bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Typhoon-Induced Design Wave Height Estimation for Ocean Engineering Applications Using High Performance Computing
Authors: Yaozhao Zhong, Lei Wang, Peitu Lin Feng Zhang, Li Zhang, Yanshuang Xie,
Published: April 2024

Coastal construction heavily depends on accurately estimating design wave parameters. This paper presents a technique for calculating ocean engineering design wave heights that addresses the challenges of inadequate wind speed and measured wave data in the field using High Performance Computing (HPC). The proposed method combines the Simulating WAves Nearshore (SWAN) wave model driven by wind with two different wind field models: the NCEP/NCAR reanalysis wind field and the Jelesnianski typhoon model wind field. By combining these models, the study reduces the underestimation of wind speed and inaccurate depiction of typhoon-related details. The method's effectiveness is demonstrated by applying it to calculate the design wave elements of the deep-sea breeding platform in Dongluo Island, Fujian. The study results show that the platform can experience a maximum wave height of 9.42 m during a 50-year recurrence period. The research shows that the platform's southwest corner is the most vulnerable location. This new calculation method is a significant improvement in ocean engineering design since it overcomes the limitations that come with the lack of wind speed and measured waves. By utilizing multiple wind field models, the calculated wave heights' accuracy and reliability is considerably enhanced. The application of this method to the breeding platform in Dongluo Island has yielded valuable insights for designing resilient structures in similar oceanic environments. This study demonstrates the important application of computational numerical simulations in the field of ocean engineering, providing high-precision predictive capabilities for structural design and risk assessment.
The Relationship between Interpreting Anxiety and Learning Motivation Assisted by Long Short-Term Memory Neural Network Algorithms
Authors: Hanhui Li Yanling Hong,
Published: April 2024

The Long Short-Term Memory (LSTM) neural network algorithm has achieved significant results in natural language processing and speech recognition. This study explores the application of the LSTM algorithm in the study of the relationship between interpreting anxiety and learning motivation. The article mainly uses questionnaire survey method to obtain research data. The participates include students engaged in translation research and professional translators. After statistical analysis of the research data, it was found that there was a significant correlation between translation anxiety and learning motivation (r=-0.62, p<0.001). According to calculation results, the average score is 3.42 and the standard deviation is 0.76. The average score of evaluating the learning enthusiasm of participants through corresponding questionnaires is 4.27, with an SD of 0.92. Further regression analysis of the data was conducted, and the results showed that respondents with low translation anxiety had stronger learning motivation（β=- 0.43, p<0.001). It can be seen that reducing learners' fear of translation can enhance their learning motivation, thereby stimulating their interest in learning. This indicates that reducing translation anxiety may be helpful to improve the learning motivation level of interpreting learners. The study is of great significance for improving the learning enthusiasm of translation learners and reducing translation anxiety and provides a new perspective and research method for future related research.
A Comprehensive Approach with Trust-Based Routing, Energy Optimization, and Multi-Routing Protocol for Enhancing Security and Performance in Mobile Ad Hoc Network
Authors: V. Vaithianathan, A. Karthikayen, P Deepa, M A Starlin Ramesh S,
Published: April 2024

In this paper, we propose an aggregate solution to boost the security, performance, and resilience of disaster-based comms networks. This involves trust-based routing, energy optimization, and more than one routing protocol to meet the unique requirements of the ever-changing and unpredictable environments facing disasters in this world. The evaluation brings 4.3 Mbps for the average throughput, 34 ms delay with a packet delivery ratio of 96.9%. Also, energy consumption was optimized and the average consumption of 2458 Joules makes sharing power resources an easy exercise even when you are in a disaster-prone zone. Security analysis of the experiments showed on average 2.7 vulnerabilities across different trials with a range from 2 to 5 in severity level. Therefore, robust security mechanisms guard against potential threats, a reminder that disaster situations can befall any person or community. Overall, disaster response performance was good despite the difficult conditions. Further research and development in this area will continue to refine and improve communication systems for disaster preparedness and response efforts.
Analyzing the Efficacy of the Relative Strength Indicator of Capital Inflows and Outflows Based on Big Data Analysis in Achieving Abnormal Returns Evidence from the Chinese Stock Market
Authors: Xingyuan Li Tianquan Liu, Shuiyang Pan,
Published: April 2024

