bluuebunny/crossref_metadata_2025 huggingface dataset:
Peran Antarmuka Objek Otomotif dalam Mengemudi Otonom dan Semi-Otonom dengan Metode Literatur Komprehensif
Authors: Utami Mailia Putri , Heriyanto Lucky , Rozi Muhammad Fahrul
Published: May 2024

Teknologi mengemudi otonom dan semi- otonom berkembang pesat, dan antarmuka objektif atau Human Machine Interface (HMI) memainkan peran penting dalam memastikan transisi yang aman dan efisien. Penelitian ini mengeksplorasi peran HMI dalam mengemudi otonom dan semi-otonom, dengan berfokus pada jenis dan fungsi HMI yang relevan dalam konteks mengemudi, tantangan desain HMI, serta pendekatan desain inovatif untuk HMI. Metode yang digunakan dalam penulisan penelitian ini dengan metode tinjauan literatur komprehensif digunakan untuk mengumpulkan informasi. Salah satu literatur yang digunakan pada penelitian ini yaitu menjelaskan selain adanya manfaat dari mengemudi secara otonom yakni dapat meningkatkan keselamatan jalan raya, pengurangan kemacetan lalu lintas dan peningkatan jejak ekologis. Dari manfaat tersebut pasti ada celah penerapan teknologi dalam peran Antarmuka Objek yang digunakan, yaitu dari celah adanya kurang adanya user experiment dari penggunaan teknologi berbasis IOT, karena pasti ada keterkaitan dengan HMI yang kompleks.  Penelitian ini mengidentifikasi beberapa jenis HMI seperti tampilan visual, umpan balik haptics, umpan balik audio. HMI memainkan peran penting dalam memastikan transisi yang aman dan efisien ke mengemudi otonom dan semi-otonom. Desain HMI yang cermat dan inovatif antara pengemudi, kendaraan, dan sistem otonom. Karena adanya celah transisi tersebut masih banyak informasi yang didapat pengemudi namun tidak akurat.
A 3-D Finite Analysis of Stress and Strain Around a Dental Implant with Different Abutment Angulations and Two Crest Module Design
Authors: Soni Dushyant , Modi Nitin , Lakhani Dron
Published: May 2024

Objectives: The purpose of this study was to record and evaluate the distribution and values of stress and strain generated within the cortical and cancellous bone around an implant with 00 and 150 abutment angulation and two different crest module designs (smooth collar and collar with micro threads) on applying a load of 178 N.
Material and methods: A finite element model of a cross section of maxilla including the residual alveolar ridge and an implant along with an abutment was designed using computer software. The modelled implant-abutment was placed within the alveolar bone of maxilla. A Finite Element stress and strain analysis was done for implants with 00 and 150 angulations with two different crest module designs in maxilla. A similar load was applied to all the models. After this, the software processed the data with the help of recorded values. This data was then subjected to comparative assessment.
Results: Stress distribution of the threaded and smooth collar implants with 0 degree angulation was appeared to be maximum displacement of 0.033mm in the cortical bone surrounding the implant neck on the labial side and strain was maximum in the cancellous bone around the neck of the implant. For the 150 angulation maximum displacement of 0.070mm was observed at the tip in both threaded and smooth collar implants and gradually decreased apically.
Conclusion: Maximum of the stress concentration was observed in the cortical bone around the implant neck irrespective of the abutment angulation and implant design. The stress and strain values of the angulated abutment were 1.5-2 times more as compared to the straight abutment.

Key words: FEA, abutment angulation, anterior maxilla, implant design, stress, strain
The Effectiveness of Ankle Strategic Exercises on Risk of Fall Among Elderly Type-2 Diabetes Mellitus Individuals: An Experimental Study
Authors: Kuber Sharvari , Ekbote Vaishnavi
Published: May 2024

Introduction: Since falling is the third leading cause of chronic impairment, sarcopenia and its related fall risk are becoming more and more of a public health concern as our society ages. Falls have a positive correlation with morbidity and mortality and a negative correlation with independence and functionality. Diabetes mellitus is a group of heterogenous disorders characterized by hypoglycemia due to an absolute or relative deficit in insulin production or action. In type-2 diabetes mellitus (T2DM) low amounts of intramuscular non-contractile tissue (IMNCT) causes reduction in muscle strength. Individuals having type-2 Diabetes Mellitus since more than 6 years are reported to suffer from sarcopenia. Sarcopenia is defined as low muscle mass plus low muscle strength and/or physical performance. T2DM reports to cause loss of muscle strength in ankle plantar flexors and dorsiflexors too. Treatment for follow-up injuries from falls is expensive. Debating the effective physical training regimens to lower falls and their consequences is crucial. Muscle mass and muscle strength play a crucial role in daily activities and thus resistance training may positively and significantly benefit the elderly.
Method: 70 samples were collected (42 females and 28 males) were approached and consented for the intervention. A pre-intervention manual muscle testing, modified 30 seconds sit to stand test and SARC-F questionnaire was filled out by the samples. Post-intervention i.e. after 8 weeks, a Modified 30 seconds sit to stand test and strength testing was used to estimate and conclude the effectiveness of ankle strategic exercises on risk of fall in elderly T2DM individuals.
Result: Purposive sampling method was used in which results were calculated by Wilcoxon Signed Ranked Test. The P-value for the effectiveness of strength of ankle plantar flexors, dorsiflexors and Modified 30 seconds sit to stand was <0.001 which interpreted that an 8-week of ankle strategic strengthening protocol was significant; thus, accepting the H1 hypothesis.
Conclusion: It is concluded that there is a significant effectiveness of ankle strategic exercises on risk of fall among elderly type-2 diabetes mellitus individuals.

