bluuebunny/crossref_metadata_2025 huggingface dataset:
Algorithmic Management. Theoretical Perspectives and Implications for Organizational Development
Authors: Sfetcu Lucian
Published: December 2024

Algorithmic management leverages data-driven algorithms and artificial intelligence to automate managerial functions traditionally executed by human managers. This paper provides a comprehensive overview of algorithmic management, exploring its definitions and emergence in gig economy platforms and traditional workplaces. It delves into key sociological and organizational theories—including Weber's bureaucracy, Critical Management Studies (CMS), and technological rationality—to frame the discussion. The impact of algorithmic management on employee autonomy, digital surveillance, and forms of worker resistance is examined, alongside its role in shaping organizational structures, enhancing efficiency, and driving innovation. Ethical implications, particularly concerning fairness, transparency, and bias, are critically analyzed. While algorithmic management offers potential benefits such as improved efficiency and decision-making, it also raises significant concerns about worker autonomy, power imbalances, and ethical considerations. The paper underscores the need for a nuanced understanding and responsible implementation of algorithmic management to harness its advantages while mitigating its drawbacks.
Avoiding the death of the newspaper industry in Indonesia and the transformation of the digital media business
Authors: Supadiyanto Supadiyanto , Murti Sudaru
Published: December 2024

The circulation of newspapers in Indonesia has been at its lowest point in the last 10 years. Print media companies have collapsed and closed, and most have shifted to online media businesses. What is the fate of various print outlets and media in Indonesia amidst the death knell of print media business in Indonesia? What is the ultimate strategy to save the print media business in Indonesia and can the business survive until 2030? The paradigm of this research is qualitative. The research took place from January 2023 to December 2024. Data collection techniques were via literature review, observation, and documentation. As a result, the circulation of various print media in Indonesia has decreased drastically by around 1 million copies per year. By the end of 2023, the circulation will be around 5 million copies. The researcher predicts that by 2030, the number of print media companies is estimated to be only 100 companies. Emergency rescue efforts for the print media business through: media transformation, attracting young readers; integration of print media, online media, and social media businesses; and maintaining content according to the needs of customers and potential readers.
Modified Sombor Spectral Radii and Modified Sombor Energies of Splitting and Shadow Graphs
Authors: Bilal Ahmad , Mobeen Munir Muhammad
Published: December 2024

Gutman et al. introduced Sombor and Modified Somber index because of the rapidly growing applications in chemistry and network analysis. Graph energy ε(G) and the spectral radius ℘(G) of graph G are essential components that are associated with the eigenvalues of the matrix of graph G and, chemically, with the intermolecular forces. These graph invariants have many useful applications in computer sciences, networking, and molecular computing. There are numerous variants of the ε(G) and ℘(G) attained by substituting another matrix in place of an adjacency matrix. Modified Sombor energy, MSε(G) is defined as the sum of absolute eigenvalues of the modified Sombor matrix or in other words we can say that modified Sombor energy, MSε(G) is the trace norm of modified Sombor matrix. Modified Sombor spectral radius, ℘MS is defined as the largest absolute eigenvalue of the modified Sombor matrix. The major focus of this article is on the MSε(G), ℘MS of the generalized shadow and splitting graphs. The only realistic problem in which we are particularly interested in how MSε(Splt(G)) and MSε(Sht(G)) are comparable to MSε(G). On similar lines we are also interested in how ℘MS(Splt(G)) and ℘MS(Sht(G)) are comparable to ℘MS(G). We were able to address these challenges by focusing on splitting and shadow graphs.
Key Drivers of Stock Price Movements: Financial and Economic Indicators in Unilever Indonesia (2014–2023)
Authors: Setiawati Rini , Herlina Listri
Published: December 2024

Multinational corporations are critical in ensuring financial market stability and boosting corporate performance, mainly through job creation and increased productivity. These contributions positively influence investor confidence and significantly impact stock market performance. As one of the leading multinational companies, PT Unilever contributes substantially to Indonesia's economic development. However, in recent years, PT Unilever has faced a notable decline in its stock prices. Such a decline can undermine investor trust, affecting the company’s investment strategies. For this reason, examining factors that influence stock price movements, including financial ratios and macroeconomic variables, becomes essential. This research investigates the influence of financial ratios and macroeconomic indicators on PT Unilever Indonesia Tbk's stock price fluctuations in 2014–2023. The variables analyzed include the Current Ratio (CR), Earnings Per Share (EPS), inflation, interest rates, and Gross Domestic Product (GDP). Using secondary data and a multiple linear regression approach, the study reveals that EPS and inflation positively and significantly impact stock prices. At the same time, interest rates and GDP exhibit a negative and significant effect. On the other hand, the CR variable does not significantly affect stock price movements
Analysis of Financial Distress and Bankruptcy Using the Altman Z-Score Method on the Indonesia Stock Exchange (IDX) in 2020 - 2022
Authors: Irwanto M. Endrik , Rodoni Ahmad , Riniawati Rina
Published: December 2024

