EAI Endorsed Transactions on Scalable Information Systems

OEE-WCRD: Optimizing Energy Efficiency in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics

2024-01-31T10:03:29+00:00

Wireless Sensor Networks (WSNs) play a pivotal role in various applications, including environmental monitoring, industrial automation, and healthcare. However, the limited energy resources of sensor nodes pose a significant challenge to the longevity and performance of WSNs. To address this challenge, this paper presents an Optimized Energy Efficient Protocol in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics (OEE-WCRD). This research paper presents a novel approach to cluster head selection in WSNs by harnessing a combination of residual energy and distance metrics. The proposed method aims to significantly enhance the energy efficiency of WSNs by prioritizing nodes with ample residual energy and proximity to their neighbors as cluster heads. Through extensive simulations and evaluations, we demonstrate the effectiveness of this approach in prolonging network lifetime, optimizing data aggregation, and ultimately advancing the energy efficiency of WSNs, making it a valuable contribution to the field of WSNs protocols.

Combining Lexical, Host, and Content-based features for Phishing Websites detection using Machine Learning Models

2023-11-20T20:59:52+00:00

In cybersecurity field, identifying and dealing with threats from malicious websites (phishing, spam, and drive-by downloads, for example) is a major concern for the community. Consequently, the need for effective detection methods has become a necessity. Recent advances in Machine Learning (ML) have renewed interest in its application to a variety of cybersecurity challenges. When it comes to detecting phishing URLs, machine learning relies on specific attributes, such as lexical, host, and content based features. The main objective of our work is to propose, implement and evaluate a solution for identifying phishing URLs based on a combination of these feature sets. This paper focuses on using a new balanced dataset, extracting useful features from it, and selecting the optimal features using different feature selection techniques to build and conduct a
comparative performance evaluation of four ML models (SVM, Decision Tree, Random Forest, and XGBoost). Results showed that the XGBoost model outperformed the others models, with an accuracy of 95.70% and a false negatives rate of 1.94%.

Research of IoT Technology Based Online Status Monitoring on Hydropower Station Equipment

2023-12-08T07:13:35+00:00

The rapid proliferation of the Internet of Things (IoT) has revolutionized the field of status monitoring for electrical equipment such as hydropower station equipment, offering enhanced efficiency and reliability in maintenance and operations. In this paper, we investigate the utilization of IoT transmission technology enhanced by multiple relays, denoted as $M$ relays, to augment the monitoring of hydropower station equipment. To further optimize the performance of the system, we employ partial relay selection, commonly referred to as selection combining. This study delves into the analysis of the system performance by deriving analytical data rates, with a focus on quantifying the benefits of employing partial relay selection in IoT transmission for electrical equipment status monitoring. Our analytical approach enables a comprehensive evaluation of system efficiency, considering factors such as data rate, reliability, and power consumption. Through our analysis, we aim to provide valuable insights into the trade-offs and advantages of incorporating partial relay selection into IoT systems, ultimately assisting in the development of more effective and efficient solutions for status monitoring on electrical equipment. By examining the impact of $M$ relays and partial relay selection on IoT transmission technology, this research contributes to the ongoing efforts to enhance the reliability and robustness of status monitoring for electrical equipment, ultimately advancing the capabilities of IoT-based solutions in the context of electrical systems and equipment maintenance.

MFRLMO: Model-free reinforcement learning for multi-objective optimization of apache spark

2024-01-04T09:18:07+00:00

Hyperparameter optimization (HO) is a must to figure out to what extent can a specific configuration of hyperparameters contribute to the performance of a machine learning task. The hardware and MLlib library of Apache Spark have the potential to improve big data processing performance when a tuning operation is combined with the exploitation of hyperparameters. To the best of our knowledge, the most of existing studies employ a black-box approach that results in misleading results due to ignoring the interior dynamics of big data processing. They suffer from one or more drawbacks including high computational cost, large search space, and sensitivity to the dimension of multi-objective functions. To address the issues above, this work proposes a new model-free reinforcement learning for multi-objective optimization of Apache Spark, thereby leveraging reinforcement learning (RL) agents to uncover the internal dynamics of Apache Spark in HO. To bridge the gap between multi-objective optimization and interior constraints of Apache Spark, our method runs a lot of iterations to update each cell of the RL grid. The proposed model-free learning mechanism achieves a tradeoff between three objective functions comprising time, memory, and accuracy. To this end, optimal values of the hyperparameters are obtained via an ensemble technique that analyzes the individual results yielded by each objective function. The results of the experiments show that the number of cores has not a direct effect on $speedup$. Further, although grid size has an impact on the time passed between two adjoining iterations, it is negligible in the computational burden. Dispersion and risk values of model-free RL differ when the size of the data is small. On average, MFRLMO produced $speedup$ that is 37% better than those of the competitors. Last, our approach is very competitive in terms of converging to a high accuracy when optimizing Convolutional Neural networks (CNN).

Fast Lung Image Segmentation Using Lightweight VAEL-Unet

2024-01-07T01:13:44+00:00

INTRODUCTION: A lightweght lung image segmentation model was explored. It was with fast speed and low resouces consumed while the accuracy was comparable to those SOAT models.

OBJECTIVES: To improve the segmentation accuracy and computational efficiency of the model in extracting lung regions from chest X-ray images, a lightweight segmentation model enhanced with a visual attention mechanism called VAEL-Unet, was proposed.

METHODS: Firstly, the bneck module from the MobileNetV3 network was employed to replace the convolutional and pooling operations at different positions in the U-Net encoder, enabling the model to extract deeper-level features while reducing complexity and parameters. Secondly, an attention module was introduced during feature fusion, where the processed feature maps were sequentially fused with the corresponding positions in the decoder to obtain the segmented image.

RESULTS: On ChestXray, the accuracy of VAEL-Unet improves from 97.37% in the traditional U-Net network to 97.69%, while the F1-score increases by 0.67%, 0.77%, 0.61%, and 1.03% compared to U-Net, SegNet, ResUnet and DeepLabV3+ networks. respectively. On LUNA dataset. the F1-score demonstrates improvements of 0.51%, 0.48%, 0.22% and 0.46%, respectively, while the accuracy has increased from 97.78% in the traditional U-Net model to 98.08% in the VAEL-Unet model. The training time of the VAEL-Unet is much less compared to other models. The number of parameters of VAEL-Unet is only 1.1M, significantly less than 32M of U-Net, 29M of SegNet, 48M of Res-Unet, 5.8M of DeeplabV3+ and 41M of DeepLabV3Plus_ResNet50.

CONCLUSION: These results indicate that VAEL-Unet’s segmentation performance is slightly better than other referenced models while its training time and parameters are much less.

