bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study to Assess Effectiveness of Nesting on Bio-Physiological Parameters and Sucking Response Among Low Birth Weight Babies in J.K. Loan Hospital Jaipur with a View to Develop an Educational Video
Authors: Sharma Jogendra , Sharma Subhash , D. Das Aruna
Published: May 2024

Low Birth Weight babies have higher morbidity and mortality because they having the complaints of altered bio-physiological parameters and poor sucking response. The nesting is a nursing skill used ordinarily within the developmental care of low-birth-weight (LBW) babies. The study aimed to assess effectiveness of nesting. A quantitative research approach and pre-experimental one group pre & post test research design was used to conduct this study. The sample consisted of 30 LBW babies who are admitted at J. K. Loan Hospital Jaipur And purposive sampling technique was used to select the samples. The Bio-physiological parameters and modified feeding assessment observational checklist were used to collect data from samples after getting consent analyzed using descriptive and inferential statistics. The findings of the study showed that –
This study assessed the pre-test and post-test scores of bio-physiological parameters and sucking response in low birth weight babies admitted to J K Loan Hospital Jaipur. The pre-test mean scores for temperature, heart rate, respiratory rate, and oxygen saturation were 98.49, 168.33, 48.53, and 81.16, respectively. The majority of participants (60%) had poor sucking response with a mean score of 4.16.
After applying nesting, the post-test mean scores for temperature, heart rate, respiratory rate, and oxygen saturation were 98 (SD 0.34), 139.6 (SD 5.03), 39.46 (SD 5.28), and 95.93 (SD 0.53), respectively. The post-test results revealed that a significant improvement occurred in heart rate, respiratory rate, and oxygen saturation, as evidenced by the calculated "paired t" values (16.59, 3.74, and 6.83, respectively). These values were greater than the tabulated "paired t" value (2.045) at 29 degrees of freedom and 0.05 level of significance, suggesting the effectiveness of nesting in maintaining these parameters.
Regarding sucking response, the majority of participants (66.67%) had good sucking response post-nesting, with a mean score of 9.33. The "paired t" value for sucking response (13.79) was also greater than the tabulated value, indicating a significant improvement in sucking response after nesting application.

Key words: Low Birth Weight, Bio- physiological parameters, Sucking response, Effectiveness, Nesting, Educational video.
Effect of Forgiveness on Need for Closure
Authors: Begal Manshi , Bapu K G Vigraanth , V R Priyanka
Published: May 2024

Aim of the present study is to find the relationship between Forgiveness and Need for Closure and to find the influence of one on the other. Convenience sampling was used to collect data from a sample of 200 young adults from different parts of India, using Heartland Forgiveness Scale (Yamhure Thompson et al., 2002) and Need for Closure Scale (Paliwal & Kumar, 2022) with good reliability and validity. SPSS 29 analysis shows no significant gender differences in forgiveness and the need for closure. Observations suggested a weak negative correlation (r = 0.161, p < 0.05) between Forgiveness and Need for Closure. Regression analysis further indicated that Forgiveness has a moderate influence on the Need for Closure (R² = 0.025), emphasizing its role as a coping mechanism that can mitigate the desire for cognitive closure. These findings open the door to future research that can deepen our understanding of the intricate relationship between forgiveness and the need for closure.

Key words: Forgiveness, Need for Closure, Gender Differences, Correlation, Regression.
Brain Tumor Classification Using Pretained Deep Convolutional Neural Networks
Authors: Meena M. , Balaswetha U. , Harini M.
Published: May 2024

Due to their complexity and sensitivity, classifying brain diseases is a very difficult task. Because brain tumors are serious and sometimes fatal, early detection and diagnosis are essential for developing an efficient treatment plan. A vital medical imaging tool, magnetic resonance imaging (MRI) allows for the detailed, non-invasive visualization of the internal structures of the brain. When it comes to diagnosing and treating brain tumors, magnetic resonance imaging (MRI) plays a critical role. Starting with dataset preprocessing, the method applies to MRI scans and clinical data from people with different brain conditions, including cases of tumors and non-tumors. Training and testing sets make up the dataset. MRI tumor detection requires a number of processes, including feature extraction, classification, and image post-processing. For classifying brain images, the system makes use of Convolutional Neural Networks with Long Short-Term memory (LSTM) a pre-trained model using the approach of transfer learning. The proposed framework not only uses the pre-trained model to improve the performance of training a better model but also uses thresholding to improve the dataset for better accuracy and data augmentation for increasing the number of images in the dataset. Preliminary outcome shows that the family of models of Hybrid algorithm performs better than previous CNN architectures because to scale all dimensions of depth, width, and resolution of an image with a constant ratio it uses the compound coefficient. The results also demonstrated that by scaling the baseline architecture the model is able to capture complicated features and thus the overall performance of the model is improved.