This research analyzes financial distress and bankruptcy using the Altman Z-Score method and its influence on share prices in construction service companies listed on the Indonesia Stock Exchange (IDX) for 2020 - 2022. The variables tested in this research consist of working capital to total assets, retained earnings to total assets, earnings before interest and tax to total assets, market value of equity to book value of debt, sales to total assets as independent variables, and share prices as dependent variables. The population in this research is construction services companies listed on the Indonesia Stock Exchange for 2020 - 2022. The sampling technique used in this research is purposive sampling, and a sample of 63 data from 21 construction services companies listed on the Indonesia Stock Exchange in 2020-2022 that meet specific criteria was obtained. The data analysis technique used in the research is panel data regression analysis and classical assumption testing using EViews version 13 statistical software. This research shows that in 2020, 18 construction service companies were in the distress zone and 3 companies were in the gray zone. In 2021, 17 construction service companies will be in the distress zone and 4 in the gray zone. In 2022, 17 construction service companies will be in the distress zone and 4 in the gray zone. The earnings before interest and tax to total assets have a significant effect and positive relationship with share prices. Working capital to total assets, retained earnings to total assets market value of equity to book value of debt, and sales to total assets do not have a significant effect and have a negative relationship to stock prices.
Enhanced Growth, Haematological and Biochemical Performance in Hybrid Catfish of Clarias gariepinus x Clarias cavernicola
Authors: Paul B. Ekpo Ekerette E. Ekerette , Etukudo Owoidihe M.
Published: December 2024

This study evaluated the growth performance, haematological indices, and biochemical parameters of four catfish breeds, including two purebreds (Clarias cavernicola and Clarias gariepinus) and two hybrids (female C. cavernicola x male C. gariepinus and female C. gariepinus x male C. cavernicola). Over four months, growth was assessed by measuring body weight, total length, and standard length. Haematological and biochemical analyses were conducted to assess the health and immune status of the different breeds. The results indicated that the hybrid of female C. cavernicola x male C. gariepinus exhibited the highest growth performance, with significant increases in body weight and length compared to the other breeds. This hybrid also demonstrated the highest white blood cell (WBC) and lymphocyte counts, suggesting a stronger immune response, which was statistically significant (p<0.05) compared to the purebreds. Granulocyte levels were consistent across all breeds, while red blood cell (RBC) and platelet counts showed no significant differences, indicating stability in these parameters. Biochemical analysis revealed that the female C. cavernicola x male C. gariepinus hybrid had the highest total protein and globulin levels, which are indicative of better health and immune function. Albumin levels were higher in both hybrids compared to the purebreds, while cholesterol, creatinine, and glucose levels were similar across all breeds. The alleles associated with better immunity are likely present in the maternal line of C. cavernicola, leading to the superior vigour observed in the C. cavernicola (female) x C. gariepinus (male) hybrid. This enhanced vigour results in improved growth, hematological, and biochemical profiles. Compared to purebred breeds, this hybrid demonstrates better health, increased disease resistance, and a higher potential for productivity in aquaculture.
A Systematic Review of Behavioural Intentions Influencing Data Analytics Adoption among Malaysian Crop Farmers
Authors: Azmi Nur Fazana , Rashid Arlinah Abd , Hanif Azlina
Published: December 2024

Technology adoption situation has changed tremendously in the agriculture industry. Farmers prefer on using the traditional way of farming as they have the opportunity of adoption technology, these changes acquire in terms of the farming system, innovation, usage of technology and many more. The systematic literature review paper aims to discover the adoption of technology strategy that contributes to the latest technology system strategy. This study attempts to pair and match with the theory of Ventakesh (2003) technology adoption strategy. There are 142 eligible articles on the subject sourced from Research Gate and Scopus databases over the past five to ten years. The PRISM protocol needs to identify new technology adoption strategy and that is performance expectancy, effort expectancy, social influence, personal computer utilization and behavioural intention. Practically these new strategy serves as a guideline for crop farmers to upgrade their farming lifestyle and innovation by using their technology adoption system to be incorporated in the agriculture industry in Malaysia.
Optimization of Whatsapp-Based Administrative Services in the Governance Section of the Kulo District Office
Authors: Pate Nasir , S Muliani , Nilwana Andi , uddin Jamal , Nur Muhammad , K Nuraini , Efendy Rasyid Rustam
Published: December 2024

This study aims to analyze the optimization of WhatsApp-based (WA) administrative services in the government section at the Kulo District Office, as well as to analyze the public’s perception and satisfaction with the WhatsApp-based administrative services implemented by the government section at the Kulo District Office.