This study originates from an analysis of market microstructure and introduces a novel statistical indicator of relative strength in capital flows through the application of big data analytics. This indicator effectively captures the impact of capital movements on future stock prices by integrating variations in stock prices with the volume of transactions within a corresponding timeframe. Building upon this foundation, the research develops an innovative momentum investment strategy based on the relative strength indicator of capital inflows and outflows, extending beyond the traditional fixed holding period momentum strategy framework. Empirical analysis reveals that this new indicator significantly demonstrates momentum effects in the Chinese A-share market; notably, in portfolios of stocks with smaller market capitalizations, the momentum strategy grounded on the new indicator achieves higher excess returns compared to traditional momentum strategies. These findings not only validate the efficacy of the new indicator but also underscore the critical role of big data analytics in enhancing the analysis of financial markets and the efficiency of investment decision-making.
Chilly Leaves Diseases Identification Using MobileNet V2 Deep Learning Model
Authors: Golda Dilip D. Prabhu,
Published: April 2024

This work describes the application of pre-trained deep learning models (Mobile Net V1, Mobile Net V2) to image classification issues such as plant disease diagnosis. Regarding this, We provide a deep learning-based feature extraction technique for classifying leaf diseases of cold plants and identifying plant species. The automated diagnosis of plant diseases has brought about a transformation in the agriculture industry. Our main goal is to use neural networks for agricultural disease diagnosis and treatment. Plant diseases seriously impair the agriculture industry's financial standing. Managing an illness is a challenging endeavor. Usually, infections appear on the leaves or stems of plants, as do their symptoms, which include colorful spots or steaks. Compared to human labor, image processing technology enables faster and more accurate illness detection. Image processing is critical in the detection of plant diseases because it gives the best results with the least amount of human intervention. Deep learning helps to diagnose diseases and provide focused treatment.
Development of Android-based Smart Classroom Applications to Enhance SMK Students' Competences that are Relevant to the Industry
Authors: Ambiyar, Unung Verawardina Said Thaha Ghafara,
Published: April 2024

The application of innovative technology in training, can enhance the competence of new worker or internship student, which corresponds to the needs of the world of work and industry. The aim of the study is to create an Android-based training media—also known as a smart classroom in the form of an application that can be accessed using a smartphone, used by internship student as a learning and training medium to improve new worker competence in the use of technology according to the needs of the world of work and industry. This application supports project-based learning even if new worker or students are in the process of internship in industry. It can be used anywhere, internship students in field work practice (PKL) in the industry. This application is designed to suit the needs of new workers in the world of work and industry. The development of this application uses R&D methods adapted to the ADDIE development model. There are five stages of development: 1) Analysis, 2) Design, 3) Development, 4) Implementation, and 5) Evaluation. The study was conducted at SMK State 1 Tanjungpinang, and the data collection was carried out through observation and filling of the cargo with 30 internship student of class 10 of the major logistics engineering (X TL). The descriptive data collection method used by materialists and media experts is the Likert scale which has four options of answers. The material expert evaluation results obtained a total score of 98.00 with an average score of 3.62 out of 27 evaluation indicators with a percentage of 90.74%, so it belongs to the category very suitable for use. With a positive student response with an average of 92.67% at the media implementation stage, this falls into a highly qualified category. Evaluation results of application use by students showed an average improvement of about 43.74% in student learning outcomes. This smart classroom application is specially designed to support new worker or internship student doing project-based learning improving student competence and student skills in working on industry-based projects that will affect student or new worker performance/skills as they descend into the world of industry.
Intelligent Data Integration: A Case Study of NOVOI in a Higher Education Institution in China
Authors: Xiaodao Lin Zhiling Yang,
Published: April 2024

Massive data of different sectors inheres in different sites, which requires integration of those data in various types and formats when dealing with system development and integration of an organization. Intelligent data integration is crucial for system development and management to enhance the effective governance of organizations at all levels. It alleviates the lagging service and meets users' needs in higher educational institutions (HEIs). From the holistic perspective, the “No More than One Visit for One Item” (NOVOI) is a paradigm emphasizing the integration of system information, which is utilized in integrated services all in one, namely, the synergy and collaboration of all offices in an HEI. By a case study of the status quo of NONOI in an HEI in China, the NOVOI framework was constructed and its outcomes were analyzed by a comparison of data in past years. It’s found that users of NOVOI vary among different service items. The most frequently used service items are academic-related, instead of teaching affairs or administrative affairs. Service items for faculty and staff have more nodes than those for students. As a result, NOVOI can improve the efficiency of the service, enhance the overall management level, promote information disclosure in HEIs, and help the HEI system develop towards diversification of services and improve a more efficient informatization process through intelligent and effective data integration.
Construction and evaluation of financial distress early warning model based on machine learning
Authors: Gao Li
Published: April 2024

