bluuebunny/crossref_metadata_2025 huggingface dataset:
Information Security and Privacy Protection Strategies in the Process of Electronic Archiving
Authors: Dan Zhao
Published: May 2024

In the era of digital transformation, electronic archiving plays a pivotal role in preserving and managing vast amounts of information. However, as organizations increasingly rely on electronic archives, ensuring robust information security and privacy protection becomes paramount. This paper proposes a comprehensive framework for enhancing information security and privacy protection strategies within the electronic archiving process. Drawing upon insights from the fields of cyber security, data privacy, and archival science, our framework addresses key challenges and offers practical solutions to safeguard sensitive information throughout its lifecycle. Through a synthesis of existing literature, case studies, and expert interviews, we identify critical vulnerabilities and propose proactive measures to mitigate risks associated with unauthorized access, data breaches, and privacy violations. Our framework integrates technical controls, policy guidelines, and organizational practices to establish a resilient architecture that fosters trust, integrity, and confidentiality in electronic archiving systems. By adopting our proposed strategies, organizations can strengthen their defiance mechanisms and uphold the principles of information security and privacy in the digital age.
A Comparative Study on the Results of College English Grade 4 Based on Multi-model Prediction
Authors: Yanwen Chen
Published: May 2024

Predictive analytics improves educational outcomes by offering insights into student performance and influencing targeted interventions. In this work, we undertake a detailed comparative analysis of predictive models for projecting College English Grade 4 exam results, to contribute to the improvement of educational predictive analytics. We compare the predicted accuracy and interpretability of several modeling approaches, including ensemble learning methods like Random Forests, interpretable models like Decision Trees, and Support Vector Machines (SVM). We evaluate each model's performance using important metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2) coefficients. Our results show that Random Forests have greater prediction accuracy, beating other models in terms of MSE, RMSE, and MAE. Despite its slightly lower R2 coefficients, Decision Trees perform competitively and provide useful insights into crucial variables. In contrast, SVM shows limitations in prediction accuracy for College English Grade 4 exam results. We highlight the relevance of these findings for educators and administrators, emphasizing the need to make educated decisions when selecting predictive models and implementing targeted interventions to promote student achievement. This work advances the field of educational predictive analytics by giving empirical evidence for the efficacy of various modeling methodologies and emphasizing the importance of model interpretability in understanding student performance determinants. More study is needed to improve predictive analytics methods and enhance their applicability in educational settings.
The Application of K-means Clustering Algorithm in the Evaluation of E-Commerce Websites
Authors: Lulu Yu
Published: May 2024

When dealing with large amounts of high-dimensional transaction data, clustering approaches often struggle with challenges including elasticity, weak processing capabilities of high-dimensional data, sensitivity to data sequence across time, independence of parameters, and ability to manage noise. These problems may prevent the methods from providing accurate predictions. Experiments conducted with data samples collected from 300 different mobile phones purchased on Taobao yielded the following results. K-means beats Single-pass in evaluating e-commerce transactions because of its higher intra-class dissimilarity and inter-class similarity. K-means clustering is an approach to the organization of massive datasets that is both effective and flexible. The outcome of the clustering algorithm is sensitive not only to the total number of clusters but also to how they were initially arranged. Because of this, it is simple to demonstrate that the results of clustering are best optimized locally. For this reason, continuing research into the elements that influence the number of clusters produced by this method as well as the starting locations of the clustering center is a crucial endeavor.
Factors Affecting Virtual Reality Interior Design Products Acceptance Behavior of Consumer from Shenzhen City, China
Authors: Baiying Qian
Published: May 2024

This study aims to determine the key attributes that impact client acceptance of the interior design industry (VRIDP). The researchers employed the purposive sampling strategy to select a total of 614 individuals who possess previous experience with VRIDP. Furthermore, the researchers utilized structural equation modeling (SEM) to examine the connections between crucial factors that impact client acceptance of VRIDP. The findings suggest that customer perceptions of VRIDP are significantly influenced by their evaluation of its usability and value. Trust is crucial in mitigating the adverse effects of perceived risk on acceptance.Moreover, it has been found that subjective criteria indirectly influence the desire to accept something by affecting perceived usefulness and trust. This emphasizes the significance of social factors in the process of adopting technology. The results of this study offer valuable insights for developers, marketers, and lawmakers who aim to enhance the acceptance of VRIDP.
An Artificial Intelligence-Based Intrusion Detection System using Optimization and Deep Learning
Authors: Satish Kumar Garapati
Published: May 2024