Integration and Innovation Path Analysis of Enterprise Marketing Data Management Based on Deep Learning

2024-01-08T12:23:43+00:00

INTRODUCTION: To explore the integration and innovation path of enterprise marketing data management based on deep learning to adapt to today's competitive business environment. With the continuous development of information technology, enterprises are faced with a large amount of marketing data, and how to efficiently manage and integrate these data has become an essential issue for enterprises to improve their market competitiveness. Deep learning, as a necessary technical means of artificial intelligence, provides enterprises with more intelligent and precise data processing tools.

OBJECTIVES: The primary purpose of the study is to solve the problems of marketing data management in traditional enterprises and to achieve better integration and management of data through deep learning technology. Specifically, the goal is to explore the potential of deep learning in improving data processing efficiency and accurately analyzing user behavior and trends. By achieving these goals, organizations can better understand market needs, develop more effective marketing strategies, and stand out in a competitive marketplace.

METHODS: This study adopts a comprehensive approach, including a literature review, case study, and empirical analysis of deep learning algorithms. First, the main issues of current enterprise marketing data management and the latest progress in deep learning were understood through an in-depth study of the literature in related fields. Second, several enterprise cases were selected to gain a deeper understanding of the challenges and needs of enterprises in marketing data management through field research and data collection. Finally, a series of deep learning algorithms were designed and implemented to validate their effectiveness in real-world applications and analyze their impact on data integration and innovation paths.

RESULTS: The results of the study show that deep learning has significant advantages in enterprise marketing data management. By using deep learning algorithms, enterprises are able to handle large-scale marketing data more efficiently and achieve intelligent data integration and accurate analysis. This not only improves the efficiency of data processing but also provides enterprises with deeper market insights that help develop more targeted marketing strategies.

CONCLUSION: The results of the study are of guiding significance for enterprises to realize data-driven marketing decision-making, which provides strong support for enterprises to maintain their competitive advantages in the highly competitive market. Future research can further explore the application of deep learning in different industries and scenarios, as well as how to optimize deep learning algorithms further to meet the changing needs of enterprises.

Investigation of Imbalanced Sentiment Analysis in Voice Data: A Comparative Study of Machine Learning Algorithms

2024-01-10T06:45:45+00:00

INTRODUCTION: Language serves as the primary conduit for human expression, extending its reach into various communication mediums like email and text messaging, where emoticons are frequently employed to convey nuanced emotions. In the digital landscape of long-distance communication, the detection and analysis of emotions assume paramount importance. However, this task is inherently challenging due to the subjectivity inherent in emotions, lacking a universal consensus for quantification or categorization.

OBJECTIVES: This research proposes a novel speech recognition model for emotion analysis, leveraging diverse machine learning techniques along with a three-layer feature extraction approach. This research will also through light on the robustness of models on balanced and imbalanced datasets.

METHODS: The proposed three-layered feature extractor uses chroma, MFCC, and Mel method, and passes these features to classifiers like K-Nearest Neighbour, Gradient Boosting, Multi-Layer Perceptron, and Random Forest.

RESULTS: Among the classifiers in the framework, Multi-Layer Perceptron (MLP) emerges as the top-performing model, showcasing remarkable accuracies of 99.64%, 99.43%, and 99.31% in the Balanced TESS Dataset, Imbalanced TESS (Half) Dataset, and Imbalanced TESS (Quarter) Dataset, respectively. K-Nearest Neighbour (KNN) follows closely as the second-best classifier, surpassing MLP's accuracy only in the Imbalanced TESS (Half) Dataset at 99.52%.

CONCLUSION: This research contributes valuable insights into effective emotion recognition through speech, shedding light on the nuances of classification in imbalanced datasets.

Multi-strategy KOA Algorithm for Optimizing Gated Recurrent Cell Networks in Automatic Writing Scoring Method Design

2024-01-16T08:02:55+00:00

INTORDUCTION: Builds an objective, robust, high-precision automatic scoring method for essays that not only improves the efficiency of exam scoring, but also provides effective feedback to help users improve their writing skills.
OBJECTIVES: Addressing the problems of current automatic writing scoring methods that fail to consider holistic and process features and lack of model accuracy.
METHODS: In this paper, a methodology approach for automatic scoring of writing based on intelligent optimization algorithm to improve recurrent neural network is proposed. Firstly, relevant features are extracted by analyzing the problem and process of automatic writing scoring; then, the gated recurrent unit network is improved by multi-strategy Keplerian optimization algorithm to construct the automatic writing scoring model; finally, the effectiveness and superiority of the proposed method is verified by simulation experiment analysis.
RESULTS: The results show that the scoring method proposed in this paper controls the scoring error within 0.04, which solves the problem of incomplete features and insufficient scoring accuracy of automatic scoring methods for writing.
CONCLUSION: The proposed algorithm can improve the accuracy and real-time performance of automatic scoring of writing questions, but the optimization efficiency needs to be further improved.

Visual Design of Digital Display Based on Virtual Reality Technology with Improved SVM Algorithm

2024-01-18T02:47:15+00:00

NTRODUCTION: With the rapid development of virtual reality (VR) technology, digital displays have become increasingly important in various fields. This study aims to improve the application of virtual reality technology in the visual design of digital displays by improving the support vector machine (SVM) algorithm. The visual design of digital displays is crucial for attracting users, enhancing experience and conveying information, so an accurate and reliable algorithm is needed to support relevant decisions.
OBJECTIVES: The purpose of this study is to improve the SVM algorithm to more accurately identify features related to the visual design of digital displays. By exploiting the nonlinear mapping and parameter optimization of the SVM algorithm, it aims to improve the performance of the model so that it can better adapt to complex visual design scenarios.
METHODS: In the process of achieving the objective, multimedia data related to digital displays, including images and videos, were first collected. Through feature engineering, features closely related to visual design were selected, and deep learning techniques were applied to extract higher-level feature representations. Subsequently, the SVM algorithm was improved to use the kernel function for nonlinear mapping, and the penalty parameters and the parameters of the kernel function were adjusted. Cross-validation was used in the training and testing phases of the model to ensure its generalization performance.
RESULTS: The improved SVM algorithm demonstrated higher accuracy, recall and precision compared to the traditional method by evaluating it on the test set. This suggests that the model is able to capture visual design features in digital displays more accurately and provide more reliable support for relevant decisions.
CONCLUSION: This study demonstrates that by improving the SVM algorithm, more accurate visual design can be achieved in digital displays of virtual reality technology. This improvement provides reliable algorithmic support for the design of digital displays and provides a more prosperous, immersive experience for users. Future research can further optimize the algorithm and iterate with user feedback to continuously improve the visual design of digital displays in virtual reality environments.

