bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of an Energy-efficient Prototype of Wind Power Plant
Authors:   NCJSC «M.Kh. Dulaty Taraz Regional University», OMAROV Zhaksylyk , VOYTKEVICH Sofiya ,   NPJSC «Abylkas Saginov Karaganda Technical University», VYTAUTAS Adomavicius ,   Renewable Energy Center of Kaunas University of Technology, KHOZHAGELDI Bolat ,   NPJSC «Abylkas Saginov Karaganda Technical University», MINAZHOVA Saulesh ,   NCJSC «Kazakh National Research Technical University named after K.I. Satpayev»
Published: November 2024

In view of the rapid development of the field of renewable energy, the development of a prototype wind power plant (WPP) is a necessary step to better understand the processes and optimize the operation of such plants. This research aims to create a functional prototype of a horizontal axis wind turbine that will serve as a basis for experimentation, performance analysis and development of improved technological solutions in the field of renewable energy. The work will study the interaction of a wind generator with atmospheric air flow, taking into account the design of the wind turbine blades. The study is based on a laboratory experiment and involves the use of an air flow concentrator. The experimental results provide an opportunity to visually study the influence of wind flow strength on the efficiency of a wind generator. Practical advice on designing and building a concentrator is offered. The findings from the study have the potential to significantly improve the technological aspects of renewable energy installations, highlighting their importance for sustainable development and improving energy efficiency.
Innovative Approaches to Digital Monitoring of Road Infrastructure
Authors:   NPJSC «L.N. Gumilyov Eurasian National University», AIYMBAY Sunggat , KUANYSH Maksat ,   NPJSC «L.N. Gumilyov Eurasian National University», DYUSSEKEYEV Kanagat ,   NPJSC «L.N. Gumilyov Eurasian National University», MATSON Eric ,   Purdue University, ZHUMADILLAYEVA Ainur ,   NPJSC «L.N. Gumilyov Eurasian National University»
Published: November 2024

Examines contemporary approaches for the digital monitoring of road infrastructure, highlighting the critical need for efficient, precise, and sustainable management techniques for road networks. Traditional methods of monitoring road infrastructure, heavily reliant on manual inspections and reactive maintenance strategies, are increasingly viewed as insufficient due to their labor-intensive nature, elevated costs, and lack of capability to provide instantaneous data feedback. The advent of digital innovations, such as sensors, the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Digital Video Recorders (DVRs) and Mobile Mapping Systems (MMS) signifies a transformative shift in road infrastructure monitoring practices. This research evaluates the most recent digital monitoring techniques, examines their real-world applications through case studies, and discusses the implications of these technologies on infrastructure management and maintenance practices. By synthesizing insights from the latest studies and examples, the paper spotlights significant technological advancements, assesses their advantages and limitations, and proposes future directions for the digital observation of road infrastructure. The findings underscore that digital monitoring not only enhances the efficiency and effectiveness of infrastructure management but also significantly contributes to creating more resilient and adaptable road networks.
Online Extraction of Aspects from the Texts of Scientific Articles for the Kazakh Language
Authors:   NPJSC «L.N. Gumilyov Eurasian National University», TASHIBAYEVA Raushan , BAKIYEVA Aygerim ,   NPJSC «L.N. Gumilyov Eurasian National University», BATURA Tatiana ,   A.P. Ershov Institute of Informatics Systems
Published: November 2024

Extracting aspects from the texts of scientific articles for the Kazakh language is an important task in the field of natural language processing and information retrieval. This task is relevant for revealing the key topics and ideas embedded in the texts of articles, which can facilitate their understanding and analysis. Currently, automatic text processing of scientific articles is becoming increasingly relevant, which includes many tasks such as term extraction, extraction of relations between terms, autoreference, etc., which is associated with an increase in the number of published works. Reading and analyzing publications in search of the necessary information is becoming an increasingly time-consuming and resource-intensive task. With the constant increase in data volumes, the development of more efficient processing methods is required to ensure the speed and scalability of information extraction systems. General methods may not always work effectively in different subject areas due to specific vocabulary, sentence structure, and context. Therefore, building knowledge bases requires not only extracting facts, but also structuring and linking them, however, this can be difficult in the absence of clear patterns or structure in the text. Aspect extraction helps in creating tools for more efficient search, indexing and categorization of scientific articles in the Kazakh language, which can improve the accessibility and convenience of working with literature.
Impact of corruption, public debt and democracy on economic growth: An analysis of SAARC member countries for the period of 1996 to 2020
Authors: Jatoi Waseem Ahmed , Ghumro Ikhtiar A.
Published: November 2024