The Intrusion Detection System (IDS) was developed to resolve the malicious activities on the networking. However, the existing IDS models face issues in achieving high accuracy, and it is prone to detection error. Hence, to address these challenges, an innovative Vulture-based Deep Belief System (VbDBNS) for enhancing the performance of detecting intrusion activity by monitoring their behaviour and features. This study utilized the NSL-KDD database for training and validating the system. Henceforth, pre-processing is employed to standardize the dataset using Min-Max normalization, and 1-N encoding, which transforms the categorical data variables. Moreover, feature extraction was utilized to capture the most significant 42 attributes from the pre-processed database. Further, a classification module was designed using VbDBN to categorize normal and malicious data in which the fitness of vulture was updated, which enables it to monitor and detect the intrusion continuously. The experimental results of the study illustrate that the designed methodology achieved better outcomes than the conventional techniques in terms of recall, accuracy, precision, F1-score, etc.
The Detection and Classification from the Multimodal Images using Artificial Intelligence for Lung Diseases
Authors: Hussein Abdulqader Hussein
Published: May 2024

Lung cancer is a disease in which healthy cells in the body gradually convert into tumour cells, resulting in a variety of medical issues. A standard dataset exists for lung cancer. With the rising incidence of lung cancer and the exponential growth of CT pictures, having a quick and effective way to evaluate CT scans can help physicians or surgeons develop an early treatment plan. In this research, two approaches are investigated for lung cancer prediction. One approach is based on training machine learning model on the features extracted from image processing techniques. And the other approach involves Artificial Intelligence models for lung cancer detection. Computed Tomography (CT) pictures are useful for determining the stage of lung cancer in a patient. As a result, CT images of the lung region are explored in this study by constructing a content-based image retrieval system using various machine learning and Artificial Intelligence techniques. For medical photos, texture analysis is critical. As a result, algorithms such as the K-Means clustering method and morphological operations such as erosion, dilation, and so on are used
Kurdish Sign Language Recognition Using Pre-Trained Deep Learning Models
Authors: Ali A. Alsaud
Published: May 2024

In the tapestry of rich human communication, sign language gleams like one of the basic threads of this art, giving voice to hundreds of deaf and hard-of-hearing individuals in the region. The technology for recognizing and translating sign language fell far behind what these communities needed. Therefore, the present investigation is going to compare the performance of three top-performing deep learning algorithms in recognizing the signs created from the database of Kurdish Sign Language. The models are going to be put to a rigorous test, with a variety of signs drawn. All the 3 models give a performance that is—from all indications—good or even excellent, this is MobileNetV2, a very good candidate that manages to walk an amazing line between the requirements of high accuracy, low space complexity, and acceptable time complexity. We conclude by looking at some exciting opportunities for future research, including integrating our models into hardware devices and expanding our study to a larger variety of sign languages. And just as any good journey would, it throws up as many questions as it answers, leaving us inspired by the many possibilities that will need to be explored to enhance communication for all.
Leadership Skills in the Al-Driven Enterprise: Identifying New Competencies for Success and Humanities Conference
Authors: Raja Mohan
Published: May 2024

Leadership in AI-driven organizations demands a multifaceted skill set to navigate the complexities of rapidly evolving technology, diverse teams, and ethical considerations. A fundamental quality for leaders in these organizations is technical acumen. A foundational understanding of AI technologies, machine learning, and data science enables leaders to make informed decisions, effectively communicate with technical teams, and envision the strategic integration of AI within the organization.This study employs a quantitative research approach through a cross-sectional survey design to capture the perspectives of 100 employees, leaders, and HR representatives on leadership skills in  AI-driven companies This study investigates diverse perspectives on leadership in technology-driven industries, examining opinions on traditional and contemporary leadership attributes. Findings highlight tensions between adaptability and resistance to change, varied views on the integration of technical knowledge, and diverse opinions on communication and emotional intelligence in leadership roles. The study underscores the need for adaptable leadership models that balance technical proficiency with traditional skills in response to the evolving nature of leadership in technology-driven environments.
Design and Application of Decision Support System for Educational Management Based on Big Data
Authors: Bai Haining
Published: May 2024

Decision support system (DSS) for educational management based on big data leverage advanced analytics and machine learning techniques to enhance decision-making processes within educational institutions. By collecting and analyzing large volumes of data from various sources such as student performance, administrative records, and institutional resources, the DSS provides valuable insights and predictive models to administrators and educators. These insights can inform strategic planning, resource allocation, student interventions, and curriculum development, ultimately improving student outcomes and organizational efficiency. With the ability to process vast amounts of data in real-time, the DSS enables timely and data-driven decision-making, empowering educational leaders to address challenges and capitalize on opportunities in today's complex educational landscape.The integration of big data analytics with the Recommender Ranking Decision Support System (RRDS) presents a transformative approach to enhancing educational management. This paper explores the application of big data analytics within the educational setting, focusing on the development and implementation of the RRDS. By leveraging large and diverse datasets, the RRDS enables personalized learning experiences, optimizes resource allocation, and enhances student outcomes. Through predictive modeling, the RRDS predicts student performance and identifies at-risk students, facilitating targeted interventions to support student success and retention. Additionally, the system offers personalized learning recommendations tailored to individual student needs, fostering engagement and motivation. The findings underscore the significance of data-driven decision-making in improving educational practices and institutional performance. As educational institutions increasingly embrace technology-driven solutions, the integration of big data analytics and RRDS stands poised to revolutionize educational management, ultimately leading to more effective teaching and learning methodologies and better outcomes for students.
Development Of English Education Using Big Data And Learning Analytics
Authors: Wu Haijun
Published: May 2024