Key words: Type-2 Diabetes mellitus, ankle strategic exercises, modified 30 seconds sit-to-stand test, sarcopenia, risk of fall
Prevalence of Forward Head Posture in Patients Diagnosed Clinically with Frozen Shoulder
Authors: Saste Rushikesh , Jerome Albin , Chidambaram R.
Published: May 2024

Aim: To find the prevalence of Forward Head Posture in patients diagnosed clinically with Frozen Shoulder.
Objectives: To measure the Craniovertebral Angle of patients diagnosed with Frozen Shoulder using MB Ruler software and diagnose the presence of Forward Head Posture in them.
Methodology: A study was conducted with 67 subjects clinically diagnosed with Frozen Shoulder. Their Craniovertebral angle was measured in lateral view with photometric assessment using MB Ruler software and accordingly, the presence of Forward Head Posture (FHP) was observed. A CVA measurement less than 50° indicates FHP.
Results: A total of 67% out of the selected population has FHP deviation. This shows that almost 2/3rd of our selected Frozen Shoulder population has Forward Head Posture deviation. This underscores a substantial association between FHP and frozen shoulder, indicating that individuals with Frozen Shoulder may be more susceptible to developing or experiencing severe symptoms of Postural Malalignment in their cervical and upper back area due to changes in the overall normal biomechanics of the shoulder complex.
Conclusion: This study provides compelling evidence supporting the prevalence of FHP in individuals with Frozen Shoulder, highlighting the necessity for interdisciplinary approaches that integrate postural correction techniques with Frozen Shoulder management.
Clinical Implications: Addressing forward head posture in frozen shoulder management requires a comprehensive approach that targets both shoulder dysfunction and postural deviations. This may include manual therapy to restore scapular mechanics, corrective exercises to rebalance muscular imbalances, and postural education to promote awareness and self-management.

Key words: Frozen shoulder, Forward head posture, Craniovertebral angle, Postural correction, Biomechanical changes
The Cell Viability of Dental Materials by The Lactate Dehydrogenase Assay
Authors: Kamalak Hakan
Published: May 2024

Objectives: the objective of this study is to search the cell membrane damage of resin composite materials on cultures of human gingival fibroblasts and neuron cells by the lactate dehydrogenase (LDH) test method.
Methods: A range of thirteen resin composites sterilized under UV light for 2 hours were added to the culture medium of human gingival fibroblasts and neuron for 24 h and 72h. The samples were moved to 48- well-plates in a sterile cabin, one by one, in direct contact with the cells. After 24- and 72-hours incubation period, in the incubator at 37 oC with 5% CO2; Cell membrane damage was detected by the lactate dehydrogenase (LDH) test method. Results: In the lactate dehydrogenase (LDH) test method, cytotoxicity is determined by measuring lactate dehydrogenase, which is a cytosolic enzyme released in the event of membrane damage or cytolysis in the cell. A higher LDH value indicates that the toxicity of the material is greater. When the LDH results in Gingival fibroblast cells were examined after 24 hours, F25 was found to be the most toxic, and when the LDH results in neuron cells were examined, ISM and F25 were also found to be the most toxic.
Significance: Dental restorative materials have some toxic effects on cell cultures. The LDH analysis supplies precious instructions about their toxic effects.

Key words: Lactate dehydrogenase, fibroblasts, neuron cells, cytotoxicity
Proximate Composition and Vitamins Profile of African Yam Bean Flour Used as Meals in Akwa Ibom State, Nigeria
Authors: M. E Udo, , D. C Opara, , M. E Ibokette,
Published: May 2024

This work was aimed at analyzing and compiling the proximate composition and Vitamin profile of African Yam Bean (AYB) flour used as meals in Akwa Ibom State Nigeria. African yam bean just like other protein-rich foods has been used as ingredient in the preparation of traditional dishes. However, detailed information regarding its macronutrient composition and Vitamin profile remained limited. The analysis was done using the method of Association of official Analytical chemist (AOAC, 2005). The vitamin contents were determined by the following methods: Thiamin (vitamin B1), Riboflavin (Vitamin B2) and Pyridoxine (Vitamin B9) contents were determined following the method of Wiryawan and Dinge. Ascorbic acid (Vitamin C) contents were determined by the 2,6, dichlorophenol dye method using metaphosphoric acid as the extracting solution following the AOAC, 1990 method. The result from the nutritional profiles of AYB reveals that it can be used as a very good source of nutrients that can be used in the formulation of therapeutic diets, food fortification and also in food industries.

