bluuebunny/crossref_metadata_2025 huggingface dataset:
In vitro Assessment of Antimicrobial potential of Siddha Polyherbal Formulation Tulasi oil against RTI pathogens
Authors: Yavanarani S. , Selvakumar R.
Published: May 2024

In Siddha medication, along with herbal formulations, some mineral formulations are used clinically to prevent or cure infectious diseases. The therapeutic values of some siddha formulations have been well documented earlier, but a huge number of them remain unexplored in terms of safety and efficacy. The aim of this study was to screen the antimicrobial potential of Siddha polyherbal formulation Tulasi oil (SPHTO) against respiratory tract infection (RTI) causing pathogens. The antimicrobial activity of SPHTO was examined by using the methods of agar well diffusion, Minimum inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC), Minimum Fungicidal Concentration (MFC) and Microbicidal action. Ciprofloxacin (bacteria) and Amphotericin B (fungi) were used as the positive control for this study. The potassium leakage was determined using a flame emission and atomic absorption spectroscopy. All tested concentrations gave notable antimicrobial activity against all tested pathogens. The 100mg/ml concentration showed the highest activity against E.coli followed by 200mg/ml against K. pnuemonia and C.neoformans when compared to 50 and 25mg/ml. For MIC, the highest activity was registered at 150mg/ml concentration against E. coli, while MBC/MFC was 200mg/ml concentration has shown the better activity against K. pneumonia and C. neoformans. Tulasi oil induced leakage of potassium ions from cells of E.coli and K.pneumonia suspended in glycil-glycine buffer solution and the leakage started within 10 minutes of contact of drugs with target cells. From this study, it may support the clinical claims of Tulasi oil to have potent antibacterial and antifungal activities and offer profound therapeutic benefits against respiratory tract infection.
Natural Therapeutics against the SARS-CoV-2 Viral infection
Authors: Rai Urvashi , Sharma Kavita , Kamani Richa , Pande Urmila , Singh Alka , Singh Arjun
Published: May 2024

Coronaviruses are a class of large, enveloped, positive-sensed, single-stranded RNA viruses that cause infections in the respiratory tracts of mammals such as humans, bats, and other animals. Coronavirus disease 2019 (COVID-19), also known as 2019 new coronavirus (2019-nCoV), is an agile respiratory disease caused by a novel coronavirus that was first discovered in Wuhan, China. It has now expanded to 216 nations and killed over 0.5 million people worldwide, prompting the World Health Organization (WHO) to proclaim it a pandemic. Current review focuses on the role of herbs and herbs-derived secondary metabolites in inhibiting the SARS-CoV-2 virus as well as in the management of post-COVID-19 complications. This strategy will promote and protect the use of medicinal plant resources to support the healthcare system. Plant-derived phytochemicals have already been shown to prevent viral infection and to alleviate post-COVID complications such as parkinsonism, kidney and heart failure, liver and lung damage, and mental problems. We investigated mechanistic approaches of herbal medicines and their phytochemicals as antiviral and post-COVID complications by modulating immunological and inflammatory states in this review.
Research on intelligent auxiliary regulation technology of large power grid section based on artificial intelligence
Authors: Kun Zhang
Published: May 2024

In the modern era, large-scale renewable energy systems are integrated with advanced power systems and provide efficient operations. Also, optimized power systems require accurate energy generation and effective control systems to manage and ensure a stable power supply. Nevertheless, uncertainties are the intermittent balance of supply and high electricity demand. In addition, conventional power sources are not applicable for this challenging task and increase electricity costs. Therefore, an efficient Neuro Fuzzy Single phase Unified Power Quality Conditioner with Maximum Power Point Tracking (NF-SP UPQC -MPPT) strategy is developed for enhancing the grid-connected power systems. Here, Neuro-fuzzy logic is used as dynamic reactive power compensation in the grid. Also, this logic can efficiently handle the Energy storage system (ESS). Then SP UPQC was used to enhance power quality in electrical distribution systems. It combines both series and shunt compensators to mitigate various power quality issues such as voltage sags, harmonics, and unbalance. After that MPPT was utilized to extract the maximum power from the grid system. Model Predictive Control (MPC) controller to determine the overall stability and performance of the system. Moreover, the developed model was implemented on the MATLAB platform and performance is analyzed in terms of voltage deviation, grid current, reactive power fluctuations and Total Harmonic Distortion (THD).
Research and Application of Key Vocabulary Extraction Algorithm in English Vocabulary Learning
Authors: Fang Yu
Published: May 2024

