bluuebunny/crossref_metadata_2025 huggingface dataset:
Comprehensive Analysis of Capital Asset Pricing Model and Arbitrage Pricing Theory in Stock Investment Decision Making
Authors: Syahwildan Muhamad , Widiastuti Widiastuti
Published: December 2024

The increasingly advanced development of technology and the phenomenon of the Covid-19 outbreak, which has caused most employees to be affected by Work From Home, has led most people, especially the millennial generation or people under the age of 30, to be interested in allocating their excess funds into stock investments. This study aims to analyze the development of a multi-factor CAPM model by including volatility as an additional factor: a new approach in asset pricing with a case study of companies listed on the Indonesia Stock Exchange included in the LQ45 Index for the 2020-2023 period. The independent variables used in this study are Risk Premium (Beta), Company Size (SMB), book-to-market ratio (HML), and Stock Price Volatility. In contrast, the dependent variable used in this study is proxied by Stock Return with Excess Return. The population in this study consists of 45 companies. The sampling method uses a Purposive Sampling Technique. The analysis method used in this study is Multiple Linear Regression. The result of the study shows that the Risk Premium and Company Size variables partially have a significant positive effect on Stock Returns. The Book to Market Ratio variable partially has a negative but not significant impact on Stock Returns. Meanwhile, the Stock Price Volatility variable has a positive but insignificant effect on Stock Returns. The independent variables in this study can explain 88,61% of the factors affecting Stock Returns, while 11,39% is explained by other factors not researched in this study.
Pemberdayaan Perempuan Di Era Digital Melalui Pelayanan Marketing Digital, Service Digital Dan Komunikasi Digital Menuju Desa Wisata Di Kecamatan Pulau Pinang
Authors: Merdasari Iin , Antoni Darius , Oktala Rosananda
Published: December 2024

This research aims to find out strategies for empowering women in developing villages in the digital era through digital marketing, digital services, and digital communication towards tourist villages in Pulau Pinang District, 2. to find out the driving and inhibiting factors for women's empowerment in developing villages in the digital era through digital marketing, digital services and digital communications to tourist villages in Pulau Pinang District. This research uses primary data and secondary data. Primary data in this research was obtained by conducting observations, interviews, and documentation. Data analysis techniques used include data analysis, data reduction, data presentation, verification, drawing conclusions, and testing data validity. This research shows that implementing government support to improve women's communication can help reduce village poverty. Government support for women's empowerment in tourist villages has been carried out. This activity aims to provide understanding and knowledge about how to market creative products in the digital era through digital marketing, digital services, and digital communications so that participants can apply them to increase sales of creative products in Pulau Pinang Village, Pulau Pinang District. Empowering Women Entrepreneurs in Pulau Pinang village aims to empower women entrepreneurs in the technology and digital fields. So that they can keep up with current developments where the world is entering industry 4.0, which uses more technology and digital capabilities; apart from that, people can get jobs without having to leave the village or the city, but by partnering with business owners who need their skills in the field of digital marketing.
Pengaruh Citra Destinasi dan Kepuasan Pelanggan Terhadap Niat Perilaku Pada Lembah Tumpang Resort Kabupaten Malang
Authors: Naifa Fairuz Hasna' , Afandi Yosi
Published: December 2024

This research used a quantitative approach. The data was collected by distributing questionnaires to 100 respondents from tourists who visited the Tumpang Valley Resort, Malang Regency, offline (directly). The sampling used non-probability sampling with a purposive sampling technique. The data analysis method in this research used multiple linear regression analysis and hypothesis testing. The results of partial hypothesis testing showed that the destination image positively and significantly affects behavioral intentions. Customer satisfaction has a positive and significant effect on behavioral intentions. The results of simultaneous hypothesis testing showed that the destination image and customer satisfaction have a positive and significant impact on behavioral intentions. From the results of this research, it can be concluded that the destination image and customer satisfaction influence the behavioral intentions in Tumpang Valley, Malang Regency
The Effectiveness of Word Chains in Enhancing the Vocabulary of Grade 10 Students
Authors: Kilat Katelyn V. , Rosete Shammah Llujah T. , Dula Josephine P. , Baluyos Genelyn R.
Published: December 2024

The Program for International Student Assessment (PISA) 2022 data highlights alarming reading proficiency statistics in the Philippines, ranking it 347th out of 476 countries, with many students failing to meet minimum standards. The study aimed to determine the effectiveness of Word Chains in enhancing Grade 10 students’ vocabulary during the Academic Year 2023-2024 in a public secondary school in Ozamiz City. This study used a classroom-based action research design with 36 students as participants using purposive sampling. Researcher-made test and interview guide questions were used to gather data. The data is interpreted using statistical tools such as mean, standard deviation, t-test, and thematic analysis. Thus, the findings revealed that the students did not meet expectations before the implementation of Word Chains; the students showed outstanding improvement after the integration of Word Chains; there was a significant difference in the student’s level of comprehension before and after the implementation of Word Chains; and additional developments were observed among the students following the use of the Word Chains in class. Therefore, the significant difference in students’ vocabulary before and after using Word Chains exhibits how effectively the tool enhances their vocabulary. Recommendations include teachers adopting innovative teaching methods across the curriculum, such as Word Chains, to foster better vocabulary acquisition and overall student academic growth.
How Can Mass Communication Research Explain Disinformation?
Authors: Grangeiro José J. M. , Pinheiro Déborah S. , D’Assumpção France H. , Peres Júlia C. A. P. , Araújo Kilma M. D. de , Silva Sara R.
Published: December 2024