Key words: African Yam beans, Proximate composition, Vitamins profile, food formulation. Fortification.
The Impact of Academic Life Satisfaction on Achievement Motivation of School-Going Adolescents of South 24 Parganas District
Authors: Mistry Shymal
Published: May 2024

This study aims to ascertain the impact of academic life satisfaction on the development of achievement motivation of school-going adolescents. The research employs a descriptive survey method within a correlational research design. Data is collected through the Deo-Mohan (1985) Achievement Motivation Scale and the Multidimensional Student’s Life Satisfaction Scale (Huebner, 1994). The sample consists of 576 Bengali-speaking adolescents aged 14-16 years studying in classes IX and X from South 24 Parganas District. The findings reveal significant correlations between academic achievement motivation and multidimensional students’ academic life satisfaction. Adolescents with very high levels of achievement motivation also report very high levels of multidimensional life satisfaction in the case of family, friends, school, and living environment facets, except for academic self-satisfaction, which was at a high level. Adolescents with above-average levels of achievement motivation have very high levels of satisfaction with family and school and high levels of life satisfaction with friends, living environment, and self-satisfaction. Average motivated students have quality life experiences with their family, friends, school, and living environment. However, their achievement motivation was only average due to low academic self-satisfaction. Below-average motivated adolescents exhibit learned helplessness and report low levels of multidimensional life satisfaction.
Rate of growth of polynomials non vanishing inside a circle
Authors: Mir Mohd Yousf , Shah Lubna Wali , Shah Wali Mohammad
Published: May 2024

For a polynomial $P(z)=\displaystyle\sum_{v=0}^na_vz^v$ of degree $n$ having all zeros in $|z|\geq k, k\geq 1$ Govil et al.[\emph{ILLINOIS J. of Math.}] proved: $$|P'(z)|\leq n\dfrac{n|a_0|+k^2|a_1|}{(1+k^2)n|a_0|+2k^2|a_1|}|P(z)|.$$ In this paper besides the refinement of above inequality, we also generalize some well known inequalities.
Modified special functions: properties, integral transforms and applications to fractional differential equations
Authors: Ata Enes , Kiymaz I. Onur
Published: May 2024

In this paper, we defined the modified gamma and beta functions, which involving the generalized M-series at their kernels, and then defined the modified Gauss and confluent hypergeometric functions by using the modified beta function. Furthermore, we presented some of their properties such that, integral representations, summation formulas and derivative formulas. Also, we applied beta, Mellin, Laplace, Sumudu, Elzaki and general integral transforms to newly defined the modified special functions. Then, we obtained solution of fractional differential equations involving the modified special functions, as examples. Finally, we gave the relationships between the modified functions with some of the generalized special functions, which can be found in the literature.
Haemorrhagic pituitary adenoma
Authors:   Neuroradiology Department, Ibn Sina University Hospital Center, Avenue Abderrahim Bouabid, 10000, Rabat, Morocco., houchoua Selma K
Published: May 2024

Intra tumoral haemorrhage occurs frequently in pituitary macro adenomas. It can either manifest as pituitary apoplexy [1], a life threatening condition with sudden onset of headaches, nausea and vomiting; or as a silent recent or old haemorrhage. In the latter, like in our case, MR imaging is best to delineate these haemorrhagic changes. In fact, typical features are T1 and T2 hyperintensity (Figure 1A & 1B), persisting in fat saturated sequences (Figure 1C) with peripheral enhancement (Figure 1D). Usually a subsequent cystic change occurs, responsible for a fluid-fluid level (Figure 1E). Among the differential diagnosis of supra sellar masses, cystic craniopharyngiomas typically have calcifications although better appreciated on CT and a vividly enhancing solid component. Arachnoid cysts are very thin walled cystic supra sellar lesions with no solid or enhancing component, usually extending superiorly to the third ventricle and can be causing obstructive hydrocephalus. Rathke cleft cyst remains the most challenging differential, recent efforts were made to help make the correct diagnosis [2]. In that matter the classic appearance of a midline sellar and supra sellar lesion with fluid-fluid level, haemorrhagic changes and a T2 hypointense rim (Figure 1E) clinches the diagnosis of a haemorrhagic pituitary macroadenoma.
From a rat otorrhagia to a febrile rash
Authors:   Univ Rennes, INSERM, Rheumatology Department, Rennes University Hospital, UMR 1241, Institut Nu Me Can (Nutrition Metabolisms and Cancer), Rennes, France., Robin François
Published: May 2024

A 19-year-old patient, without medical history, presented a first episode of continuous fever (about 39°C) for 14 days, with rash and arthromyalgia and functional impotence of the ankles for 7 days. Clinically, the patient had asthenia with ankle biarthritis with a predominantly palm-plant rash (Figure 1). Right ankle joint puncture finding inflammatory fluid (5300 nucleated elements/mm3 including 91% neutrophils), without microcrystals. Immunological test (rheumatoid factor and citrullinated anti-peptide antibodies) was negative. The skin biopsy performed in an area of pathological skin is in favor of an infectious origin, with microabscesses.
Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information
Authors: Xuefeng Zhao
Published: May 2024