An effective key vocabulary extraction is significant for English Language Learning, assisting the learners to achieve proficiency in English. However, the process of vocabulary extraction is often challenging and multifaceted, leading to inaccurate vocabulary acquisition, which reduces the learner's proficiency. To address these issues, we presented a novel key vocabulary extraction using the combination of Radial Basis Function Neural Network (RBFNN) and Emperor Penguin Optimizer (EPO). The primary objective of this research is to identify the most relevant vocabulary from the huge English language corpus to assist learners in improving their proficiency levels and learning process. The proposed work commences with the collection of English corpus and the collected database undergoes pre-processing steps like tokenization, stop word removal, and stemming to improve the efficiency of subsequent analysis. Then, the developed EPO-RBFNN was designed to extract the most informative and relevant features from the pre-processed database. The RBFNN module was trained using the pre-processed database to learn and capture the semantic patterns and interconnections within the corpus, while the EPO was employed for selecting the vocabulary sequence considering their relevance and importance in English learning. The proposed framework was implemented in the Python tool, and the results are evaluated in terms of accuracy, precision, recall, and f-measure. Furthermore, a comparative assessment was made with existing vocabulary extraction methodologies to validate the outcomes. The comparative analysis showcases that the proposed strategy outperformed the conventional models, making it a suitable solution for key vocabulary extraction.
Safety and Security of E-commerce Transactions Based on Blockchain Technology
Authors: Aifang Zhang
Published: May 2024

The safety and security of e-commerce transactions are critical in today's digital landscape, where cyber risks abound. Blockchain technology's decentralized, transparent, and immutable ledger system offers a viable solution to these concerns. This study investigates the use of Distributed Ledger Technology (DLT), specifically Decentralized Identifiable Distributed Ledger Technology (DIDLT), in conjunction with the innovative Blockchain Consent Algorithm (BCA), to improve the safety and security of e-commerce transactions. Secure data storage and retrieval in e-commerce can be achieved by accurate digital signature production, key generation, blockchain building, and validation. This paper investigates how DIDLT, which combines decentralized identity management with blockchain technology, and BCA, a cutting-edge consensus algorithm designed for blockchain environments, work together to protect e-commerce transactions from identity theft, payment fraud, and data manipulation. This paper throws insight into DIDLT and BCA's potential to transform the e-commerce safety and security market by providing an in-depth analysis of their implementation. Using the incorporated DIDLT-BCA model significantly improves the safety effectiveness of the network, resulting in 98% security, shorter performance times of as much as 150 milliseconds, and mining times of up to 0.98 s.
Interactive Creation and Performance of Digital Music Based on Virtual Reality Technology
Authors: Leiyan Jiang
Published: May 2024

It explores the application of Virtual Reality (VR) technology to the development and performance of digital music on the e-MARZ platform, which promotes Malaysian arts and culture. Following the ADDIE model's systematic approach, the emphasis is on the Design phase, which involves conceptualizing and realizing the goal for interactive digital music experiences. Through painstaking attention to user requirements and immersive design concepts, e-MARZ strives to provide entertaining and informative experiences that inspire creativity and enthusiasm for Malaysian music. Using VR technology, users are transported to virtual spaces where they can explore, compose, and play music in novel ways. By adhering to user-centred design principles and pedagogical effectiveness, e-MARZ seeks to give a transforming trip across the realms of digital music within a virtual landscape, enriching the cultural tapestry of Malaysian arts and music. The results of the usability show that the e-MARZ module effectively addressed the usability needs of both professionals and high school students, offering an immersive and engaging learning experience in Malaysian arts and music
Movie Scene Simulation and Rendering Based on Virtual Reality Technology
Authors: Yuanyuan Ji
Published: May 2024

Movie scene simulation and rendering with virtual reality (VR) technology have emerged as a game-changing method in the film industry, providing filmmakers with unparalleled creative opportunities and immersive storytelling experiences. This article investigates the use of VR technology in movie scene simulation and rendering, with a focus on sophisticated modelling techniques and rendering methods. Filmmakers can effectively control scene complexity and enhance rendering speed by utilizing VR-based modelling approaches such as multi-resolution decomposition and level of detail (LOD) rendering. These modelling techniques enable the development of highly complex and realistic virtual worlds, allowing filmmakers to bring their creative visions to life with unparalleled precision. In addition, VR-based rendering technologies, such as viewpoint-based geometric chunking rendering, improve frame rates and overall scene rendering quality, resulting in a seamless and immersive watching experience for audiences. Using VR technology, filmmakers can push the boundaries of traditional filmmaking, create fascinating cinematic experiences, and reimagine narrative in the digital age. This research explores the revolutionary impact of VR technology on movie scene simulation and rendering, emphasizing its potential to change the way films are created, seen, and appreciated by audiences throughout the world. Finally, they compare and assess the scene effect, LOD detail level, and interactive experience effect before and after simplification. scene design for virtual reality technologies generally scores 5-6 on each of the measures, whereas common technology's scenario design usually gets 3 to 4, showing whether the scenario design of virtual reality technology works properly.
Design and Integration of Intelligent Classroom for Preschool Education Majors Based on Computer Visual Recognition Technology Computer
Authors: Yabo Yang
Published: May 2024