As the economy of our nation has expanded, the harm resulting from the financial risks associated with listed corporations has intensified, significantly impeding the survival and expansion of businesses. We must create a reliable financial crisis early warning system to prevent financial risks from endangering the business. This article aims to analyse the development of financial early warning models that use machine learning techniques. The model employs the Archimedes optimization method (AOA) method to optimize the parameters of SVM and selects 20 financial risk assessment indices as the input to anticipate the financial crises. The results suggest that the proposed model surpasses current models in terms of both prediction accuracy and resilience. They also highlight the ensemble model's greater predictive capacity when compared to individual algorithms, highlighting its efficacy in spotting early warning signs of financial instability in a variety of market scenarios. Financial organisations and governments may improve their risk management procedures and prevent future crises by incorporating machine learning techniques into the early warning system.
Construction of Network Database for Innovative Entrepreneurial Practice Course
Authors: Tongxin Xue, Gang Xiao Yuhan Xiao,
Published: April 2024

The Innovative Entrepreneurial Practice Course offers a dynamic learning experience tailored to aspiring entrepreneurs seeking to navigate the complexities of the business world. This course provides a comprehensive overview of entrepreneurial concepts, strategies, and practical skills essential for success in today's competitive landscape. Through a combination of interactive lectures, case studies, and hands-on projects, participants gain invaluable insights into ideation, business planning, market analysis, and venture creation. The course emphasizes experiential learning, encouraging students to apply theoretical knowledge to real-world scenarios and challenges. By fostering creativity, critical thinking, and collaboration, the Innovative Entrepreneurial Practice Course equips students with the tools and mindset needed to thrive as innovative entrepreneurs in the global marketplace. This paper presents the construction of a network database tailored for the Innovative Entrepreneurial Practice Course, employing the Weighted Software-Defined Clustering Database (WSD-CD) framework. The network database serves as a comprehensive repository of resources, insights, and case studies relevant to entrepreneurial practice, offering students a rich and dynamic learning environment. Through the WSD-CD model, data is organized and clustered based on weighted attributes, allowing for efficient retrieval and analysis of information. The database encompasses a wide range of entrepreneurial topics, including business models, market trends, funding strategies, and success stories, curated from diverse sources such as industry reports, academic journals, and expert interviews. This networked approach enables students to explore interconnected concepts and gain holistic perspectives on entrepreneurial challenges and opportunities. Through simulated experiments, the efficacy of the database construction process was evaluated, yielding promising numerical results. For instance, the WSD-CD model demonstrated a 30% improvement in data retrieval speed compared to traditional clustering methods, enhancing the efficiency of information access for course participants. Additionally, the database achieved a 25% increase in user satisfaction, as evidenced by feedback surveys indicating higher levels of perceived relevance and usability.
IoT Approach for Ensuring Safety in smart Cities for Energy Saving
Authors: Kapil Netaji Vhatkar, Devyani Jadhav, Vinod V. Kimbahune Atul B. Kathole, Jayashree Katti, Swapnaja Amol Ubale,
Published: April 2024

The security of city dwellers is of the utmost importance due to the increasing number of smart cities and the speed with which they are being developed. As a whole, smart city safety can be improved through the use of the Internet of Things (IoT), which is the focus of this article. Several safety concerns, such as public safety, emergency management, and disaster preparedness, can be monitored and addressed with the help of the Internet of Things (IoT) framework's network of interconnected devices, sensors, and data analytics. In order to improve situational awareness, reaction times, and overall safety results, this research dives into important Internet of Things (IoT) applications such smart sensors, real-time analytics, and surveillance systems. In addition to looking at potential energy savings, the study analyses the pros and cons of using an Internet of Things (IoT) safety infrastructure in smart cities.
A Novel Deep Learning Approach for Brain Tumor Segmentation and Classification
Authors: Rahul Mapari Sangeeta Kakarwal,
Published: April 2024

Automated brain tumor detection and segmentation are crucial in medical diagnostics as they give details about functional structures and also potential unusual tissue that is required to define surgical strategies. Segmentation of images is one of the most difficult issues in the area of MRI scan analysis research. However, due to low contrast, automatic tumor segmentation, accuracy issues, and ill-defined boundaries remain difficult. For the prevention of brain tumors, an investigation was developed for implementing a novel framework. To overcome the problem faced by traditional existing techniques, this paper proposed an automatic brain tumor segmentation and detection system using the proposed dense convolution neural network without human intervention, where an intrinsic set of features are extracted to perform the classification task. The main aim of this paper is to segment the tumor portion from the affected MRI using a novel clustering approach. Moreover, a lot of scholars have researched the diagnosis of brain tumors, yet the accuracy-based performance metric in diagnosis results is low. A deep learning framework is proposed to evaluate brain disease more accurately using a Black Widow Optimization driven Dense Convolution Neural Network (BW-DCNN). The various performances are assessed, and the results are compared to those of other existing classifiers and approaches.
Extraction of Heterogeneous Features from Various Fruits and Classification of diseases using Deep Learning Techniques
Authors: Mukesh Shrimali Prakash J Parmar,
Published: April 2024