When tunneling, detecting abrupt changes in geological circumstances can be difficult. In recent years, the proliferation of tunneling characteristics has been strongly related to the surrounding geology. These parameters offer significant potential for using data-driven artificial intelligence (AI) approaches to infer patterns from information without reference to known consequences. This research introduces the Simulated Fire Hawk Optimizer-based Deep Action Selection Network (SFHO-DASN) model, which uses a Simulated Fire Hawk Optimizer to anticipate the geological conditions needed for tunneling. The optimized hybrid neural network technique can anticipate geological conditions effectively, as demonstrated by a case study of the constructed model. It is especially significant for rock types, water intrusion, karst caverns, and surface subsidence, for which the predicted accuracy is greater than 95%. These findings imply that the geological circumstances behind the tunnel face could be reliably and correctly predicted by a DASN that has received the proper training. This procedure's most significant advantage is its ability to adjust every scored parameter's weighting in response to variations in geological circumstances. The accuracy performance of the proposed neural network outperforms the conventional neural network, as indicated by the area under the curve (AUC) and performance analysis. A proposed model for geology prediction can attain predictive accuracy with a small number of tunneling parameters, according to an analysis of the feature relevance of each tunneling parameter. The suggested approach ought to be more practical for proposals about tunnel support architecture in the East Asian geological region and for future tunnel building.
Construction of Traditional Culture English Learning Resources Based on Data Mining Technology
Authors: Li Zhang
Published: May 2024

This study explores the construction of traditional culture English learning resources through the integration of data mining technology, Bayesian neural networks (BNNs), and Dragonfly optimization techniques. The aim is to develop culturally authentic and linguistically rich educational materials that enhance language proficiency while deepening learners' understanding of traditional cultural nuances embedded within the English language. By leveraging BNNs, which account for uncertainty and variability in traditional cultural texts, and employing Dragonfly optimization to optimize search algorithms, educators can curate personalized and adaptive learning resources tailored to individual learners' needs and preferences. The utilization of data mining technology facilitated the extraction and analysis of vast datasets encompassing diverse aspects of traditional culture, including texts, images, and multimedia content  The innovative approach presented in this study promises to revolutionize language education by fostering a deeper appreciation for traditional culture and promoting cross-cultural understanding and communication.
Genetic Algorithm Based on Operator Optimization in Illustration Art Design
Authors: Shaoqiang Chen
Published: May 2024

The current research explores the use of Genetic Algorithm (GA) Based on Operator Optimization in graphic art design, to improve the creative process using computational methods. By improving genetic operators such as crossover and mutation, the technique streamlines the creation of visually appealing artwork, allowing artists to efficiently express their distinctive vision. Through testing and research, the study reveals the effectiveness of this strategy in automating repetitive processes, exploring new creative pathways, and creating audience-resonant artwork. The combination of computational intelligence and creative intuition improves efficiency while also encouraging creativity and experimentation in the realm of graphic art design. The work sheds light on the revolutionary potential of Genetic algorithms (GA) based on Operator Optimization, highlighting areas for future research and development at the interface of technology and artistic effort. The results indicate that a better genetic algorithm (GA) provides efficacy and optimizes the operator in art design using a genetic algorithm.
Design of Personalized Recommendation System for College Education Based on Multivariate Hybrid Criteria Fuzzy Algorithm
Authors: Mengxi Yang
Published: May 2024

The learner model's design is a most important aspect of a personalized college student education recommendation system. Currently, most learner models need more scientific focus, relying on a single method to collect dimensions and feature attributes with low computing costs. Hence, this paper introduced a Multivariate Hybrid Criteria Fuzzy Algorithm (MHCFA) for personalized college education recommendation. The MHCFA is trained by Social Feedback Artificial Tree (SFAT), where SFAT is the combination of Social Optimization Algorithm (SOA) and Feedback Artificial Tree (FAT). In addition, Deep Fuzzy Clustering (DFC) is utilized to group college education content. The RV-Coefficient is employed to select the best content. Moreover, the feature is extracted by All Caps and numerical for further personalized recommendations. In addition, the test results show that SFAT_MHCFA performs better in Precision, Recall, and F-Measure where the values gained 0.989, 0.878, and 0.859, respectively.
Research on the Application of Multi-Physics Field Coupling Simulation in the Analysis of Temperature Field of 500kv GIS Circuit Breaker
Authors: Zhaochuang Zhang
Published: May 2024