The article explores the role of mass communication theories in understanding disinformation, tracing historical examples and theoretical foundations. It examines how communication technologies, initially tools for societal connection, became instruments for authoritarian control, particularly in the 19th and 20th centuries. Key theories by Walter Lippmann, Harold Lasswell, and Paul Lazarsfeld are analyzed to illustrate how mass communication manipulates public opinion, shaping pseudo-environments and reinforcing ideological cohesion. The paper emphasizes the enduring influence of opinion leaders in spreading disinformation and discusses contemporary digital challenges, including the fragmentation of public discourse and the psychological impacts of cognitive overload.
Marathon Enthusiasm in Malaysia: An Explanatory Study Using PLS-SEM Analysis
Authors: Wasli Zaidi , Abu Bakar Muhammad Fauzan , Mohd Rathi Mohd Razif , Jimel Clement
Published: December 2024

This study investigates the factors influencing marathon enthusiasm in Malaysia by examining the roles of lifestyle, self-esteem, confidence, discipline, and sports culture. Using an explanatory research design and a quantitative method, data were collected through a survey employing a 5-point Likert scale. A purposive sampling technique was used to select 69 marathon runners as participants. The study utilized Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis, with bootstrapping applied to assess the significance of relationships between the variables, and Importance-Performance Map Analysis (IPMA) used to identify the key drivers of marathon enthusiasm. The results revealed that lifestyle significantly impacts marathon enthusiasm, highlighting the importance of health-conscious behaviors in promoting participation. Marathon enthusiasm was found to positively influence confidence, discipline, and perceptions of longevity, underscoring the psychological benefits of endurance sports. Although self-esteem and sports culture contributed moderately to marathon participation, health issues had a negligible impact on motivation. The IPMA results further emphasized lifestyle as the most important factor, with high performance in promoting marathon participation. These findings suggest that lifestyle factors are critical in shaping marathon enthusiasm, while the psychological benefits of participation further reinforce this engagement. The study recommends targeted interventions to promote active living and enhance self-esteem and sports culture to boost participation rates in Malaysia. The insights provide valuable guidance for future efforts to foster marathon participation and overall public health.
Foods and Food Habits of Rajbanshi Community of Indo-Bangladesh Border Area in Cooch Behar: A Brief Historical Analysis
Authors: Barman Ratan , Chandra Adhikary Dr. Madhab
Published: December 2024

Rajbanshi is the only majority indigenous community in Cooch Behar and Northern part of West Bengal popularly known as ‘North Bengal’. The Northern Part of Bengal is bounded by the Indo- Bangladesh border line. Most of the people of this study area belong to the Rajbanshi community including Naisya shekh. Generally it is believed that the people of the Rajbanshi community belong from a mixed Mongoloid and Dravidian community. They have their own society and culture which is different from other communities. With the use of multitudinous wild plants they prepared their foods and traditional delicious food items which are Chhyaka, Pelka, Sidal, Shukta, Shukati, Foktani, Panta Bhat, Horpa etc. These traditional and delicious food items are biologically beneficial for the human body. But nowadays, it has been found that under the affect of modernity and globalization process, the own and basic culture of the Rajbanshi community of Cooch Behar is losing its identity and moving towards changes. In this paper I have highlighted some basic major foods, traditional foods, the process of preparation of the food items and their biological significance.
Holistic Approaches to Monitoring Dementia Progression Beyond Cognitive Measures
Authors: Jayasena Bimsara Dinura , Dahanayaka Rashmi Tharindya , Krishara Jenny , Abeysinghe Nithya Ravishini , Buddhika Janith , Samaraweera Mihiri
Published: December 2024

This research aims to advance the diagnosis and management of vascular dementia by integrating machine learning and natural language processing into traditional diagnostic frameworks. The enhanced system targets the early detection of vascular dementia, a condition often overlooked by current neuropsychological tests. Unlike conventional methods, this proposed system provides continuous monitoring and real-time data analysis, enabling dynamic disease management. This approach not only increases diagnostic accuracy but also allows for timely interventions that could significantly alter disease progression and improve patient outcomes. Additionally, the system’s capability for personalized care helps tailor treatment plans to individual patient needs, optimizing therapeutic efficacy. The research also explores the potential for reducing healthcare costs through early diagnosis and efficient resource management. Overall, this study contributes to the field by offering innovative solutions that could revolutionize dementia research and treatment, potentially leading to broader applications for various cognitive disorders.
AVALIAÇÃO TOMOGRÁFICA DO REPARO DE PERFURAÇÃO CORONÁRIA INTRAÓSSEA UTILIZANDO AGREGADO TRIÓXIDO MINERAL: RELATO DE CASO
Authors: De Holanda Fabrício Rennan Silva , Bezerra Vinicius Grangeiro Leite , Marinho Maria Cecília Pereira , Dantas Márcia Valente de Brito , Dos Santos Anne Caroline Brito Cabral , Marinho Tomás Manuel Braz , Da Silva Filho Ricardo Sérgio Fernandes , Matos Felipe de Souza
Published: December 2024