This type of research is categorized as field research using a qualitative research approach. The study was conducted in the government section of the Kulo District Office. The data in this research consists of information from the public and staff of the government section. Data collection techniques included interviews, observation, and documentation. The data analysis technique was carried out in three stages: data reduction, data presentation, and conclusion drawing.The results of this study conclude that WhatsApp-based administrative services at the Kulo District Office have shown broad adoption and are well received by the public, with the majority of informants finding this service easy to use and more efficient compared to conventional services. Out of 20 respondents, 18 used WhatsApp-based services, with varying usage frequencies. The most accessed services are ID cards, birth certificates, and moving certificates. The majority of the public finds the WhatsApp-based administrative services implemented at the Kulo District Office easy to use, with 50% of respondents stating the service is very easy, and 25% finding it easy. Additionally, 40% of respondents are satisfied with the response and services provided, while 30% are somewhat satisfied.
Exploring Students’ Perceptions of ChatGPT: A Study of Public University in Malaysia.
Authors: Singh Kuldip , Agustine Elizabeth Caroline , Samasu Nur Farhani Binti , Kipli Nur Aida Binti
Published: December 2024

ChatGPT has sparked both excitement and skepticism in education. The release and rapid diffusion of ChatGPT has forced teachers and researchers around the world to grapple with the consequences of artificial intelligence (AI) for education. To analyze its impact on teaching and learning it is crucial to understand how students perceive ChatGPT and assess its potential and challenges. This study investigated students’ perception of ChatGPT in higher education. About 360 students responded to a questionnaire which gauged their perceptions regarding use of ChatGPT in teaching and learning at their university. Most of the students rated their level of awareness as very aware and extremely aware. For ability to use ChatGPT they rated themselves as medium or High. The results show that the students admire the capabilities of ChatGPT and find it interesting, motivating, and helpful for study and work. The results show that the students admire the capabilities of ChatGPT and find it interesting, motivating, and helpful for writing assignments However, many students feel that ChatGPT’s answers are not always accurate and most of them are knowledgeable about potential benefits and limitations of ChatGPT. So, most students think that ChatGPT needs to be improved but are optimistic that this will happen soon. Most students also mentioned that ChatGPT can provide to students’ instant feedback and enhance the learning process and experience. When it comes to the negative impact of ChatGPT on learning academic cheating is the issue they highlighted. We conclude that ChatGPT can and should be used for learning. However, students should be aware of its limitations. The study a found positive relationship between awareness and attitudes to ChatGPT and perceptions towards ChatGPT. The main determinant of perception towards ChatGPT is attitudes toward ChatGPT. Educators should try using ChatGPT and guide students on effective prompting techniques and how to assess generated responses. The developers should improve their models to enhance the accuracy of given answers. The study provides insights into the capabilities and limitations of ChatGPT in education and informs future research and development.
Advancing Computational Models for Personalized Medicine: Enhancing Predictive Performance, Interpretability, and Practical Implementation for Equitable Healthcare.
Authors: Ilyas Ayesha Ahmed , Ilyas Shoeb Ahmed , Rubina Rubina
Published: December 2024

Personalized medicine (PM) represents a transformative approach in healthcare, utilizing computational models to tailor medical interventions based on individual patient data, including genomic, environmental, clinical, and lifestyle factors. While PM has demonstrated promising outcomes, significant challenges remain in enhancing these models’ prediction performance, interpretability, and clinical implementation. Key issues include prediction inaccuracies due to data heterogeneity, interpretability barriers associated with complex AI-driven models, and gaps in real-world application, which complicate clinical integration. Addressing these gaps through advanced computational techniques, robust validation frameworks, interdisciplinary collaboration, explainable AI (XAI), and culturally adaptive practices is essential to realizing PM’s full potential. This review highlights the importance of socio-cultural and ethical considerations, particularly in promoting equitable access and culturally sensitive healthcare, as well as ensuring data privacy and informed consent. Future research should improve model generalizability across populations, develop culturally responsive models, and advance XAI techniques for greater clinical usability. These efforts are critical for advancing personalized medicine toward more precise, effective, and equitable healthcare.
Social Capital Formation through Grassroots Entrepreneurship Development: Evidence from Civil Society Organizations in Rural Uganda
Authors: O. V. Falade , F.S. Omweri , Asuman Bateyo
Published: December 2024