The success of the farming business and the economy as a whole is directly tied to each individual farmer's output. Micro-level illnesses that emerge during fruit development may have a significant influence on output. There is an overwhelming variety of viruses, necessitating improvements in image processing and other informational resources before effective treatments can be proposed. Unparalleled approaches to segmenting, extracting characteristics, and classifying in order to identify overt conditions constitute a substantial portion of the research in this domain. However, in the event of larger and more in-depth datasets, machine learning and artificial intelligence technologies like deep learning and the like are necessary to discover illnesses that would otherwise be invisible to the human eye. Machine designers are not always sure what kinds of illnesses can be identified by mixing algorithms since there are so many possible combinations. In this paper we proposed a fruit disease detection and classification using deep convolutional neural network. The VGG-16 and VGG-19 both deep learning frameworks are utilized for detection of heterogeneous fruits. The both deep learning frameworks are archives 96.10% and 98.10% average accuracy on heterogeneous fruit dataset. In overall analysis the VGG-19 obtains higher accuracy with 200 epoch size which is higher the VGG-16 and other conventional machine learning and deep learning frameworks.
Fruit Quality Analysis and Disease Detection using Deep Learning Techniques
Authors: Pawan Bhambu Sneha Ramdas Shegar,
Published: April 2024

Defective fruits are the main reason for worldwide financial catastrophes in agricultural production. It affects both the dependability and quality of the fruits. Post-harvest, quality checking requires a significant amount of time and labor-intensive skill. Automatically identifying fruit quality enables saving time and labor during harvest. Various algorithms have been created using machine learning and image processing methods to detect and categorize fruit quality. A system using Convolutional Neural Networks (CNN) and transfer learning techniques has been developed to enhance the fruit classification process. Two methods are suggested for predicting fruit freshness. One tailored CNN architecture is proposed by modifying the network's parameters to suit the dataset. The second technique utilizes the pre-trained VGG model with transfer learning to assess the freshness of the fruit. The proposed models are capable of differentiating between fresh and spoiled fruit by analyzing the input photos. This research used 70 diverse types of fruit, such as apples, bananas, oranges, and others. The first CNN obtains 98.55% accuracy for heterogeneous fruit dataset while second VGG16 achieves 99.05% accuracy on similar dataset. The plant village global dataset is utilized with various fruit categories. As a result, The VGG16 provides higher accuracy than conventional CNN and other deep learning algorithms.
Enhancing Security and Energy Efficiency in Wireless Sensor Networks for IoT Applications
Authors: Aqeel Ibrahim
Published: April 2024

Wireless Sensor Networks (WSNs) are fundamental components within the Internet of Things (IoT) scene, empowering information collection and transmission in different applications. This research papers on upgrading the security and vitality effectiveness of WSNs for ideal IoT usefulness. Novel strategies were created and assessed to address these vital perspectives. Security improvements included the usage of lightweight cryptographic calculations, intrusion detection systems (IDS), and secure directing conventions. These measures essentially reinforced organized strength against cyber dangers, with interruption discovery rates surpassing 95% and secure steering conventions accomplishing a strength score of over 97%. Vitality productivity optimizations enveloped communication convention improvements, information accumulation strategies, and energetic control administration methodologies. These procedures come about in outstanding decreases in vitality utilization, with optimized conventions diminishing vitality utilization by up to 30% and information conglomeration techniques yielding vitality reserve funds of up to 50%. Test assessments were conducted utilizing both simulation-based approaches and real-world organizations. Recreation comes about, obtained utilizing NS-3, showcasing the viability of the proposed strategies over different network topologies, activity designs, and attack scenarios. Real-world experimentation employing a WSN testbed approved the viable achievability and viability of the techniques. Comparative investigations with existing writing highlighted the oddity and importance of the commitments, checking considerable progressions in WSN security and vitality proficiency.
An Effective Heart Disease Detection and Classification using Machine Learning
Authors: Sanjay Y. Azade, Deepali Sawane, Sarfaraz Pathan Shabeena Naaz Khan,
Published: April 2024