With English education using big data and learning analytics represents a transformative approach to enhancing language learning outcomes and pedagogical practices. By harnessing the power of big data analytics, educational institutions can collect, process, and analyze vast amounts of learner data, including student interactions with digital learning materials, performance on assessments, and engagement metrics. Through sophisticated learning analytics techniques, educators gain valuable insights into students' learning behaviors, preferences, strengths, and areas for improvement. The paper explores the application of big data analytics and learning analytics in English language education within Chinese universities. Utilizing a diverse dataset, including proficiency levels, engagement metrics, and demographic characteristics, the study employs Median Clustering Fuzzy Learning Analytics (MCFLA) to predict students' engagement levels based on language proficiency scores. Findings reveal varying proficiency levels among students, alongside mixed patterns of engagement. The integration of MCFLA proves effective in identifying trends and guiding educational interventions. Median scores provide benchmarks for assessing performance, while demographic factors offer additional insights. Findings reveal varying proficiency levels among students: 40% beginner, 30% intermediate, and 30% advanced. Mixed patterns of engagement are observed, with 50% high engagement, 30% medium, and 20% low. Integration of MCFLA effectively predicts engagement trends, with 75% accuracy. Median scores provide benchmarks for assessing performance: beginner (Vocabulary: 63, Grammar: 66, Reading: 68, Oral Communication: 55), intermediate (Vocabulary: 78, Grammar: 79, Reading: 80, Oral Communication: 72), and advanced (Vocabulary: 90, Grammar: 88, Reading: 95, Oral Communication: 80). Demographic factors such as gender (50% male, 50% female) and socio-economic status (40% middle class, 30% upper class, 30% lower class) offer additional insights.
Design and Evaluation of English Vocabulary Learning Aids Based on Word Vector Modelling
Authors: Long Yun
Published: May 2024

English vocabulary learning aids based on word vector modeling involve creating tools that leverage advanced techniques to enhance vocabulary acquisition. Word vector modeling, often using methods like Word2Vec or GloVe, represents words as high-dimensional vectors capturing semantic relationships. These models can power vocabulary learning aids by offering context-based word suggestions, personalized word quizzes, or interactive visualizations of word associations.. The paper introduces the Hierarchical Spiking Vocabulary Deep Learning (HSV-DL) framework, a novel approach aimed at enhancing vocabulary learning and classification tasks. With word vector modeling and spiking neural networks, HSV-DL offers a sophisticated methodology for accurately categorizing vocabulary words into their respective semantic categories. The Hierarchical Spiking Vocabulary Deep Learning (HSV-DL) framework introduces novel methodologies for vocabulary learning and classification tasks, achieving outstanding performance metrics. Experimental results demonstrate high accuracy (95%), precision (96%), recall (94%), and F1-score (95%) in categorizing vocabulary words into their semantic categories. Moreover, HSV-DL exhibits robustness to noise and efficient resource utilization, showcasing its potential for real-world applications in natural language processing.
Application of Decision Tree Algorithm in Teaching and Learning Management in Colleges and Universities
Authors: He Xiaohu
Published: May 2024

In modern educational settings, effective decision-making plays a crucial role in ensuring student success and fostering academic excellence. The decision tree algorithm is a valuable tool in teaching and learning management in colleges and universities, offering insights that inform strategic decision-making processes. By analyzing student data, including academic performance, engagement metrics, and demographic information, decision tree algorithms can identify patterns and trends that help optimize educational practices. These algorithms aid in course planning, student advising, and resource allocation by predicting student outcomes, identifying at-risk students, and recommending personalized interventions. This paper explores the application and impact of the Decision Tree Wrapper Sampling Class Imbalance Knowledge Assimilation (DT-WSCIKA) method in enhancing educational decision-making processes. Through a comprehensive analysis of teaching and learning aspects, classification performance metrics, and student performance outcomes across various scenarios, we investigate the efficacy of DT-WSCIKA in addressing class imbalances, optimizing decision-making, and improving educational outcomes. The findings reveal significant improvements in key metrics following the implementation of DT-WSCIKA. For example, classification accuracy increased by an average of 8%, precision improved by 10%, recall by 6%, F1 Score by 7%, and ROC AUC by 9% over multiple epochs. Additionally, student performance outcomes showed an average increase of 7.5% in Scenario 1, 6.2% in Scenario 3, and 8.3% in Scenario 7 when DT-WSCIKA was employed.
Course Evaluation and Improvement Based on Association Rule Mining in English Online Teaching and Learning
Authors: Liying Li
Published: May 2024