Childhood development students may greatly benefit from the creation and implementation of a cognitive smart lesson that utilizes computer vision technologies. By analyzing and interpreting visual information gathered during lessons in real time, this equipment may be utilized to improve instruction while giving pupils more opportunities for individualized education. Participants' existence, gestures, and movements may be detected in interactive classrooms by installing many sensors and analyzing the resulting pictures via artificial intelligence techniques. This information is then utilized to give pupils more specific input, modify the learning method, and pinpoint problematic areas for improvement. Implementation of such devices in school settings has the potential to raise educational standards by promoting greater pupil involvement and engaged study. In addition to tracking learner growth, instructors may utilize such equipment to pinpoint areas of struggle so they can better customize the material for every learner. One objective of such a study is to create an engaging picture-based study and teaching platform tailored to preschoolers by using intelligent sensing' picture-of-image technologies. Utilizing the algorithm for BP neural networks and the ImageNet, Microsoft Common Objects in Context (MS COCO) , Modified National Institute of Standard and Technology (MNIST), and Chars74K datasets, this article creates a photograph recognition framework that can carry out the majority of its identification acts and integrates it into the educational program using an imagine for pictures. The picture machine learning algorithm has a high precision rate, with a total precision rate of 85.16%. The system offers an improved identification rate, greater instructional effectiveness, and more interactive features than the conventional approach to early childhood education. It has an excellent dynamic impact on learning because it can identify nearly every one of the items kids touch. Early childhood children may benefit greatly from an extra dynamic and individualized educational setting made possible by the introduction of technological vision technologies into a smart teaching. The adoption of computerized visual identification technologies within intelligence classrooms may not only improve the educational and learning experience but also provide vital data for research. Researchers and instructors may learn more about students' learning styles and most beneficial methods of instruction via real-time monitoring of pupil conduct and educational patterns. This information may be utilized to better improve the field of pediatrics by informing the creation of new methods of instruction, resources for learning, and technological tools.
Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks
Authors: M. Narender Pavan
Published: May 2024

The Spectrum is a scarce resource in wireless networks, hence these resources need to be perfectly channelized for their better utilization. By the study of the previous decades, it is found that the spectrum is colossally underutilized and the main reason for the underutilization found out to be the policies that are fixed and not dynamic. The dynamic spectrum allocation of frequency bands may overcome this problem. Cognitive radio provides an important concept that can used to solve the problem of underutilization of spectrum. Reinforcement learning is a key technique that is widely used to learn the spectrum allocation behaviour and maximize the system’s efficiency. Therefore, in this work, we have designed and developed a Reinforcement learning-based model to allocate the channels among secondary users. Also, a Deep Reinforcement learning-based channel allocation algorithm (DRLCA) has been proposed. The proposed DRLCA is compared with existing JPCRL [1]. In our algorithm, the Python libraries were used for simulation. From the simulation results and analysis, it is found that the DRLCA outperforms the JPCRL in terms of channel utilization by 5%.
Critical Analysis of Active Filter for Harmonic Reduction under Varying Load
Authors: Rituraj Jalan
Published: May 2024

In this paper, we give the main focus on the proliferation of non-linear loads in power systems, the mitigation of harmonics has become a critical concern to ensure stable and efficient operation. This study presents an analysis of an advanced control active filter (ACAF) designed for harmonic reduction under varying load conditions. The ACAF is a sophisticated solution that employs advanced control algorithms to effectively suppress harmonics and maintain power quality. The research investigates the performance of the ACAF under varying load conditions, which represent real-world scenarios where power consumption fluctuates. A comprehensive analysis is conducted to evaluate the filter's ability to adapt and maintain harmonic suppression efficiency across different load profiles. The study considers various load scenarios, including steady-state operation, transient load changes, and dynamic load fluctuations. The findings of this research contribute to the understanding of advanced control active filters in practical power systems with non-linear loads and varying operating conditions. The analysis provides valuable insights into the design, optimization, and implementation of ACAFs for harmonic mitigation, ultimately improving power quality and system reliability in modern electrical networks. A classified list of 38 publications on this topic is also given for the quick reference.
Predictive Analytics and Machine Learning in Disease Diagnosis: A Review of Recent Advances
Authors: Kannan Vishwanatth
Published: May 2024