About 20.5 million people die yearly due to cardiovascular disease (CVD). Early prediction of heart disease and accurate heart severity identification can save the induvial life with timely medications. In many cases, most deaths occur due to inaccurate diagnosis of the heart. In computer science, various existing researchers have dealt with the classification of heart disease on synthetic as well as real-time datasets. But still, those systems have a challenge such as low classification accuracy, high error rate and inaccurate heart severity detection. After identifying all these challenges, we proposed an effective heart disease detection and classification using hybrid machine learning techniques. In this article, we describe how various feature extraction and hybrid machine learning classifiers produce accurate severity of heart disease on real-time datasets. In the first phase, we collected a Cleveland dataset from Kaggle and then applied preprocessing and normalization for data balancing. Various feature extraction and selection methods, such as TF-IDF, Co-relation coefficient, N-Gram, and bi-Gram features, are used for practical module training. The different machine learning we used on normalized datasets for classification. The five supervised machine learning algorithms are used, such as Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and Hybrid Machine Learning (HML) etc. The HML achieves 99.30% accuracy on the Cleveland dataset using Weka 3.8 machine learning framework. As a result, the proposed system compares with various heart disease predictions using machine learning techniques.
3D Convolutional Neural Networks based Movement Evaluation System for Gymnasts in Computer Vision Applications
Authors: Chen Ying
Published: April 2024

This study introduces an innovative Movement Evaluation System, which utilizes state of art 3D CNNs in grassland of computer vision. This system presents a significant advancement in analyzing and assessing complex gymnastic movements, providing a comprehensive understanding of both spatial and temporal dynamics. By incorporating pose estimation algorithms, it accurately identifies body joints and positions to extract detailed spatial features. The 3D CNN further captures the temporal evolution of these features, enabling a precise analysis of the fluidity, rhythm, and synchronization of gymnastic movements over time. The effectiveness of this system is evaluated through performance metrics such as precision, recall, and accuracy. Aimed at enhancing sports science and athlete training, this research offers coaches, judges, and gymnasts a sophisticated tool for objective and standardized movement assessments. It has the potential to streamline the evaluation process in gymnastics and provide constructive feedback efficiently. The integration of 3D CNNs in this system marks a paradigm shift in utilizing advanced computer vision techniques to improve the understanding and refinement of gymnastic performances.
Transferability of Learned Knowledge in Neural Networks: Impact of Trained Weights on Untrained Networks
Authors: Udit Chaturvedi, Neha V. Sharma Adwaita Tulsyan,
Published: April 2024

This research explores the intricate dynamics of neural networks (NNs) with a focus on understanding the profound implications of the training process on their performance. In a departure from conventional transfer learning, this study delves into the manual initialization of untrained neural networks with weights and biases extracted from trained networks of identical architecture. Despite their plain congruence, we meticulously investigate the nuanced distinctions between trained and untrained networks and analyze the disparities in their performance. This study introduces mathematical foundations, empirical findings, and implications that underscore the significance of the training process in the realm of neural networks. Applying the ideas of weight extraction, this research is inspired by the unique process of visual learning and “mirroring” seen in humans; the ability to mimic something simply by seeing the methods involved.
Unlocking Machine Learning Model Decisions: A Comparative Analysis of LIME and SHAP for Enhanced Interpretability
Authors: Sarvesh Koli Komal Bhat Prajwal Korade Deepak Mane  Anand Magar Om Khode
Published: April 2024

XAI is critical for establishing trust and enabling the appropriate development of machine learning models. By offering transparency into how these models make judgements, XAI enables researchers and users to uncover potential biases, admit limits, and eventually enhance the fairness and dependability of AI systems. In this paper, we demonstrates two techniques, LIME and SHAP, used to improve the interpretability of machine learning models. Assessing Explainable AI (XAI) approaches is critical in searching for transparent and interpretable artificial intelligence (AI) models. Explainable AI (XAI) approaches are designed to provide insight into how complex models make decisions. This paper thoroughly analyzes two prominent XAI methods: Shapley Additive explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). This study aims to understand the decision made by a machine learning model and how the model came to that decision. We discuss the approaches and framework of both LIME and SHAP and assess their behavior in predicting the model's outcome.
Building A Smart Campus: Designing An Architectural Framework For Campus Security Gates And Vehicle Asset Management With GPS Tracking Technology
Authors: Thelma D. Palaoag Reina T. Payongayong,
Published: April 2024

Modern technology must be integrated into educational institutions as they develop into "smart campuses" in order to guarantee the security and effectiveness of day-to-day operations. This study combines GPS tracking technology to present an architectural framework for the deployment of intelligent security gates and automotive asset management systems. The objective is to build a secure and effective environment that improves campus security and maximizes the use of available resources. The suggested architecture combines a centralized software platform with innovative hardware elements, including GPS trackers, RFID readers, and security cameras. RFID-enabled security gates improve access control by limiting entrance to the college grounds to only authorized people and vehicles. Surveillance cameras simultaneously offer real-time monitoring to guarantee adherence to security procedures.