A Hybrid CNN Approach for Unknown Attack Detection in Edge-Based IoT Networks

2024-01-18T09:54:36+00:00

INTRODUCTION: In the constantly growing Internet of Things (IoT), device security is crucial. As IoT gadgets pervade our lives, detecting unforeseen assaults is crucial to protecting them. Behavioral analysis, machine learning, and collaborative intelligence may be needed to protect against new dangers. This short discusses the need of detecting unexpected IoT attacks and essential security strategies for these interconnected environments.

OBJECTIVES: This research uses the BoT-IoT dataset to create an enhanced IoT intrusion detection system. The goals are to optimize a CNN architecture for effective pattern recognition, address imbalanced data, and evaluate model performance using precision, recall, F1-score, and AUC-ROC measures. Improving IoT ecosystem reliability and security against unknown assaults is the ultimate goal.

METHODS: The proposed methods use the BoT-IoT dataset to create a comprehensive IoT intrusion detection system. This involves tuning a Convolutional Neural Network (CNN) architecture to improve pattern recognition. Oversampling and class weighting address imbalanced data issues.

RESULTS: The comprehensive evaluation of our innovative unknown attack detection method shows promise, suggesting it may be better than existing methods. A high accuracy, precision, recall, and f-measure of 98.23% were attained using an advanced model and feature selection methods. This achievement was achieved by using features designed to identify unknown attacks in the dataset, proving the proposed methodology works.

CONCLUSION: This research presents an improved IoT Intrusion Detection System using the BoT-IoT dataset. The optimised Convolutional Neural Network architecture and imbalanced data handling approaches achieved 98.23% accuracy.

Visual Knowledge Graph Construction of Self-directed Learning Ability Driven by Interdisciplinary Projects

2024-01-22T06:10:25+00:00

INTRODUCTION: The application of interdisciplinary information technology is becoming more and more widespread, and the application of visual knowledge mapping in the process of students' independent learning is also becoming more and more important; therefore, in this context, takes the history discipline as a starting point to study the construction of visual knowledge mapping of students' independent learning ability under the drive of interdisciplinary projects.

OBJECTIVES: To enrich the means of student independent learning aids in China's history discipline and enhance the modernization level of China's history discipline construction; to solve the problem that student independent learning ability under the drive of China's interdisciplinary projects can not be visualized and observed; to further improve China's distance education environment and to enhance the educational capacity of the history discipline.

METHODS: Firstly, the relevant modeling uses a visual knowledge map. Secondly, the neural network model assesses students' independent learning ability in history learning. Finally, the convolutional neural network model is used to assess the efficiency of the knowledge map.

RESULTS: The Sig and Tanh function models have better robustness, and the ReLU and PReLU functions have weaker interdisciplinary driving performance. However, the iterative Knownledge1 and Knownledge2 models have better robustness of the visualized knowledge graph.

CONCLUSION: In studying history, the interdisciplinary, project-driven, and independent learning ability of students could be more vital, and our country should vigorously develop new information network technology to improve the status quo of history discipline education in China.

Performance Evaluation and Improvement of Deep Echo State Network Models in English Writing Assistance and Grammar Error Correctionn

2024-01-26T07:57:51+00:00

INTRODUCTION: The research on the performance evaluation model of English writing tutoring and grammar error correction is very necessary, which is not only conducive to the rational allocation of teachers' writing tutoring resources, but also more conducive to the timely and effective correction of students' grammatical errors.

OBJCTIVES: Aiming at the problems of non-specific quantification, low precision, and low real-time performance evaluation methods for English writing grammar error correction in current methods.

METHODS: This paper proposes a grammar error correction performance evaluation method based on deep echo state network with gold rush optimisation algorithm. Firstly, by analysing the process of English writing assistance and grammatical error correction, we extract the evaluation features of grammatical error correction type and construct the performance evaluation system; then, we improve the deep confidence network through the gold rush optimization algorithm and construct the grammatical error correction performance evaluation model; finally, we analyse it through simulation experiments.

RESULTS: The results show that the proposed method improves the evaluation accuracy, robustness. The absolute value of the relative error of the evaluation value of the syntactic error correction performance of the method is controlled within the range of 0.02.

CONCLUSION: The problems of non-specific quantification, low precision and low real-time performance of the application of English writing grammar error correction performance assessment methods are solved.

Research on Music Classification Technology Based on Integrated Deep Learning Methods

2024-01-28T15:11:47+00:00

INTRODUCTION: Music classification techniques are of great importance in the current era of digitized music. With the dramatic increase in music data, effectively categorizing music has become a challenging task. Traditional music classification methods have some limitations, so this study aims to explore music classification techniques based on integrated deep-learning methods to improve classification accuracy and robustness.

OBJECTIVES: The purpose of this study is to improve the performance of music classification by using an integrated deep learning approach that combines the advantages of different deep learning models. The author aims to explore the effectiveness of this approach in coping with the diversity and complexity of music and to compare its performance differences with traditional approaches.

METHODS: The study employs several deep learning models including, but not limited to, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory Networks (LSTM). These models were integrated into an overall framework to perform the final music classification by combining their predictions. The training dataset contains rich music samples covering different styles, genres and emotions.

RESULTS: Experimental results show that music classification techniques based on integrated deep learning methods perform better in terms of classification accuracy and robustness compared to traditional methods. The advantages of integrating different deep learning models are fully utilized, enabling the system to better adapt to different types of music inputs.

CONCLUSION: This study demonstrates the effectiveness of the integrated deep learning approach in music classification tasks and provides valuable insights for further improving music classification techniques. This approach not only improves the classification performance but also promises to be applied to other areas and promote the application of deep learning techniques in music analysis.

E-GVD: Efficient Software Vulnerability Detection Techniques Based on Graph Neural Network

2024-02-07T02:28:50+00:00

INTRODUCTION: Vulnerability detection is crucial for preventing severe security incidents like hacker attacks, data breaches, and network paralysis. Traditional methods, however, face challenges such as low efficiency and insufficient detail in identifying code vulnerabilities.
OBJECTIVES: This paper introduces E-GVD, an advanced method for source code vulnerability detection, aiming to address the limitations of existing methods. The objective is to enhance the accuracy of function-level vulnerability detection and provide detailed, understandable insights into the vulnerabilities.
METHODS: E-GVD combines Graph Neural Networks (GNNs), which are adept at handling graph-structured data, with residual connections and advanced Programming Language (PL) pre-trained models.
RESULTS: Experiments conducted on the real-world vulnerability dataset CodeXGLUE show that E-GVD significantly outperforms existing baseline methods in detecting vulnerabilities. It achieves a maximum accuracy gain of 4.98%, indicating its effectiveness over traditional methods.
CONCLUSION: E-GVD not only improves the accuracy of vulnerability detection but also contributes by providing fine-grained explanations. These explanations are made possible through an interpretable Machine Learning (ML) model, which aids developers in quickly and efficiently repairing vulnerabilities, thereby enhancing overall software security.