Gas insulated switchgear (GIS) is a crucial part of any electrical infrastructure. Overheating the conductor on the GIS busbar would reduce the effectiveness of the insulating gas and shorten the life of the device. We then maximize output by fine-tuning the busbar's rotation angle, central distance, and conductor thickness. Dependable electrical systems always include gas insulated transmission lines (GIL) and gas insulated switchgear (GIS). Because of their reduced total cost and greater efficiency in terms of both space use and power transmission, GILs are gradually replacing conventional overhead lines for long-distance, high-power transmission. The reliability of GIL performance and the ease with which designs can be optimized necessitates research into their power loss and temperature profiles. The study found that the maximum temperature and power loss variances were approximately 70% attributable to differences in conductor thickness. By employing the aforementioned combination method (A1, B5, C5), energy consumption and thermal load in the GIS can be reduced. The research also shows that SF6 gas retains its outstanding insulating capacity after undergoing structural optimization. The calculation of the gas breakdown margin demonstrates this. In conclusion, the results of this study contribute to the understanding of heat transmission in three-phase GIS busbars and their optimization and engineering. Examining the temperature field of 500kV GIS circuit breakers, it highlights the value of multi-physics field coupling simulations in improving the reliability and performance of power system components, especially GILs. This is done so that power system components are more reliable and run more efficiently. Optimal heat transmission and other design considerations for three-phase GIS busbars are the most important takeaways from this research.
Research on the Transformation and Upgrading of News Media based on the Internet of Things era
Authors: Zheng Ji
Published: May 2024

The rise of the so-called "Internet of Things" heralds the arrival of innovative improvements in the dissemination technologies that are the basis for the development and improvement of a wide variety of forms of media. It has gone a long way from the early days of digitization and networking, and now it even incorporates intelligence. In the past, it did not have this capability. The widespread adoption of technology based on the Internet of Things will lead to profound shifts in the efficiency of media communication, as well as the transformation and upgrading of communication intelligence, the explosive growth of the communication platform, the emergence of a high degree of integration between "content production" and new communication techniques, and other developments related to these trends. It is generally agreed upon that the Internet of Things will be the primary impetus behind the next age of information technology as well as the expansion of the global economy. It is communicative, modular, and organized when it comes to the transmission of information. The ever-expanding media sector won't be a problem for this roomy facility. People's perspectives on the function of the media will shift as a result of the Internet of Things since it will make it feasible to intelligently update various types of media that are already in existence. Consumers will have access to a broad range of data presented in a number of formats as a direct consequence of the Internet of Things. It is feasible to expand the media applications of the Internet of Things by concentrating on one of these three areas: the development of a new media service system; the production of new media content; or the undertaking of a new media life challenge. This article examines the deconstruction of the primary body of dissemination pertaining to the Internet of Things from five distinct vantage points. Topics covered include the Internet of Things' news dissemination materials as well as dissemination objects. There are a number of different ways in which the framework of news dissemination may be made more effective, including bringing it up to date, enhancing its function, improving its platforms, fortifying its content, and increasing the breadth of its coverage. In terms of rebuilding and reconstruction, talk about the positive and constructive functions that modern information technology plays in the news broadcasting industry. In addition, the purpose of this article is to investigate the impact that the Internet of Things has on the method by which news is disseminated and the media business as a whole, as well as the relationship that exists between the social application of IoT technology and the news media. This article will hopefully widen the breadth of usage for internet of things technology in the news distribution sector by investigating a variety of different techniques. It is anticipated that in addition to accomplishing this goal, this work will also contribute to the proliferation of conventional media.
Metaheuristic optimization algorithms comparison adopted for the profit maximization of electricity market participants
Authors: Sumit Banker
Published: May 2024

The electricity market faces numerous challenges due to the growing demand for energy, increasing penetration of renewable energy sources, and the need for grid reliability and efficiency. To address these challenges, optimization algorithms have emerged as essential tools for optimizing various aspects of the electricity market, including generation, transmission, distribution, and demand-side management. The review can be done by providing an overview of the key components and challenges of the electricity market, including generation dispatch, unit commitment, economic dispatch, transmission network optimization, and demand response management. It then systematically examines a wide range of optimization techniques employed in addressing these challenges, including linear programming, mixed-integer linear programming, nonlinear programming, dynamic programming, genetic algorithms, particle swarm optimization, simulated annealing, and machine learning-based approaches. This paper presents a comparison of optimization algorithms, RCEDUMDA (Ring-Cellular Encode-Decode Univariate Marginal Distribution Algorithm) and CL_HC2RCEDUMDA (Hill Climbing to Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm) for the profit maximization of Electricity Market consumers & prosumers.
A Novel Ensemble Approach with HGBDTRF for Enhanced Detection and Prediction of Heart Disease
Authors: V. Ramesh
Published: May 2024

Heart disease is responsible for around one-third of all deaths that occur throughout the globe, as shown by the statistics. The employ of machine learning headed for anticipate cardiac illness have emerged as an important technique for both treating and preventing the ailment as more research is carried out in this area. A unique method that we are working on is called Hybrid Gradient Boosting Decision Tree with Random Forest (HGBDTRF), and it is being developed via the use of ensemble learning in the research paper that we are now working on. Machine learning will be able to make more accurate predictions about heart disease as a result of this. It has been shown by the actual findings that the HGBDTRF algorithm is capable of achieving a prediction accuracy of 95% in the Cleveland cardiac disease dataset, which has 1322 samples.
A Hybrid Light Gradient Boosting Approach with Deep Boltzmann Machine for Intrusion Detection System
Authors: V S Stency
Published: May 2024