The study sought to examine the mechanisms through which Civil Society Organizations’ (CSOs) services contribute to social capital formation via grassroots entrepreneurship development in Bushenyi District, Uganda, with particular emphasis on the empirical relationship between educational service provision, income generation capacity building initiatives, and human rights advocacy vis-à-vis community development metrics. The methodological framework employed a mixed-methods research design, incorporating both descriptive and correlational approaches, with a systematically derived sample of 178 respondents selected through stratified and simple random sampling techniques from a target population of 320. Data acquisition procedures encompassed triangulated methodologies, utilizing structured questionnaires, semi-structured interview protocols, and systematic observation frameworks. The analytical framework integrated both quantitative methodologies, including descriptive statistical analysis, bivariate correlation coefficients, and regression modeling, alongside qualitative thematic content analysis. Statistical findings demonstrated robust positive correlations between specific CSO service dimensions and community development indicators: educational services exhibited a strong positive correlation (r = 0.82**, p = 0.000 < 0.05), as did income generation initiatives (r = 0.76**, p = 0.000 < 0.05), while human rights advocacy demonstrated a moderate positive correlation (r = 0.481, p = 0.001 < 0.05). The study’s scholarly contribution lies in its empirical elucidation of community development dynamics within the Ugandan context, generating substantive theoretical and practical insights into development promotion frameworks, while establishing evidence-based parameters for policy formulation, particularly emphasizing the imperative for governmental intervention in educational service provision as a catalyst for sustainable community development through enhanced human capital formation.
VIOLÊNCIA OBSTÉTRICA E A AUSÊNCIA DE TIPIFICAÇÃO PENAL NO ORDENAMENTO JURÍDICO BRASILEIRO
Authors: Bezerra de Lima Alícia Regianne , Pereira Furtunato Carolina , Azevedo Rodrigues Fillipe
Published: December 2024

O presente artigo tem por objetivo versar sobre a violência obstétrica e a ausência de tipificação penal dessa prática no ordenamento jurídico brasileiro. Nesse contexto, são abordadas questões relacionadas a violência de gênero e contra a mulher, desenvolvendo os aspectos conceituais e as atitudes passíveis de proteção legal que se enquadram em condutas delituosas. Assim, o objetivo específico deste trabalho é evidenciar que apesar de ter havido uma evolução no Direito das Mulheres, a legislação ainda caminha a passos lentos no que diz respeito a punição da prática de violência obstétrica. Foi utilizado o método dedutivo, por meio de pesquisas bibliográficas e coleta de dados em órgãos oficiais. O estudo se justifica na relevância social e jurídica desse tema a toda sociedade, principalmente às mulheres.
Hernia diafragmática traumática derecha
Authors: Burgos Burgoa Rene , Montaño Ramirez Ivan , Camacho Apaza Jhony
Published: December 2024

La hernia diafragmática traumática es una patología rara que con frecuencia no es diagnosticada de forma temprana, que se puede complicar con encarcelación, estrangulación y compromiso pulmonar las mismas tienen una elevada morbilidad. Actualmente la TAC de abdomen se considera el gold estándar para el diagnostico de esta patología y el tratamiento es quirúrgico con la síntesis diafragmática con o sin uso de malla es el tratamiento mas apropiado a pesar de la vía de abordaje. El objetivo es describir un caso de un paciente con hernia diafragmática traumática derecha, su diagnostico y su tratamiento quirúrgico. Tenemos un Paciente masculino de 46 años de edad con herida traumática en hemitórax derecho por arma blanca de mas o menos 15 años de evolución, quien el mismo presentaba cuadros abomínales (dolor, nauseas y vómitos, durante su hospitalización se le realiza una placa de tórax y una TAC en las que se evidencia en hemitórax derecho intestino grueso y lóbulo derecho del hígado que atraviesan anillo herniario y que llegan a segundo espacio intercostal, por lo cual se ingresa a quirófano en donde se realiza una toracotomía lateral derecha y se repara el diafragma con puntos continuos con hilo no absorbible y la colocación de pleurostomia. La hernia diafragmática es una patología muy infrecuente que no se cuenta con muchas publicaciones en nuestro medio la misma siempre se tiene que considerar en los pacientes con trauma torácico. La anamnesis y el examen físico son importantes juntos con los estudios imagenológicos que ayudan a confirmar el diagnostico. La reparación del diafragma tiene buenos resultados a largo plazo.
Robotics in STEM Education: Enhancing Engagement, Skills, and Future Readiness
Authors: Prity Choudhary  , Pooja Potdar
Published: December 2024

The research explores the influence of robotics education on students' engagement, development of competencies, and career preparation in the field of STEM (Science, Technology, Engineering, Mathematics). With the increased integration of robotics into traditional curriculum, the development of fundamental 21st-century skills and better preparation for future careers are possible. However, evidence about its effectiveness in education has been scant. This, therefore, brings in the need for the current research, focusing on STEM students who are robotics enthusiasts, to explore how robotics activities influence engagement, competency development, and career readiness. Through a data-driven approach entailing surveys, interviews, and performance assessments, this study examines how robotics could effectively be integrated into STEM education to yield better student learning outcomes. The findings provide evidence-based recommendations on how best robotics integration can be optimized in educational settings by educators and curriculum developers. Besides technical competencies, it underlines the development of creativity, collaboration, and flexibility, or critical competencies at the core of a fast-changing technology environment.
Enhanced Spaces: The Art of Curating with Thoughtful Place Accessories using DJANGO
Authors: Adil T Mohammad , K Dr. Chitra
Published: December 2024