Integrating Metaheuristics and Two-Tiered Classification for Enhanced Fake News Detection with Feature Optimization

2024-02-08T06:35:17+00:00

INTRODUCTION: The challenge of distributing false information continues despite the significant impact of social media on opinions. The suggested framework, which is a metaheuristic method, is presented in this research to detect bogus news. Employing a hybrid metaheuristic RDAVA methodology coupled with Bi-LSTM, the method leverages African Vulture Optimizer and Red Deer Optimizer.

OBJECTIVES: The objective of this study is to assess the effectiveness of the suggested model in identifying false material on social media by employing social network analysis tools to combat disinformation.

METHODS: Employing the data sets from BuzzFeed, FakeNewsNet, and ISOT, the suggested model is implemented on the MATLAB Platform and acquires high accuracy rates of 97% on FakeNewsNet and 98% on BuzzFeed and ISOT. A comparative study with current models demonstrates its superiority.

RESULTS: Outperforming previous models with 98% and 97% accuracy on BuzzFeed/ISOT and FakeNewsNet, respectively, the suggested model shows remarkable performance.

CONCLUSION: The proposed strategy shows promise in addressing the problem of false information on social media in the modern day by effectively countering fake news. Its incorporation of social network analysis methods and metaheuristic methodologies makes it a powerful instrument for identifying false news.

A Self-learning Ability Assessment Method Based on Weight-Optimised Dfferential Evolutionary Algorithm

2024-02-22T02:26:04+00:00

INTRODUCTION: The research on the method of cultivating college students' autonomous ability based on experiential teaching is conducive to college students' change of learning mode and learning thinking, improving the utilisation rate of educational resources, as well as the reform of education.

OBJECTIVES: Addressing the current problems of unquantified analyses, lack of breadth, and insufficient development strategies in the methods used to develop independent learning skills in university students.

METHODS: This paper proposes an intelligent optimisation algorithm for the cultivation of college students' independent learning ability in experiential teaching. Firstly, the characteristics and elements of college students' independent learning are analysed, while the strategy of cultivating college students' independent learning ability in experiential teaching is proposed; then, the weight optimization method of cultivating college students' independent learning ability based on experiential teaching is proposed by using the improved intelligent optimization algorithm; finally, the validity and feasibility of the proposed method are verified through experimental analysis.

RESULTS: The results show that the proposed method has a wider range of culture effects.

CONCLUSION: Addressing the problem of poor generalisation in the development of independent learning skills among university students.

An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO Algorithms

2024-02-22T06:32:32+00:00

INTRODUCTION: In an effort to enhance the quality of user experience in using music services and improve the efficiency of music recommendation platforms, researching accurate and efficient music recommendation methods and constructing an accurate real-time online recommendation platform are the key points for the success of a high-quality music website platform.
OBJECTIVES: To address the problems of incomplete signal feature capture, insufficient classification efficiency and poor generalization of current music recommendation methods.
METHODS: Improve the deep confidence network to construct music recommendation algorithm by using big data and intelligent optimization algorithm. Firstly, music features are extracted by analyzing the principle of music recommendation algorithm, and evaluation indexes of music recommendation algorithm are proposed at the same time; then, combined with the deep sleep optimization algorithm, a music recommendation method based on improved deep confidence network is proposed; finally, the efficiency of the proposed method is verified through the analysis of simulation experiments.
RESULTS: While meeting the real-time requirements, the proposed method improves the music recommendation accuracy, recall, and coverage.
CONCLUSION: Solves the questions of incomplete signal feature capture, insufficient classification efficiency, and poor generalization of current music recommendation algorithms.

Visualization Process of International Trade and its impact on GDP through Multi-criteria Decision Model: A Case Study of India’s Merchandise Trade

2024-03-04T10:41:17+00:00

When it comes to international trade, India is one of the most important nations. This paper intends to analyze the effect of International Trade on a nation’s GDP growth through the process of visualizing the current trends. For this research, some statistical (economical) data is considered and its effect on the GDP is analyzed for the previous accounting years ranging from 2015 to 2021. The data considered for this include – the monetary value of exports from India (in US$ Millions), the monetary value of imports from India (in US$ Millions), India’s share of exports to the nation out of all the nations, India’s share of imports to the nation out of all the nations in, export growth rate, import growth rate, currency exchange rate, Inflation rate. This paper examines and explains how these economic factors influence a country’s (India’s) GDP growth through the process of visualization.

Research on Credit Risk Prediction Method of Blockchain Applied to Supply Chain Finance

2024-03-05T01:39:15+00:00

INTRODUCTION: From the perspective of blockchain, it establishes a credit risk evaluation index system for supply chain finance applicable to blockchain, constructs an accurate credit risk prediction model, and provides a reliable guarantee for the research of credit risk in supply chain finance.

OBJECTIVES: To address the inefficiency of the current credit risk prediction and evaluation model for supply chain finance.

METHODS: This paper proposes a combined blockchain supply chain financial credit risk prediction and evaluation method based on kernel principal component analysis and intelligent optimisation algorithm to improve Deep Echo State Network. Firstly, the evaluation system is constructed by describing the supply chain financial credit risk prediction and evaluation problem based on blockchain technology, analysing the evaluation indexes, and constructing the evaluation system; then, the parameters of DeepESN network are optimized by combining the kernel principal component analysis method with the JSO algorithm to construct the credit risk prediction and evaluation model of supply chain finance; finally, the effectiveness, robustness, and real-time performance of the proposed method are verified by simulation experiment analysis.

RESULTS: The results show that the proposed method improves the prediction efficiency of the prediction model.

CONCLUSION: The problems of insufficient scientific construction of index system and poor efficiency of risk prediction model of B2B E-commerce transaction size prediction method are effectively solved.