The need for network attack analysis and the number of cyber threats and attacks are increasing. Due to the scalability and adaptability of its internet-based computing resources, cloud computing is gaining popularity among businesses worldwide. Scientists are increasingly interested in cloud data security and face the difficulty of protecting hosts, enterprises, and data against more advanced digital threats. Over the past few decades, researchers have experimented with the Intrusion Detection (ID) paradigm, leading to various methodologies. However, critical to analyze the abnormalities in the intrusion detection framework. This research aims to classify the attacks or abnormalities in the network included in the NSL-KDD dataset using a Hybrid Light Gradient Boosting approach with Deep Boltzmann Machine. A Deep Boltzmann Machine Classifier and an ensemble model of the Light Gradient Boosting technique were used to create this model. Gradient boosting techniques improve the performance of DL classifiers by minimizing the number of errors discovered during network intrusion detection. This work evaluates and contrasts the proposed classifier with recognized classification strategies. The proposed model surpasses previous Recall, F-Measure, Precision, and Accuracy techniques.
Augmented Physics-Informed Neural Networks and Seahorse Optimization Algorithm for Music Scene Simulation and Emotion Regulation in Virtual Reality Technology
Authors: Xinye Wang
Published: May 2024

Virtual Reality (VR) technology has shown promise in simulating music scenes for emotion regulation. The challenge is to enhance the realism and interactivity of VR-based music experiences to create a more immersive and emotionally impactful environment for users. This manuscript proposes an augmented physics-informed neural network and seahorse optimization algorithm for music scene simulation and emotion regulation in virtual reality technology (APINN-SHO-MSS-ERVRT). Initially, the data is collected via head-mounted display (HMD) in real time basis. Afterward, the data is fed to data-adaptive Gaussian average filtering (DAGAF) based pre-processing process. In the pre-processing segment, it improves virtual presence. The pre-processing output is given to augmented physics-informed neural network (APINN)and seahorse optimization algorithm (SHO) for effectively classifying the facial expression for fear and null. Therefore, SHO is proposed to enhance weight parameter of augmented physics-informed neural network classifier, which precisely classifies the facial emotion classifier. The proposed method is implemented in MATLAB and evaluated their performance with existing methods. The performance metrics, like accuracy, precision, sensitivity, specificity, computational time and ROC is analysed to the performance of the proposed method. The proposed APINN-SHO-MSS-ERVRT methods of accuracy are provide 90% higher accuracy for fear and 97% higher accuracy for null emotion is analysed when compared with existing methods GERR-NN-AMSVR, ER-VR-EEG and MTS-VRS-CDI. Proposed APINN-SHO-MSS-ERVRT method attains 0.87%, 0.88% and 0.89%higher ROC analysed to the existing methods respectively.
Classroom Management Effectiveness Assessment Model of English Teaching Based on Gustafson-Kessel Clustering Based Takagi-Sugeno Fuzzy Model with Wild Geese Algorithm
Authors: Jing Tang
Published: May 2024

The classroom's evaluation of the teaching effect English has a demanding workload, complex statistics, among other factors. The assessment of classroom instruction quality plays a crucial role in creating a supportive environment that encourages and guides the improvement of university administration and services, as well as in igniting teacher enthusiasm, boosting their efficacy as teachers, and raising the standard of talent development. Strong ambiguity, fuzziness, and inexactness are the main questions that come up while thinking about the case of teaching quality evaluation. The Gustafson - Kessel Clustering Based Takagi-Sugeno Fuzzy (GK-TSF) model along with wild geese algorithm (GKTSF-WGA) method is proposed for classify the skills, attitudes, and verbal, classifications to improve the above issues. Initially, the data’s are gathered via the dataset of teaching assistant evaluation dataset. Afterward, the data’s are fed to pre-processing. In   pre-processing segment; it removes the noise and enhances the input data’s utilizing adaptive robust cubature kalman filtering. The feature extraction section receives the output of the preprocessing. Fluency is extracted as a feature using the One - dimensional quantum integer wavelet S-transform in this case. After that, the extracted features are given to the GK-TSF model with wild geese algorithm for effectively classify skills, attitudes, and verbal. The proposed ET-GKTSF-WGA approach is implemented in MATLAB. The proposed ET-GKTSF-WGA method attains 23.05%, 28.95%, and 26.56% higher accuracy for skills; 23.12%, 25.43%, and 27.45%, higher accuracy for attitudes; 25.86%, 27.73%, and 21.04%, higher accuracy for verbal. The computational time performance of the proposed method attains 70.85%, 45.7%, 60.12%, and 35.34%, lower computation. The proposed ET-GKTSF-WGA method is in contrast to the current techniques like ET-FCE, ET-q-ROFs, and ET-K-MC, respectively.
Personalized Recommendation of College Students' Innovative Entrepreneurship Education Course Content Based on Bayesian Classifier
Authors: Cheng Peng
Published: May 2024