Key words: Brain tumor classification, convolutional neural network, medical imaging, deep learning, transfer learning.
Molecular Characterization and Tolerance Potential of Culturable Crude Oil-Degrading Microbes in Santa-Barbara River, Bayelsa State, Nigeria
Authors: B. Allen-Adebayo , U. O. Maureen , S. I. Odaro
Published: May 2024

There has been chronic contamination of the surrounding aquatic and terrestrial environments in the Niger Delta region of Nigeria due to the enormous activities of crude oil exploration. This study aimed to characterize and assess the crude oil tolerance potential of indigenous microbes from crude oil spills in Santa Barbara River, Bayelsa State, Nigeria, that could serve as potential consortia for bioremediation of the crude oil spills. Total crude oil hydrocarbon-utilizing bacteria count (THUBC) and total crude oil hydrocarbon-utilizing fungi count (THUFC) in the samples were determined with a culture-dependent spread plate technique. Bacterial and fungal isolates were characterised using standard phenotypic and 16S/Internal Transcribed Spacer region rRNA gene sequencing techniques, respectively. The tolerance of autochthonous bacterial isolates to different concentrations of crude oil was subsequently determined. THUBC and THUFC in crude oil-polluted water samples ranged from 1.88 log10 CFU/ml to 2.74 log10 CFU/ml and from 0.00 log10 CFU/ml to 1.70 log10 CFU/ml, respectively. Representative strains of Proteus mirabilis, Bacillus subtilis, Pseudomonas aeruginosa, Micrococcus luteus and Aspergillus flavus isolates obtained from crude oil-polluted water samples were deposited in the GenBank (NCBI) under accession numbers OQ969924, OQ969951, OQ969987, OQ970009 and OQ975908. Proteus mirabilis, Pseudomonas aeruginosa and Aspergillus flavus demonstrated the most significant tolerance to crude oil pollutants (minimum crude oil inhibitory concentrations (MIC) = 80%) followed by Bacillus subtilis and Micrococcus luteus (MIC= 40%). The findings from this study are pointers to the potential role of the microbial isolates as bioremediation consortia to remediate the polluted Santa Barbara River.
Development and evaluation of a low-cost facial model for training in sutures, incisions, and biopsies
Authors: Andrade Laryssa Ellen Moura de , Viana Fernando André Campos , Medeiros José Rômulo de , Silva Bruno Frota Amora
Published: May 2024

Atividades de simulação cirúrgica são fundamentais em ambientes acadêmicos na área da Odontologia. Esta prática exerce a função de planejar e simular habilidades básicas, como realização de incisões, biópsias e suturas, fazendo com que os estudantes, mediante os treinamentos repetitivos aprimorem as técnicas, melhorando capacidades manuais e possibilitando a formação de profissionais mais seguros e confiantes. Desta maneira, foi desenvolvido um modelo facial de baixo custo para avaliação e simulação por meio dos docentes, para o treinamento de suturas, incisões e biópsias. Trata-se de um estudo transversal, quantitativo e observacional, onde foram realizados com 10 professores das áreas de Periodontia, Cirurgia e Estomatologia do Curso de Odontologia da Universidade de Fortaleza (UNIFOR), mediante a escala Phrase Completion e seus níveis de satisfação pessoal. Os resultados mostraram uma boa avaliação em todos os quesitos, resultando numa avaliação positiva quanto à similaridade do modelo com os tecidos reais da face, o manuseio do modelo, bem como sua utilidade nas práticas clínicas com o treinamento de incisões, biópsias e suturas. A utilização do modelo facial de baixo custo em práticas pré-clínicas apresentam vantagens para diversas especialidades na graduação em Odontologia, principalmente áreas como Periodontia, Cirurgia Oral e Estomatologia. O treinamento no modelo que simula incisões, biópsias e suturas se mostrou eficaz no presente estudo, para o desenvolvimento e aperfeiçoamento das habilidades dos discentes.
An Investigation of Science And Mathematics Teachers’ Thoughts on STEM Education
Authors: Gok Tolga
Published: May 2024

This study examines the views of high school physics, chemistry, biology, and mathematics teachers on STEM education. The research uses a phenomenological research design. The sample involved three public high schools and eight teachers. Participants had prior experience and knowledge of STEM education. The experience of the participating teachers ranged from approximately 9 to 30 years. Data for the study were collected using a semi-structured interview form. The interview form consists of eight questions. As a result of the analysis of the interviews, 8 themes and 67 codes were categorized. Results reveal that the implementation of STEM will greatly contribute to the development of students and teachers. Many teachers think that STEM applications will be extremely effective in creating an environment where teachers can be motivated, develop projects and discover their students’ talents. Results show that many teachers do not get enough information about STEM seminars held at universities. In addition, it reveals that teachers have problems with time, equipment and classroom control during STEM practices.
Analysis Of The Attitude Of Secondary School Teachers Towards The Use Of Bichronous Online Learning
Authors: Panda Sushanta Kumar , Neha
Published: May 2024