The vehicle asset management system utilizes GPS technology to monitor campus vehicles in real-time, increasing fleet management efficiency and enhancing general campus security. GPS tracking integration allows scheduling maintenance, optimizing routes, and notifying authorities of unwanted vehicle movements.
A Comprehensive Multimodal Approach to Assessing Sentimental Intensity and Subjectivity using unified MSE model
Authors: Faiyaz Ahamad Mohd Usman Khan,
Published: April 2024

In the dynamic realm of multimodal learning, where Representation Learning serves as a pivotal key, our research introduces a groundbreaking approach to understanding sentiment and subjectivity in audio and text. illustration from self-supervised learning, we've innovatively combined multi-modal and Unified--modal tasks, emphasizing the crucial aspects of consistency and distinctiveness. Our training techniques, likened to the art of fine-tuning an instrument, harmonize the learning process, prioritizing samples with distinctive supervisions. Addressing the pressing need for robust datasets and methodologies in combinational text and audio sentiment analysis, we present the Multimodal Opinion-level Sentiment Intensity dataset (MOSI). This meticulously annotated corpus offers insights into subjectivity, sentiment intensity, text features, and audio nuances, setting a benchmark for future research. Our method not only excels in generating Unified-modal supervisions but also stands resilient against benchmarks like MOSI and MOSEI, even rivaling human-curated annotations on the challenging datasets. This pioneering work paves the way for deeper explorations and applications in the burgeoning field of sentiment analysis.
Improving Task Scheduling in Cloud Datacenters by Implementation of an Intelligent Scheduling Algorithm
Authors: Bassim M. Salih Omer K. Jasim Mohammad,
Published: April 2024

The need for mobile and online Apps and services has led to rapid growth in cloud computing services.  The rapid increase in growth highlights the importance of reducing the time required for scheduling and efficiently utilizing resources in a dynamic setting. Consequently, numerous scheduling algorithms have been devised to address these problems by employing intelligent scheduling approaches, including Genetic Algorithms, greedy algorithm, Antlion Optimizer, Ant Colony optimization, and Cuckoo Intelligent Algorithm. This study provides an overview of intelligent optimization strategies, with a specific focus on the Cuckoo intelligent methodology. Additionally, it presents a proposed implementation of a Cuckoo-based cloud computing environment as an efficient algorithm that is projected to yield improved results in task scheduling.
Data Leakage, Security, Privacy Issues and Challenges in Cloud Computing Services: A Systematic Survey
Authors: Omer K. Jasim Mohammad Bassim M. Saliha,
Published: April 2024

Cloud Computing services are widely expanded all over the world because of it comfort and reliable services offered to the user on demand. The majority of the users who are using different pervasive devices are directly or indirectly dependent on the cloud services. The consumer data stored at vendor end has lot of security concerns. Though many cloud computing mechanisms are offered by cloud vendors still the data leakages are challenging issues to be addressed in various levels. This paper presents the systematic investigation of data leakages which occur in cloud services. In our study we present systematic analysis of (i) data leakages in cloud environment, (ii) security issues related to cloud environment, (iii) cloud security mechanisms and finally we present the challenges in cloud security that need proper solutions.
Optimized Deep Learning for Human age Estimation
Authors: R. P. Bhavsar, B. V. Pawar Kirti A. Patil,
Published: April 2024

Background/ Introduction: Human age assessment plays a crucial part in diagnosing genetic problems, and development abnormalities in children. They are also used in several applications, like forensic investigation and criminal scenes. Generally, human age is estimated from X-ray images of bones, and the manual estimation of human age is highly subjective, as they depend on the medical experience of the professionals. Further, they are prone to error and are laborious, thereby requiring automated Bone Age Assessment (BAA) for determining human age with high accurateness.

Materials and methods: This work presents a novel approach using Deep Learning (DL) and optimization considering the X-ray images of hand. Here, human age is estimated using the Deep Residual Network (DRN), whose parameters are trained using the proposed Beluga whale lion optimization (BWLO) algorithm. Further, several processes, like pre-processing, Region of Interest (RoI) extraction, Image augmentation, and feature extraction are used to enhance the accuracy of the estimation model.

Results and conclusion: The BWLO_DRN is examined for its superiority considering metrics, like accuracy, Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV).
The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study
Authors: Sonali Kadam Vaishali Mishra,
Published: April 2024

The automotive industry has witnessed a significant increase in cyber threats as vehicles become more connected and reliant on software-driven systems. To safeguard against these threats, effective cybersecurity measures must be implemented. This paper explores the use of Support Vector Machine (SVM) algorithms as a means of bolstering automotive cybersecurity attack prevention. SVM algorithms have demonstrated remarkable capabilities in various domains due to their ability to handle complex and high-dimensional data. By leveraging SVM algorithms, this research aims to enhance the detection and prevention of cybersecurity attacks targeting automotive systems.

The proposed approach involves training SVM models using labeled datasets that include both normal and anomalous driving scenarios. By analyzing various features and patterns extracted from the data, the SVM models can learn to differentiate between normal and malicious behaviour. These models can then be used in real-time to identify and mitigate potential cyber threats.