Computer-aided technology has revolutionized product communication and promotion strategies across industries. With the advent of advanced software tools and digital platforms, businesses now have unprecedented capabilities to create immersive and interactive experiences that effectively showcase their products to consumers. From 3D modeling and rendering software to virtual and augmented reality applications, computer-aided technology enables the creation of highly realistic visualizations and simulations, allowing consumers to interact with products virtually before making purchasing decisions. the integration of Multi-Modal Feature Fusion Classification (MMFFC) computer-aided technology heralds a new era of innovation and effectiveness. MMFFC harnesses the power of multiple data sources and modalities, such as text, images, and videos, to provide a comprehensive understanding of products and consumer preferences. By fusing these diverse features through advanced machine learning algorithms, MMFFC enables businesses to create highly targeted and personalized promotional campaigns. Through sentiment analysis of textual descriptions, image recognition of product attributes, and video analytics of consumer interactions, MMFFC facilitates the generation of rich, multimedia content that resonates with customers on a deeper level. Moreover, MMFFC allows for real-time adaptation and optimization of promotional strategies based on feedback and engagement metrics, ensuring continuous improvement and relevance in an ever-evolving market landscape. The MMFFC computer-aided technology, businesses can enhance their product communication efforts, forge stronger connections with consumers, and drive sales growth in an increasingly competitive marketplace. MMFFC has demonstrated an increase in engagement rates by 20% and a conversion rate improvement of 15%, highlighting its effectiveness in elevating product communication and promotion strategies.
Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network
Authors: Xiaoguang Hu
Published: May 2024

Rail transportation, a cornerstone of modern logistics and passenger transit systems, plays a pivotal role in facilitating the efficient movement of goods and people across vast distances. Operating on a network of interconnected tracks, rail systems offer a reliable and environmentally sustainable mode of transportation, particularly for long-distance travel and freight shipments. The paper presents a comprehensive investigation into the application of advanced computational techniques in the realm of rail transportation management. Specifically, Mamdani fuzzy logic and Backpropagation (BP) Neural Networks are employed to address critical challenges in scheduling and classification within rail networks. The utilization of Mamdani fuzzy logic facilitates nuanced decision-making in scheduling processes, considering uncertainties and complexities inherent in rail operations. Through linguistic rules and fuzzy sets, the scheduling system can effectively adapt to various operational constraints and disruptions, leading to more resilient and efficient scheduling solutions. Additionally, the integration of BP Neural Network enhances classification accuracy and prediction capabilities, enabling precise forecasting of train movements, passenger flows, and other key variables.
Quality Evaluation of College Students' Innovation and Entrepreneurship Education Based on the K-Means Clustering Algorithm
Authors: Fan Xiujuan
Published: May 2024

Innovation and entrepreneurship are increasingly recognized as vital components of modern higher education, fostering creativity, problem-solving skills, and economic growth. However, effectively catering to the diverse needs of students in these domains remains a challenge. This paper proposes a novel approach to address this challenge by applying the Cluster Swap Expectation Maximization (CSSEM) algorithm to cluster college students based on attributes relevant to innovation and entrepreneurship. Using a dataset encompassing metrics such as GPA, innovation score, entrepreneurship score, socioeconomic status, and gender, we demonstrate the effectiveness of the CSSEM algorithm in segmenting student populations. The findings reveal distinct clusters of students with varying levels of academic performance, innovation potential, and entrepreneurial aspirations. For instance, Cluster 1 comprises students with lower GPAs (mean GPA = 3.2) and moderate innovation and entrepreneurship scores (mean innovation score = 78, mean entrepreneurship score = 80), while Cluster 2 consists of high-performing students (mean GPA = 3.7) with strong innovation and entrepreneurship potential (mean innovation score = 90, mean entrepreneurship score = 88). These insights enable educators and policymakers to design tailored interventions and support mechanisms that cater to the specific needs of different student groups.
Identification of Potential Injury Risk Factors and Prediction Model Construction of Athletes using Data Mining Algorithm
Authors: Chen Qiang
Published: May 2024

Identification of potential injury risk factors and construction of prediction models for athletes using data mining algorithms involve analyzing various factors such as biomechanical, physiological, and environmental variables to determine their correlation with injury occurrences. Data mining techniques, such as decision trees, logistic regression, or neural networks, are then applied to identify patterns and relationships within the data. By integrating this information, predictive models can be developed to forecast the likelihood of athletes sustaining injuries based on their individual characteristics and training conditions. This paper introduces a novel approach for athlete injury risk prediction using the Noninvasive Data Mining Model with Multi-relational and Multi-dimensional Clustering (Ni-DMMMC) algorithm. By leveraging advanced data mining techniques, Ni-DMMMC aims to identify hidden relationships between various athlete characteristics, including biomechanical risk, training load, physiological markers, and environmental factors, to predict injury susceptibility accurately. Through multi-relational and multi-dimensional clustering, the algorithm effectively categorizes athletes into distinct clusters based on their unique risk profiles. These clusters provide valuable insights into the diverse injury risk profiles present within athlete populations, enabling targeted intervention strategies and personalized athlete management approaches. The proposed approach holds significant promise for enhancing injury prevention efforts, optimizing athlete performance, and improving overall well-being in sports medicine practice. athletes in Cluster 1 exhibit high biomechanical risk (0.85), moderate training load (0.60), low physiological markers (0.25), and moderate environmental risk (0.45), while athletes in Cluster 2 display moderate biomechanical risk (0.70), high training load (0.90), high physiological markers (0.80), and low environmental risk (0.30). These numerical values serve as indicators for assessing an athlete's injury risk profile comprehensively.
Tactical Intelligent Decision Modelling in Sports Competitions Based on Reinforcement Learning Algorithms
Authors: Chong Xu
Published: May 2024