This project presents a full-stack web application for online furniture shopping, developed using Python and the Django framework. The application enables users to browse, select, and purchase furniture from various categories. Leveraging the efficiency and flexibility of Django, the application provides a streamlined user experience, facilitating easy navigation and secure transactions. Key features include user registration and login, product browsing by category, shopping cart functionality, online payment options, and email confirmation of transactions. By execution a comprehensive full-stack solution, this project aims to enhance customer convenience, improve business efficiency, and demonstrate the potential of Python and Django in e-commerce development.
Face Detection Using Machine Learning
Authors: Singh Ananya , Sree R Usha
Published: December 2024

Face Detection plays an essential part in numerous operation exertion including facial identification, security systems, and commerce between people and computers. The prospect of face detection using machine learning shall be explored with main methodologies, which include the use of Haar cascades, CNNs, and SVMs. When comparing the methods, the study focuses on their performance within these criteria of accuracy, computational complexity, and reliability. The experimental findings show that CNNs have better accuracy, achieving high stability of results regardless of the conditions for classification. Also, the study discusses the applicability of these techniques to practical problems, the complexity and time required to execute methods, how the approaches can be scaled up and down depending on the problem’s characteristics. The results indicate that although different approaches like Haar falls are effective for the problem, they fail in more complicated and ever- changing surroundings. That is why, despite the fact that CNNs require greater computational resources, modern face detection systems are based on them due to their accuracy and versatility.
Beyond the Surface: Investigating Ethical Hacking for Cyber Defense
Authors: Singh Ananya , Sree R Usha
Published: December 2024

Ethical hacking is basic for proactively distinguishing and settling security vulnerabilities in data frameworks. As cyber dangers gotten to be more modern, solid defense components are more critical than ever. This paper examines how ethical hacking enhances cybersecurity by combining traditional penetration testing with modern machine learning techniques. A case study on using the insulation timber algorithm for anomaly discovery demonstrates its effectiveness in relating implicit security breaches in network business. The paper also underscores the need to follow legal and ethical guidelines in hacking practices. Integrating ethical hacking with advanced analytical methods helps organizations better detect and address vulnerabilities, ultimately strengthening their defenses against cyberattacks. In addition to technical strategies, the paper emphasizes the importance of adhering to legal and ethical standards in all hacking activities. Ethical hacking must be conducted within a framework of clear guidelines and permissions to ensure it remains a constructive force for improving security rather than becoming a potential threat itself. Integrating ethical hacking practices with advanced analytical methods allows organizations to develop a more resilient defense posture, facilitating the early detection and mitigation of vulnerabilities and reducing the risk of successful cyberattacks.
Intelligent Agriculture System using KNN Algorithm
Authors: Dubey Ananya , Sree R Usha
Published: December 2024

The agricultural sector faces the challenge of maximizing crop yields while maintaining sustainability and resource efficiency. Nostalgic or Romantic Views, oftenreliant on manual monitoring and subjective decision-making, can result in inefficiencies and lower productivity. To address these issues, an intelligent agriculture system utilizing the K-Nearest Neighbors (KNN) algorithm has been proposed. This paper presents an intelligent agriculture system that leverages the K- Nearest Neighbors (KNN) algorithm to enhance crop management and yield prediction. By analyzing various environmental factors such as soil moisture, temperature, and humidity, the system accurately classifies the optimal planting and harvesting times for different crops. Additionally, it provides real-time recommendations for irrigation and fertilization based on predictive analysis. The integration of KNN allows for adaptive learning from historical data, thereby improving decision-making processes for farmers. This approach aims to optimize resource utilization, increase productivity, and promote sustainable agricultural practices. The proposed intelligent agriculture system leverages the K- Nearest Neighbors (KNN) algorithm to enhance crop management and sustainability. By analyzing real-time environmental data and historical agricultural records, the system provides precise recommendations for optimal planting and harvesting times, efficient irrigation schedules, and tailored fertilization strategies. This data-driven approach aims to increase crop yields, optimize resource utilization, and reduce the environmental impact of farming practices. The system's continuous learning capability allows it to adapt to changing conditions, ensuring that farmers receive up-to-date and accurate guidance. Overall, the integration of KNN in agriculture represents a significant advancement towards sustainable and efficient farming.
Sign Language to Text Conversion
Authors: CM Annapoorna , Sree R Mrs. Usha
Published: December 2024