Bichronous online learning is a recently coined terminology that refers to a blended form of synchronous (real-time online meeting) and asynchronous (anywhere, anytime learning) online learning. The present study aims to analyse the attitude of secondary school teachers towards bichronous online learning based on gender, locality, age, and type of institution. The sample consists of 100 secondary school teachers from 20 schools (10 each from government and private) in the Samba district. The investigator adopted the descriptive survey method and used a self-developed attitude scale for collecting data. The reliability and validity of the scale were also established by the investigator. For the analysis of data, Mean (M), Standard Deviation (SD), and Critical Ratio (CR) were used. The findings of the study reveal that there is a significant difference between the attitude of secondary school teachers based on gender (male and female), locality (urban and rural), age (below 40 and above 40), and type of institution (government and private). The results of the present investigation will help the stakeholders to take necessary steps to develop willingness among the teachers to use bichronous online learning in the teaching-learning process and make it more interesting and innovative.
Being A Distance Student: View Of Preservice Teachers In Turkey
Authors: Yılmaz Ozelci Serap
Published: May 2024

A hybrid model, supported by technology, is used for teacher training in many countries. Sometimes, this is mandatory, such as during the COVID-19 pandemic or an earthquake and other times it is a conscious choice. This study examines the opinions of preservice teachers on their experiences with distance education. The research is a qualitative case study, and the data collection is through a focus group interview, which was evaluated using content analysis. The findings reveal that the experiences during the courses and the attitude of the instructors affect the perception and satisfaction of the participants. The participants evaluated the opportunities offered by their universities as positive efforts but emphasized that being able to benefit from such opportunities is a personal situation. The participants’ study skills, self-discipline, technological equipment, internet access, and environment to study made them either advantageous or disadvantageous in distance education.
On a new variant of I-convergence in topological spaces
Authors: Hoque Jiarul , Modak Shyamapada
Published: May 2024

In this write-up, we mainly introduce b-I-convergence of sequences, b-convergence and b-Iconvergence of nets in topological spaces, and put forward some important topological investigations. Existence of b-ω-accumulation point is presented via admissible ideal and b-I-cluster point of sequence. It is shown that a map f : Z → W is quasi-b-irresolute if and only if for every net (s_d)_d∈D converging to zo, the image net (f(s_d)_d∈D) b-converges to f(z_o). Notion of b-I-cluster point of net is disclosed along with its a nice characterization as: ‘Corresponding to a given net s : D → Z, there exists a filter G on Z such that z_o ∈ Z is a b-I-cluster point of the net (s_d)_d∈D if and only if z_o is a b-cluster point of the filter G’. Another characterizationof b-I-cluster point of net with respect to a certain type of class of subsets is demonstrated. Further, we show that b-I-cluster point of a net in a b-compact space always exist.
Best proximity point results for generalized proximal $Z$-contraction mappings in metric spaces and some applications
Authors: Goswami Nilakshi , Roy Raju
Published: May 2024

In this paper, we define generalized proximal Z-contraction mappings of first and second kind in a metric space (X, d). The existence of best proximity point is shown for the defined mappings under some specific conditions which generalizes and extends some existing results of Olgun et al. [23] and Abbas et al. [1]. Suitable examples are given to justify the derived results. Some applications are also shown via fixed point formulation for such mappings in variational inequality problem and homotopy result.
Picture fuzzy normed linear space
Authors: Sinha Kalyan , Majumdar Pinaki
Published: May 2024

Picture fuzzy set (PFS) is a recent advancement tool to deal with vulnerability. It is an immediate expansion of intuitionistic fuzzy set that can display vulnerability in such circumstances including more responses of these kinds: indeed, decline, no. In this manuscript the idea of Picture fuzzy normed linear space (PFNLS) is discussed for the first time. Naturally PFNLS is an hybrid concept of PFS and normed linear space. Also Convergence in PFNLS are shown. Later on Completeness property on PFNLS are explored. Finally boundedness of Cauchy sequence in PFNLS is analysed.
Formulation Development and Evaluation of Ubidecarenone Chewable Tablet
Authors: Vijay Pagar Vaishali , P. Sonawane Mitesh , D. Kothawade Parag , K. Pagar Pratibha , S. Patil Shamal
Published: May 2024