Association rule mining in English online teaching and learning involves the analysis of patterns and relationships within large sets of data to uncover meaningful insights that can enhance the teaching and learning experience. By examining student interactions, performance data, and usage patterns on online learning platforms, educators can identify correlations between different learning activities, student characteristics, and learning outcomes. This paper explores the application of the Frequent Pattern System Modelling Association Rule (FPSMAR) in the realm of online English teaching and learning. FPSMAR offers a data-driven approach to analyzing student interaction data, uncovering meaningful patterns and associations between different elements of the online learning environment and student outcomes. Through the analysis of association rules and frequent patterns, educators gain valuable insights into effective instructional strategies, learning patterns, and areas for improvement. The study presents association rules indicating relationships such as the correlation between multimedia resource usage and high language proficiency or active online discussion participation and enhanced speaking ability. The study reveals association rules indicating strong relationships, such as a correlation coefficient of 0.80 between multimedia resource usage and high language proficiency, or a confidence level of 0.85 indicating enhanced speaking ability through active online discussion participation.
Factor and Correlation Analysis for Predicting Marathon Race Performance Using Machine Learning Algorithms
Authors: Chen Ji
Published: May 2024

A marathon race is a long-distance running event typically spanning 26.2 miles or 42.195 kilometers. It is a test of endurance, stamina, and mental fortitude, attracting participants from all walks of life, ranging from elite athletes to recreational runners. The origins of the marathon can be traced back to ancient Greece, where legend has it that a messenger named Pheidippides ran from the battlefield of Marathon to Athens to deliver news of victory before collapsing and dying from exhaustion. Machine learning has increasingly become a valuable tool in optimizing training strategies, performance prediction, and injury prevention for marathon runners. By analyzing vast amounts of data collected from wearable devices, training logs, and race results, machine learning algorithms can identify patterns, trends, and correlations that help runners improve their training regimens and race-day strategies. This paper introduces a novel approach, the Factor Analysis Probabilities Prediction Ranking Machine Learning (FA-PP-R-ML) methodology, for predicting marathon race outcomes. With historical race data, factor analysis, and machine learning techniques, the FA-PP-R-ML methodology aims to accurately estimate marathon finish times and rank predicted outcomes based on their probabilities. Through a comprehensive analysis of marathon race data, including training metrics, environmental conditions, and physiological parameters, the FA-PP-R-ML model identifies latent factors influencing race performance. Through factor analysis, latent factors influencing race performance are identified, with values ranging from 0.5 to 0.9. Machine learning algorithms utilize these factors to predict marathon finish times, resulting in accurate predictions with an average error of ±0.1 hours.
cFL：Data distribution edge clustering algorithm based on deep Federated learning
Authors: Xide Wang
Published: May 2024

Federated learning (FL) allows end-devices to train local models on their respective local datasets and collaborate with the server to train a global predictive model, thus achieving the goal of machine learning while protecting the privacy and sensitive data of end-devices. However, simultaneous access to the server by a large number of end devices may result in increased transmission latency and some local models may have malicious behavior, converging in the opposite direction to the global model. As a result, additional communication costs can occur during the federation learning process. Existing research has mainly focused on reducing the number of communication rounds or cleaning dirty local data. In order to decrease the overall amount of local updates, we provide an edge-based model cleaning and device clustering strategy in this study. By computing the cosine similarity between local update parameters and global model parameters over a multi-dimensional space, the approach assesses if local updates are necessary while preventing pointless communications. In addition, end devices are grouped together based on where they are connected to the network and connect to the cloud via mobile edge nodes in clusters, therefore lowering the latency associated with high concurrent server access. Convolutional neural networks and Soft max regression are used, for instance, to perform MNIST handwritten digit recognition, and the effectiveness of the suggested approach to enhancing communication efficiency is confirmed. According to experimental findings, the edge-based model cleaning and device clustering technique decreases the quantity of local updates by 60% and speeds up the model's convergence by 10.3% when compared to the conventional FL approach.
Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
Authors: Muhammad Zharif Mat Aziz
Published: May 2024

Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimization problems. This may also occur in power system optimization problem. Load curtailment is one of the important issues in power systems since its approach can help control the power system loss. In general, it is termed as loss minimization so that the delivery of electricity to the consumers can be smoothened. This paper proposes a new optimization technique, Embedded Real Swarm Evolutionary Programming (ERSEP) for identify contingencies occurrence in power system. ERSEP is the integration of real mutation swarm operator with the traditional evolutionary programming (EP) which aims to produce better results in terms of achieving lower optimal solution. Comparative studies were conducted to observe the advantages of ERSEP over the traditional. Results exhibited that the proposed ERSEP outperformed the traditional EP in achieving lower optimal solution validated on IEEE 30-Bus Reliability Test System (RTS). Significant results deduced from this study revealed that total transmission loss reduction worth 52.83% was achieved by EP, 54.09% solved by PSO and 74.09% by ERSEP in Case 1 for chosen load condition. In Case 2, ERSEP maintains to achieve the highest loss reduction worth 54.97%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. ERSEP maintains to achieve highest loss reduction worth 61.63%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. This implies that ERSEP is superior in all cases to reach the lowest minimized transmission loss.
Intelligent Composite Compensation Under Load Variation in Power Transmission System.
Authors: Saiful Amri Ismail
Published: May 2024

The power transmission system is in a stressed condition due to the increasing electricity demand. This stress, exacerbated by the deregulated power system environment, necessitates an urgent additional supply to maintain system adequacy. One important initiative to alleviate the transmission burden which meets the load demand can be rectified by composite compensation scheme. This paper showcases intelligent composite compensation under varying loads, emphasizing loss minimization. The approach integrates a loss control scheme involving Distributed Generation (DG) and Optimal Reactive Power Dispatch (ORPD) with multi-DG installation termed composite compensation, employing a novel optimization technique called Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP). The study identifies optimal locations and sizes for composite compensation in the power transmission system, demonstrating its superiority over traditional optimization techniques namely the evolutionary programming (EP) and artificial immune systems (AIS). Results are demonstrated for four cases involving single DG, 3 DGs and composite compensation validated on IEEE 30-Bus Reliability Test System (RTS). ICAMEP is superior over EP and AIS in achieving the highest loss reduction.
Development and Impact Analysis of a Multi-Pandemic Real-Time Data Dashboard: A Comparative Analytical Approach
Authors: Swathi Buragadda
Published: May 2024

This research presents the development and implementation of a Multi Pandemic Real-Time Data Dashboard, designed to facilitate enhanced comparative analysis across various global pandemics. Utilizing a combination of real-time data acquisition, statistical modeling, and predictive analytics, the dashboard provides a dynamic platform for analyzing pandemic trends and outcomes. Our methodology involved integrating diverse data sources, employing mathematical models such as the SIR (Susceptible, Infected, Recovered) model and machine learning algorithms for data processing and trend prediction. Key findings indicate that the dashboard effectively identifies patterns in pandemic spread and impact, offering valuable insights for public health decision-making. The research underscores the significance of advanced data analytics in managing public health crises, highlighting the dashboard’s potential in aiding proactive pandemic response and policy formulation.
Advanced DDos Attack Detection in SD-IoT Using DNFN and Nature-Inspired Optimizations
Authors: Kumbhar Kalpana
Published: May 2024

The purpose of this research is to improve the detection of Distributed Denial of Service (DDoS) attacks in systems that employ Software-Defined Internet of Things (SD-IoT). First, feature selection techniques such as PCA is used to improve the Deep Neuro Fuzzy Network (DNFN) model's detection accuracy of DDoS assaults. With an overall accuracy of 0.969, the findings show that the DNFN model has above average accuracy rates when applied to feature selection technique. To further improve the DDoS detection capabilities, optimization approaches like Elephant Herding Optimization (EHO) and the hybrid Elephant-Herding-Water-Cycle-Algorithm (EHWCA) are then developed. The EHWCA approach is superior than the current EHO method, as shown by a comparative study that compares the two. The DNFN model achieves an accuracy of 0.99 when optimized using EHWCA, whereas it only achieves 0.97 with EHO. The suggested system's scalability and efficiency are greatly enhanced by the inclusion of the water cycle in the optimization process. Overall, this research contributes to the development of strong cybersecurity solutions for IoT networks by demonstrating the efficacy of sophisticated optimization approaches, in particular EHWCA, in improving the detection of DDoS assaults in SD-IoT settings.
Exploratory Data Analysis using Machine learning – Behaviour Based Safety
Authors: Jerin Johnkutty
Published: May 2024

This study takes a gander at the usage of machine learning techniques for exploratory data examination (EDA) in the field of behavior-based safety (BBS), with a particular focus on the examination of safety dimension datasets got from worker reviews drove in industrial settings. We utilize a methodology that integrates an extent of visualization methods, statistical examinations, and parameter evaluations to uncover complex encounters into safety perceptions and ways of behaving. We do this by utilizing the Python programming language and its strong data investigation libraries, including MATplotlib, Seaborn, and Pandas. Our survey means to assist proof based decision-making strategies, proactively distinguish potential for developing safety protocols, and develop a organizational culture that is safety-centric driven by eagerly examining worker feedback and behavioral patterns. This research features the significant role of EDA and machine learning in deciphering complex datasets, advancing substantial improvements in occupational safety, and putting a high priority on workers' well-being in dynamic workplaces through synergistic cooperation between data analytics and domain expertise.
Teaching Quality Analysis of Health Management Professionals in Colleges and Universities Based on Big Data Decision Tree Algorithm
Authors: Liu Jian
Published: May 2024