The purpose of this study was to know the impact of corruption, public debt, and democracy on the growth of economy-centering developing countries, especially six (6) South Asian Association for Regional Cooperation (SAARC) countries, as they are highly affected by it, and this type of research has not been carried out before based on intense literature review. Data has been collected from the World Bank’s reliable indexes, such as; perspective mode and world governance indicator index, and democracy index from the Freedom House Index. Fixed Effect, Random Effect, and Pooled OLS models, have been used to analyze the data for the period of 1996 to 2020. Based on the findings, we may conclude that public debt is significantly and adversely affecting countries' growth, so SAARC countries should form policies to tackle that debt related issues and make it profitable for their countries; although corruption impact is insignificant and negative, and democracy impact is insignificant and positive, so countries' should relax their anti-corruption measures, especially in those sectors that foster economic growth although corruption in the form of money laundering, kickbacks, and non-development projects should be stopped and governments should provide more civil and political rights to its people in SAARC countries, so that will give freedom to choose sincere and honest governments and improve economic growth.
Educational Prospects in Promoting and Strengthening Peace in Pakistan: A mixed method study
Authors: Khan Mumtaz , Rasool Shafqat , Mahmood Tariq
Published: November 2024

Education plays a vital role in the establishment of Peace in a country. It does so by developing tolerance, unity of the human race, and respect for all human beings among the people. The study aimed to explore educational prospects for promoting and strengthening Peace in Pakistan. Two thousand teachers for the teacher survey (TS) were selected by multi-stage stratified and convenience sampling. Interviews of educationists and students were conducted to make cross discussion on results of TS. A mixed method, convergent parallel research design was used in this study. A Likert-type survey scale was used to collect quantitative data, and interviews were conducted to collect qualitative data about the research problem. Mean ANOVA, correlation, and regression were applied to analyze quantitative data, and thematic and content analyses were used to analyze qualitative data. The results of TS reveal that teachers are not sure that the aims of education include the promotion of Peace in the country as an objective and curriculum of the secondary and higher secondary level has sufficient material for promoting Peace in the country. The teachers agree that they adopt a peace-promoting teaching strategy and evaluation. However, the results of student interviews reveal that the teaching strategy adopted by the teachers for promoting Peace is very poor, and the evaluation of peace attitudes is informal, not formal. The results of educationists' interviews reveal that education can play a crucial role in promoting Peace in the country through aims and objectives, curriculum, teaching strategy, and evaluation.
Impact of Leadership & Motivational Skills, Teamwork & Problem-Solving Skills, and Communication Skills on Graduate Employability: Evidence from Universities of Sindh, Pakistan
Authors: Malokani Dodo Khan Alias Khalid , Siddiqui Masood Ahmed , Shar Muhammad Idrees , Zaidi Ali Raza
Published: November 2024

The current study aims to examine the role of three important skills, such as Leadership and Motivation Skills, Teamwork and Problem-Solving Skills, and Communication Skills, in graduates' employability in the service sector of Sindh, Pakistan. A survey approach was adopted, and a questionnaire was distributed among the service sector employees of Sindh, Pakistan, among those who are part of the hiring process in their respective firms. In order to test the hypothesis, Smart PLS (version 3) is used, and structural equation modeling (SEM) is tested. The findings of this study revealed that all three skills included in this study were found to have positive and significant impacts, such as Leadership and Motivations Skills, Teamwork and Problem-Solving Skills, and Communication Skills, on graduate employability in the service sector in Sindh, Pakistan. However, Communication Skills were considered with a higher beta value as compared to other skills may be due to customer dealing in the service sector. This study directed to the top management of Sindh's Universities that these skills should be developed among their graduates within the classroom and by designing additional curricular activities on the campus. Additionally, the findings of this study confirmed the existing literature review that these skills are important and cannot be ignored, especially in Universities.
The Relationship between Personality Traits and Academic Achievements of Pakistani ESLs: Factors, Implications, and Future Directions
Authors: Taskeen Samiya , Ahmed Khalid
Published: November 2024

This study intends to investigate the relationship between personality types and academic performance of Pakistani English as second language learners. The research design includes qualitative and quantitative data strands, which are processed and combined to answer the research question. Carl Jung's theory of personality types is applied, which is the most famous theory in the field of psychology. The study considers personality type as an independent variable and academic performance in terms of writing as the dependent variable. The population of the study comprises English as a second language (ESL) learners in Pakistan, with a sample frame of students from PAF College Fazaia Sargodha. A purposive sampling technique is used to select 66 intermediate students in grade 11, with 53 male and 13 female participants. The correlation between personality type and writing was measured at three levels of writing namely in grammar, in sentence structure, and in vocabulary. Researchers found no significant association between personality type and writing score in grammar, the relationship between personality type and writing score in the organization of ideas is also not significant however a weak negative correlation between personality type and writing scores in sentence structure was found. Overall results declare that personality type plays a less significant role in English as a second language classroom.
A Case Report of Taeniasis in a 29yrs old Woman from Owerri, Imo State Nigeria.
Authors: A.l Nwannah , S.o Asiawuchi ,   Microbiology/Parasitology Department, Federal University Teaching Hospital, P.M.B. 1010, Owerri, Imo State, Nigeria, C.n Ukaga ,   Department of Animal and Environmental Biology, Imo State University, P. M. B. 2000, Owerri, Imo State, Nigeria., S.o Nwannah ,   Department of Project Management Technology, Federal University of Technology, P. M. B. 1526, Owerri, Imo State, Nigeria., I.c Mgbemena ,   Department of Biotechnology, Federal University of Technology, P. M. B. 1526, Owerri, Imo State, Nigeria.
Published: November 2024