Sound Art of Internet of Things Explore: Sound Sensors in the Fusion of Painting and Music

2024-03-12T04:53:49+00:00

INTRODUCTION: The combination of sound art and Internet of Things (IoT) technology offers new possibilities for artistic creation. With the rapid development of the Internet of Things (IoT), sound sensors have become a powerful tool for capturing environmental sounds. This study aims to explore the application of suitable sensors in the fusion of painting and music by utilizing IoT technology to create more prosperous and interactive artworks for artists.
OBJECTIVES: The study's primary purpose is to explore the potential uses of sound sensors in art creation, especially in integrating painting and music. By deeply analyzing the interaction between sound and visual art, the researchers aim to discover new creative possibilities, thus expanding the boundaries of artistic creation.
METHODS: The study utilized a comprehensive methodology that included a literature review, field research, and artistic practice. First, the literature on sound art, IoT, and art fusion was extensively reviewed to provide a theoretical foundation for the study. Second, through field research, the researchers collected practical examples of sound sensors applied in the field of art. Finally, through hands-on art creation in the artist's studio, the researchers verified the potential applications of suitable sensors in the fusion of painting and music.
RESULTS: The study found that sound sensors have many applications in painting-music fusion. By embedding suitable sensors into painting tools and music devices, artists can capture the sounds of their surroundings and transform them into visual and auditory artistic expressions. Examples from practice demonstrate how this technological innovation can bring a richer, more sensual experience to artistic creation while engaging the audience in interactive participation.
CONCLUSION: The results of this study show that the combination of sound sensors and IoT technology offers new possibilities for artistic creation. By integrating proper elements in painting and music, artists can create more profound and engaging works. This innovation expands the boundaries of art and provides the audience with a more intimate and interactive experience with the artwork. In the future, further research can delve into the application of sound sensors in other artistic fields, thus promoting the integration of art and technology to move forward.

Smart Painting Exhibitions: Utilizing Internet of Things Technology Creating Interactive Art Spaces

2024-03-12T05:25:19+00:00

INTRODUCTION: With the rapid development of science and technology, intelligent painting exhibitions have gradually attracted people's attention with their unique forms. This study aims to create an interactive art space using Internet of Things (IoT) technology to provide audiences with a more prosperous and deeper art experience.
OBJECTIVES: The primary purpose of this study is to explore how to use IoT technology to transform a painting exhibition into a digital space that can interact with the audience. By fusing art and technology, the researchers aim to promote innovation in traditional art presentations and stimulate the audience's freshness and interest in art.
METHODS: In the Smart Painting exhibition, the researchers used advanced Internet of Things (IoT) technology to incorporate the audience's movements, emotions, and feedback into the artworks through sensors, wearable devices, and cloud computing. The digital devices in the exhibition space could sense the audience's presence and generate and adjust the art content in real-time according to their movements or emotional state, creating a unique display that interacted with the audience.
RESULTS: After implementing the Smart Painting exhibition, the audience's sense of participation and immersion in the art display was significantly increased. Through IoT technology, viewers can interact with the artwork in real-time and feel a more personalized art experience. The digitized exhibition space provided the audience a new level of perception, deepening their understanding and appreciation of the artworks.
CONCLUSION: This study demonstrates the feasibility of using IoT technology to create interactive art spaces and shows that this innovation can inject new vitality into traditional painting exhibitions. Through digitalization, the interactivity of the art space is enhanced, providing the audience with a more profound art experience. This approach provides artists with new possibilities for creativity and opens up a fresh vision of participatory art for the audience. The Smart Painting Exhibition is expected to become a new model for integrating art and technology, pushing the art world towards a more innovative and open future.

A Solution to Graph Coloring Problem Using Genetic Algorithm

2024-03-15T13:25:27+00:00

INTRODUCTION: The Graph Coloring Problem (GCP) involves coloring the vertices of a graph in such a way that no two adjacent vertices share the same color while using the minimum number of colors possible.

OBJECTIVES: The main objective of the study is While keeping the constraint that no two neighbouring vertices have the same colour, the goal is to reduce the number of colours needed to colour a graph's vertices. It further investigate how various techniques impact the execution time as the number of nodes in the graph increases.

METHODS: In this paper, we propose a novel method of implementing a Genetic Algorithm (GA) to address the GCP.

RESULTS: When the solution is implemented on a highly specified Google Cloud instance, we likewise see a significant increase in performance. The parallel execution on Google Cloud shows significantly faster execution times than both the serial implementation and the parallel execution on a local workstation. This exemplifies the benefits of cloud computing for computational heavy jobs like GCP.

CONCLUSION: This study illustrates that a promising solution to the Graph Coloring Problem is provided by Genetic Algorithms. Although the GA-based approach does not provide an optimal result, it frequently produces excellent approximations in a reasonable length of time for a variety of real-world situations.

Evaluating Performance of Conversational Bot Using Seq2Seq Model and Attention Mechanism

2024-03-18T15:08:59+00:00

The Chat-Bot utilizes Sequence-to-Sequence Model with the Attention Mechanism, in order to interpret and address user inputs effectively. The whole model consists of Data gathering, Data preprocessing, Seq2seq Model, Training and Tuning. Data preprocessing involves cleaning of any irrelevant data, before converting them into the numerical format. The Seq2Seq Model is comprised of two components: an Encoder and a Decoder. Both Encoder and Decoder along with the Attention Mechanism allow dialogue management, which empowers the Model to answer the user in the most accurate and relevant manner. The output generated by the Bot is in the Natural Language only. Once the building of the Seq2Seq Model is completed, training of the model takes place in which the model is fed with the preprocessed data, during training it tries to minimize the loss function between the predicted output and the ground truth output. Performance is computed using metrics such as perplexity, BLEU score, and ROUGE score on a held-out validation set. In order to meet non-functional requirements, our system needs to maintain a response time of under one second with an accuracy target exceeding 90%.

Research on User Interface Design and Interaction Experience: A Case Study from "Duolingo" Platform

2024-03-21T05:11:58+00:00

INTRODUCTION: In today's information age, user interface design and interaction experience are crucial to the success of online platforms.

OBJECTIVES: Through in-depth analysis of the user interface design features and user interaction experience of the "Duolingo" platform, this study reveals the potential correlation between them and proposes effective improvement methods to enhance user satisfaction and efficiency.

METHODS: Interaction design principles were adopted to guide the improvement and optimization of the user interface. These principles include usability, consistency, and feedback to improve overall user satisfaction with the platform by actively considering user behavior and needs in the design. At the same time, specific mathematical models and equations are used to quantitatively analyze the efficiency and smoothness of the user interaction process, providing designers with more precise directions for improvement.

RESULTS: Optimized user interface design and interaction experience can significantly improve user satisfaction and usage efficiency. Users operate the platform more smoothly, which provides useful reference and guidance for the design and development of e-learning platforms.

CONCLUSION: Through in-depth analysis of the case of the "Duolingo" platform and the introduction of user experience evaluation methods and interaction design principles, this study has come up with a series of effective improvement measures and verified their effectiveness through experiments. It has certain theoretical and practical significance for improving the user experience of online learning platforms and promoting the design and development of Internet products.

