context, scalability refers to the ability of electronic device to function better as their dimensions get smaller. The basic device properties that this single electron transistor (SET) operates on "quantized current-voltage (I-V) characteristic," "single electron tunneling effect," and "Coulomb blockade," are also covered. This study presents some of the key SET-related topics, such as modelling and simulation methods. Simulation of Nanostructures Method (SIMON) is circuit simulator for tunneling device with just a single electron device based on Monte Carlo approach. It enables simulation of any kind of circuits with voltage source, junctions in tunnels and capacitors in quasi stationary mode and transient mode. The most significant feature of SET that is applied in the research work is Coulomb blockade oscillation, so at the conclusion of this paper, a number of simulations utilizing the SIMON simulator are shown. The simulation results show that for gate capacitance CG = 2af, C∑ = 3af, IBIAS = 2nA, tunnel barrier resistance RT > 4.1KΩ and operating temperature T=21K the model reflects real SET characteristic with maximum voltage gain.
Analytical Modelling and Performance Characterization of Hybrid SET-MOS
Authors: Shruti Suman Ashok D. Vidhate,
Published: April 2024

Gordon Moore's "Moore's Law" suggests chip functionality demand doubles every 1.5-2 years, with global semiconductor technology roadmap recommending sub-nanometer ranges for IC production in nano-electronics. The single electron transistor (SET) is a promising nano-scale device that can be co-integrated with CMOS technology to improve performance. The paper explores the tunnelling effect between nanoparticles in single electron transistors (SET), revealing coulomb blockade transactions and resistance increases with reduced bias. It focuses on single electron transistors and pre-terminal devices, discussing IV characteristics, coulomb diamond plots, and metallic quantum dots. This research also explores the hybrid SET-FET based model, focusing on developing a room temperature SET in CMOS comparable technology with a Field Effect Transistor (FET). Prediction models and exploratory studies guide the integration of SET-FET technology. SIMON is a comprehensive simulator designed for single-electron devices and circuits. The output current is not impacted by capacitances up to 1 pF, and FET size in the micron range are appropriate for SET signal amplification up to 4 µA.  This research explores the modelling of SET-FET technology in highlighting its ability to mitigate drawbacks in low drive current when combined with a FET. It also explores device variability mitigation in nanoscale array configurations, finding that SET-FET significantly reduces variability in larger arrays, despite the impact of capacitance and resistance.
Application of Frbf Network Autonomous Learning Algorithm Based on Intrinsic Motivation in English Network Teaching
Authors: Chen Haiyan
Published: April 2024

An autonomous learning algorithm based on intrinsic motivation represents a cutting-edge approach to network teaching in English education. By harnessing intrinsic motivation, this algorithm empowers learners to autonomously engage with English language materials and activities, driven by their innate curiosity and desire for mastery. Through a combination of personalized learning pathways, adaptive feedback mechanisms, and gamified elements, the algorithm fosters a sense of ownership and agency in learners, leading to more effective and enjoyable learning experiences. Learners are encouraged to explore, experiment, and persist in their learning journey, guided by their intrinsic drive to understand and communicate in English. This paper introduces an innovative application of the FRBF network autonomous learning algorithm, grounded in intrinsic motivation principles, in the domain of English network teaching, further enhanced by the LogRegression Federated Radial Basic Function (LogR-FRBF). This approach leverages intrinsic motivation to foster autonomous learning behaviors among students, encouraging active engagement and exploration in English language acquisition. The LogR-FRBF model facilitates personalized learning pathways by integrating federated learning techniques with radial basic functions, enabling adaptive and context-aware recommendations tailored to individual learner needs and preferences. Through simulated experiments and empirical validations, the efficacy of the proposed framework is assessed, with promising results. The LogR-FRBF autonomous learning algorithm demonstrates superior performance in English network teaching, achieving higher levels of learner engagement, proficiency, and satisfaction compared to traditional methods. The LogR-FRBF autonomous learning algorithm demonstrated remarkable performance, achieving an average proficiency improvement of 25% among students compared to conventional teaching methods. Moreover, learner engagement levels increased by 30%, indicating heightened interest and participation in English language learning activities. Additionally, satisfaction surveys revealed a significant positive impact, with 90% of students expressing higher levels of satisfaction with the LogR-FRBF-enhanced autonomous learning experience. The LogR-FRBF autonomous learning algorithm demonstrated remarkable performance, achieving an average proficiency improvement of 25% among students compared to conventional teaching methods. Moreover, learner engagement levels increased by 30%, indicating heightened interest and participation in English language learning activities. Additionally, satisfaction surveys revealed a significant positive impact, with 90% of students expressing higher levels of satisfaction with the LogR-FRBF-enhanced autonomous learning experience.
Research on Intelligent Recognition and Verification System of Football Offside Penalty Based on Artificial Intelligence
Authors: Leqiang Zhang, Xiaodong Feng Gang Liu,
Published: April 2024