A 29yrs old woman from Obowo in Imo State Nigeria presented with a mild abdominal cramp and intermittent diarrhoea in one of the health facilities in Owerri. She had been discharging tapeworm segments since 2020 from her anus and sometimes these were found around her vagina without any major discomfort.
The Influence of Self-Efficacy and Family Environment on Student Interest Learning in SMK PGRI 2 Kediri
Authors: Fadhila Syarafina , Afandi Tjetjep Yusuf , Surindra Bayu
Published: November 2024

The purpose of this study is to see how self-efficacy and family environment at SMK PGRI 2 Kediri affect student learning motivation. With a population of students of SMK PGRI 2 Kediri, especially accounting majors, this study used quantitative methodology. A total of 75 people were selected as samples using saturated sampling technique. The findings of the analysis show that the Sig. value of the self-efficacy variable, family environment, and interest in learning are all less than 0.05: the value of the self-efficacy variable is 0.001, the value of the family environment variable is 0.007, and the value of the interest in learning variable is 0.000. Analysis of research data using the F test (simultaneous test) obtained the results of Fcount = 36.432 with a significance value of 0.000 so that the learning interest variable (Y) is simultaneously influenced by the self-efficacy variable (X1) and the family environment (X2).

Keywords: Self Efficacy, Family Environment, Learning Interest
Development of Artificial Intelligence in Business Ethics and Regulatory Responsibilities in the Era of Artificial Intelligence
Authors: . Purwanti , Arifudin Nur , Sonaria Mita
Published: November 2024

The purpose of this study is to analyze development of artificial intelligence in business ethics and regulatory responsibilities in the era of artificial intelligence. The research methods applied in this study are literature study, comparative regulatory analysis, regulatory analysis, and regulatory risk analysis. Through this research method, it is expected to provide a comprehensive picture of the regulatory framework, ethics, and responsibilities related to the development of artificial intelligence in a business context. The results of the study show that the implementation of a regulatory framework for the use of artificial intelligence in business faces a number of complex challenges, such as regulatory uncertainty, unclear regulatory obligations, data privacy and security, algorithmic bias, compliance with regulations and standards, ethical challenges in autonomous decisions, accountability in cases of failure, understanding by stakeholders, risks at the personal level, and changes in job and market dynamics.

Keywords: Development, Artificial Intelligence, Business Ethics, Regulatory Responsibilities, Era of Artificial Intelligence
Addition of Bean Sprout Extract (Vigna radiata) in Feed for Rematuration of Female Tilapia Fish (Oreochromis niloticus)
Authors: . Mukhlisah , Ramadhana Sri , Susastra Pandu Ranu
Published: November 2024

The availability of protein, vitamins, minerals and calcium in feed are also very important in the process of gonad development of tilapia. This research aims to examine the reproduction of tilapia fish that add bean sprout extract to feed at different doses. Addition of bean sprout extract to feed at different doses for rematuring female tilapia fish that are kept for 18 days. The feed used in the research was CPP floating feed with a protein content of 31%, mixed with bean sprout extract according to the treatment: A (control without bean sprout extract), B (10% bean sprout extract), C (30% bean sprout extract), and D (50 % bean sprout extract). Data were analyzed based on the parameters of gonad maturity level, gonad maturity index, and fecundity. Gonad maturity level IV, ready to spawn. Bean sprout extract did not have a significant effect on the gonad maturity index, with the highest gonad maturity index at a dose of 10% (3.96%) compared to the control (3.29%). The highest fecundity was also found at a dose of 10% (3,380 grains), decreasing at higher doses, but still higher than the control (1,824 grains). The addition of bean sprout extract increased fecundity but did not significantly affect the gonad maturity index.

Keywords: Tilapia, Feed, Bean Sprouts, Rematuration
Theoretical Study of Position-Dependent Mass Particle in Cosmic String Space-Time and External Magnetic Field
Authors: Faniandari Suci , Syamputra Dhani Nur Indra , Fahmi Muhammad
Published: November 2024

In this work, we discuss the case of a non-relativistic quantum particle in the cosmic string space-time under the influence of the presence of external magnetic and exponential potential. It’s shown with the help of the Laplace transform method the second-order differential equation of the Schrodinger equation is solvable where it is reduced to a first-order differential equation. We obtain the eigenfunction and the non-relativistic energy spectrum. In addition, these results may be useful in the future study of thermodynamic, optic, and magnetic properties of the atomic interaction of a system particle.