Application of Sports Equipment Image Intelligent Recognition Response APP in Sports Training and Teaching

2024-03-20T06:43:48+00:00

INTRODUCTION: The paper addresses the integration of intelligent technology in university physical education, highlighting the need for improved analysis methods for sports equipment image recognition apps to enhance teaching quality.

OBJECTIVES: The study aims to develop a more accurate and efficient APP use analysis method for sports equipment image recognition, utilizing intelligent optimization algorithms and kernel limit learning machines.

METHODS: The proposed method involves constructing an APP usage effect analysis index system, improving kernel limit learning machines through talent mining algorithms, and validating the model using user behavior data. The method integrates a talent mining algorithm to enhance the kernel limit learning machine (KELM). This integration aims to refine the learning machine’s ability to accurately analyze the large datasets generated by the APP's use, optimizing the parameters to improve prediction accuracy and processing speed.

RESULTS: Preliminary tests on the sports equipment image intelligent recognition response APP demonstrate improved accuracy and efficiency in analyzing the APP's usage effects in physical education settings. The study compares the performance of the TDA-KELM algorithm with other algorithms like ELM, KELM, GWO-KELM, SOA-KELM, and AOA-KELM. The TDA-KELM algorithm showed the smallest relative error of 0.025 and a minimal time of 0.0025, indicating higher accuracy and efficiency. The analysis highlighted that the TDA-KELM algorithm outperformed others in analyzing the usage effects of sports equipment image recognition apps, with lower errors and faster processing times.

CONCLUSION: The study successfully develops an enhanced APP use analysis method, showcasing potential for more accurate and real-time analysis in the application of sports equipment image recognition in physical education.

A Web-Based Augmented Reality System

2024-03-20T10:58:56+00:00

Web-based augmented reality (AR) systems have many use cases and opportunities in Product Visualisation, Education and Training, Advertising and Marketing, Navigation and Wayfinding, Virtual Try-On, Interactive Storey Telling, Museums and Cultural Heritage, Training and Simulation, Gamification and more. As such, this research paper, A Web-Based Augmented Reality System, will explore these technologies and their use cases in the form of a literature review and several examples utilising the likes of Vectary, Blippar, Model Viewer and World Cast AR. The purpose of which, is to demonstrate a level of understanding of these virtual technologies, to develop them and to develop their future with practical use cases.

Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8

2024-03-21T10:22:43+00:00

INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data.

OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images.

METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation.

RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result.

CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

2024-03-27T15:30:48+00:00

INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain.

OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions.

METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities.

RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster.

CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.

Smart Contracts for Ensuring Data Integrity in Cloud Storage with Blockchain

2024-04-04T09:07:47+00:00

INTRODUCTION: Data integrity protection has become a significant priority for both consumers and organizations as cloud storage alternatives have multiplied since they provide scalable solutions for individuals and organizations alike. Traditional cloud storage systems need to find new ways to increase security because they are prone to data modification and unauthorized access thus causing data breaches.

OBJECTIVES: The main objective of this study is to review usage of smart contracts and blockchain technology to ensure data integrity in cloud storage.

METHODS: . Case studies, performance evaluations, and a thorough literature review are all used to demonstrate the effectiveness of the suggested system.

RESULTS: This research has unveiled a revolutionary approach that capitalizes on the fusion of smart contracts and cloud storage, fortified by blockchain technology.

CONCLUSION: This theoretical analysis demonstrate that smart contracts offer a dependable and scalable mechanism for maintaining data integrity in cloud storage, opening up a promising area for further research and practical application.

The Digital Transformation of College English Classroom: Application of Artificial Intelligence and Data Science

2024-04-04T12:01:51+00:00

A major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study seeks to address. Even though there have been great strides in educational technology, conventional English classes still have a hard time meeting the demands of their different student bodies and offering individualized lessons. This is a major problem that prevents English language training from being effective, according to the material that is already available. In this study, we provide an approach to this issue called English Smart Classroom Teaching with the Internet of Things (ESCT-IoT). Utilizing data science techniques, artificial intelligence (AI) algorithms, and Internet of Things (IoT) sensors, ESCT-IoT intends to provide a personalized learning environment that is both immersive and adaptable. The fuzzy hierarchical evaluation technique is used to determine the assessment's final result, which measures the smart classroom's instructional impact. To overcome the limitations of conventional education, ESCT-IoT gathers and analyses data in real time to give adaptive material, individualized feedback, and learning suggestions. There are noticeable benefits as compared to traditional methods of instruction when it comes to evaluation metrics like student engagement, learning outcomes, and teacher satisfaction. Furthermore, ESCT-IoT is excellent in encouraging active learning, improving language fluency, and boosting overall academic achievement, according to qualitative comments from both students and teachers.

IoT Protocols: Connecting Devices in Smart Environments

2024-04-06T13:51:43+00:00

The study delves into the implications of various IoT protocols on communication efficiency and energy consumption within smart environments. The RVRR (routing via respective reducer) protocol emerges as a standout performer, showcasing notable advantages over other conventional protocols. Specifically, the results demonstrate a substantial reduction in communication costs with RVRR, exhibiting improvements of 22.72%, 43.46%, and 49.04% when compared to ILP, SDN-Smart, and R-Drain, respectively. excels in data transmission, achieving commendable reductions in Round-Trip Time (RTT) and enhancing overall energy efficiency. It registers an 18.80% decrease in energy consumption compared to ILP, 28.65% compared to SDN-Smart, and a significant 37% reduction when compared to R-Drain. This suggests that RVRR is adept at optimizing resource usage (routing via respective reducer )and minimizing energy consumption, crucial aspects in the context of IoT applications. The study reveals that RVRR contributes to an extended network lifespan, outperforming other protocols by substantial margins. It showcases a 19.45% improvement over ILP, 39.16% over SDN-Smart, and an impressive 54.60% over R-Drain. This underscores the sustainability and longevity benefits offered by RVRR (routing via respective reducer), making it a promising protocol for efficient and enduring IoT applications within smart environments.

Intelligent manufacturing: bridging the gap between the Internet of Things and machinery to achieve optimized operations

2024-04-07T11:19:49+00:00

The access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities.