Artificial intelligence (AI) refers to the utilisation of computer information to exhibit intelligent behaviour with minimal human intervention, whereas machine learning (ML) is seen as a subset of AI methodologies. Typically, this form of intelligence is widely recognised to have originated with the advent of robotics. Given the slow progression of diseases, it is crucial to make early predictions and administer appropriate medication. Hence, it is imperative to present a decision model that may aid in the diagnosis of chronic illnesses and forecast future patient prognoses. The primary objective of this study is to investigate the application of Predictive Analytics and ML in the field of Disease Diagnosis. The study will focus on reviewing the latest advancements in this area. This work specifically emphasises the significance of ML prediction models in disease diagnosis within the AI field, amidst other approaches available.  The study utilises a qualitative research methodology. ML has been prominent in the medical field as it offers methods for analysing disease-related data, as indicated by this study. ML algorithms are crucial in attaining early disease detection. Another crucial finding in this research is that the accuracy and performance of the model can be enhanced by employing an alternative technique to generate a single ensemble model.
Performance Benchmarking of Redpanda an event streaming platform using SBK and Open Messaging Frameworks
Authors: Sanjay Kumar N V
Published: May 2024

Redpanda is an event-driven, publish/subscribe based messaging system. The two main performance benchmarking frameworks, Storage Benchmark Kit (SBK) and open messaging benchmark are used here to test the performance benchmarking of redpanda’s event processing system. SBK is a software framework for measuring any storage device or client's performance benchmarking and open messaging benchmark framework which supports benchmarking for a wide variety of messaging platforms. Well formulated fine tuning system for benchmarks is still an open research problem. In this paper, SBK and open messaging frameworks have been experimentally compared with respect to their messaging capabilities (Message sending and receiving throughput) on redpanda’s event processing system. A number of experimental test scenarios have been conducted on data streaming workload, the performance evaluation results revealed that SBK gives maximum throughput performance compared to open messaging benchmark.
Practice and Exploration of University English Teaching Reform Based on Virtual Reality Technology
Authors: Yuan Siyuan
Published: May 2024

The university English teaching, the integration of virtual reality (VR) technology represents a groundbreaking frontier poised to revolutionize traditional pedagogical approaches. As education evolves to meet the demands of a digital age, incorporating immersive technologies like VR offers transformative opportunities to enhance learning experiences and outcomes. In the dynamic landscape of university English teaching, the integration of cutting-edge technologies like Augmented Virtual Feature Classification (AVFC) heralds a transformative era of pedagogical innovation. AVFC represents a pioneering approach that harnesses the power of virtual reality (VR) technology to reshape traditional teaching methodologies. By leveraging the power of VR, AVFC redefines traditional teaching methodologies by immersing students in virtual environments enriched with augmented features. These features augment the learning experience by providing interactive elements, visual aids, and simulated scenarios tailored to enhance language learning. The implementation of AVFC has yielded remarkable results, demonstrating its effectiveness in driving positive learning outcomes.  By immersing students in virtual environments enriched with augmented features, AVFC offers an unprecedented level of engagement and interactivity in language learning. AVFC implementation has demonstrated a 30% increase in student engagement and a 25% improvement in language proficiency compared to conventional teaching methods, highlighting its effectiveness in driving positive learning outcomes.
Algorithm Design and Implementation of Automatic Assessment and Expansion of English Vocabulary
Authors: Niu Xiaoqing
Published: May 2024

Automatic assessment and expansion of English vocabulary play a crucial role in language learning and proficiency evaluation. In this paper, we propose the use of an Optimized Bi-directional Long Short-Term Memory Deep Learning (Obi-LSTM-DL) model for these purposes. We conduct experiments using a comprehensive vocabulary dataset and evaluate the model's performance across various scenarios. The results demonstrate the effectiveness of the Obi-LSTM-DL model in accurately assessing vocabulary proficiency levels and expanding vocabulary knowledge. Through optimization experiments, we show that increasing the vocabulary size leads to improved model performance. Additionally, the model's proficiency level assessment capability allows for tailored instruction and support for language learners. Comparative analysis with baseline models further confirms the superiority of the Obi-LSTM-DL model. The results demonstrate the effectiveness of the Obi-LSTM-DL model in accurately assessing vocabulary proficiency levels and expanding vocabulary knowledge. Through optimization experiments, we show that increasing the vocabulary size leads to improved model performance, with an average increase in accuracy of 1.5% for every 1,000 words added to the vocabulary. Additionally, the model's proficiency level assessment capability allows for tailored instruction and support for language learners. Comparative analysis with baseline models further confirms the superiority of the Obi-LSTM-DL model, with an average increase in accuracy of 3.2% over traditional models.
Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA
Authors: Yalin Nie
Published: May 2024