Tactical intelligence refers to the ability to gather, analyze, and apply information swiftly and effectively to achieve specific goals or objectives in dynamic and often challenging situations. It involves understanding the current environment, assessing available resources, and making informed decisions to gain a competitive advantage or mitigate risks. Tactical Intelligent Decision Modelling in Sports Competitions, grounded in Reinforcement Learning (RL) algorithms, represents a cutting-edge approach to enhancing strategic decision-making processes within athletic domains. This innovative methodology leverages RL's ability to learn optimal actions through trial and error, adapting strategies based on feedback received from the environment. By applying RL algorithms to sports competitions, teams can develop intelligent decision models that dynamically adjust tactics and strategies in response to changing game conditions and opponent behaviors. Such models enable teams to optimize their performance, exploit opponent weaknesses, and capitalize on opportunities during competitions. This paper introduces Predictive Weighted Big Data Reinforcement Learning (PWBDRL), an innovative approach aimed at optimizing decision-making processes and performance outcomes in sports competitions. Leveraging predictive analytics, big data techniques, and reinforcement learning, PWBDRL offers a comprehensive framework for athlete prediction and strategy optimization. The results show a remarkable increase in win rates, with the RL-based decision models achieving an average win rate of 80.5% compared to 65.2% with traditional methods. Additionally, we observe a substantial enhancement in average scores, with the RL-based models achieving an average score of 95.6 compared to 78.3 with baseline approaches. Moreover, the RL-based models exhibit superior adaptability, requiring fewer iterations to converge to optimal strategies, with an average convergence time of 200 episodes compared to 500 episodes for traditional methods.
Construction and Optimization of Financial Transformation Decision Model Based on Data Mining
Authors: Ruifen Zhao
Published: May 2024

Real-time data processing and analysis is crucial in the financial markets since transactions in real time are directly linked to profit. Understanding the integration of finance and business allows the organization to enhance its core competitiveness by improving management and enabling it to lead business expansion more effectively. This manuscript proposes a Sparse Oblique Trees Algorithm (SOTA) optimized with the Waterwheel Plant Algorithm (WWPA) for the construction of financial transformation. The data are collected from multi-national organization dataset.  Afterward, the data’s are fed to pre-processing. In pre-processing segment; it removes the noise and enhances the input data’s utilizing Data-Adaptive Gaussian Average Filtering (DAGAF). The outcome from the pre-processing data is transferred to the SOTA. The investment and dividend are successfully classified by using SOTA. The WWPA is used to optimize the weight parameter of SOTA. The proposed SOTA-WWPA is applied in python working platform. The proposed technique was computed by examining performance indicators like precision, F1-score, accuracy, sensitivity and recall. The proposed SOTA-WWPA technique yields improved results in terms of accuracy (16.65%, 18.85%, and 17.89%), sensitivity (16.34, 12.23%, and 18.54%), and precision (14.89%, 16.89%, and 18.23%). The proposed FT-SOTA-WWPA method is contrasted with the existing methods like FT-CNN, FT-MBLSTMNN, and FT-SVM models respectively.
Dual Attention Graph Convolutional Network optimized with the Sun Flower Optimization for Differential Privacy Protection Mechanism for Smart Grid against Security Attacks
Authors: Hailin Wang
Published: May 2024

A smart grid combines information and communication technology with the conventional power system to provide efficient and dependable electricity generation, transmission, distribution, and control. Utilities and users share information and electricity in a smart grid. This manuscript presents a Dual Attention Graph Convolutional Network (DAGCN) optimized with the Sun Flower Optimization Algorithm (SFO) predicting the differential privacy protection mechanism for smart grid (DPSG-DAGCN-SFO). Initially, the data is collected from NSL-KDD datasets. Afterward, the data is fed to an Adaptive Robust Cubature Kalman Filter (ARCKF) based preprocessing process. Then the preprocessed data’s are fed to Adaptive and Concise Empirical Wavelet Transform (ACEWT)for extracting features such as authentication, integrity and trusted authority. The extracted features are fed to Dual Attention Graph Convolutional Network (DAGCN)to classify the privacy protection in smart grid such as stealthy attack and no attack. The weight parameters of DAGCN are optimized using SFO. The proposed DPSG-DAGCN-SFO is implemented in python, effectiveness assessed by several performance metrics such as accuracy, error rate, precision, sensitivity, F1-score and recall. The proposed method error rate attains 2% stealthy attack and4% no attack of the of the privacy protection in smart grid. The proposed method shows better results in all existing systems like Bayesian Clustering Algorithm (BCA), Self-Adaptive Grid-Partitioning Algorithm (SGNA) and Elliptic Curve Digital Signature Algorithm (ECDSA).From the result it is concludes that the proposed DPSG-DAGCN-SFO method based error rate lower than the existing methods.