Sign language is crucial for Deaf and non-verbal individuals to communicate, yet it remains unfamiliar to many, and interpreters are often scarce. To address this challenge, we developed a real-time system that recognizes fingerspelling in ASL using neural networks. Our process begins by filtering hand images to enhance key features. These enhanced images are then classified by a CNN. This method achieves an impressive 98.00% accuracy in recognizing all 26 letters of the alphabet, making it a highly effective tool for improving communication.
Plant Disease Recognition through Deep Learning and CNNS
Authors: L Chaithanya , K Dr. Chitra
Published: December 2024

Plant conditions have a substantial impact on agrarian productivity and quality, venturing global food security. The homemade examination and expert knowledge that are the main factors of traditional illness opinion systems can be labor- ferocious, time- consuming, and prone to mortal mistake. Recent developments in convolutional neural networks (CNNs) and deep literacy present a feasible volition that's more accurate and effective for automated factory complaint discovery. Traditional styles of factory complaint recognition involve homemade examination by experts, which can be time- consuming, labor- ferocious, and frequently private. To overcome these challenges, the field has decreasingly turned to automated styles using advances in deep literacy, particularly Convolutional Neural Networks (CNNs)..
Stock Market Price Prediction Using ARIMA Model
Authors: Karthik C  , Priyanka Mohan
Published: December 2024

Stock market price prediction involves forecasting future stock values using techniques like technical analysis, fundamental analysis, and machine learning. Technical analysis looks at historical price movements, while fundamental analysis evaluates a company's financial health. Machine learning models use large datasets to identify patterns. This paper introduces a vital approach to time series analysis and forecasting, emphasizing its widespread practical applications. Time series data consists of an ordered sequence of data points recorded at equal time intervals. The stock market, one of the most complex financial systems, experiences significant fluctuations in stock prices over time. Stock market forecasting aims to uncover market trends and help investors maximize returns while minimizing risks. Given the market's sensitivity to rapid changes, this research focuses on developing innovative approaches to forecast stocks for high profits. Specifically, this paper analyzes the time-series data of the Indian stock market and builds a statistical model to predict future stock prices efficiently
Cryptocurrency Price Prediction using Machine Learning
Authors: Nandan S  , Usha Sree R
Published: December 2024

Cryptocurrency price prediction is a complex task due to the volatile and dynamic nature of the market. To tackle this challenge, a study was conducted to compare the effectiveness of two popular machine learning models: Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). These models are designed to capture temporal dependencies in sequential data, making them suitable for predicting price movements based on historical trends. The study utilized historical price data, specifically focusing on the closing prices of various cryptocurrencies. The data was collected using the yfinance library, a tool that provides easy access to financial data from Yahoo Finance. By concentrating on closing prices, the study aimed to simplify the prediction task and improve the accuracy of the models.
Keyless Bike Control System
Authors: Nisha A R  , Mrs. Usha Sree R
Published: December 2024

Including warning mechanisms is essential in modern car safety systems to improve driver awareness and reduce hazards. A phone call alerting system that activates when a car's ignition is turned on is shown in this abstract. The goal of the suggested solution is to solve the frequent problem of drivers unintentionally leaving their cell phones behind or in dangerous positions while driving. With the ignition state of the vehicle as a trigger, the system guarantees the rapid transmission of a phone call alert to the driver's mobile device. This warning can encourage safer driving habits and lessen distractions by reminding the driver to find and secure their phone before starting their trip. To ensure dependability and user-friendliness, the installation of such a system requires the smooth integration of mobile communication technologies with ignition status sensors. According to preliminary assessments, this alarm system greatly raises driver awareness and lowers the risk of driving while distracted by a phone.
SecureCloud: A Shield Against Virtual Threats
Authors: Rakshitha M V  , Dr. Chitra K
Published: December 2024

Cloud computing is a constituent of today and tomorrow innovative information and computing technologies which provides useful and convenient services like storage and access etc. But it has important problems which aggravate with security aspects of a system with regards to the data confidentiality and integrity. These challenges are met in this paper by conducting a detailed theoretical study of literature on data security risks in single and multi-cloud models. Some of the threats include; break-ins, theft of information and loss of information. A comparison of extant literature on security measures like encryption and access control in the cloud based systems is done. It is shown that the solutions available currently have certain deficits, which contribute to the necessity of enhancing safety features. Thus, to fill these gaps, it is suggested to have one layer of protection against data access and another one in relation to data downloads. That way it is protective of the data, useful and clients’ information privacy will be well protected. Thus, the results of the study can be valuable for researchers and cloud service providers who are working on the improvement of the information security in cloud environments
Work Sync 360
Authors: Tanuja M  , Dr Chitra K  , Mrs. Harilakshmi V M
Published: December 2024