Background: Coenzyme Q10 has a role in mitochondrial transport system and can be utilized as a supplement in migraine prevention. This study's objective is to assess Coenzyme Q10's effectiveness when used in conjunction with conventional medications to prevent migraines. Adding 300mg of coenzyme Q10 each day to prescription medications, while another group of patients continued receiving standard care. Over the course of a three-month treatment period, data on attack frequency, headache pain intensity, monthly migraine days, and associated symptoms like nausea, vomiting, phonophobia, and photophobia were analyses. Ubidecarenone, a medicinal component. The goal is to produce Ubidecarenone in a 300 mg chew-able tablet form. In vitro release studies and organoleptic properties, such as taste and flavour, will be evaluated. investigations in the early stages, tests for medicine excipient compatibility, and analyses of the water content Quality Assurance for the Process Hausner's Ratio, Bulk Density, Tapped Density, Comprehensibility Index, and Angle of Repose for calculating derived properties for medications and mixtures. ingredients that have been optimised for their concentrations, such as tastes, sweeteners, disintegrants, wetting agents, glidants, and lubricants. putting the best possible ingredients together to make a chew able pill. Evaluation of chewable tablets for post compression parameters including uniformity of weight, thickness, hardness, brittleness, friability, dissolution, assay, and in vitro drug release properties. An evaluation of the optimised batch's palatability is required. The intention is to create chewable tablets of ubidecarenone. Method: The wet granulation method is used to make chewable tablets of ubidecarenone. Mannitol is used as a diluent, crospovidone as a disintegrant agent, sodium starch as a disintegrate agent, saccharin sodium as a sucrose sweetener, orange flavour, peppermint oil as a flouring agent, and colloidal silicon dioxide as a gliding. The Ubidecarenone (300 mg) is one of the best first-line treatments for migraine prevention.
Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning
Authors: Zhiqiang Wang
Published: May 2024

The power grid presents several obstacles for demand forecasting and resource scheduling, such as a substantial amount of data, a growing number of factors influencing the demand profile, uncertainty in the generation profile of distributed energy from renewable sources, and a shortage of historical data. Here, we provide a unique market-oriented energy storage method based on artificial intelligence (AI) that aims to optimize operational profit in the electricity market between consumers, energy storage, and grid service providers. The approach is divided into two parts; the first is the Residual Network-50 (GR-ResNet-50) algorithm, which is used to overcome future uncertainties related to load needs and power pricing. The second uses deep learning based on the Radial Heap Basis Algorithm (RHBA) to determine the most effective charging or discharging operation considering the grid peak states, load demand, and the state of the batteries. The performance of the proposed method in this study is compared with the conventional method in terms of 0.007% of low MAPE and 20 seconds of less execution time. The research shows that the proposed strategy significantly lowers on-peak power, improves operational profit, and improves effective performance.
Research on the Application of Three-Dimensional Virtual Reality Technology in Landscape Architecture Design
Authors: Yunzhou Chen
Published: May 2024

This study investigates the depth of and engaging experiences for designers and users could transform conventional landscape architecture design approaches through the use of 3D virtual reality technology. Research assesses the advantages and difficulties of using 3D virtual reality (VR) into landscape architectural practice by means of an extensive analysis of the body of current literature and case studies. The study looks into how 3D virtual reality (VR) technology affects the ideation, imagining, collaboration, and presentation phases of the architecture and landscape process of design. It examines how virtual reality simulations can improve communication among design teams, facilitate client engagement, and enhance decision-making processes. The study also looks at the practical and technical aspects of using 3D virtual reality technologies in landscaping design firms, including hardware requirements, software compatibility, and training requirements. In the end, the research's findings deepen our understanding of how 3D virtual reality technology might revolutionize landscape architecture, practice, and spur creative thinking in design processes.
Design and Development of an Oral English Evaluation System Based on Speech Recognition Technology and A Scoring Algorithm
Authors: Haiyan Li
Published: May 2024

Proficiency in oral English plays a significant role in different professional and academic settings. However, evaluating oral English proficiency precisely is challenging because of the subjective nature of oral communication. In this study, we developed an innovative Oral English Evaluation System (OEES) by combining the efficiencies of speech recognition technology and scoring algorithms. Initially, an intensive oral English database was collected, containing numerous persons' voice recordings. The collected voice recordings are pre-processed to remove the background noise, ensuring clarity in the voice signals. Then, an automatic speech recognition algorithm was developed using the recurrent neural network (RNN) to transcribe voice signals into text. This module trains the system to map the voice signals to their corresponding textual characteristics. Finally, an adaptive scoring algorithm was incorporated into the designed OEES to evaluate the oral English proficiency of the individuals. The adaptive scoring algorithm considers different factors like fluency, grammar, pronunciation, and vocabulary for evaluating the individual's oral English proficiency as "excellent," "good," and "poor." The presented framework was modeled in Python and validated across diverse natural English-speaking databases. The experimental results are assessed in terms of accuracy, precision, recall, and error rate. The implementation outcomes suggest that the proposed OEES framework accurately evaluates oral English proficiency.
Design and Development of IIOT Based Prototype Model by Using Machine Learning Techniques for Workflow Management in Industry 4.0
Authors: Nirmala N Kamble  , Nirmala N Kamble
Published: May 2024