An automatic recognition method for ball trajectory and penalty is extensively studied, leveraging computer vision technology. The core hardware components of this system are designed to effectively filter out noise and extract crucial information from the moving track. This enables the system to accurately identify the speed and position of the ball in real-time. To enhance the precision of image segmentation, a novel approach is introduced that utilizes the threshold vector in high-resolution color spaces. This method determines the color of each pixel through bitwise "and" operations between pixel points and subsets. Additionally, a moving window image filtering algorithm is proposed, incorporating trajectory prediction theory. Experimental results demonstrate that this approach significantly reduces recognition errors, resulting in a notably higher accuracy level, making it a promising solution for enhancing the performance of soccer robot systems.
Implementation of Ant Colony Optimization and Principal Component Analysis for Early Prediction of Tomato Leaf Diseases: A Feature Reduction Approach
Authors: Prasanth Yalla, Karanam Madhavi Lavanya Gottemukkala,
Published: April 2024

This paper details the implementation of a sophisticated algorithmic framework designed for the early prediction of various tomato leaf diseases, crucial for enhancing crop management and yield. The research deploys image processing techniques to extract pivotal features such as Red Mean, Green Mean, Blue Mean, Height, Width, and Defect Color channels from high-resolution images of tomato leaves. These features serve as indicators for diseases including Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, and infestations by Two-spotted Spider Mites. The core of the implementation lies in the integration of Ant Colony Optimization (ACO) with Principal Component Analysis (PCA) for feature reduction, which streamlines the dataset while retaining critical information. This combination not only reduces computational load but also improves the accuracy of the early prediction model. The paper demonstrates the application of this hybrid approach and compares its performance with existing models, emphasizing its efficiency and accuracy in early-stage disease prediction. The findings indicate that the proposed method outperforms traditional techniques, offering a reliable and scalable solution for agricultural disease management.
Hybrid Multi Kernel Polar Code Construction and Optimized Decoding Algorithm for 5G Mobile Communications
Authors: Sunita Panda B.Yamini Pushpa,
Published: April 2024

5G mobile communications faces difficulty in channel coding to satisfy user requirements. This paper evaluates flexibility, reliability and complexity for different channel coding schemes(Turbo codes, Low Density Parity check codes, mother polar codes, Multi Kernel-polar codes) on regular channel AWGN with BPSK modulation of code rate R=1/2. The proposed hybrid Multi Kernel-polar codes will have greater flexibility and reliability for short and large code words. The optimized decoding of Multi Kernel polar codes reduces the number of computations by using repeated LLR’s of sub-kernel matrices.
Internet of Things Based-Early Diagnosis System of Diabetes Using Optimized Neural Network
Authors: Mustafa Ridha Al-Yasari Mohammed Qasim Alazzawi,
Published: April 2024

One of the most common diseases today is diabetes. This disease has an initial phase, if it is diagnosed on time, it helps to control and treat this disease to a great extent. But early diagnosis of this disease is very challenging due to the difficulty of repeated tests, especially in some geographical areas. Therefore, in smart health systems, a series of information is frequently taken from users by mobile phone, and by analyzing this information using artificial intelligence and machine learning algorithms and with the help of the Internet of Things, the doctor can diagnose diabetes in the first steps remotely. In this paper, we used a database that has 16 features related to early diabetes. Our proposed method for early diagnosis of diabetes is based on feature ranking along with MLP neural network optimized with whale optimization algorithm (WOA). also, the NCA algorithm is used to rank the features, and the WOA algorithm is used to optimize the parameters of the MLP neural network. Finally, based on the simulation results of the proposed method, we achieved 98.1% accuracy in the early diagnosis of diabetes.
Machine Learning Approach to Forecast the Tensile Strength of Bamboo
Authors: Saurabh Dubey, Deepak Gupta Mainak Mallik,
Published: April 2024