The results of this study is compared with other machine learning algorithm like Logistic Regression, Naive Bayes, KNN, Decision Tree, highlight the effectiveness of SVM algorithms in detecting and preventing automotive cybersecurity attacks. The models exhibit high accuracy rates in distinguishing between normal and anomalous behavior, providing a robust defense mechanism against potential threats.

In conclusion, this research emphasizes the significance of leveraging SVM learning algorithms for automotive cybersecurity attack prevention. By harnessing the power of machine learning, automotive systems can be fortified against cyber threats, ensuring the safety and integrity of vehicles and their passengers.
Machine Learning based Financial Stock Market Trading Strategies with Moving Average, Stochastic Relative Strength Index and Price Volume Actions for Indian and Malaysian Stock Market
Authors: Rupali Atul Mahajan, Arup Kadia, Monika Singh Rajesh Dey, Salina Kassim, Sudhanshu Maurya,
Published: April 2024

The study related to a new and very powerful stock, index and commodities trading strategy based on machine learning with Moving Average (MA), Stochastic Relative Strength Index (SRSI) and Price Volume (PV) analysis to generate three types of financial market or stock trading signals such as buy, sell and no trade. Moving averages and moving average crossover are widely used to smooth price data and identify trends, whereas Stochastic RSI is a powerful oscillator that measures the relative strength of recent stock prices. Combination of multiple indicators in machine learning can improve strategies and provide more accurate decision for investors and traders which generate financial worth. This model is very much useful for Indian and Islamic stock markets.
Data Mining-driven Multiple Regression Analysis of Cultural Identity in International Students’ Perceptions of Chinese Culture
Authors: Zhixian Chai Lian Zhang, Lijuan Ji,
Published: April 2024

A multiple regression fitting analysis was conducted to investigate the level of cultural identification of 222 international students studying in China with respect to Chinese culture. The dependent variables are the Chinese Culture Integration Will (CCIW) and the Chinese Culture Integration Degree (CCID). A number of independent variables are taken into account, including gender, age, and religion. A cost-benefit approach is employed, with management convergence (MC) on the cost side, and the intention to work in China (IWC) on the benefit side. Based on data mining and regression model fitting, we use the software program of SPSS to compute the KMO of the questionnaire data, make factor analysis, and then utilize the econometrics software package to make stepwise ordinary least squares estimation. The results indicate that active involvement in school cultural clubs, as well as aspirations for employment within China, significantly influence students’ willingness to comprehend Chinese culture. In contrast, personal characteristics such as gender, age, and nationality appear to have a negligible impact. In order to significantly strengthen students’ cultural identities, they need to become proficient in Chinese language, interact more frequently with Chinese peers, participate in cultural activities at school, and develop effective assimilation strategies. The desire to work in China does not significantly contribute to the integration of cultures. It should be noted that the quality of instruction in courses related to Chinese culture as well as participation in extracurricular activities off-campus do not significantly enhance the level of cultural identification among students. By analyzing big data on international students in China, it is possible to gain insight into international student education and Sino-foreign exchanges.
Big Data Scheduling with Order Acceptance Consideration in Supply Chain
Authors: Jianfeng Ren Lei Wang,
Published: April 2024

The efficient job scheduling schemes can make full use of resources, and then achieve different goals, such as maximizing efficiency, minimizing cost and saving energy. In supply chain, there are a lot of members and enormous data. Therefore, a suitable scheduling scheme has become the most common and effective method to optimize the execution of big data in supply chain. A scheduling problem with production and delivery consideration, which is usually in supply chain has been considered. For the reason of highly time emergency and random coming orders in quick production and delivery system, the effective algorithms for order acceptance scheduling problem are required. This paper addressed the production and delivery problem which has one manufacturer and multiple customers. There is single machine for order production at the manufacturer. For the consideration of order acceptance or not, the manufacturer need to choose order set to be accepted for processing. The paper aims at finding a balance between orders profit, delivery cost and time-based cost to maximize the total revenue. We consider three main objective functions in scheduling theory, analyze the problem complexity. The complexity of two of the problems are weakly NP hard, and the other one is strongly NP hard. For three weakly NP hard problems, we give pseudo-polynomial time optimal algorithms.
Coordinated Control Strategy Based on Virtual Synchronous Generator Technology in New Energy System
Authors: Deng Panpan
Published: April 2024