Manufacturing is undergoing a revolutionary shift known as Industry 4.0 (I4.0), which unifies production and business operations through data-driven End-to-End digital integration. I4.0 goes beyond the digital ecosystem of the Third Industrial Revolution to include autonomous tech-human convergence and real-time data interchange. The Industrial Internet of Things (IIoT) and machine learning are driving breakthroughs in cyber-physical systems, which form the basis of this. In order to advance I4.0, technology and human talents must be balanced. It is essential to upskill the workforce and engage in coordinated transdisciplinary activities. A holistic approach, integrating human factors and tech advancements, ensures smooth automation transition. The proposed study introduces a cost-effective liquid filling system based on a Raspberry Pi, addressing automation challenges for smaller firms.
Echocardiography Based Cardiac Ischemic Detection Using Red-CNN Classifier
Authors: Jyothi P
Published: May 2024

One of the most vital and hardest-working organs of the human body is the Heart. Nevertheless, a condition termed Cardiac Ischemia or CAD is engendered by the inadequate supply of blood along with oxygen to the heart muscle. So, with the aid of a Regularisation Dropout-centered Convolutional Neural Network (ReD-CNN) classifier, an efficient CAD detection system is proposed. At first, the input videos and the data are amassed from the Echonet Dynamic dataset. Next, frame conversion is undergone by the input video. For further processing, keyframes are extracted and preprocessed after frame conversion. Afterward, by means of a Contraction Expansion-Residual Network (CE-ResNet), the heart chambers are segmented, and then the features are extracted. Simultaneously, utilizing the Feynman Kan Formalism-based Tissue Doppler Imaging (FKF-TDI) approach, wall motion is estimated. Meanwhile, the features are extracted from the data and grouped via Average Distance-centric Fuzzy C Means (AD-FCM). After that, mapping of the features extracted from the frames and the grouped features occurs and the resultant data is divided into training, testing, along with validation stages. Then, utilizing the Cosine Similarity-centered Circulatory System Based Optimization (2(CS)BO) methodology, the Feature Selection (FS) is done. Therefore, to compute the Ischemic index, the features that are selected and the estimated wall motions are wielded. Lastly, for effective classification, the Ischemic index and the selected features are fed into the ReD-CNN classifier.  Finally, to ascertain the superiority of this technique, the proposed system’s performance is contrasted with the prevailing methodologies.
Moderated Mediation Model of Student Expectation and Satisfaction in Blended Learning: Context of Mathematics Learning in Chinese Higher education institutions
Authors: Luling Duan
Published: May 2024

With the continuous development of educational informatization, blended learning, which combines the advantages of online and offline teaching, has become a research hotspot in the field of education. This study aims to discuss the influencing factors of blended learning satisfaction. Using purposive sampling method, 529 undergraduate students majoring in mathematics from 16 higher education institutions in Guangxi were surveyed. Furthermore, validity and reliability tests, confirmatory factor analysis, correlation analysis, and structural equation modeling were employed to process the data. Empirical results indicate that in blended learning, student expectation and perceived value have significant positive effects on learning satisfaction. Student expectation also have a significant positive effect on perceived value. Moreover, perceived value plays a mediating role in the relationship between student expectation and learning satisfaction. Self-efficacy moderates the relationship between student expectation and learning satisfaction. This study contributes to enhancing the quality of blended courses and improving the satisfaction of university students with blended learning.
Advances in Traditional Electrical Validation of High-Speed Serial Connections Using Innovative Debugging Methods and Machine Learning Models.
Authors: Mohammad Ahmar Khan
Published: May 2024

As technology continues to shrink and more heterogeneous blocks are combined into single System on a chip (SOCs), the design of high-performance integrated circuits is getting more and more difficult. In the course of silicon validation, which includes testing (Wafer Testing, Automatic Test Equipment), digital validation (Functional Data route, FSM), analogue bench validation (Electrical, Interoperability), and system validation, a number of challenges that arise from the complicated design are uncovered. The process, methods, and constraints of each domain are unique. Electrical problems that have already passed through earlier stages of design are the primary focus of analogue validation. The analogue signal or mix signal nature of IPs makes it impossible for digital validation or any of the other post-silicon domains to discover electrical flaws on its own. As a result, studies focusing on analogue bench validation have grown in prominence. Recent developments in analogue bench validation, focusing on high-speed serial interface in particular, are highlighted in the proposed study. In addition, this article summarises recent technological developments that provide avenues for further study regarding analogue bench validation.
Ad-Tl: Alzheimer’s Disease Prediction Using Transfer Learning
Authors: Archana Gopinadhan
Published: May 2024

Although many statistical methods and machine learning algorithms have been explored in both clinical and research settings to extract these patterns from neuroimaging data, differentiating between Alzheimer's disease and healthy brain data in older adults (age > 75) has proven challenging due to highly similar patterns of brain atrophy and image intensities. Medical image analysis is just one field that has benefited from the widespread use of deep learning technologies in recent years. This research paper proposed AD prediction using transfer learning (AD-TL) methods. The MRI dataset has been normalized using the Multi-Layer Perception model (MLP) with the CNN algorithm. In order to improve images, the CLAHE (Contrast-Limited Adaptive Histogram Equalization) technique has been used. Image segmentation has been done with Watershed Image segmentation. The training has been done with the Residual network (ResNet 50) with Alex net. Finally, the classification has been done with the Deep Convolutional neural network (DCNN) algorithm.According to the experimental data, the classification accuracy of the technique provided in this study may reach 99%.
Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for Smart Machine
Authors: Xiujun Du
Published: May 2024