Wireless Sensor Networks (WSNs) play a pivotal role in modern data-driven applications, yet concerns persist regarding the privacy and security of sensitive location information. The attributes of Wireless Sensor Networks (WSNs) make them susceptible to eavesdropping, enabling attackers to intercept data packets across single or multiple communication links. This interception allows for the extraction of sensitive data from various sensor information, presenting a significant challenge to location privacy. Consequently, it becomes crucial to implement effective measures for safeguarding the privacy of training sample data when utilizing WSNs. In this manuscript, Dynamically Stabilized Recurrent Neural Network (DSRNN) optimized with Mother Optimization Algorithm (DSRNN-MOA) is proposed. Initially data is taken from WSN dataset. Afterward the data is fed to Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) based pre-processing process. The pre-processing output is provided to the Dynamically Stabilized Recurrent Neural Network to enhance source location protection while addressing challenges related to recurrent network stability and gradient issues. The learnable parameters of the DSRNN is optimized using MOA. The proposed strategy, LPWSN-DSRNN-MOA, is implemented in MATLAB, and its effectiveness is assessed using a number of performance evaluation measures, including ROC analysis, accuracy, precision, recall, f1-score, mean squad error, and recall. The proposed LPWSN-DSRNN-MOA method shows the highest accuracy of 98%, precision of 99%, specificity of 98% and F1-score of 99% while comparing other existing methods such as Location Protection for Wireless Sensor Networks based on Artificial Neural Network(LPWSN-ANN), Location Protection for Wireless Sensor Networks based on Deep Neural Network (LPWSN-DNN), and Location Protection for Wireless Sensor Networks based on Machine Learning (LPWSN-ML) respectively.
Corpus-based Research on English-Chinese Translation Teaching Combining Vocabulary Learning and Practice
Authors: Nan Zhang
Published: May 2024

As the global economy is developing, real time translation among major influential languages such as English, Chinese has become a necessity. Translation from English to Chinese is difficult since Chinese has a different grammar and ambiguous word boundaries. Furthermore, there are issues with the current Chinese-English machine translation, including difficult-to-understand extended sentences and inaccurate word translation. To address this drawback, the methodology for more precisely categorizing vocabulary in both English and Chinese is described in this research. The data are collected from UM-Corpus news dataset. Then, using SEGBOT: Neural Text Segmentation method, English words are segmented from the sentences available on the dataset. Afterward, the data are fed to pre-processing. In pre-processing segment; words missing in translation from the news dataset are eliminated and enhances the input data utilizing Iterated Square-Root Cubature Kalman Filter. The outcome from the pre-processing data is transferred to the Pyramidal Convolution Shuffle Attention Neural Network (PCSANN). The word order, grammatical structure, consistent style and smooth flow for English to Chinese translation are successfully classified by using PCSANN. The Waterwheel Plant Algorithm (WWPA) is used to optimize the weight parameter of PCSANN. The proposed PCSANN-WWPA is applied in python working platform. Performance metrics, like accuracy, precision, F1-score, and recall are examined to compute proposed method. The gained results of the proposed PCSANN-WWPA method attains higher accuracy of 16.65%, 18.85%, and 17.89%, higher sensitivity of 16.34%, 12.23%, and 18.54%  and higher precision of 14.89%, 16.89%, and 18.23%. The proposed ECTT-PCSANN-WWPA method is compared with the existing methods such as ECTT-RNN, ECTT-DNN, and ECTT-MLPNN models respectively.
Artistic Virtual Reality Scene Analysis Taking into Account the Interactive Digital Media Pattern Generation Technology
Authors: Hong Zhu
Published: May 2024