Keywords: Schrodinger equation; Laplace transform method; Position dependent mass; External magnetic field; Cosmic string
Enhanced Optoelectronic Properties of Few-Layer Graphene for Transparent Conducting Electrode: Density Functional Theory Perspective
Authors: Yutomo Erik Bhekti , Putra Oki Ade , Faniandari Suci
Published: November 2024

The challenge in utilizing graphene for transparent conducting electrodes (TCE) is to increase the electronic conductivity without significantly decreasing its transmittance. Forming a few-layer structure is an effective way to modify the electronic properties of graphene. In this paper, we used the density functional theory (DFT) method to study the effect of the number of few-layer graphene (FLG) layers on the electronic conductivity and transmittance. We find that increasing the number of layers leads to an increase in electronic conductivity. On the other hand, a decrease in transmittance was observed when the number of layers was increased. Based on the performance analysis, the optoelectronic properties of FLG with more than three layers were better than those of conventional TCE materials, such as indium tin oxide (ITO) and aluminium-doped zinc oxide (AZO). The calculated electronic conductivity and transmittance of a 4-layer FLG were 2.23×1019 (1/Ω.m.s) and 95.69%, respectively. This finding can be used as guidance for experimental research in developing graphene-based TCE materials.

Keywords: density functional theory, electronic conductivity, few-layer graphene, transmittance
Analysis of the Completeness of Medical Records in RSJ. Prof. Dr. Saanin Padang
Authors: Nasution Nurhasanah , Mandia Sayati
Published: November 2024

Complete medical records are very useful for knowing in detail the patient's history, examinations that have been carried out, and planning further actions. To achieve this, the medical records department is obliged to carry out quality monitoring by carrying out quantitative, qualitative, and statistical analysis. To know whether a medical record file is complete or incomplete, it is necessary to carry out a quantitative analysis, namely four reviews consisting of an identity review, recording review, reporting review, and authentication review. Based on the results of a preliminary study with existing medical record officers, it was stated that every patient's medical record file received from inpatient care was always assessed for completeness but was still not optimal, and incomplete values ​​were always obtained. One of the problems, among others, is in terms of input, namely the high workload of medical record officers, facilities, and infrastructure, especially storage space and technology, which are not yet optimal. It is important to discuss this problem so that researchers research to find out the completeness of medical record files in RSJ. Prof. HB. Saanin Padang City. This research was carried out in March 2024 at RSJ. Prof. HB. Sa'anin Padang. From the study, it was found that the highest percentage of completeness in filling out identification components was the name item at 97.98%, the highest percentage of completeness in important report components was found in the discharge summary form, CPPT and General consent each at 100%, the percentage of completeness in the recording method component was highest in You can use item type x at 100%. In the medical record file, no type-x was used. There is a need for policies regarding filling rules and periodic audits regarding the completeness of medical record files.

Keywords: Completeness, Medical record, files, hospital, analysis.
The Impact of COVID-19 Pandemic on the Performance of Food and Beverage Micro and Small Enterprises (MSEs): A Case Study of Langsa City, Aceh Province
Authors: Dalmi Muhammad , . Sukardi , . Rujiman
Published: November 2024

The PSBB and PPKM policies were perceived to have caused economic downturns by restricting community activities, including productive economic endeavors.
The primary issue in this study is the decline in the GDP of the accommodation food, and beverage sectors in Langsa City in 2020. The research objectives are formulated as follows: (1) to analyze the impact of the Covid-19 pandemic on the income of food and beverage MSMEs in Langsa City; (2) to analyze the impact of the Covid-19 pandemic on the production levels of food and beverage MSMEs in Langsa City; (3) to analyze the impact of the Covid-19 pandemic on the assets of food and beverage MSMEs in Langsa City; and (4) to analyze the impact of the Covid-19 pandemic on the workforce of food and beverage MSMEs in Langsa City.
The study was conducted in Langsa City, Aceh Province, utilizing primary data, A purposive sample of 50 MSME owners in Langsa City was selected. Respondents were not categorized by business scale (micro or small businesses). The accidental sampling method was used to determine respondents and data was collected through structured interviews using a questionnaire.
The impact of the Covid-19 pandemic on the performance of food and beverage MSMEs in Langsa City was analyzed using for indicators: (1) MSME income, (2) sales volume, (3) assets, and (4) workforce size. A descriptive analysis was performed, followed by a paired sample t-test (with a = 0.05) to compare conditions before and during the Covid-19 pandemic.
The analysis concluded that: (1) there was a significant decline in the income of food and beverage MSMEs in Langsa City during the Covid-19 pandemic; (2) there was a significant decline in sales volume; (3) there was a significant reduction in the value of MSME assets; and (4) there was a significant decrease in the number of employees.