In smart meter technology, the reliability of Backplane to Bezel (BTB) Liquid Crystal Displays (LCDs) is crucial for efficient functioning within intelligent grid systems. Defects in these LCD screens can significantly impact overall smart meter performance, necessitating effective detection methods for proper management and utilization. Current detection approaches, which combine manual and automatic methods based on machine vision, have shown unsatisfactory performance. This research addresses this challenge by proposing a novel method for advanced fault detection in BTB LCDs of smart meters, Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for smart machine (BTB-LCD-FD- MCDSGAN) is proposed. The study begins by collecting datasets specifically tailored for LCD screen localization and defect detection. To enhance data quality, a Window Adaptive Extended Kalman Filter (WAEKF) is applied during preprocessing for noise removal. Feature extraction follows, utilizing Parameterized Multi Synchrosqueezing Transforms (PMST) with a primary focus on Gray Level Co-occurrence Matrix features. These extracted features serve as input for classification by a Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDSGAN), categorizing defects into five types likes normal display, no display, abnormal display, liquid crystal rupture, and incomplete display. Furthermore, the proposed method optimizes the MCDSGAN weight parameter using the Red Fox Optimization Algorithm (RFOA) to achieve accurate LCD fault defect prediction. The entire approach is implemented in Python, performance metrics likes accuracy, precision, recall, F-score, computational time are thoroughly analyzed. Performance of proposed BTB-LCD-FD-MCDSGAN approach attains 20.89%, 33.67% and 25.98% high accuracy, and 17.98%, 23.78% and 33.45% higher recall compared with existing methods such as automatic detection of display defects for smart meters depend on deep learning (AD-AM-DL), deep learning-enabled image content-adaptive field sequential color LCDs by mini-LED backlight (DL-AFSC-LCD) and new multi category defect detection method depend on convolutional neural network technique for TFT-LCD panels (MDF-CNN-LCD), methods respectively.
Deep Learning Backward Recognition Model to Improve Pronunciation Accuracy in the Teaching of Spoken English
Authors: Liu Jiahua
Published: May 2024

The advent of speech recognition algorithms has opened new avenues for enhancing pronunciation accuracy in the teaching of spoken English. As English continues to be a global lingua franca, proficiency in spoken communication holds paramount importance for individuals across diverse professional and academic domains. This paper introduces Forward Backward Recognition Deep Learning (FBRDL), a novel approach aimed at leveraging speech recognition algorithms to enhance pronunciation accuracy in the teaching of spoken English. FBRDL incorporates advanced deep learning techniques such as recurrent neural networks (RNNs) and transformers, which excel in modeling sequential data and capturing long-range dependencies. By leveraging these powerful architectures, FBRDL can effectively handle the inherent variability and complexity of speech signals, enabling robust and accurate recognition even in noisy or adverse environments. Moreover, FBRDL is characterized by its adaptability and scalability, making it well-suited for a wide range of applications across industries. Whether in the realm of virtual assistants, automatic transcription, or voice-controlled devices, FBRDL offers a versatile solution capable of meeting the demands of modern speech recognition tasks. FBRDL integrates principles from deep learning with advanced speech recognition techniques to provide learners with real-time feedback and guidance on their pronunciation. By analyzing spoken English inputs and identifying phonetic discrepancies, FBRDL offers targeted interventions tailored to individual learners' needs. FBRDL achieves an average increase in pronunciation accuracy of 20% compared to traditional teaching methods. Moreover, qualitative assessments underscore the effectiveness of FBRDL in facilitating more precise and efficient acquisition of spoken English skills.
Sentiment Analysis of Modern Chinese Literature Based on Deep Learning
Authors: Shen Xiaohui
Published: May 2024

Recent advancements in deep learning have facilitated sentiment analysis of modern Chinese literature. By leveraging techniques such as recurrent neural networks (RNNs) and transformer models like BERT, researchers can effectively gauge the sentiment expressed within literary texts. These models learn intricate patterns and context-specific nuances, enabling them to discern the emotional tone of Chinese literature accurately.Sentiment analysis, a crucial task in natural language processing, plays a pivotal role in understanding human emotions and opinions expressed in textual data. In this paper, we propose a novel deep learning framework, termed BERT-LLSTM-DL, for sentiment analysis in Chinese literature. The framework integrates Bidirectional Encoder Representations from Transformers (BERT) for language representation, Long Short-Term Memory (LSTM) networks for sequential learning, and deep learning techniques for feature extraction. We evaluate the proposed model on a dataset comprising Chinese literature texts and achieve promising results in terms of accuracy, precision, recall, and F1-score.
Magnetic Field Mutation Mechanism and Its Non-Stationary and Stationary State Analysis in Power and "Ring" Applications
Authors: Mingxian Liu
Published: May 2024