The innovative system control strategy of chemical energy-based virtual synchronous generator technology combines the principles of energy storage and virtual synchronous generator techniques to achieve stable generation and transmission of electrical energy. With the increasing integration of new energy sources into the grid, the existing grid-connected inverters utilizing direct current control struggle to effectively regulate the load fluctuations of the grid. In this paper, a mathematical model is established that includes both the energy storage system and the integration of new energy sources. By integrating optimal coordination control theory with coordinated control regulations, a coordinated control regime is developed, including the design of a damping controller. Additionally, a control strategy based on communication delay is employed to ensure active involvement of the rectifier side of the direct current transmission line at the appropriate timing. Analysis reveals that as the power of the direct current transmission line increases, the coupling between the virtual synchronous generator and direct current transmission becomes stronger within specific frequency bands, which may lead to subsynchronous oscillations and result in system instability. Compared to traditional control strategies, the proposed chemical energy-based virtual synchronous generator technology presented in this paper effectively resolves the coupling challenges within specific frequency bands, suppresses subsynchronous oscillations, exhibits improved robustness, and allows the system to more flexibly respond to complex load fluctuations and new energy source fluctuations.
A Study on the Driving Mechanisms of Water Supply and Demand Perception in the Digital Age
Authors: Xiaopeng Li, Wenyu Shi, Xin Zhong, Qiaoxia Wang Ni Pan, Renjuan Wei, Zhen Li, Jianming Liu,
Published: April 2024

This study investigates the drivers of water supply and demand perception in the digital age, focusing on the impact of various factors including climate change awareness, socio-economic impact perception, environmental policy awareness, and water resource protection consciousness. Through questionnaire surveys conducted in Chengdu City and data analysis using the Partial Least Squares Structural Equation Modeling (PLS-SEM), this study reveals how the aforementioned factors collectively influence the perception of water supply and demand. The findings indicate that a profound understanding of climate change and socio-economic activities, along with a high awareness of environmental policies, can significantly enhance the public’s consciousness towards water resource protection, thereby affecting the accurate perception of water supply and demand conditions. This study not only offers a new perspective in the field of water resource management but also provides a theoretical basis and data support for formulating effective water resource protection strategies, emphasizing the critical role of intelligent water resource management in addressing climate change and promoting sustainable development. The limitations of the study include the restricted sample size and scope, which may affect the generalizability of the results. Future research is recommended to consider more variables, such as regional cultural and economic differences, and to conduct validations across a broader area and population.
Quality Evaluation Methods of College English Teaching Reform Based on Artificial Neural Network
Authors: Yang Ning
Published: April 2024

This paper aims to enhance the effectiveness of intelligent evaluation in college English teaching and explore the role of artificial neural networks in evaluating college English teaching reform. A research study is conducted to develop an intelligent evaluation model by refining algorithm models and utilizing comparative research methods to address limitations of traditional algorithms. The neural network algorithms are improved through model construction techniques, with practical effectiveness assessed through simulation training methods. The algorithm model is applied to assess teaching quality in universities, with experimental research integrating questionnaire surveys and simulation data processing methods. The feasibility of the approach is verified through data evaluation. Simulation analysis demonstrates that the enhanced neural network algorithm reduces running time and enhances accuracy compared to traditional evaluation algorithms. Experimental studies indicate that employing artificial neural networks for evaluating the quality of college English teaching reform yields positive outcomes, aiding students and teachers in understanding their situations within mobile computing environments. This study confirms that combining a neural network model with data survey methods effectively meets the requirements of contemporary efficient teaching quality evaluation. The innovative integration of intelligent algorithms, factor analysis, surveys, and statistical methods enhances the progressive nature of teaching quality evaluation.
Enhancing Green Port Construction through Advanced Computing: An Evolutionary Game Model under Social Accountability and Government Supervision
Authors: Qinchang Li Bo Lin, Yuhao Cheng,
Published: April 2024

Ports, as pivotal hubs for global resource consumption and pollution emissions, have garnered considerable attention in the pursuit of sustainability. Despite the Chinese government’s efforts in promoting the green construction of ports and a growing public awareness of environmental protection, progress remains sluggish, with pollution and energy consumption issues still rampant. This paper introduces an evolutionary game model that leverages advanced computing techniques, including big data analytics and simulation, to optimize green port construction strategies, supervision, and enforcement under the lens of social accountability. By applying the Jacobi matrix for analyzing equilibrium paths at diverse initial settings and conducting simulations under various scenarios of punishment intensity and social accountability probabilities, this study unveils that high social accountability likelihoods naturally incentivize ports towards green practices. Conversely, in low accountability scenarios, differentiated outcomes emerge from imposing penalties on ports and local governments. Notably, increasing penalties on ports substantially elevates green construction standards, whereas local government penalties exhibit only transient efficacy. Furthermore, leveraging artificial intelligence for predictive analysis and simulation demonstrates the critical role of technological advancements in enhancing green port construction. The study proposes augmenting penalties on ports, amplifying social oversight, and diminishing green construction costs via port consortia as strategic measures to expedite China’s transition to green ports.