Keywords: Business, Assets, Number of Employees, Income, Sales, MSMEs
Design and Simulation of Adaptive Filters in Real-Time Environment
Authors: Okoekhian Joshua , Guiawa Mathurine , Onyegbadue Ikenna
Published: November 2024

The results of the study showed that adaptive filters were designed and simulated to enhance signal processing capabilities in real-time environments. The study focused on the LMS and NLMS algorithms for echo and noise cancellation in audio signals, exploring the impact of different mu values (0.01, 0.05, 0.1, and 0.5) on algorithm performance. A comprehensive literature review underscored the importance of effective adaptive filtering techniques in real-time audio processing and highlighted the trade-off between convergence speed and stability. The results indicated that a mu value between 0.05 and 0.1 optimally balances convergence speed and stability for both LMS and NLMS algorithms. The NLMS algorithm consistently outperformed LMS due to its superior adaptation to signal power variations and better stability at higher mu values. Recommendations include using the NLMS algorithm for most applications, careful tuning of the mu value, and further research into adaptive and hybrid methods. These findings aimed to enhance echo and noise cancellation, resulting in clearer and more intelligible audio signals in real-world applications.

Keywords: Adaptive Filters, LMS Algorithm, NLMS Algorithm, Real-Time Signal Processing
The Role of Digital Training for Human Resources in the Relevance of Research Within the University
Authors: Laachari Driss , Faraj Adil
Published: November 2024

This article examines the role of digital human resources training in the mastery of digital research management (DRM) in universities. For they are primarily responsible for structural change and innovation in university research. Notwithstanding this, this paper aims to detect and identify the technical and digital deficiencies that mitigate the level of achievement in research.
Method: Our approach is based on an empirical study via a sample of 135 research specialists who will be interviewed and organized as follows: 34 teachers and 34 PhD students from the université Chouaib Doukkali in El Jadida, and 33 teachers and 34 PhD students from the université d' Ain Chock in Casablanca. Nevertheless, we focused on the use of SPSS software. The chi-square test and Cramer's Phi V will form the basis of our analysis, in order to process and interpret the data. At the same time, we will assess the correlation and strength of the relationship between the dependent and independent variables
Our study is based on a hypothetico-deductive approach that takes into consideration a mixed reflection based on qualitative and quantitative methods. The results of this study reveal that human resources are not being provided with specific training in MDR research, which is seen as a means of strengthening and modernizing research in higher education.
Result: The results attest those human resources are not trained by digital management strategies to carry out research projects, however, cel impacts relevance, excellence and above all innovation in scientific research.
Conclusion: In short, the aim of this article is to explore and determine the level of training of human resources for a digital transformation that positively impacts the world of creation, invention and innovation of new professional knowledge, reflexive and professionalizing, specific to scientific research on a national and international scale.

Keywords: Digital management, research, university, challenges, prospect
Improving Student Learning Outcomes Through Vidio Learning Models In Class IVA Mathematics MIS Islamiyah GUPPI Medan
Authors: Hasibuan May Fitriana , Saragih Marice
Published: November 2024

The aim of this research is to improve the mathematics learning outcomes of class IVA MIS Islamiyah GUPPI students by using a video learning model. This research is classroom action research. The data collection techniques used are conducting reviews, data collection and observation. The data collected is a test of student learning outcomes carried out in the second cycle. From the analysis of the data obtained, it can be concluded that there is a significant increase in learning outcomes after using the video learning model. It can be seen from the average (mean) value of test I, which is 76.67, while the learning results in test II have an average (mean) value of 87.33, which means there is an increase in each test carried out. So we can conclude that learning using instructional video media can improve the mathematics learning outcomes of class IVA MIS Islamiyah GUPPI students on the subject of simple fractions.
Keywords : Learning Outcomes, Simple Fractions, Learning Video Models
THE IMPACT OF STRESS DURING THE COVID-19 PANDEMIC ON THE DENTAL HEALTH OF MU-VARNA EMPLOYEES
Authors:   Medical College, Medical University – Varna, Bulgaria., Angelova Svetlana , Milev Minko ,   Medical College, Medical University – Varna, Bulgaria., Borisov Boris ,   Medical College, Medical University – Varna, Bulgaria.
Published: November 2024

Introduction: One of the negative manifestations of stress and anxiety related to dental health is the unconscious clenching or grinding of teeth, which gradually leads to tooth wear. Aim: The aim of this article is to determine the impact of the COVID-19 pandemic on the dental health of MU-Varna employees, to follow up on the therapeutic effect of wearing a bruxism mouthguard, and for the results to be applied in preparing a model for health promotion based on a multidisciplinary approach. Materials and Methods: From July 2022 to now, 995 patients have been examined, and 40 of them, university lecturers and administrative staff at MU-Varna, Bulgaria, have been prescribed a bruxism mouthguard. The following instruments were used: a unified form for defining the dental status and a survey about the therapeutic effect of the bruxism mouthguard. Results and Discussion: A voluntary, anonymous survey was conducted among MU-Varna employees in October 2023. The SPSS (version 22) software was used to process the data from the survey. The assessment of the therapeutic effect of the mouth guard was based on the results from the secondary exam at the clinician’s office and those from the conducted individual survey among the people wearing mouth guards for a specified period of time. Conclusion: The analysis indicates that the employees have a positive attitude toward wearing the mouthguard in the future. Its use helped decrease the frequency of conditions such as affect, anxiety, and quick tiredness.
Small intestinal bacterial overgrowth
Authors:   Department of Internal Medicine European Hospital Marseille France, Escoda Thomas
Published: November 2024