In this study, we investigate whether the big data decision tree approach can be utilised to evaluate the effectiveness of teaching in higher education by health management professors. In light of the expanding need for high-quality healthcare education by the professionals, it is absolutely necessary to evaluate and enhance the teaching efficacy of healthcare management specialists. The application of big data analytics towards the creation of a method for the evaluation of instructors that is both objective and precise is the focus of this study. Every educational system may be broken down into its two primary components: teachers and pupils. The students are not only passive recipients of the instruction that their teachers provide. All of the desired instructional achievements must be driven by the effort of the students in order to fall within the category of "subjective initiative." Therefore, the first step in any educational activity should be encouraging pupils to build their own sense of initiative and intrinsic motivation to learn. This should be the case in any educational endeavour. If teachers have access to and make use of reliable employment data for statistical analysis, they will be able to have a more significant impact on the kids they teach. Based on a preliminary set of actual university employment data from 2012, this study compares the results of employing the C4.5 approach with a decision tree generating technique for the assessment of the teaching quality by the professionals. Both approaches were used to analyse the data. The findings demonstrate that a straightforward decision tree structure may be obtained using the decision tree approach that makes use of the multiscale rough set model. In addition, our approaches don't need to use overlapping data sets, and they're quite efficient from a computing standpoint. The relevance of this study lies in the fact that it sheds light on how the expertise and motives of teachers who are healthcare professionals to be precise, reflect on the learning of their students and their relevant field skills.
GHE-Ensemble: Enhanced Hybrid Image Enhancement Model for Night Vision Multi-Object detection in Autonomous Vehicle
Authors: Ranjitha P
Published: May 2024

Autonomous vehicle is the exponential growing topic in the research filed due its huge demand in the current industry. Where object detection plays the major role to take any major decision. Object detection in the normal vision yields the excellent performance due to deep learning models. But object detection in the night vision does not yield the good performance due to various challenges in it. In the proposed work created a night vision database called BDD-Darko where it contains night vision images with multi objects. Applied novel GHE-Ensemble model for image enhancement technique and trained using deep learning model Yolov5 for the multi-object detection in the night vision. Which resulted in 65.3 % accuracy. Proposed model is yielding better results for image enhancement technique for night vision than the previous existing model and detecting the multi object in the single frame with better performance. This paper proposes
(1)A new database called as BDD-Darko which contains night vision image with multiple objects. (2) A Novel GHE-Ensemble model for image enhancement technique for night vision images
Natural Language Processing of Grammar Checker Tools for Academic Writing: A Systematic Literature Review
Authors: Paulo Miguel A. Cano
Published: May 2024

Grammar correction is crucial for essential documents such as research, business articles, formal papers, but with language evolving every century, some rules for grammar correction are added or changed. There have been created grammar correction online, which has helped most students create academic writing. Some tools focus on correcting the paper by following specific rules; some run an algorithm based on what type of document you are writing. In this paper, we reviewed documents published from 2017 up to 2021 related to grammar correction and Grammar error detection, with numerous Natural Language Processing and models provided by past researchers, which may aid this paper towards a solution on creating a grammar correction tool.
Estimation and Evaluation of Ionospheric Scintillations at Various NavIC Signal Frequencies using Real Time Data
Authors: G. Chandra Shekar
Published: May 2024

The ionospheric scintillations affect radio waves from the NavIC satellites through the ionosphere. The ionospheric scintillations lower receiver tracking accuracy, integrity, and continuity. This paper presents the computation of ionosphere parameters such as TEC, ROTI and scintillation index (S) using pseudo range and Carrier to Noise density ratio (C/No) measurements of NavIC L5 and S-band signals. Also, investigates the ionospheric scintillations, which are common in the equatorial and low latitude region. The correlation between the amplitude scintillation index S4 and ROTI is investigated using real data of NavIC receiver at Osmania University, Hyderabad. The results obtained confirm that the scintillations impact varies with radio signal frequency and it is severe on low frequencies during post sunset hours at low latitude regions.
Wearable T – Shaped Metamaterial Microstrip Patch Antenna with Split Ring Resonators for Wi-Fi Applications
Authors: Mehaboob Mujawar
Published: May 2024

This paper presents the design of a T-shaped metamaterial microstrip patch antenna integrated with split ring resonators, tailored for IEEE 802.11a Wi-Fi applications. The antenna utilizes a non-woven polypropylene geotextile substrate, measuring 1.9 mm thick with a dielectric constant of 2.2, while the copper sheet thickness is 0.1 mm. Performance metrics such as reflection coefficient, antenna gain, and VSWR are evaluated. Simulated results show a reflection coefficient of -21 dB at 5 GHz, which closely matches the measured value of -22 dB. VSWR values of 1.5 and 1.3 are obtained through simulation and measurement respectively. Additionally, SAR analysis indicates suitability for wearable applications, with the SAR value exceeding 10 grams of tissue falling within acceptable limits.
Lightweight active back exosuit reduces muscular effort during an hour-long order picking task
Authors: Quirk David Adam
Published: May 2024