The phenomenon of magnetic field mutation, a complex yet pivotal aspect in various scientific and engineering domains, has garnered significant attention due to its profound implications on system behavior and performance. Magnetic fields, inherently dynamic in nature, exhibit mutations characterized by temporal variations in strength, direction, and spatial distribution. These mutations can stem from diverse sources, including external environmental factors, system dynamics, and material properties.This paper presents a comprehensive investigation into the magnetic field mutation mechanism and its implications for power and "ring" applications. The study encompasses theoretical analysis, simulation studies, and experimental validation to elucidate the dynamic behavior of magnetic fields in various contexts. The findings reveal insights into the temporal evolution and state analysis of magnetic fields, highlighting both stationary and non-stationary characteristics. Through analysis tables, we demonstrate the varying magnetic field strengths observed at different nodes within a ring application over consecutive time intervals. The implications of these findings for the stability and performance of magnetic field systems, particularly in power applications. The study underscores the importance of considering non-stationary factors in magnetic field analysis, emphasizing the need for adaptive modeling techniques and real-time monitoring strategies. The insights gained from this research contribute to a deeper understanding of magnetic field dynamics and offer valuable implications for the design, optimization, and operation of magnetic field-based systems in diverse applications.
Research on Indoor Space Layout Based on Redirection Algorithm
Authors: Zhang Le
Published: May 2024

Research on indoor space layout based on redirection algorithm explores innovative approaches to optimizing the arrangement of indoor environments for various purposes. By utilizing redirection algorithms, which focus on guiding individuals through spaces efficiently, researchers aim to enhance the functionality, usability, and aesthetics of indoor spaces. These algorithms consider factors such as traffic flow patterns, spatial constraints, and user preferences to dynamically adjust space layouts, furniture placements, and circulation paths. The goal is to create environments that promote better navigation, user satisfaction, and productivity while maximizing space utilization and minimizing congestion. This paper investigates the implementation of a redirection algorithm to optimize indoor space layout and organization. The primary objective is to enhance navigation efficiency, improve user experience, and maximize space utilization within indoor environments. Through the utilization of the redirection algorithm, furniture and fixtures within various rooms are strategically arranged to create more functional and aesthetically pleasing layouts. The effectiveness of the optimization process is evaluated through metrics such as navigation times, trajectory smoothness, and collisions with obstacles. Results demonstrate significant improvements in layout design, with optimized arrangements leading to reduced navigation times, smoother trajectories, and minimized collisions.
Design and Application of Intelligent Assisted Learning System for Higher Education Students
Authors: Lin Yushi
Published: May 2024

The Intelligent Assisted Learning System for Higher Education Students is an innovative educational platform designed to enhance the learning experience for college and university students. Leveraging artificial intelligence, machine learning, and data analytics, this system provides personalized learning pathways, adaptive tutoring, and real-time feedback to students based on their learning styles, preferences, and performance. By analyzing student interactions, engagement levels, and academic progress, the system dynamically adjusts course materials, assignments, and assessments to optimize learning outcomes. This paper explores the integration of Cooperative User Knowledge Modelling (CUKN) into Intelligent Assisted Learning Systems (IALS) tailored for higher education. Through a systematic investigation of CUKN's role within IALS, we elucidate its potential to enhance personalized learning experiences for students. By leveraging user data, collaborative filtering techniques, and adaptive learning strategies, CUKN empowers IALS to dynamically tailor content recommendations, learning paths, and collaborative tools to individual student needs, preferences, and proficiency levels. Simulation results suggest significant improvements in student engagement and performance, with metrics such as a 30% increase in learning efficacy and an 85% satisfaction rate with feedback mechanisms. While promising, challenges remain in refining algorithms and ensuring seamless integration into educational practices.
Construction of Athletes' Physical Condition Monitoring and Analysis System Using Biometrics Recognition Technology
Authors: Lv Meng
Published: May 2024