A 46 year old man with no comorbidity presented with chronic digestive troubles for around ten years following acute viral gastroenteritis
Evolutionary Optimization of Deep Learning Models: Enhance Performance through Hyperparameter and Structural Tuning with Evolutionary Algorithms
Authors: Kale Anil Wasudeorao
Published: November 2024

Evolutionary optimization is a strong way to improve the performance of deep learning models by using evolutionary algorithms (EAs) to fine-tune both structure elements and hyperparameters. This method is based on biological evolution and uses processes like crossing, mutation, and selection to make model setups better over time. When the model and hyperparameter space get more complicated, it can be hard to use traditional methods for adjusting hyperparameters, like grid search or random search, because they need a lot of computing power and don't work as well. Evolutionary algorithms, on the other hand, can quickly move through these high-dimensional spaces and find the best or almost best designs that make the model work better. This research looks into how evolutionary algorithms can be used to improve deep learning models. It focuses on two main areas: setting hyperparameters and making changes to the models' structures. Hyperparameters, like learning rate, batch size, and the number of layers, have a big effect on how the model learns and how accurate it is in the end. The evolutionary method encodes these hyperparameters into chromosomes and then iteratively evolves a population of models, picking the best ones based on a fitness function that usually shows how accurate or lost the models are. Random changes are made by mutations, and traits from high-performing models are combined in crosses. This creates variety and stops things from convergent too soon. Changing the model's design, such as the amount of neurons in each layer, the type of activation functions, and the patterns of connections, is called structural tweaking. These building blocks can be changed on the fly by evolutionary algorithms, which can find new, efficient designs that human creators might miss. This feature is especially helpful when making complicated networks like convolutional neural networks (CNNs) or recurrent neural networks (RNNs), where the best structure isn't always clear. Results from real life show that evolutionary optimization not only makes models more accurate, but it also makes them more stable and able to generalize. This method gives a scalable and adaptable strategy for optimizing deep learning models. This makes it a very useful tool for students and professionals who want to get the best performance in a wide range of situations.
Deep Learning for early Identification and Prediction of Knee Osteoarthritis
Authors: Ravindra D. Kale
Published: November 2024

Evaluation of persons with knee osteoarthritis (OA) whose structural progression is anticipated is essential to the development of medications that treat diseases in different ways. Deep learning approaches have rapidly gained popularity as the preferred tool for analyzing medical image segmentation. This study explores many contributions in the field of Deep Learning in medicine, covers the most common difficulties in recent years, and also analyzes the principles of Deep Learning ideas relevant to medical image categorization. We have used Deep Learning to make predictions from X-ray images. Deep Learning research can be applied to image classification, object identification, segmentation, registration, and other applications. Medical image analysis has a number of problems, such as poor picture enhancement and low image analysis dependability, that deep learning has been developed to define and solve. We propose research to address these current problems and advance the development of medical image segmentation. This feasibility research shows the interest in deep learning for osteoarthritis and could help even experienced radiologists with the difficult challenge of determining people who are at risk for illness development.
MHNFSP: Design of an Efficient Model for Advanced MRI Image Classification Using Hybrid Neural Networks and Optimized Feature Selection Process
Authors: Swati k. Mohod
Published: November 2024