This clinical case report shows the repair of intraosseous coronary perforation in a maxillary canine, treated with Mineral Trioxide Aggregate (MTA), through clinical evaluation and cone beam computed tomography (CBCT) after 6 and 12 months of follow-up. Case report: The perforation occurred in the second session of endodontic treatment during the preparation of the access cavity and was located in the cervical region of the vestibular wall of the pulp chamber, communicating it with the bone tissue, at the subcristal level. Immediately after the accident, the area was cleaned with 2% chlorhexidine digluconate, the perforation was sealed with MTA Angelus and the vestibular wall of the pulp chamber was sealed with composite resin. Discussion: Intraosseous coronary perforation is an accident that occurs during the coronal opening stage, characterized by communication between the pulp cavity and the periradicular tissues, including the periodontal ligament, alveolar bone and/or gingival tissue. Sealing intraosseous perforations with bioactive materials such as MTA increases the success rate of treating these perforations. However, the long-term prognosis is not yet well established in the literature and few studies have evaluated the success of treating perforations with MTA using CBCT. Conclusion: Clinical and tomographic evaluation after 6 and 12 months showed bone healing and periodontal tissue health, demonstrating success in the treatment of intraosseous coronary perforation.
EDUCAÇÃO PARA A EXTREMA DIREITA: UMA REVISÃO BILBIOGRÁFICA DO USO DE VIDEOGAMES COMO FERRAMENTA DE RADICALIZAÇÃO DA JUVENTUDE
Authors: Oliveira Machado Paula Isabelle , Dias de Oliveira Paz Carlos Eduardo , Rodrigues dos Santos Bruno
Published: December 2024

Videogames are one of the most popular and relevant types of media today, driving a multi-million dollar global industry and instrumentalizing social, political and cultural issues. However, there is a constant and growing presence of hate speech and harassment in online gaming environments. This study aims to carry out a systematic literature review through an electronic search of articles in databases, using the recommendations of the PRISMA 2020 protocol, focusing on the interactions between far-right movements and the videogame culture and gamer communities, and the influences of this extremist discourse in these contexts. It was possible to observe recurring questions about the history of the video game industry, forms of recruitment and radicalization in gamer spaces, community involvement with far-right agents and ideologies, and practices of appropriation and use of characteristics specific to video games or its community in the propagation of extremist discourses.
Pólipos gástricos, a propósito de un caso
Authors: Suárez-Guzmán Francisco Javier
Published: December 2024

Los pólipos gástricos son lesiones asintomáticas descubiertas accidentalmente en las endoscopias digestivas altas. Los pólipos hiperplásicos son los más comunes en los países occidentales (1-6%), su frecuencia varía dependiendo del tipo de población, características del estudio, tipo de equipo, etc. Varón de 29 años, con epigastralgia y pérdida de unos 12 kg, diagnosticado de pólipos hiperplásicos por endoscopia y confirmados por biopsia. El 90% de los pólipos gástricos hiperplásicos son asintomáticos. El aumento del uso de la gastroscopia ha incrementado el número de diagnósticos. Suelen ir asociados a infección por H. pylori. Posteriormente se deben realizar biopsias y endoscopias para descartar lesiones malignas y tener un seguimiento. La polipectomía fue optativa del paciente, ya que, debido al tamaño de los pólipos, existía un importante riesgo de hemorragia.
Reemergencia del virus parainfluenza serotipo 1 (HPIV-1) como agente causal predominante de infecciones respiratorias agudas en niños durante un brote en Lambayeque, Perú
Authors: Aguilar-Gamboa Franklin R. , Aparicio-Sigueñas Johana E. , Villegas Chiroque Miguel
Published: December 2024

El HPIV es un importante patógeno respiratorio pediátrico que puede causar hospitalizaciones por infecciones respiratorias agudas (IRA) y que recientemente se ha relacionado a neumonías graves, con tasas de mortalidad similares a la influenza. Sin embargo, reportes de brotes por este virus son poco frecuentes. Se realizó seguimiento a 4 casos pediátricos con IRA durante un brote en noviembre-diciembre 2023 en Lambayeque, Perú. Se analizaron hallazgos clínicos, de laboratorio e imágenes. Tres niños requirieron hospitalización, mientras que todos presentaron cuadros de vía respiratoria superior con tos, rinorrea y dificultad respiratoria, además de conjuntivitis. Exámenes mostraron infiltrados pulmonares compatibles con neumonía viral. Pruebas de inmunofluorescencia directa identificaron HPIV-1 en todos los casos. Este brote mostró la reemergencia de HPIV-1 como agente causal predominante de neumonías pediátricas, probablemente debido a disminución transitoria de la inmunidad de rebaño por la pandemia. El reconocimiento oportuno de HPIV-1 es clave para un adecuado manejo clínico y control de la morbilidad respiratoria infantil evitando uso innecesario de antibióticos.
Auto Mobilize Pro
Authors: Routaray Ankita Priyadarsini , K Dr. Chitra
Published: December 2024