With the development of science and technology and the continuous strength of Chinese spiritual civilization, digital media patterns have penetrated into various fields and played a central role in them. This article studies the endless emergence of various new media, and the channels of information dissemination are becoming more and more abundant. With the development of science and technology, new media are gradually derived, such as: IPTV, electronic magazines, etc., which have developed on the basis of traditional media. But there is a qualitative difference from traditional media. Digital media has a profound impact on mass society.This article proposes that digital media has become the most important means of communication in the information society. The digital media creative industry and the country's economic development have formed a close relationship of interdependence and mutual promotion. Based on the interactive digital media model generation technology, the research of artistic virtual and real scene analysis is the main issue today. The experimental results of this paper show that through the experimental investigation and analysis in 2019 and 2020, it is found that the application trend of company A and B in the virtual reality situation has increased significantly. The application rate of company A and B in 2019 reached about 43%, but the trend is not unstable. However, in 2020, it can be clearly seen that the application rate of companies A and B in applying digital media patterns to virtual and real scenarios has stabilized, exceeding 35% every month. It can be seen in the text below that when the four models are in different periods, the value of the optimal solution is different, and when the model is at 0.1 and 31, the optimal solution appears.Experiments show that digital media is widely used in the analysis of virtual and real scenarios. Based on the new research field formed by the fusion and intersection of digital media patterns, starting with the changes in the main body, objects, tools and forms of virtual and real scene communication in the context of digital media, it conducts in-depth research on the creation of artistic virtual and real scenes from many aspects.
Application of ELAN in Human-Machine Mutual Translation of English for the News
Authors: Jie Cheng
Published: May 2024

The translation of English news means the process of converting news articles published in English into another language. These articles may also be referred to as English news. This process comprises understanding the substance of the source article and accurately translating its tone and intent into the target language. A human translator or machine translation software can be used to perform the translation manually. In the disciplines of software globalization and technological translation, the Human-Machine Mutual Translation technique has grown significantly with the advancement of civilization and the quickening growth of research and innovation. Because of this practical need, translators have begun to focus more on machine translation and have conducted some productive research in this area. As a result, we suggested the EUDICO Linguistic Annotator (ELAN) in Human-Machine mutual translation of English for the news. EUDICO stands for European Distributed Corpora Project. The Max Planck Institute for Psycholinguistics created the EUDICO Linguistic Annotator as a software program for the linguistic annotation of language data. For this study, the English news dataset was used. The data were pre-processed by using normalization. The results were evaluated using performance metrics such as translation quality, precision, BLEU, recall, and computation time. The findings show that the recommended procedure provides an accurate translation of English news when compared to previously used methods. The results demonstrated that translation quality and computation time of proposed model are 95% and 60s respectively.
Diagnosis of Medical Images Using Convolutional Neural Networks
Authors: Yogita K. Desai
Published: May 2024

Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging.  This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.
Construction of Qــ Ary(n,Μ,d )ــ Codes in PG (2,9)
Authors: Dunya  Fareeq  Fendi
Published: May 2024

This study's main objective is to offer the connection between the projective plane of order nine & the field of coding theory. An incidence matrix has been developed using the parameters n (code length), d (code min. distance), and e (code error correcting). The greatest value of size code M across a limited field of the ninth order has also been determined. There are a few theorems and examples provided.
Intelligent Internet of Things and Privacy Protection Technology for IPE Data Analysis
Authors: Xiaolin Guo
Published: May 2024

In the process of shaping the personality and forming the ideal and belief of innovative talents, education plays a unique role that cannot be replaced by other forms of education. The application of an intelligent Internet of Things has been well developed in college teaching. AI is the reality of technological development in the training of higher talents in the new era, and it is the realistic demand for IPE in the training of higher talents, which requires political and ideological empowerment. Positioning the role of "educational assistant" in AI and then defining it as a "quasi-subjective object", is the basic positioning of the introduction of higher-talent IPE at this stage. 1) Build a model of teacher-student interaction supported by "human-computer cooperation"; 2) Construct intelligent learning and intelligent teaching modes supported by "algorithm databases"; 3) Establish the mechanism principle of "people-oriented" assessment and goal orientation, and further improve the quality of IPE in China's higher education personnel training. This paper aims to explore the use of artificial intelligence technology to build an ideal ideological and political education paradigm in the process of training higher-level talents in Chinese universities.
Research on the Design Method of Rural Lodging Based on the Integration of Architecture and Environment
Authors: Jing Hu
Published: May 2024

The concept of modern tourism intelligent application in China is to fully meet the information needs of tourists and customer experience, and the development of intelligent tourism relying on Internet of Things (IoT) is its most basic guarantee condition. With the rise of ethnic village tourism and the change of new consumer lifestyle, B&B shows an explosive development. Tourism consumers are also shifting from hotels to home stays in the process of tourism. Therefore, based on IOT application as the starting point, this paper firstly, gives a more detailed description of the development process and marketing status of home stays, and discusses its various problems. Then, based on the TP theory of ethnic villages, Porter's five forces analysis model and the SWOT analysis method of ethnic villages, the marketing environment of homestays is studied in depth to discover their advantages.The HTOE model develops a marketing mix strategy for B&Bs that meets the actual situation of ethnic village development
The Style Transformation of Gu-Zheng Music Based on the Improved Model of Cyclegan
Authors: Yifan Li
Published: May 2024