Bamboo holds significant global economic importance as a non-timber forest product. Its fibers can be utilized in concrete reinforcement, a concept known as bamboo fiber reinforced concrete. Nevertheless, further investigation is required for Bamboo Fiber Reinforced Concrete, with a specific focus on bamboo tensile strength. This study aimed to foretell the BTS through several machine learning techniques, including artificial neural network, extreme learning machine, and support vector regression. A total of 30 samples from the previous literature article were considered for predicting Bamboo Tensile Strength. The dataset was split into two parts, with 80% allocated for training data and 20% for testing data. The outcome from the data testing shows that the extreme learning machine predict very sensitive to random errors in the observed target. The results show that a positive correlation was found between key input parameters, such as the shorter dimension of bamboo, the longer dimension of bamboo, cross- sectional area, modulus of elasticity, and area, in relation to the output bamboo tensile strength.
Smart Hydroponic Systems: Optimizing Nutrient Levels with IoT Connectivity
Authors: Kunal Kulkarni Pradnya Vishram Kulkarni, Vinaya Gohokar,
Published: April 2024

Maintaining precise nutrient levels is essential for the thriving success of hydroponic systems. This comprehensive study delves into the intricate interplay of climatic conditions and nutrients, meticulously analysing various parameters like Total Dissolved Solids (TDS), pH, and electrical conductivity (EC) within a sophisticated smart hydroponic vertical Nutrient Film Technique (NFT) setup designed for leafy greens. The innovative system integrates a wireless sensor network and Internet of Things (IoT) connectivity, employing ESP32 microcontrollers as the primary unit alongside complementary hardware components for continuous and real-time monitoring of critical nutrient parameters. The investigation delves into the dynamic relationship between environmental factors and the growth patterns of spinach plants. Through a thorough examination of the extensive dataset gathered via deployed sensors, the study unveils variations in environmental parameters throughout various stages of plant development, including germination and root establishment. The study aims to elucidate the significance of each parameter in optimizing plant growth in hydroponic environments by analysing data associations. Results show higher Total Dissolved Solids (TDS) values correlate with better plant growth, while reduced pH levels harm plants. These findings are crucial for interpreting disease symptoms, aiding farmers in identifying issues early and optimizing yields. Understanding how nutrient values affect plant health enables proactive problem-solving. This research provides valuable insights into smart hydroponic systems, informing precision agriculture decisions.
Enabling Natural Language Processing and AI Research in Low-Resource Languages: Development and Description of an Assamese UPoS Tagged Dataset
Authors: Shikhar Kumar Sarma Kuwali Talukdar,
Published: April 2024

This paper describes in detail the Universal Parts of Speech (UPoS) tagged dataset for the Assamese language. PoS tagged dataset in a language is crucial for experimenting and creating resources for various Natural Language Processing (NLP) and AI research. With the growing usage of Universal Dependency standards, tagged dataset with Universal PoS tags are becoming very much essential for contemporary experiments in the NLP community. NLP research in Assamese, and Indo-Aryan language, is relatively new, and the language is considered a Low Resource language. The dataset of UPoS tagged Assamese text is created with an aim of contributing towards enriching this low resource language for NLP and AI tasks. The size of the dataset is 283506 tokens of Assamese vocabulary, against total 20280 sentences, tagged with 17 standard UPoS tags of core lexical categories. The raw data are taken from an open-source corpus originally tagged with BIS tagset. The original size of 453457 tokens against 29504 sentences, after subjected to data filtering, was reduced to this clean resource of 283506 tokens. Lexical categories mapping is done with linguistic expertise, from BIS to UPoS tagsets. Mapped pattern was used for a first-level conversion of BIS tags to UPoS tags. Linguistic validation is also performed with linguistic experts and inter annotator agreement/disagreements were recorded. Second level validation resulted in deciding on the agreement, producing the final version of the dataset. This Assamese UPoS tagged dataset is the first of its kind with UPoS annotations and shall serve a wider Assamese NLP research community for model training using Machine Learning/Deep Learning Techniques.
Arabic Sentiment Analysis Evaluation of Saudi Arabia's Telecommunication Using Several Deep Learning Algorithms
Authors: Fahad Alotaibi Sara Almutairi,
Published: April 2024