Occupational back exoskeletons and exosuits aim to reduce low back injuries in the workplace. For these technologies to be adopted, it is important that they provide biomechanical beneﬁts to the wearer and do not disrupt job performance. To address this challenge, here we developed a lightweight, soft, active back exosuit that can autonomously control virtual impedance to apply differing assistance during lowering and lifting. This video highlight reviews some factors that were considered in the development of our soft active back exosuit actuation strategy. Despite noted limitations in our approach, we highlight how these decisions contributed to our results presented in Nature Communications Engineering that demonstrate: In usability tests, participants rated the exosuit as easy to learn and use and reported feeling conﬁdent while wearing it. In an experiment involving an hour long order picking task we demonstrated that the exosuit reduced peak and median muscle activations in the back by 18% and 20%, respectively. Despite the complexity of the movements required, such as walking, bending, and navigating around obstacles while lifting boxes from under a rack, our controller demonstrated impressive robustness with only 14 mistriggers out of 9600 lifts (0.1%). Collectively we hope this video review highlights how active exosuit technology can be iterated to provide it the potential to be a highly usable solution to aid warehouse workers in real-world settings.
Facilitators and barriers to supply-side maternal, newborn, and child health service availability in DRC: a systematic review and narrative synthesis
Authors: Duff Rani , Patel Ffyona , Dumouza Alexandre , Brown Lisanne , Embeke Narcisse , Fataki Joseph , Haile Mignote , Conners Abigail , Pickett Caleigh
Published: May 2024

Background

This study is a systematic review of barriers and facilitators of supply-side maternal, newborn, and child health (MNCH) service availability in the Democratic Republic of the Congo (DRC). Given gaps in understanding of the factors affecting supply-side service availability, authors reviewed the literature to describe the DRC’s landscape and inform future research and programming.

Methods

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, authors searched two databases and grey literature, using the Quality Assessment with Diverse Studies tool to assess quality. Authors identified key themes and used a narrative approach to analyze extracted data.

Results

The search identified 2160 articles, with 26 meeting the inclusion criteria. Authors grouped them according to the following key themes: facility readiness, human resources for health (HRH), geography and transportation, conflict, financing, and governance. MNCH service availability was hindered by lack of supplies, logistical challenges, weak infrastructure, unmet staffing needs, HRH turnover, poor HRH distribution, career and payment obstacles, long distances to facilities and poor transportation, system strain and disruption due to conflict, low government expenditure, high out of pocket costs, fragmented stakeholders, and low government prioritization. Authors found facilitating factors in each key theme, including parallel supply chains and buffer stocks, proactive staffing policies, motivated HRH, novel transportation methods, health posts, concentrations of stakeholders in conflict zones, universal health coverage policies, provider payment reforms, stakeholder coordination, and horizontal integration efforts.

Conclusions

The DRC faces substantial supply-side barriers to MNCH service availability. Progress towards the Sustainable Development Goals and better MNCH outcomes requires additional work to build on existing facilitators and investments in service availability. Additional research on demand-side factors and quality of care could inform these efforts.
„Egészségpart” mobil nyári szűrő- és egészség-edukációs programsorozat: a 2021–2023. évek között megvalósult programok értékelése
Authors: Dr. Pénzes Melinda , Dr. Mikesy Gergely , Kenesei-Kalló Andrea , Jóni András Dániel , Lengyel Lívia , Bertókné Tamás Renáta , Árváné Egri Csilla , Gál Veronika , Joó Tamás
Published: May 2024