A car rental analysis solution uses current data analytics to simplify and enhance rental operations management. It consists of numerous modules that monitor vehicle utilisation, client preferences, and operational effectiveness. The system employs big data and machine learning algorithms to forecast demand trends, increase fleet utilisation, and enhance customer satisfaction through tailored recommendations. It also provides real-time insights into maintenance requirements, which reduces downtime and costs. The system also includes financial analysis tools for analysing sales, expenses, and profitability, resulting in a comprehensive view of organisational performance. Overall, a car rental analysis system enhances decision-making, boosts operational efficiency, and encourages business expansion.
Sentiment Analysis and Rating Predicting for Hotel Review
Authors: Routaray Ankita Priyadarsini , K Dr. Chitra
Published: December 2024

This paper provides a comprehensive system for sentiment analysis and rate prediction of hotel reviews, combining modern natural language processing techniques with machine learning algorithms. The major purpose is to automatically assess consumer input and forecast related scores based on textual reviews. The system employs regression models to predict star ratings from the textual data and sentiment analysis to categorise reviews into good, negative, or neutral categories. The dataset consists of a large corpus of hotel reviews collected from various online platforms. Experimental results demonstrate the effectiveness of the proposed approach, with high accuracy in sentiment classification and rating prediction. The findings indicate that automated sentiment analysis and rating prediction can provide valuable insights for hotel management, helping to enhance customer satisfaction and improve sequence.
Demand Forecasting for Aqua Tech Technical Textiles
Authors: Chaithanya L  , Dr. Chitra K
Published: December 2024

Demand forecasting plays a significant role in the Aquatech industry, where innovation integration with aquacultural practices drives efficiency and effectiveness. This abstract outline the importance, challenges, techniques, and implications of demand forecasting in the sector. Demand forecasting is essential for businesses to anticipate trends, optimize resource allocation, and enhance operational efficiency. By accurately predicting future demand for aquacultural products, services, and technological outcomes, companies can make informed decisions regarding production, operations, and marketing strategies. However, demand forecasting in this industry faces several challenges, including seasonal variations, data availability, technological complexity, and uncertainties. Aquaculture demand is influenced by factors such as precipitation patterns, regulatory policies, and consumer preferences, making it difficult to develop precise forecasting models. Various techniques are used for demand forecasting, including statistical methods, data analytics, and machine learning algorithms. Time series analysis, regression analysis, and hybrid forecasting approaches are commonly employed to analyze historical data and predict future demand patterns. Accurate demand forecasting has significant implications for businesses, including improved resource planning, supply chain management, and customer satisfaction. By aligning production capacities with anticipated demand and optimizing supply levels, companies can minimize costs, reduce waste, and enhance competitiveness in the market. Demand forecasting is a critical aspect of strategic planning and decision-making in the specialized industry. By utilizing advanced techniques and effectively addressing challenges, businesses can enhance their ability to anticipate trends, meet customer demands, and drive sustainable growth in an increasingly dynamic and competitive environment
UPI Fraud Detection using Machine Learning
Authors: Kavya K R  , Usha Sree R
Published: December 2024

The goal of this project is to use machine learning techniques to identify fraudulent transactions within the Unified Payments Interface (UPI). Although UPI has completely changed digital payments in India, an increase in fraudulent activity has resulted from its quick adoption. A sizable dataset of UPI transactions will be gathered in order to address this, and outliers, missing values, and category factors will be suitably handled. The efficacy of several machine learning methods, including decision trees, random forests, logistic regression, and neural networks, in spotting fraud will be assessed. We will evaluate the model's performance using measures such as F1-score, recall, accuracy, and precision. Deep learning models and ensemble techniques achieve higher detection rates, according to preliminary investigations
Leveraging Machine Learning for Predicting Chronic Kidney Disease: Techniques an Application
Authors: Kavya K R  , Usha Sree R
Published: December 2024

Renal work steadily declining over time is the trademark of constant kidney malady (CKD), a dynamic ailment. Noteworthy side impacts from this sickness incorporate frailty, bone anomalies, and cardiovascular issues. Kidney illness runs in the family, and diabetes and hypertension are hazard components. Early location is troublesome since the condition is regularly asymptomatic. With pharmaceutical, way of life alterations, and perhaps dialysis or transplantation in more progressed stages, administration points to ease issues, direct fundamental causes, and decrease the disease's course. In arrange to move forward persistent results and quality of life, awareness-raising and early intercession are essential.
Machine Learning Classifies the Smoking and Drinking Impact
Authors: Kottur Siddaraju Gari Sai Kiran  , Prof. Mrs. Usha Sree R
Published: December 2024

This research main objective of this paper is to show the impact of the smoking and drinking habits on our body and making comparative analysis between algorithms to find out which algorithm provides better accuracy. With the help of techniques such as EDA we find out the relationship between the smoking and drinking habits and how interrelated one habit is to another and with the help of Data Visualization techniques we’ll be using such as graphs and other methods to show data visually for better understanding. Algorithms like Logistic Regression, Random Forest, XGBoost, LGBM classifier are used for comparative analysis and the main intention of the paper is to raise awareness in the society that how dangerous these habits are, showing them the impact of these habits having on our body
Prediction of Indian Currency Using Artificial Neural Network
Authors: Lakshmi U  , Dr. Chitra K
Published: December 2024