To optimize project and task management in businesses, WorkSync 360 is a stand-alone internet solution that doesn't require any additional tools. It offers a user-friendly interface for keeping track of, arranging, and enhancing work processes.
Timely job distribution, deadline control, and real-time progress tracking are critical components that enhance productivity and teamwork. With its news feed, discussion boards, and tools for involving coworkers, WorkSync 360 acts as a safe haven for internal communication, encouraging transparency and information sharing.
Task dependencies, connected calendars, file sharing, and other essential technologies are combined by WorkSync 360 to centralize project management operations. Teams are able to improve teamwork, achieve operational efficiency, and effectively manage projects in a single digital environment.
Object Detection for Autonomous Vehicles Using YOLO Algorithm
Authors: Thrupthi C P  , Dr. Chitra K  , Mrs Harilakshmi V M
Published: December 2024

The YOLO (You Only Look Once) algorithm is a real-time object recognition system that classifies objects as regression problems and predicts bounding boxes and class probabilities directly from the whole image in analysis. It is known for its speed and accuracy in real-time object detection. One of the best detection systems in autonomous vehicles is YOLO (You Only Look Once). It predicts bounding box and class probabilities from the entire image in a single evaluation using the same convolutional neural network. It works well in real-time applications such as autonomous driving due to its high-speed capability. YOLO’s one-step detection pipeline streamlines the process and reduces the computational burden. As a result, drivers and other road users are safer and have better situational awareness and decision-making skills.
Real-Time Driver State Detection Based on OpenCV and MediaPipe Libraries
Authors: Varshini H S  , Dr. Chitra K
Published: December 2024

Empowering street security requires being able to distinguish occupied driving. A real- time way to keep an eye on a driver's condition is to utilize driver-state diversion. We will screen varieties within the driver's eye and look developments by utilizing the eye-aspect proportion (EAR) and the look score. A dashboard-mounted camera gives us with video, which we record and prepare utilizing the OpenCV and Mediapipe modules. Deciding the driver's sign of languor is made simpler by the EAR. Discover out in the event that the drivers are occupied utilizing the look score. As a implies of instantly distinguishing and settling such issues, the framework persistently checks the driver's confront. With broad testing, we appear that our approach can effectively caution drivers, lessening the chance of collisions and progressing everyone's security when driving
Forecasting Rate of Crime Using Machine Learning
Authors: Venkatesh S D  , Dr. Chitra K
Published: December 2024

Criminalities and predicting are indispensable when it comes to crime identification and prevention, and recognition of areas with a high probability of criminal occurrences, as well as simulation of crime scenes. Finally, and based on the methods of data mining, patterns can be obtained from large amount of unstructured data that can help in predicting future crimes. Crime is still a social issue that impacts the population on all the main aspects including quality of life, economic growth, and image of the nation, and thus, calls for more elaborate and effective systems and strategies for the safety of the population. With the help of the DM techniques proposed below, the goal is to design a system that is capable of identifying, studying, and these predicting types of crimes in certain areas. In particular, the current paper focuses on the methodological approaches of crime analysis and crime forecast. Thus, this work examined temporal and spatial features of crime to establish patterns and trends necessary to predict subsequent occurrences. Classification, clustering and association rule mining are utilized mainly in crime data classification process, in order to make distinctions and identify relationships between certain crime characteristics. Quantitative methods of analysis which include regression analysis, decision trees and artificial neural networks forecast future formulations of criminal incidences
Intrusion Detection System using Machine Learning
Authors: Yogesh Maurya  , Dr. Chitra K
Published: December 2024

The objective of this venture is to make an interruption location framework (IDS) that can recognize and halt any security dangers in organize activity by applying machine learning strategies. The system's objective is to successfully distinguish irregularities and designs of unauthorized get to by utilizing administered learning procedures. Real- time observing, tall danger location precision, and adaptability in reaction to changing cyberthreats are among its key characteristics. We are going prepare the framework with an broad dataset of both authentic and malevolent arrange movement. Within the conclusion, the IDS looks for to make strides arrange security and effectively halt information breaches
Climate Change Prediction
Authors: Yogesh Maurya  , Dr. Chitra K
Published: December 2024

The method of anticipating future climate patterns includes analyzing verifiable climate information and applying machine learning calculations and complex computational models. The objective of this inquire about is to make strides the accuracy of climate expectations by combining information from different sources, such as temperature, precipitation, and nursery gas outflows. These models can offer critical bits of knowledge into conceivable impacts on environments, climate designs, and human action by spotting patterns and peculiarities. The ultimate objective is to help within the execution of proficient climate adjustment and relief plans, subsequently supporting worldwide endeavors to handle climate alter
Image Watermark Embedding Method Based on Security Service Secure
Authors: Mr. M. Ajaykumar  , Mr. K. Bharath Prakash  , Mr. Md. Kamloddin  , Mr. K. Praneeth Reddy
Published: December 2024