In the field of medical imaging, particularly regarding magnetic resonance imaging (MRI) and the analysis of feature extraction in the MRI, there exists an immediate and pressing need for precision and clarity in the accuracy of such. Current methods, though useful, have inherent weaknesses with respect to dealing with noise, specific feature identification, and an ability to effectively merge different types of data. The explained developments show the need of innovation in MRI image classification, especially for distinguishing slight but critical variations in brain images and samples. These gaps underline the urgency for innovation in MRI image classification, especially for differentiating delicate nuances of brain images and samples. The proposed model has addressed these gaps through an innovative workflow for MRI image classification, identified by a novel, synergistic approach process. The workflow begins with the scrupulous acquisition of MRI and fMRI images, capturing the intricate structural and functional nuances of the brain. Image segmentation is employed for innovative execution using Fuzzy C-Means (FCM), picked for its excellent noise resistance and the advantage of soft clustering that is important for a more exact definition of the boundaries of the regions of interest in the image set. The feature extraction phase involves three phases with Fourier, Entropy, and Convolution features from an elevated perspective. This trio is very carefully picked to encapsulate a comprehensive image representation, harmonizing the shape, texture, and local structure. The workflow of this model is streamlined from the feature set with the employment of Whale Optimization, which quite efficaciously sieves the feature set, retaining the most informative while curtailing redundancy and overfitting issues within the process. Eventually, a Hybrid Convolutional Neural Network (CNN) emerges at the top of this model, incorporating Naive Bayes, k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) methods. This selective multi-classifier classification provides a robust and heterogeneous set of functions for the classification. Tested on different OASIS and ADNI datasets and samples, this model has outperformed current methods with a 4.9% increase in precision, a 4.5% enhancement in accuracy, a 3.5% rise in recall, a 4.3% augmentation in AUC (Area Under the Curve), a 3.4% improvement in specificity, and a 2.9% cutdown in delay. Such advancements not only signify a great leap in MRI image classification but also carry very much significance for medical diagnosis and research, and that could see its way into the clinical field. In this model, the amalgamation of disparate methods sets a new benchmark in medical imaging, opening doors to better accuracy, higher efficiency, and greater reliability of diagnostic tools.
Advanced Algorithmic Approaches for Accurate Classification and Statistical Analysis of Brain Tumors: Enhancing Diagnostic Precision
Authors: Dinesh B. Bhoyar
Published: November 2024

Brain tumor classification and examination are exceptionally vital in therapeutic examinations since they have a huge impact on how medicines are arranged and how well patients do. Utilizing cutting-edge machine learning and profound learning strategies, this consider looks into progressed factual ways to move forward the exactness of classifying brain tumors. We need to create brain tumor discovery more exact and dependable by utilizing these complex equations. This will offer assistance us get around the issues with current strategies. Our consider employments a huge collection of diverse sorts of brain tumors to prepare and test a number of progressed models, such as convolutional neural systems (CNNs), bolster vector machines (SVMs), and outfit learning strategies. To discover out how well each strategy works, we see at measures like precision, affectability, specificity, and the zone beneath the collector working characteristic bend (AUC-ROC). In expansion, we do a full measurable examination of the collection to discover vital patterns and connections that deliver us more data around the tumors and how they develop. The recommended strategies work superior at classifying tumors, which incredibly brings down the number of off-base analyze. Our comes about show that these progressed calculations can be valuable apparatuses in clinical settings, making a difference specialists make more precise analyze and come up with more personalized treatment plans. In expansion, utilizing factual examination makes a difference us get it how tumors carry on, which includes to our in general understanding of neuro-oncology. This consider appears how imperative it is to keep coming up with unused computer methods for therapeutic determination. We are making strides the way brain tumors are classified and analyzed, which leads to superior quiet care and comes about. This appears how AI has the capacity to totally alter healthcare. Within the future, analysts will center on combining distinctive sorts of information and making real-time determination apparatuses to assist specialists make superior choices.
Artificially Intelligent Strength of Tertiary Cement Concrete, Natural Aggregate Is Anglizated with Recycled Aggregate
Authors: Uday  Shankar  Patil
Published: November 2024

The researchers have studied RCA in concrete. Compressive strength is a fundamental criteria, although it has been shown that identical concrete may be created regardless of the quality of RCA. This study aims to demonstrate that ternary blended cement concretes made from RCA are a viable alternative in structural concrete and can remain so in the future. The results of this investigation seeks the best ratio of RCA aggregate to natural coarse aggregates in ternary blended cement concrete by testing the compressive concrete strength made with supplementary cementitious materials & RCA that have been blended together in a consistent manner using a chemical admixture that is effective at high concentrations of water reduction.  Compressive strengths at 7 and 28 days were measured for two distinct concrete grades (40 MPa & 60 MPa) using natural coarse aggregate replacement ratios (Rr) of 0.0, 0.2, 0.4, 0.5, 0.6, 0.8, & 1.0 for the recycled coarse aggregates. A total of 84 test specimens in the shape of a cube were cast & examined. According to the results of the experiments, To obtain an equivalent M60 concrete, we can use RCA with a percentage of Rr = 0.5, & with Rr = 1.0, we can achieve an equivalent M40 concrete.
Aluthge Transformation of (℘,≀,ϱ)-Paranormal and (℘,≀,ϱ)-*-Paranormal Operators
Authors: D. Senthilkumar
Published: November 2024

Present a newborn class of an operator -Paranormal and -*-paranormal operators. We examine properties and Aluthge Transformation,*-Aluthge Transformation, -Paranormal and -*-paranormal operators.
BlockSCM: Integrated Customized Blockchain based Supply Chain Management for Decentralized Networks
Authors: Harshad D. Markad
Published: November 2024