The ANN-based currency identification system aims to enhance the accuracy and speed of recognizing diverse Indian currency denominations. Traditional methods fall short due to the complexity of Indian money's varied designs, sizes, and security features. Deep learning and ANNs are well-suited for currency classification by analyzing extensive data and detecting intricate patterns. The framework trains on a dataset of Indian banknotes and coins to identify distinctive features such as color and design. This system can improve financial transactions, minimize errors, and bolster security across banking, retail, and security sectors.
Predictive Modeling for Diabetic Management: A Machine Learning Approach
Authors: Lakshmi U  , Dr. Chitra K
Published: December 2024

Effective diabetic management is crucial for improving patient outcomes and reducing healthcare costs. This study investigates the application of machine learning techniques to develop predictive models for diabetic management. By leveraging comprehensive patient data, including demographics, medical history, and lifestyle factors, various algorithms such as decision trees, random forests, support vector machines, and neural networks were evaluated. The models demonstrated high accuracy in predicting blood glucose levels, potential complications, and the effectiveness of different treatment regimens. These predictive insights facilitate personalized treatment plans and timely interventions, enhancing patient care. The approach aims to empower healthcare providers with data-driven tools to optimize diabetic management strategies, ultimately improving the quality of life for diabetic patients and minimizing the risk of severe complications
Credit Card Fraud Detection Using Machine Learning
Authors: Lohith E  , Mrs. Priyanka Mohan
Published: December 2024

The financial industry is very concerned about credit card theft because it can result in large losses. The Random Forest technique, which is renowned for its accuracy and capacity to handle unbalanced datasets, is used in this project to create a strong fraud detection system. Based on several characteristics, the model that was trained on a labeled dataset of credit card transactions can differentiate between authentic and fraudulent activity. Using Flask, HTML, CSS, and JavaScript for a responsive front end and Python and JavaScript for a reliable back end, the system is integrated into a full-stack web application. For safe data storage, MySQL is used. The web tool uses sophisticated data visualization to provide insights while enabling real-time transaction monitoring, fraud warnings, and historical data analysis.
Optimizing Test Automation with Selenium for Enhancing Web Application Quality
Authors: Mahesh G. M  , Usha Sree R
Published: December 2024

A comprehensive study on Selenium, a widely-used tool for automating web applications for testing purposes, is presented. By examining various methodologies, frameworks, and implementations from existing research, the paper highlights the advantages of Selenium in enhancing software testing efficiency and reliability. The analysis includes case studies on Selenium's application in cloud environments, cross-browser testing, and integration with continuous integration/continuous deployment (CI/CD) pipelines. This work provides a detailed understanding of Selenium's capabilities, challenges, and best practices, contributing to the ongoing advancements in test automation technology
FINSYNC AI: Stock Market Analysis
Authors: Saketha N  , Dr. Chitra K
Published: December 2024

The goal of the study "FINSYNC AI" is to enhance the accuracy and effectiveness of financial market forecasts by introducing a complex stock market prediction system. This work makes use of the frameworks Gradient Boosting Machine, Random Forest, Long Short-Term Memory (LSTM), and the proposed FINSYNC AI. The process involves acquiring historical stock market data, cleaning and normalizing it, and then feature engineering to produce new features like rolling means and trend components. These preprocessed data are then used to test and train several machine learning models in order to assess their predictive accuracy. Given the inadequacies of previous techniques, the FINSYNC AI is proposed as a means to improve their effectiveness. The project is broken down into three primary modules to guarantee a comprehensive process: creating and refining the framework, obtaining and prepping the data, and the prediction stage and evaluation. The Python implementation of the machine learning tasks makes use of scikit-learn, pandas for data management, yfinance for acquiring stock data, and matplotlib for visualization. The results show that the FINSYNC AI performs better than traditional one, making it a more useful tool for value trading in the stock market
Crop Selection and Cultivation Using Machine Learning
Authors: Sanjay Aswath K S M  , Chitra K
Published: December 2024

Arranging for agribusiness and guaranteeing nourishment security depend intensely on trim yield estimate. With the utilize of assorted information sources like obsequious symbolism, climate figures, soil characteristics, and past trim execution, modern machine learning calculations are able to accurately foresee future yields. In arrange to estimate comes about beneath different circumstances, these models look at designs and relationships show in the information. Relapse examination, neural systems, and gathering approaches are illustrations of procedures that are as often as possible utilized. Arranging for horticulture and guaranteeing nourishment security depend intensely on trim yield figure. With the utilize of different information sources like fawning symbolism, climate estimates, soil characteristics, and past edit execution, advanced machine learning calculations are able to absolutely anticipate future yields. In arrange to figure comes about beneath different circumstances, these models look at designs and relationships display in the information. Relapse investigation, neural systems, and gathering approaches are illustrations of procedures that are regularly utilized
Detecting Missing Persons Using Face Recognition
Authors: Shobha D S  , Dr. Chitra K
Published: December 2024