Sentiment analysis (SA) is a technique that applies natural language processing (NLP) in order to analyze and classify the emotion in sentiment reviews. SA is responsible for analyzing people's feelings, opinions, and experiences that are shared through the Internet and social networks. In this paper, we focus on investigating, evaluating, and improving Arabic sentiment analysis (ASA) models, datasets, and challenges. ASA has several difficulties, like language’s morphological features, many dialects, no clear and uniform corpora, low accuracy, and restricted ASA material. In order to do that, we do a full analysis and evaluation of Arabic sentiment analysis models and datasets that target e-marketing services such as telecommunication, health, and books. We evaluate our data set, called Sara, with several Arabic sentiment datasets in terms of brief description, dataset size, source of collecting data, field type, and abbreviation. We enhanced our previous models by using ensemble learning average techniques. The accuracy of our enhanced model has increased and now reaches around 97%. Also, we evaluate our developed ASA using deep learning (DL) algorithms with other ASA models in the field of e-marketing. Our models have significant improvements in terms of performance compared with other works, where our three models, CNN-Model, LSTM-Mode2, and CNN+LSTM-Model3, have accuracy of 96.83%, 94.74%, and 96.91%, respectively.
Optimized Approach for Heart Disease Prediction by using Enhancing Diagnostic Method through Meta-Features
Authors: T. Sajana, Nitin Rakesh Monali Gulhane,
Published: April 2024

In today's world, pollution is worsening, and bad habits like not eating regularly, eating lots of junk food, and not exercising enough are becoming more common. This can cause health problems, and the global need for the detection of heart diseases is increasing. The American Heart Association, in 2023, has given an update that heart disease is the leading cause of death. Implementing machine learning models has given significant results, but due to the limitations of requirement balanced data, repeat training model complexity has led to unreliable results in some cases. Hence, the proposed model overcomes the limitations by comparing machine learning models for cardiovascular diseases. Performance of proposed work is evaluated with the base models KNN and DT with and without smooth. Thus, the comparison included increased accuracy in DT, but the proposed model GBM as a meta learner has led the performance metric with 92% accuracy, with recall of 0.89% and F1 score of 0.86%. Thus, the proposed approach has achieved the highest accuracy in cardiovascular diseases using meta-subjects. Future developments for the research will focus on applying the model to a larger dataset and analysing cases according to complex machine learning models.
Node2Vec and Machine Learning: A Powerful Duo for Link Prediction in Social Network
Authors: Mukesh M. Raghuwanshi, Pradnya S. Borkar Sachin U. Balvir,
Published: April 2024

Link prediction in social networks is a challenging task that attempts to uncover hidden linkages and forecast future connections. The link prediction problem is addressed in this research article by utilizing the capabilities of Node2Vec and machine learning algorithms. To learn high-dimensional node representations that capture both local and global network structures, the Node2Vec technique is used. Then, in order to forecast potential connections, these node embedding’s are put into various machine learning models. Two real-world social network datasets are used to test the suggested methodology, and the findings show a considerable improvement in link prediction accuracy. It achieves a deeper comprehension of the hidden relationships in social networks by fusing the semantic richness of Node2Vec embedding’s with the predictive powers of machine learning methods. The results of this study extend link prediction approaches in social networks by revealing hidden ties and providing insightful predictions for upcoming connections. The suggested method indicates the potential for real-world applications in a number of fields, including recommender systems, targeted advertising, and social influence studies.
Machine Learning Based Automated Trading Strategies for Indian Stock Market
Authors: Rupali Atul Mahajan, Arup Kadia, Monika Singh Rajesh Dey, Salina Kassim, Sudhanshu Maurya,
Published: April 2024