Improving Mobile Ad hoc Networks through an investigation of AODV, DSR, and MP-OLSR Routing Protocols

2024-04-08T14:40:27+00:00

Mobile Ad Hoc Networks (MANETs) pose a dynamically organized wireless network, posing a challenge to establishing quality of service (QoS) due to limitations in bandwidth and the ever-changing network topology. These networks are created by assembling nodes systematically, lacking a central infrastructure, and dynamically linking devices such as mobile phones and tablets. Nodes employ diverse methods for service delivery, all while giving priority to network performance. The effectiveness of protocols is crucial in determining the most efficient paths between source and destination nodes, ensuring the timely delivery of messages. Collaborative agreements with MANETs improve accessibility, allow for partial packet delivery and manage network load, ultimately minimizing delays and contributing to exceptional carrier performance. This article conducts a comparative analysis of simulation parameters for AODV, DSR, and MP-OLSR protocols to explore QoS limitations associated with different routing protocols. The study primarily focuses on evaluating various quality metrics for service improvement, assessing protocol performance. Simulation results underscore the DSR protocol's 80% superior throughput compared to AODV and MP-OLSR. However, in terms of delay and packet delivery ratio, the hybrid protocol outperforms both AODV and DSR protocols. These findings provide a distinct perspective for testing the compliance services of MANETs.

Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning

2024-04-09T08:21:49+00:00

In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.

Exploring the Impact of Mismatch Conditions, Noisy Backgrounds, and Speaker Health on Convolutional Autoencoder-Based Speaker Recognition System with Limited Dataset

2024-04-09T12:04:44+00:00

This paper presents a novel approach to enhance the success rate and accuracy of speaker recognition and identification systems. The methodology involves employing data augmentation techniques to enrich a small dataset with audio recordings from five speakers, covering both male and female voices. Python programming language is utilized for data processing, and a convolutional autoencoder is chosen as the model. Spectrograms are used to convert speech signals into images, serving as input for training the autoencoder. The developed speaker recognition system is compared against traditional systems relying on the MFCC feature extraction technique. In addition to addressing the challenges of a small dataset, the paper explores the impact of a "mismatch condition" by using different time durations of the audio signal during both training and testing phases. Through experiments involving various activation and loss functions, the optimal pair for the small dataset is identified, resulting in a high success rate of 92.4% in matched conditions. Traditionally, Mel-Frequency Cepstral Coefficients (MFCC) have been widely used for this purpose. However, the COVID-19 pandemic has drawn attention to the virus's impact on the human body, particularly on areas relevant to speech, such as the chest, throat, vocal cords, and related regions. COVID-19 symptoms, such as coughing, breathing difficulties, and throat swelling, raise questions about the influence of the virus on MFCC, pitch, jitter, and shimmer features. Therefore, this research aims to investigate and understand the potential effects of COVID-19 on these crucial features, contributing valuable insights to the development of robust speaker recognition systems.

Manifesto of Deep Learning Architecture for Aspect Level Sentiment Analysis to extract customer criticism

2024-04-09T12:56:19+00:00

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

Development of Standards for Metadata Documentation in Citizen Science Projects

2024-04-09T13:31:03+00:00

Introduction: Citizen science has generated large volumes of data contributed by citizens in the last decade. However, the lack of standardization in metadata threatens the interoperability and reuse of information.

Objective: The objective was to develop a proposal for standards to document metadata in citizen science projects in order to improve interoperability and data reuse.

Methods: A literature review was conducted that characterized the challenges in metadata documentation. Likewise, it analyzed previous experiences with standards such as Darwin Core and Dublin Core.

Results: The review showed a high heterogeneity in the documentation, making interoperability difficult. The analyzes showed that standards facilitate the flow of information when they cover basic needs.

Conclusions: It was concluded that standardizing metadata is essential to harness the potential of citizen science. The initial proposal, consisting of flexible norms focused on critical aspects, sought to establish bases for a collaborative debate considering the changing needs of this community.

Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization

2024-04-10T08:34:29+00:00

INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends.

OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment.

METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance.

RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment

CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.

Enhancing Privacy Measures in Healthcare within Cyber-Physical Systems through Cryptographic Solutions

2024-04-11T08:21:54+00:00

INTRODUCTION: The foundation of cybersecurity is privacy, standardization, and interoperability—all of which are essential for compatibility, system integration, and the protection of user data. In order to better understand the complex interrelationships among privacy, standards, and interoperability in cybersecurity, this article explains their definitions, significance, difficulties, and advantages.

OBJECTIVES: The purpose of this article is to examine the relationship between privacy, standards, and interoperability in cybersecurity, with a focus on how these factors might improve cybersecurity policy and protect user privacy.

METHODS: This paper thoroughly examines privacy, standards, and interoperability in cybersecurity using methods from social network analysis. It combines current concepts and literature to reveal the complex processes at work.

RESULTS: The results highlight how important interoperability and standardization are to bolstering cybersecurity defences and preserving user privacy. Effective communication and cooperation across a variety of technologies are facilitated by adherence to standards and compatible systems.

CONCLUSION: Strong cybersecurity plans must prioritize interoperability and standardization. These steps strengthen resilience and promote coordinated incident response, which is especially important for industries like healthcare that depend on defined procedures to maintain operational security.

Design Method for Travel E-commerce Platform Based on HHO imparoved K-means Clustering Algorithm

2024-04-15T08:11:57+00:00

Convenient and intelligent tourism product recommendation method, as the key technology of tourism E-commerce platform design, not only provides academic value to the research of tourism E-commerce platform, but also improves the efficiency of personalized recommendation of tourism products. In order to improve the quality of tourism recommendation, this paper proposes a tourism E-commerce platform design method based on HHO improved K-means clustering algorithm. Firstly, the Harris optimization algorithm is used to improve the K-means algorithm to construct a user-oriented tourism product recommendation strategy; then, combined with the XGBoost algorithm, an item-oriented tourism product recommendation strategy is proposed; secondly, the two strategies are mixed to construct a personalized tourism product recommendation model. Finally, the effectiveness of the proposed method is verified by simulation experiment analysis. The results show that the recommendation accuracy of the tourism E-commerce platform design method proposed in this paper reaches more than 90%, and the recommendation response time meets the real-time requirements, which can provide personalized tourism product recommendation for platform users and enhance the purchase of tourism products.

Effective preprocessing and feature analysis on Twitter data for Fake news detection using RWS algorithm

2024-02-20T15:14:06+00:00

The tremendous headway of web empowered gadgets develops the clients dependably strong in virtual redirection affiliations. Individuals from social affairs getting moment notices with respect to news, amusement, training, business, and different themes. The development of artificial intelligence-based classification models plays an optimum role in making deeper analysis of text data. The massive growth of text-based communication impacts the social decisions also. People rely on news and updates coming over in social media and networking groups. Micro blogs such as tweeter, facebooks manipulate the news as faster as possible.