In response to the challenge of finding missing people, we developed an advanced facial recognition model integrated with the TensorFlow machine learning platform. Traditional methods are often inefficient and slow, but our innovative solution uses facial recognition technology to optimize the search process. Missing people's facial information is fed into the system, which searches social media, CCTV footage, and public places for matches. Once a match is identified, carers and local authorities are immediately notified, enabling fast and accurate searches. Through advanced algorithms and machine learning, our solutions significantly increase your chances of success while ensuring a high level of privacy and security. Integration with the Django framework provides a powerful and scalable platform that improves the overall effectiveness and efficiency of locating missing people and reuniting them with their families.
Predictive Analytics for Maintenance
Authors: Tanuja M  , Dr Chitra K  , Mrs. Harilakshmi V M
Published: December 2024

We present the development of a machine learning-based predictive maintenance tool tailored to the industrial industry. Predictive maintenance and operational optimization have turned manufacturing into a manufacturing revolution because to machine learning's capacity to learn from data and generate precise predictions. 96% accuracy was attained in the initial training of a K-means clustering model. An algorithm known as Random Forest was used to increase forecast accuracy, and the outcome was a very good 98% accuracy. Thus, it was decided to apply the Random Forest model. With Flask, a predictive maintenance web interface was created, and the learnt model was easily included to provide real-time predictions. Through dramatically lower equipment downtime and improved operational efficiency, this application highlights the value of machine learning in predictive maintenance in industrial settings.
Unveiling The Shadows: A Guide For Diagnosing Leukemia And Better Outcome
Authors: Thrupthi C P  , Dr. Chitra K  , Mrs Harilakshmi V M
Published: December 2024

Diagnosis of leukemia is performed through blood tests and a bone marrow diagnostic assay, with blood cell counts playing a critical role in the healthcare industry. Traditionally, hospital laboratories manually count blood cells using a hemocytometer. This approach is tedious, prone to errors, and time- consuming. The research introduces a fully automated method for identifying various types of leukemia and detecting nursing platelets in blood samples. This proposed technique employs a multi-class classifier to overcome the limitations and missed opportunities often encountered with traditional cell classification methods. This technique employs geographical metrics to identify various color feature statistics within the context of supervised machine learning. The model, trained and validated using several machine learning approaches, achieves an accuracy of 92%.
Insurance Claim Prediction
Authors: Varshini H S  , Dr. Chitra K
Published: December 2024

The Health Insurance Claim Prediction system forecasts insurance claims based on health metrics using Flask and MySQL. It features secure client registration, login, and data management. Bulk information can be added through CSV uploads, maintaining an up-to-date database essential for accurate predictions. The dashboard provides key metrics like user demographics for quick insights. Predictive modeling uses SVM and KNN algorithms trained on historical data to forecast insurance claims, offering real-time feedback based on inputs like age, sex, and BMI. Data preprocessing ensures clean and analyzable data. This system helps insurance companies assess risk, tailor plans, and improve customer service
Seasonal And Trend Analysis of Drug Sales using Time Series Analysis
Authors: Vimal Kumar E S  , Dr Chitra K  , Mrs. Harilakshmi VM
Published: December 2024

The SARIMA model is to analyse and forecast seasonal drug sales data, enhancing accuracy and understanding the complex of seasonal and trend patterns in fields like pharmaceutical sales, unlike the ARIMA model that only handles non-seasonal data. The model aims to improve predictions and provide deeper insights into the intricate seasonal and trend patterns in drug sales data. This study analyses drug sales behaviour across different seasons using drug store data from 1992 to 2010, grouping data by seasonal periods. Time series analysis is a method used to analyse data points over time to identify trends, patterns, and seasonal variations, providing valuable insights into drug sales. The study emphasizes the significance of drug sales data in real-time monitoring of best values of p,d,q activity
Anomaly Detection System using Machine Learning Algorithms
Authors: Vishal Anand N  , Dr. Chitra K
Published: December 2024

The "Anomaly Detections Systems" is a advanced web application created to upgrade framework security and execution observing through machine learning strategies. This application, highlighting a strong login and dashboard interface for clients, empowers real-time location and examination of peculiarities inside complex frameworks. By leveraging progressed machine learning calculations, the framework can distinguish abnormal designs and behaviors that go astray from built up standards, subsequently giving early notices of potential issues or breaches. The user-friendly dashboard offers comprehensive bits of knowledge and visualizations, permitting directors to screen framework wellbeing and execution proficiently. Clients can get to point by point reports and real-time alarms, encouraging incite examination and reaction to recognized peculiarities. This proactive approach to framework observing not as it were improving security by recognizing potential dangers some time recently they heighten but moreover makes strides generally framework unwavering quality and execution. The integration of machine learning guarantees nonstop change and adjustment to advancing dangers and operational conditions. This venture underscores the basic part of innovation in keeping up vigorous and secure frameworks, exhibiting the potential of machine learning in proactive framework administration and irregularity location
Finger Vein Detection using Deep Learning
Authors: Yashaswini Kini  , Dr. Chitra K  , Mrs. Harilakshmi V M
Published: December 2024