In recent years, generative adversarial networks have excelled in the field of image style migration, however their performance in the field of music has been mediocre. Existing music style migration does not work well for style migration of gu-zheng music. In order to solve these problems, we first extract the features of gu-zheng music and the Mel-spectrum features, then use CycleGAN to do style transformation on the combined features and Mel-spectrum features, and then use WaveNet vocoder to decode the migrated spectrograms, and finally achieve the style migration with gu-zheng music. The proposed model was evaluated on the publicly available dataset FMA, and the average style migration rate of the compliant music reached 94.07%. Compared to other algorithms, the music produced by this method outperformed other algorithms in terms of style migration rate and audio quality.
Analysis of Henan Tourism Demand Difference Based on Data Mining and Intelligent Analysis
Authors: Qiong Zhang
Published: May 2024

Relevant tourists fields in Henan have conducted in-depth investigations on Henan tourism, in order to provide reference for subsequent tourism research; Based on the increase in the number of Henan tourists, studying outbound tourism activities from the perspective of tourists can explore its development law and formulate effective marketing strategies. Based on this, this paper proposes a data mining method to analyze the differences between different consumer groups in tourism needs and marketing strategies. The joint example analysis shows that our data mining analysis is very reasonable and effective. Our data experiments show that based on the increase in the number of Henan tourists going abroad, the tourism strategy designed in this paper can promote tourists' interest and increase the number of trips.
Harnessing Traditional Business Culture Resources: Scalable Parameter Server Architecture with Distributed Machine Learning
Authors: Chengcai Xing
Published: May 2024

This paper explores the development and utilization of Traditional business culture resources, focusing on Yan'an's rich historical significance and its potential political, cultural, educational, and economic value. Leveraging distributed machine learning systems within a parameter server architecture, the research addresses challenges related to scalability and robustness, particularly in the face of random node downtime and network interruptions. Through intelligent simulation and experimentation, the study demonstrates the effectiveness of employing machine learning techniques to achieve high accuracy in modeling Traditional business culture's development and utilization.
Detection of Covid-19 Cases from Chest X-Ray Images using CNN
Authors: Vinston Raja R.
Published: May 2024

The occurrence of COVID-19 has brought the worldwide threat to the living society. The severity of the unwellness is often understood by the huge variety of deaths and affected patients globally. If the identification is fast, the illnesses are often controlled in a very higher manner. The factitious intelligence researcher’s square measure focusing their experience data to develop mathematical models for analyzing this epidemic scenario mistreatment nationwide shared information. To contribute towards the well-being of living society, during this project, Deep learning algorithmic program are going to be wont to determine the COVID infected patients through chest X-Ray pictures. By using, Convolutional Neural Network and Deep Neural Network, the chest X-Ray pictures are going to be analyzed to observe, whether or not the actual patient has the signatures of COVID-19 viruses or not. The performance of each the model is going to be compared on the experimental basis and deliver the simplest model to the clinical and health care domain.
Application of Modern Technology in Minimal Invasive Dentistry - A Narrative Review
Authors: Chanchal Gupta
Published: May 2024

Preserving human anatomy and physiology is the ultimate goal of all health-related endeavours.The conservation of healthy tooth structure is the goal of minimal-intervention dentistry. The concept of "minimum/minimal intervention dental care" first appeared in the early 1980s. From its origins in general medicine, the minimum intervention philosophy in dentistry was created, focusing patient-oriented oral and dental care to prevent and treat oral disorders and maintaining over all oral health. A more conservative approach to tooth preparation and restoration in operative dentistry is called minimally invasive restorative dentistry. This terminology is relatively new. It comprises prompt identification prior to the onset of cavitation, sealing fissures and pits in uninfected areas, fluoride administration, minimum surgical repair of carious lesions, caries risk evaluation, and patient education regarding self-care. A wide range of methods are used, from initial identification of any demineralized tooth structure to evaluation and therapy, including air abrasion, laser cavity preparation, chemo-mechanical caries removal, and hand instrumentation. Adopting the current advanced method requires sufficient knowledge. The trio that comprises tissue histology, dental biomaterials science and professional patient care and management of materials are considered by the "golden triangle" of minimally invasive treatment options for dental caries in order to enable the successful application of minimally invasive dentistry in all patients. The term "minimal intervention dentistry" (MID) was first used in the early 2000s in a policy statement. Maintaining the greatest amount of healthy tooth structure and keeping teeth functional for life are the goals of MID. This approach is endorsed by the World Dental Federation (FDI) as the modern approach to dental caries management
Predicting Accident Severity using Machine Learning
Authors: Rudresh Deepak Shirwaikar
Published: May 2024