The quality of classification of fake news and real news depends on the processing steps. The proposed articles focused on deriving a significant method for pre-processing the dataset and feature extraction of the unique data. Dataset is considered as the input data for analyzing the presence of fake news. The extraction of unique features from the data is implemented using Bags of relevant tags (BORT) extraction and Bags of relevant meta words (BORMW).

Research on the application of data mining algorithm in photojournalism and short video communication

2024-02-22T10:48:24+00:00

Short films and photojournalism are crucial parts of video communication, yet there are issues including poor video integration, inconsistency between short videos and photojournalism, and slow communication speed. In order to evaluate and analyse video communication, this research offers a data mining algorithm. First, the database's images and videos are chosen and analysed using a mining algorithm, and then the indicators are reduced by dividing them into groups based on the specifications for video dissemination. video transmission distractions. The mining algorithm then analyses the video propagation, creates a plan that satisfies the criteria, and refines the plans that satisfies the criteria. Analyse. The integration level, timeliness, and compliance rate of data mining algorithms for video propagation are superior to traditional video dissemination method under specific analysis criteria, according to MATLAB simulation.

Smart Attendance System using Face Recognition

2024-02-26T10:53:02+00:00

Face recognition offers a wide range of valuable applications in social media, security, and surveillance contexts. The software used for building facial recognition algorithms is Python and OpenCV. "Attendance using Face Recognition" is a method for tracking and managing attendance that makes use of facial recognition technology. By seamlessly integrating the 'Face Recognition' module, a native Python feature, and the OpenCV library, our system excels in accuracy and dependability. The system then stores attendance records in a database and provides real-time reports. In this article, we demonstrate how to create a face recognition system in Python utilizing the built-in "Face Recognition" module and the OpenCV library. Our results show that our system achieves high accuracy and robustness while being efficient and scalable, catering to a wide spectrum of educational institutions, organizations, and enterprises.

Research step of PID control method of stepper motor based on improved fuzzy control algorithm

2024-02-26T13:19:46+00:00

The significance of PID control within the management system of stepper motors is noteworthy; nonetheless, it is worth noting that stepper motors are susceptible to issues such as low power, step loss, and vibration. The conventional Proportional-Integral-Derivative (PID) control method is insufficient in addressing the control challenge specific to stepper motor management systems. Hence, this research work presents an enhanced fuzzy control method that integrates the principles of fuzzy control theory with traditional PID control theory. The integration of fuzzy control into the P ID control is undertaken to create a fuzzy controller that satisfies the demands of stepper motor control. Additionally, the division of indices is conducted in accordance with the specifications of the fuzzy controller in order to mitigate the disruptive elements of PID control. then, the use of fuzzy control rules is employed to achieve control over the stepper motor, resulting in the development of an enhanced scheme that is then subjected to rigorous validation. The present study employs a MATLAB simulation to compare the performance of the enhanced fuzzy control algorithm with that of the P-ID control method. The results demonstrate that the improved fuzzy control algorithm significantly enhances the stability and dynamic performance of the stepper motor. Superior to traditional Proportional-Integral-Derivative (PID) control.

A Realizable Data Encryption Strategy

2024-02-28T15:49:40+00:00

As technology continues to advance, data has become an increasingly important element in the sphere of Information Technology. However, enormous data generated by devices presents a major challenge in handling it in real time. Data encryption is a crucial component in ensuring data security and privacy during its transmission in network. Unfortunately, many applications disregard data encryption in order to achieve higher performance. The work proposes a solution to this problem by introducing a data encryption process that is, the Realizable Data Encryption Strategy (RDES) and Deoxyribonucleic Acid (DNA) computing, a revolutionary cryptographic method that improves information security by preventing authorized access to sensitive data, being used. Information security is improved by DNA symmetric cryptography being suggested. The outcomes show that plain-text encryption is a very secure procedure. The RDES approach is designed to improve privacy protection within the constraints of real-time processing. By implementing the RDES approach, data privacy and security can be significantly enhanced without compromising performance.

Truculent Post Analysis for Hindi Text

2024-04-04T14:51:36+00:00

INTRODUCTION: With the rise of social media platforms, the prevalence of truculent posts has become a major concern. These posts, which exhibit anger, aggression, or rudeness, not only foster a hostile environment but also have the potential to stir up harm and violence.

OBJECTIVES: It is essential to create efficient algorithms for detecting virulent posts so that they can recognise and delete such content from social media sites automatically. In order to improve accuracy and efficiency, this study evaluates the state-of-the-art in truculent post detection techniques and suggests a unique method that combines deep learning and natural language processing. The major goal of the proposed methodology is to successfully regulate hostile social media posts by keeping an eye on them.

METHODS: In order to effectively identify the class labels and create a deep-learning method, we concentrated on comprehending the negation words, sarcasm, and irony using the LSTM model. We used multilingual BERT to produce precise word embedding and deliver semantic data. The phrases were also thoroughly tokenized, taking into consideration the Hindi language, thanks to the assistance of the Indic NLP library.

RESULTS: The F1 scores for the various classes are given in the "Proposed approach” as follows: 84.22 for non-hostile, 49.26 for hostile, 68.69 for hatred, 49.81 for fake, and 39.92 for offensive

CONCLUSION: We focused on understanding the negation words, sarcasm and irony using the LSTM model, to classify the class labels accurately and build a deep-learning strategy.

Blockchain based Quantum Resistant Signature Algorithm for Data Integrity Verification in Cloud and Internet of Everything

2024-03-20T15:28:04+00:00

INTRODUCTION: The processing and storage capacities of the Internet of Everything (IoE) platform are restricted, but the cloud can readily provide efficient computing resources and scalable storage. The Internet of Everything (IoE) has expanded its capabilities recently by employing cloud resources in multiple ways. Cloud service providers (CSP) offer storage resources where extra data can be stored. These methods can be used to store user data over the CSP while maintaining data integrity and security. The secure storage of data is jeopardized by concerns like malicious system damage, even though the CSP's storage devices are highly centralized. Substantial security advancements have been made recently as a result of using blockchain technology to protect data transported to networks. In addition, the system's inclusive efficacy is enhanced, which lowers costs in comparison to earlier systems.

OBJECTIVES: The main objective of the study is to a blockchain-based data integrity verification scheme is presented to provide greater scalability and utilization of cloud resources while preventing data from entering the cloud from being corrupted.

METHODS: In this paper, we propose a novel method of implementing blockchain in order to enhance the security of data stores in cloud.

RESULTS: The simulations indicate that the proposed approach is more effective in terms of data security and data integrity. Furthermore, the comparative investigation demonstrated that the purported methodology is far more effective and competent than prevailing methodologies.

CONCLUSIONS: The model evaluations demonstrated that the proposed approach is quite effective in data security.