The deep learning is a part of machine learning that uses neural networks with many layers (deep architecture) to fit the intricate patterns. Deep learning has shown remarkable performance in image classification mainly due to its ability in learning and extracting features from the raw inputs. In this research study, some techniques of image preprocessing were described and utilized to machine learning algorithm for image classification. The purpose was to promote the specific preprocessing techniques on the impact, which are commonly used in classification model of images, relating to time and precision improvement. The techniques that are applied in this documentation include; image enhancement, binarization, transformation, edge detection and scaling. The present research study therefore comes handy in trying to establish the significance of enhancing statistic and the impact on the performance of the model. The following preprocessing methods have been used because the image datasets need preprocessing to be input for the machine learning models for better image classification
Design and Implementation of Device Circuit of Microcontroller-Based Smart Traffic Light Control System
Authors: Maduka N. C.  , Okafor, C. S.  , Etinamabasiyaka E. E.  , Ibrahim M. H.
Published: December 2024

In this paper, the design and implementation of a microcontroller-based smart traffic light control system is presented. The proposed smart traffic light is meant to mitigate or entirely eliminate hassles encountered by road users, mostly at road intersections. The system is sensitive to road density and therefore operates in smart and normal modes, with normal mode as the default; however, it switches to smart whenever the need arises. The approach adopted includes; typical intersection design, circuit diagram design, circuit analysis and overall system development and implementation. The microcontroller, which serves as the overall system brain is programmed using proteus electronic software; and then interfaced with already calibrated ultrasound modules and other components. Afterwards, it is initiated to confirm that research objectives have been met. The design employs a crossbar to prevent drivers from using a lane when not supposed. The traffic light control system, if implemented would significantly minimize traffic frustrations and ugly experiences road users encounter daily on Nigeria roads. However, this work is centred more on circuit operation and design, circuit analysis and overall system development and implementation.
Commercially Available Supercapacitors –A Perspective on Global, African and Nigerian Markets
Authors: L. Sa’adu  , I. Abdullahi  , A. B. Isah  , Y. Yusuf
Published: December 2024

Supercapacitors are considered as promising energy storage devices for their remarkable properties.This makes their global market on constant improvement over the last decade, with the potential to have more market share. As more and more Supercapacitors (SCs) are becoming commercially available, we need to have their representative for comparing other newly developed technology. Achieving this involves studying their behaviors through simulations, experiments or combined approach.However, thisis time consuming if we consider simultaneous comparison of many SCs, since different technologies are available. Thus, there is the need to have a compilation of the SC technologies/manufacturers,as this information could easily help to compare and hence choose the most widely accepted technology.To meet this need, the study takes review approach through survey of relevant literatures and come up with the collation.The study considers commercial supercapacitors in the global, African and Nigerian markets,as the product availability from a given company, in these regions, is a mark of acceptance, robustness, and hence originality. The study identifies Maxwell technology to havemarket dominance over the three classified regions of consideration, followed by the Eaton technology which dominates two of the regions.Hence, based on the result of this study, Maxwell and Eaton SCs could be adopted as the standard technologies for comparingother and newly developed products.
Pengembangan Karir Manajemen Sumber Daya Pendidik dan Tenaga Kependidkan di MA Manba’ul Khoiriyatil Islamiyah Bangsalsari
Authors: Azizah St. Nur , Jannah Ulfia Rhodotul , Sya’diyah Halimatus , Hidayat Rofiq
Published: December 2024

Pengembangan karir dalam manajemen sumber daya pendidik dan tenaga kependidikan merupakan investasi jangka panjang yang krusial bagi keberhasilan suatu lembaga pendidikan. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruhi efektivitas program pengembangan karir serta dampaknya terhadap kinerja dan kepuasan kerja pendidik dan tenaga kependidikan. Melalui studi kasus pada MA Mamba’ul Khoiriyatil Islamiyah Bangsalsari, ditemukan bahwa hasil penelitian, misal: program mentoring yang terstruktur, kesempatan pelatihan yang relevan, dan adanya jalur karier yang jelas sangat berkontribusi pada peningkatan motivasi, produktivitas, dan retensi tenaga kerja. Implikasi dari penelitian ini adalah pentingnya bagi lembaga pendidikan untuk merancang program pengembangan karir yang komprehensif dan berkelanjutan. Penelitian ini bertujuan untuk mengembangkan model pengembangan karir yang sesuai dengan kebutuhan pendidik dan tenaga kependidikan di MA Mamba’ul Khoiriyatil Islamiyah Bangsalsari. Model yang diusulkan menggabungkan elemen-elemen dari berbagai teori pengembangan karir, seperti teori minat, teori sosial kognitif, dan teori karier-life span. Melalui proses validasi, model ini terbukti relevan dan dapat diterapkan dalam konteks pendidikan di Indonesia. Implementasi model ini diharapkan dapat meningkatkan efektivitas program pengembangan karir dan memberikan kontribusi pada peningkatan kualitas pendidikan secara keseluruhan.
Behmenyâr’ın Makâle Fî Ârâi’l-Meşşâiyyîn Fî Umûri’n-Nefs ve Kuvâhâ Eseri Üzerine Bir İnceleme
Authors: Yılmaz Sadi
Published: December 2024