Stock market is a very important and one of the main financial platforms for a country. It measures the growth of all financial components associated with the base of economy. Firms can raise capital through bonds, IPO, FPO etc. and investors can participate in this market. Different stock exchanges are regulatory bodies are responsible to regulate the system and provides the platform to the investors for the participation into market. In India investors participating in this marketplace over than 10 crores demat accounts. Investors analyses the fundamental, technical, price action and different research on the financial parameters of market or stocks before investing. This Machine Learning and Artificial Intelligence based proposed research provides an automated platform for the Indian investors to take decision about a strategy considering the different financial tools and price action. This research considering candlestick pattern and Exponential moving average (EMA) (10,50,100 and 200 EMAs) crossover together. Now a days large portfolio investors and different international financial intuitions such as Morgan Stanley, JP Morgan, Bank of America and the different domestic institutions such as LIC, SBI, HDFC mutual fund using different algo trading strategies for trading and investing.
Taxonomy of the GDPR-based Privacy Research by Scientometric Analysis
Authors: Shan Liu Rolly R. Tang, Jae Kyu Lee,
Published: April 2024

Will General Data Protection Regulation (GDPR) be adopted globally in business? The GDPR was approved in the European Union (EU) in April 2016 and officially put into effect in May 2018, thus the research in this field has an obvious upward trend. The development of GDPR is aimed at adapting to new trends, conducting scientific econometric analysis in the fields of privacy and GDPR, and analyzing and visualizing emerging trends. First, summarizing the privacy and GDPR studies publicly published between 1995 and 2023 through statistical analysis of terminology categories and high-yield journals. Then, understand the overall research status of privacy rights and GDPR from the perspectives of author, journal, literature co citation analysis, and collaborative networks. Finally, based on keyword analysis and literature co citation cluster analysis, a knowledge graph was constructed that includes knowledge domains, evolutionary trends, and future research directions. As a globally influential regulation, GDPR emphasizes the protection and lawful processing of personal data, which is of great significance for protecting personal data privacy and enhancing data security.
Enhancing Transshipment Decision-making in Transportation Companies through Computational Intelligence: A Bayesian Game Model with Data-Driven Simulations at Guangzhou Port
Authors: Qinchang Li, Mengyue Huang Yuhao Cheng, Bo Lin,
Published: April 2024

The challenge of port congestion significantly impedes the efficiency of global trade flows and the economic vitality of hinterland regions. Addressing this issue, our study harnesses computational intelligence to construct a sophisticated Bayesian game model between the government and multiple transportation companies, where the transshipment costs, considered as private information to the companies, play a pivotal role. This research integrates data analytics and machine learning techniques to analyze the strategic decision-making processes of transportation companies, which decide on transshipment based on a critical cost threshold influenced by government subsidies and a probabilistic assessment of transshipment costs. Utilizing backward induction, the study outlines how the government can leverage computational models to devise an optimal subsidy strategy for transshipment, taking into account the anticipated responses of the transportation companies. The Bayesian Nash equilibrium identified through our model suggests that companies with costs below a predefined threshold are incentivized by government subsidies to opt for transshipment. This conclusion is further validated through evolutionary game theory analysis, enriched by data-driven simulations. Employing real-world data from Guangzhou Port, we conducted extensive computational simulations to quantify the impact of transshipment subsidies. The findings reveal a substantial alleviation in port congestion, with a 33.7% reduction in congestion levels and a 35% decrease in congestion-related costs, alongside a notable 1.9% increase in government revenue. These simulations, powered by advanced computational algorithms and data analytics, not only underscore the effectiveness of informed subsidy strategies in mitigating port congestion but also demonstrate the potential of integrating computational approaches in logistical and transportation decision-making. This study contributes a novel computational framework to the logistics and transportation literature, offering practical insights for policymakers to tackle the enduring problem of port congestion through data-driven strategies.
A Privacy Protection Method Based on Trajectory Location Point Association
Authors: Hao Ji Yu Qiao,
Published: April 2024

Location-based services (LBS) technology provides personalized services for users. However, private location information faces the risk of being leaked while users are enjoying the LBS services. In order to prevent this from occurring, this study proposes an anonymous privacy protection method based on trajectory location association, abbreviated as AS-AP, which considers the association between the user’s current location and the background information. The association between locations is mined from time and query preferences, and then fake locations and queries are generated as part of the anonymous interference requests. This conforms to the k-anonymity feature, which prevents attackers from inferring the user's real intent from the network information, thereby enhancing the credibility of network technology, and protecting data privacy. The experimental results indicate that AS-AP can provide more personalized privacy protection services while ensuring that users' sensitive data is protected.