Számos országban az ún. mobil egészségklinikák egyrészt az egészségműveltséget javító, figyelemfelhívó és egészségedukációs mozgó platformként működnek, másrészt a daganatok- és más nem fertőző betegségek szűrésében is közreműködnek. A 2021-ben indított „Egészségpart” programsorozatunk célja a Balatonnál nyaraló, elsősorban munkaképes korú, magyar felnőtt lakosság számára ingyenes szűrések, életmód-tanácsadás és egészség-edukáció biztosítása a betegségek korai felismerése és az egészségműveltség javítása érdekében.
Tanulmányunk célja a 2021–2023. évek nyári hónapjaiban, Balaton-parti településeken megvalósult, „Helybe visszük a szűrővizsgálatokat” szűrőprogramhoz kapcsolódó „Egészségpart” programsorozat értékelése bemeneti- (partnerség, humánerőforrás), folyamat- (aktivitások – szűrés és egészség-edukáció) és kimeneti (résztvevők száma és szociodemográfiai jellemzői) indikátorok alapján, leíró statisztikai elemzéssel.
A három év során 13 „Egészségpart” nap valósult meg, átlagosan napi 18–31 humánerőforrás jelenléte és bővülő szakmai partnerségek mellett. A programaktivitások közül nem mindegyik volt jelen mindhárom évben, de összességében a szűrő- és egészség-edukációs aktivitások száma évről évre bővült (maximum 15, illetve 8 aktivitás). A szociodemográfiai adatokkal regisztrált résztvevők száma n=727–906 között alakult, átlagéletkoruk kissé 50 év feletti, a nők részvételi aránya 60% körüli volt, legnagyobb arányban minden évben a 45–64 éves korcsoport vett részt. A kiindulási évhez képest jelentősen emelkedett a >65 évesek érdeklődése a program iránt. A Balaton-környéki lakosok részvételi aránya az első évhez képest (27%) jelentősen megemelkedett a harmadik évre (53%).
Az „Egészségpart” program komplex értékelése elengedhetetlen annak érdekében, hogy a későbbiekben fejlesztendő területek a programtervezésben és -megvalósításban azonosíthatókká váljanak. A résztvevők esetenként változó összetételére és igényeire dinamikus reagálás szükséges, valamint elengedhetetlen a szakmai partnerségek akár évről-évre történő felülvizsgálata és humánerőforrás allokálásának átgondolása.
Congenital absence of the lateral meniscus: A case report
Authors: Alkhunayfir Hamad Ali , AlQahtani Abdulaziz Ali , Korkoman Abdulrahman Jalwi
Published: May 2024

BACKGROUND

Congenital absence of the menisci is a rare anatomical variation characterized by the absence or underdevelopment of one or both menisci in the knee joint. The menisci are crucial in load distribution, joint stability, and shock absorption. Understanding the clinical presentation, diagnosis, and management of this condition is important for optimal patient care.

CASE SUMMARY

A 27-year-old male with a long-standing history of knee pain underwent diagnostic arthroscopy, revealing a congenital absence of the meniscus. The patient's clinical findings, imaging results, surgical procedures, and pertinent images are detailed. This case presents a unique aspect with the congenital absence of the meniscus, contributing valuable insights to the literature on rare anatomical anomalies.

CONCLUSION

This case of congenital absence of the menisci highlights the diagnostic challenges posed by rare anomalies. The diagnostic arthroscopy played a crucial role in identifying the absence of the meniscus and providing an explanation for the patient's persistent knee pain. The case underscores the importance of individualized treatment approaches, including physical therapy, for optimal management of rare meniscal anomalies. Further research is warranted to explore effective management strategies for the aforementioned cases and to expand our knowledge of these rare conditions.
Is there sufficient evidence to support the use of antibiotic holiday just before the second stage of an infected total hip or knee arthroplasty revision surgery?
Authors: Tsikopoulos Konstantinos , Sidiropoulos Konstantinos
Published: May 2024

The practice of implementing an antibiotic holiday before the second stage of hip or knee arthroplasty is currently controversial due to limited evidence for this approach, as per the International Consensus Meeting 2018 on Musculoskeletal Infection. A greater understanding of this issue could augment the quality of Alrayes and Sukeik’s mini-review (2023) on diagnosing, managing, and treating periprosthetic knee infections. However, a significant lack of literature exists concerning the optimal duration for the antibiotic holiday, calling for more research before establishing any clinical guidelines.
Debate on direct-anterior vs posterior approach for hip hemiarthroplasty: The authors’ insights
Authors: Kumar Deepak , Thami Tarkik , Nishani Manjunath
Published: May 2024

We read and discussed the study entitled “Complication rates after direct anterior vs posterior approach for Hip Hemiarthroplasty in elderly individuals with femoral neck fractures” with great interest. The authors have done justice to the topic of comparison of anterior and posterior surgical approaches for bipolar hemiarthroplasty which has been an everlasting debate in the existing literature. However, there are certain aspects of this study that need clarification from the authors.
Hyperbaric oxygen therapy – part 2. Possibilities of use in medicine
Authors: Chochowska Małgorzata , Martowski Łukasz
Published: May 2024

Wzrost zainteresowania wykorzystaniem hiperbarycznej terapii tlenowej w medycynie skłonił autorów do przeanalizowania dostępnych przeglądów badań i metaanaliz oraz uszeregowania danych na jej temat. W drugiej części artykułu przedstawiono zastosowanie hiperbarycznej terapii tlenowej w leczeniu oparzeń, przeszczepów skóry i trudno gojących się ran, zespołu stopy cukrzycowej, zatorów gazowych, choroby dekompresyjnej, niedokrwistości, chorób układu moczowo-płciowego, zatruć czadem, przewlekłych zapaleń kości i szpiku, zgorzeli gazowej, martwicy popromiennej oraz przetok odbytu.