Bu çalışmada Behmenyâr’ın Makâle Fî Ârâi’l-Meşşâiyyîn Fî Umûri’n-Nefs ve Kuvâhâ adlı makalesinin içeriği incelemektedir. İbn Sînâ’nın en önde gelen öğrencilerinden biri olan Behmenyâr’ın onun felsefî düşüncelerini benimseyen Meşşâî felsefe temsilcisi olduğu bilinir. Behmenyâr’ın, felsefî eseri Kitâbu’t-Tahsîl’i İbn Sînâ’nın eş-Şifâ, el-İşârât ve’t-Tenbâhât, Kitâbu’n-Necât ve Dânişnâme-i ‘Alâî gibi temel felsefî eserlerdeki felsefî düşünceleri özet nitelikte telif ettiği söylenebilir. Behmenyâr’ın Makâle Fî Ârâi’l-Meşşâiyyîn Fî Umûri’n-Nefs ve Kuvâhâ isimli eserindeki düşüncelerinin de özelde et-Tahsîl’in genelde ise İbn Sînâ’nın felsefî düşüncelerinin özeti niteliğinde olduğu söylenebilir. Nitekim o, makalenin dibacesinde nefsin yetileri ve nefse ilişkin konular hakkında Meşşâî bilginlerin görüşlerini bir araya getirdiğini ve bu alanda yazılan kitaplara dayandığını belirtir. Cisim kavramına ilişkin bilgiler bağlamında farklı fiillerin nefisten kaynaklanmasından ötürü nefsin cisim olmaktan farklı bir varlık olduğuna dikkat çeken Behmenyâr, nefsin varlığının bedenden önce değil, bedenle birlikte olduğu düşüncesindedir. Nefis düşüncesi bağlamında idrake ilişkin görüşlerine yer veren Behmenyâr, idrak eden özne ve idrak edilen nesne ilişkisi bağlamında idraki Tanrı’nın kendisi dışındaki varlıkları idraki, yıldızların nefislerinin idraki ve ay-altı âlemde yer alan nefislerin idraki olmak üzere üç kısım olarak açıklar. Filozof, idrak düşüncesi bağlamında her idrakin farklılık gösterdiğini, akıl ve reviyyenin birbirinden farklı oldukları görüşündedir. Risalenin sonunda lezzet ve elem kavramlarına yer veren Behmenyâr, uygun olanın idrakinin lezzet ve uygun olmayan şeyin idrakinin ise elem olduğunu savunur. Behmenyâr’ın ilgili risalesinin sadece bir nüshası günümüze ulaşmıştır.
İlahiyat Fakültesi Öğrencilerinin Akademik Erteleme Davranışları ile Akademik Öz Yeterlikleri Arasındaki İlişkinin İncelenmesi
Authors: Uçar Recep , Semerci Ayşegül
Published: December 2024

Öğrencilerin hem akademik başarılarının düşmesine hem de kişilik gelişimlerine olumsuz yönde etki eden akademik erteleme davranışı özellikle üniversite öğrencileri arasında giderek yaygınlaşmaktadır. Bu durum yüksek din eğitiminde de geçerlidir. Akademik ertelemenin pek çok sebebi olmakla birlikte akademik öz yeterliğin de bu sebeplerden biri olduğu düşünülmektedir. Bu araştırmanın amacı, ilahiyat fakültesi öğrencilerinin akademik erteleme davranışı ile akademik öz yeterlikleri arasında nasıl bir ilişki olduğunu ortaya koymaktır. Araştırmada karma yöntemin açıklayıcı ardışık deseni kullanılmıştır. Araştırmanın nicel bölümünde basit tesadüfi örnekleme yöntemiyle seçilen 500 öğrenciye anket uygulanmış; nitel bölümünde ise nicel verilerden hareketle hazırlanan yarı yapılandırılmış mülakat formları kullanılarak maksimum çeşitlilik örneklemesi yöntemiyle seçilen 10 öğrenciyle görüşme yapılmıştır. Araştırmanın nicel verileri “Akademik Erteleme Ölçeği” ile “Akademik Öz Yeterlik Ölçeği” kullanılarak elde edilmiştir. Sonuç olarak öğrencilerin hem akademik erteleme davranışlarının hem de akademik öz yeterliklerinin orta düzeyde olduğu tespit edilmiştir. Ayrıca araştırmada öğrencilerin akademik erteleme davranışları ile akademik öz yeterlikleri arasında %90 güven aralığında zayıf düzeyde ve pozitif yönde istatistiksel olarak anlamlı bir ilişki olduğu sonucuna ulaşılmıştır. Öğrencilerin akademik erteleme davranışları ile akademik öz yeterlikleri arasındaki ilişki cinsiyet, mezun olunan okul türü ve bulunulan sınıf düzeyi gibi değişkenler açısından incelenmiş ve bu değişkenler ile öğrencilerin akademik erteleme davranışları ve akademik öz yeterlikleri arasında anlamlı bir fark bulunamamıştır.