The blockchain is an emerging technology that is widely employed in several industries for transactional systems, including logistics in Supply Chain Management (SCM). One of the primary justifications for implementing a blockchain in distribution network management is its ability to lower overall production costs while ensuring the security of the system. However, existing inventory control systems that utilize blockchain technology are vulnerable to cyber-attacks. The objective of this study is to analyses actual risks and deficiencies in system design as a result. Ensuring trustworthiness and security in the supply management system is an essential necessity. This study focuses on the unique problems related to integrity and logistics in Supply Chain Management. The proposal presents a streamlined algorithm that utilizes blockchain technology to guarantee the integrity of data in Supply Chain Management systems enabled by the Internet of Things (IoT). The customized algorithms have been constructed using a private blockchain infrastructure. Topics covered include hash creation, smart contracts, mining, and other consensus algorithms. Proof of work (PoW) and proof of stake (PoS) are efficient methods for executing tasks in a decentralized network of peers. The proposed system was thoroughly examined and confirmed through experimental inquiry. The results were compared with several state-of-the-art methods, demonstrating the efficiency of our technique. This system demonstrates the utilization of blockchain technology to construct an e-transaction system. The concept is evaluated from a practical standpoint, considering both its development and usage contexts. This study serves as a prospective guide for the use of blockchain technology in facilitating intricate applications. The online transaction system allows end users to access and make transactions without the need for third-party validation.
Selective Features Based Machine Learned Intrusion Detection Framework for Wireless Sensor Networks: A Need for Cryptographic Approach
Authors: Minal Ghute
Published: November 2024

Intruder attacks are a curse to wireless sensor network (WSN) nodes that affect their regular performance differently. The attacker cheats the cooperative characteristics of sensing elements pretending as a trustworthy node. Such intruders drastically degrade the WSN performance by affecting the sensing, collecting, processing, and transmitting capabilities of ideal nodes in the network. Due to their pretending behaviour, detecting such harmful nodes in the network is difficult since they are sometimes foes and other times friends. This paper introduces a selective feature-based machine-learning (SF-BML) framework that can detect an intruder node with a higher accuracy. The WSN-DS dataset constructed for four different attacks (Flooding, Blackhole, Grayhole, and Scheduling) is experimented with different feature attributes. The optimum features are found and tested for detection accuracy relating to all five categories.  The experimental results using six significant features (SF) and a combination of eight other semi-significant features (SSF) showed that the normal-attacker node's detection accuracy increases with increasing features up to fourteen. The maximum training and test accuracy using fourteen features for the support vector machine (SVM) was 99.17% and 99.00% respectively.
Predicting Software Vulnerabilities with Advanced Computational Models
Authors: Vijay D. Sonawane
Published: November 2024

Predicting software flaws is a key part of making current software systems safer and more reliable. The goal of this study is to find out how advanced computer models can be used to predict software weaknesses so that possible security risks can be found and reduced before they happen. Machine learning techniques, such as deep learning and ensemble methods, are used in the study to look at code trends, past data on vulnerabilities, and software measurements. These models are taught to find small signs of security holes that might not be obvious using normal static analysis methods. The suggested method includes a big sample with data from many different open-source and paid software projects. This makes sure that the research is fair and varied. To make the models more accurate at making predictions, feature extraction methods are used. These include natural language processing for code comments and dependency analysis for third-party libraries. The study also uses oversampling and cost-sensitive learning to deal with the problem of datasets that aren't fair, which comes up a lot when trying to predict vulnerabilities. The testing results show that the advanced computer models are good at predicting vulnerabilities with high accuracy and memory. The models not only find known flaws very accurately, but they also work well with code they haven't seen before, which shows they could be used in the real world. The study also shows how important it is to keep learning and changing the models to keep up with how software development methods change and new security threats appear. This study adds to the field of software security by creating a strong framework for predicting software flaws. This framework can be used throughout the software development process to improve security generally.
Object Detection in Satellite Images with Hierarchical Collective Hunter Algorithm Optimized Ensemble Classifier
Authors: Mayur V. Tiwari
Published: November 2024

Object detection in remote sensing images plays an essential role in computer vision task, which significantly identifies the position and class of the located objects with arbitrary orientation information. Recently, object detection plays an important role in various applications such as Surveillance and security in airports, organizations, and so on. However, target tracking is a very challenging task in natural images to attain appropriate detection results together with they obtain certain issues such as limited availability of high-resolute images, background complexity, and so on.  To avoid these limitations, developed a Hierarchical collective Hunter algorithm with a Deep convolutional Neural network and a Bidirectional long short-term classifier (2HC-DBTM) model, which improved the interaction range of feature extraction by reducing the background complexity. Additional attributes mechanism such as the Hierarchical Collective Hunter (HCH) algorithm and hybrid attention mechanism are integrated with the proposed model for improve the ability and performance with minimum computational complexity and achieves better detection accuracy. The experimental results obtained in the evaluation of the model are achieved with high robustness and efficiency when compared with other conventional methods. The achieved performance evaluation of the model for evaluation metric F1-score is 97.71%, precision is 97.16%, recall is 98.27% and accuracy is 98.05%.