Biometrics recognition technology utilizes unique biological characteristics such as fingerprints, iris patterns, facial features, or voiceprints to identify and authenticate individuals. Through advanced algorithms and pattern recognition techniques, biometric systems capture and analyze these physiological or behavioral traits to verify a person's identity. This technology offers high levels of security and accuracy, making it valuable for various applications including access control, time and attendance tracking, border security, and digital payments. The construction of an athletes' physical condition monitoring and analysis system utilizing biometric recognition technology represents a significant advancement in sports science. By integrating cutting-edge biometric sensors and recognition algorithms, this system provides real-time insights into athletes' physiological parameters and performance metrics. Athlete performance monitoring and analysis play pivotal roles in optimizing training strategies, preventing injuries, and enhancing overall athletic performance. In this paper, we propose a novel approach leveraging the Hybrid Multi-Instance Ensemble Classifier (HMIEC) combined with biometric recognition technology to accurately classify athlete data and assess their physical condition. Our study explores the effectiveness of HMIEC across multiple biometric modalities, including ECG-based, fingerprint-based, and facial recognition, in identifying athletes and monitoring their physical parameters. Through a series of experiments and analyses, demonstrate HMIEC's superior classification performance compared to other classifiers, with high accuracy, sensitivity, specificity, and area under the curve (AUC). The reductions in heart rate from 75 to 65 beats per minute, increase in oxygen saturation levels from 98% to 99%, decreases in blood pressure readings from 120/80 mmHg to 110/70 mmHg, and enhancements in flexibility from 30 to 35 centimeters, muscle strength from 100 lbs to 120 lbs, and endurance capacity from 20 to 25 minutes.
Text Mining and Financial Analysis Modeling for Financial Statement Disclosure
Authors: Kang Huiying
Published: May 2024

This paper explores the integration of financial analysis, text mining, and blockchain technology to enhance financial statement disclosure and analysis processes. Leveraging text mining algorithms, the study extracts valuable insights from unstructured financial data, unveiling hidden patterns and sentiments within financial statements. Additionally, blockchain technology is employed to ensure secure and immutable storage of financial data, fostering transparency and trust in financial transactions and disclosures. Through the automation and digitization of financial reporting processes, stakeholders can streamline financial statement analysis, identify risks and opportunities, and make data-driven decisions with greater accuracy and efficiency.
Design a Platform for Sharing Educational Resources for Student Based on Dual Attention Graph Convolutional Networks
Authors: Xiaoyong Chen
Published: May 2024

A key component of raising educational standards is always the sharing of educational resources throughout higher education institutions. The development of digital and network infrastructure among colleges and universities can propel the sharing and exploitation of excellent educational resources to new heights. Nevertheless, there is a lack of an ideal platform for resource selection and integration in the current crop of information-based teaching tools. To overcome this drawback, the methodology used in this study was described in this research in order to classify student and teacher data more precisely. Ideological and Political Education (IPE) is the source of the data collection. The Modified Hamilton filter is used to preprocess the data during the pre-processing step. The excellent course videos, classroom performance, and interactions are all successfully classified by Dual Attention Graph Convolutional Networks (DAGCN).The neural network's weight parameter is optimized using the Lotus Effect Optimization Algorithm (LEA) to improve the DAGCN. The suggested DAGNN-LEA used with the MATLAB platform. The suggested approach was calculated using performance measures such as accuracy, precision, sensitivity, F-score, computation time, and recall. In comparison to the existing method, the suggested DAGNN-LEA method yields better results in terms of high accuracy 16.65%, 18.85%, and 17.89%, high sensitivity 16.34, 12.23%, and 18.54%, high precision 14.89%, 16.89%, and 18.23%, and low computing time 82.37%, 94.47%, and 87.76%.
Design of Music Emotion Analysis and Creation Aid System Based on Machine Learning
Authors: Jiang Qi
Published: May 2024

In human-centric contexts, visual vision plays an increasingly important role in decision making, learning, communication, and situation awareness. Music is a language that conveys emotion to all creatures, including plants and animals. These emotional characteristics are included in the emotion found in music, along with other feelings conveyed via melodies, rhythms, and composer-highlighted moments. Human interaction, communication, decision-making, and cognitive processes are all heavily influenced by emotion. However, Subjective music emotion classification is made more difficult by pure machine classification due to its objectivity; additionally, emotion classification has not reached the same level of accuracy as style classification due to the complex and poorly understood mechanisms underlying how humans perceive music and generate emotions. This manuscript proposes a Multi-Scale Adaptive Graph Neural Network (MSAGNN)optimized with the Remora Optimization Algorithm (ROA) for predicting music emotion MEA-MSAGNN-ROA. Initially data is taken from Music Video Emotion (MVE) dataset Afterward the data is fed to Attribute-based Neural Collaborative Filtering (AbNCF) based pre-processing process. The outcome from the pre-processing data is transferred to the MSAGNN. The music emotions are successfully classified by using MSAGNN are exciting, fear, sad and relaxation. The ROA is used to optimize the weight parameter of MSAGNN. The proposed MEA-MSAGNN-ROA is applied in Python working platform. Performance parameters, like accuracy, precision, sensitivity, F1-score, ROC and recall analyzed to compute proposed approach. The proposed MEA-MSAGNN-ROA approach yields improved results in terms of accuracy (22.46%, 38.58%, 21.74%), sensitivity (21.97%, 33.88%, 25.52%), and precision. reduced computation times of 21.86%, 36.76%, 28.95%, and 81.46%, 95.97%, 86.77%.The proposed MEA-MSAGNN-ROA method is compared with the existing methods such as MEA-CNN, MEA-BPNN, and MEA-FNN, respectively. From the result it is concludes that the proposed MEA-MSAGNN-ROA method based accuracy is higher than the existing methods.