Each year, millions of people are killed in automobile accidents. Predicting the severity of an occurrence allows local authorities to respond quickly and save many lives. Information and data on traffic accidents made available by public organizations can be used to categories these incidents based on their nature and severity, and then attempt to construct predictive models that can be further investigated to identify fatal accident risk factors. The study provides ways to develop a system for determining the severity of accidents. To evaluate the severity of the collision, we use a variety of weather-related characteristics such as temperature, humidity, visibility, and pressure, as well as several other circumstances, such as the presence of a traffic light or a junction. The Select-K-Best features selection algorithms were used to choose the best characteristics from a list of 47. The accuracy of both balanced and unbalanced data was measured, and the balanced data was chosen for further analysis. The balanced dataset is then trained, analyzed, and compared with a number of machine learning approaches, including the Random Forest Classifier (RFC), K closest neighbour (KNN) classifier, and Naive Bayes classifier. The RFC classifier outperformed with an AUC of 95%, whereas the naïve bayes underperformed. Furthermore, the accuracy is improved by using all of the aforementioned methods as base learners for stacking ensemble models with logistic regression as meta learner. This stacking ensemble strategy outperforms RFC with improved precision and accuracy, resulting in an AUC of 96.92%. The results revealed that the model has a 96.92% AUC in predicting accident severity. The work can be extended to investigate the complex correlations between a few key parameters and accident severity.
A Simplified Power Regulation Strategy for Grid-tied PMSG-based Marine Current Turbine System in High-speed Marine Current Conditions
Authors: Adesh K. Mishra
Published: May 2024

Marine current turbine systems (MCTSs) play a crucial role in harnessing renewable energy from ocean currents. These systems are designed with specific rated values of marine current speed, dictating their optimal operational parameters. However, the dynamic nature of marine currents can lead to fluctuations, occasionally exceeding the rated values for which these systems are designed. This poses significant challenges to the efficient and safe operation of MCTSs, necessitating the development of effective power regulation strategies. In response to these challenges, this paper presents a simplified power regulation strategy to address the complexities of high-speed marine current conditions. The strategy focuses on constraining the reference torque command, a pivotal parameter governing power generation within MCTS. By precisely managing the reference torque command, the strategy aims to ensure that power output remains within designated limits, thereby safeguarding the MCTS despite high-speed marine current conditions. The proposed strategy leverages a computational approach to determine the generator's reference torque command, utilizing key parameters such as the rated power of the MCT and the mechanical angular rotor speed. It facilitates seamless transition between unconstrained reference torque command (URTC) and constrained reference torque command (CRTC) modes, enabling precise power regulation. This flexibility empowers the MCTS to respond promptly to changes in marine current conditions, facilitating precise and responsive power regulation. To validate the effectiveness of the proposed strategy, the paper conducts a comprehensive analysis of MCTS performance across both URTC and CRTC modes utilizing Typhoon HIL604 real-time simulator. Through detailed examinations of MCT speed responses, generator torque, output power, and grid-side parameters, the study provides valuable insights into the dynamics of MCTS operation. This comprehensive understanding enhances the stability and adaptability of MCTSs across diverse operating conditions, thereby advancing their potential as a sustainable energy solution.
Hybrid RNN-LSTM Networks for Enhanced Intrusion Detection in Vehicle CAN Systems
Authors: Nujud Al-Aql
Published: May 2024

Electric vehicles (EVs) use electric motors for propulsion, relying on electric energy stored in batteries or other energy storage devices. The standard communication protocol used in EVs is the Control Area Network (CAN), a communication protocol widely used in the automotive industry for networking and communication between various components within a vehicle. CAN protocol, designed without any care about protection, as automotive systems become more connected, the vulnerability to cyber threats, including intrusion attacks. The most common intrusion attacks on EVs are Denial of Service (DoS), Fuzzy, and Impersonation Attacks. These become a significant challenge due to the imperative need for robust Intrusion Detection Systems (IDS) in CAN networks. This paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to enhance the effectiveness of intrusion detection in the EV domain. We will use a hybrid Deep-learning model to improve the analysis. First, we will apply the RNN model, and the output will come as input for the second model, LSTM. Our proposed hybrid model achieved an accuracy of 93%. The outcomes of this research contribute to advancing cybersecurity measures in vehicular networks, ensuring the integrity and safety of connected vehicles. The applicability of RNN and LSTM techniques in the context of CAN networks demonstrates their potential to evolve as integral components of next-generation intrusion detection systems, fostering a secure and resilient automotive ecosystem.