To solve the problem of privacy leakage and response latency in outsourced image watermark embedding in cloud computing, an efficient and privacy-preserving watermark embedding method for outsourced digital images was proposed by introducing edge computing technology. We had proposed a perturbing encryption method with homomorphism to ensure the information security and the correctness of discrete wavelet transformation in the encrypted domain. In addition, the framework was designed to guarantee the safety of singular value decomposition that edge server could not recover the original image matrix. The experimental results show that the proposed method is superior to similar secure watermarking schemes in terms of encryption/decryption time and ciphertext expansion. The proposed method enables the watermarking operation to be performed in an unsafe outsourced environment while achieving a watermarking effect similar to the plaintext equivalent
Optimizing Cloud Security Through Data Splitting and Replication
Authors: P. Anusha  , P. Sridhar  , S. Yeshwanth  , Adnan
Published: December 2024

Outsourcing data to third-party administrative control in cloud computing introduces significant security concerns due to potential attacks from malicious users and compromised nodes within the cloud infrastructure. To address these issues, this paper proposes the Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) methodology, which simultaneously enhances data security and system performance. In DROPS, files are fragmented into smaller pieces and distributed across cloud nodes, ensuring that no single node contains meaningful information. To further secure the data, the nodes are spatially separated using graph T-coloring, preventing attackers from deducing fragment locations. Unlike traditional cryptographic techniques, DROPS eliminates the computational overhead associated with encryption-based methods while maintaining robust security. Our analysis demonstrates an extremely low probability of compromising all nodes containing fragments of a file. Performance comparisons with ten existing schemes reveal that DROPS offers superior security with minimal performance trade-offs, making it an efficient and scalable solution for cloud environments
Trends in Gender Disparities in Life Insurance Coverage in India
Authors: Dr. Bhanuprakash B E  , Dr. Nagaraja Poojari
Published: December 2024

This study explores gender disparities in the life insurance sector in India, focusing on policy ownership, women's participation in marketing, and regional variations. Despite significant growth in the sector, women remain underrepresented in both policyholders and the workforce. Using data from the Insurance Regulatory and Development Authority of India (IRDAI), the study examines trends from 2018–2023, revealing that while the proportion of women purchasing policies has increased, men still dominate the sector. The research also highlights regional variations, with states like Karnataka and Kerala showing higher female participation due to better literacy rates and targeted marketing. Furthermore, women’s involvement in life insurance marketing is increasing, especially among private insurers, who are adopting gender-sensitive recruitment practices. However, Life Insurance Corporation (LIC) has seen a decline in female workforce participation, suggesting a need for structural changes. The study recommends policy interventions, such as financial literacy campaigns and gender-specific products, to close the gender gap in life insurance. By addressing these disparities, the life insurance sector can contribute to financial inclusion and women’s empowerment in India
Conversion from Below: A Comparative Analysis of Colombian Indigenous Peoples’ Transformations of FPIC
Authors: Sandoval-Rojas Nathalia
Published: December 2024

This article examines how previously excluded social actors transform democratic institutions that offer limited, subordinated inclusion. It introduces the concept of “conversion from below,” which refers to endogenous institutional change driven by historically marginalized groups. By comparing three indigenous mobilizations aimed at broadening Free, Prior, and Informed Consultation and Consent (FPIC), the article demonstrates that institutional changes from below occur when there is ambiguity in the definition of the institution; this ambiguity can be exploited in favorable venues and when groups can signal their disruptive power. This study enhances our understanding of endogenous institutional change and sheds light on the dynamics of engagement and resistance of newly included groups with inclusionary institutions in Latin America.
Characterization of Soil and Growth Response of Rice on Different Positions along the Toposequence in Awka, Anambra State
Authors:   Department Of Soil Science and Land Resources Management, Faculty of Agriculture, NnamdiAzikiwe University, Awka, Anambra State, Nigeria, Ogechi IGWESI, Sandra , Chinedu NNABUDE, Peter Chinedu , Onyeka MADUEKE, Chike , Nduka OJEH, Vincent ,   Department of Geography, Faculty of Social Sciences, Taraba State University, Jalingo, Nigeria
Published: December 2024

This study examined soil characteristics and the growth response of Rice along different positions of a toposequence in Awka, Anambra State, Nigeria. The soils were delineated using the Geomorphon algorithm in the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) environments. This was then readapted to reflect the Uplands, midslopes, and Lowlands. The results show that in the uplands, bulk density increased down the profile, silt decreased down the profile, and clay fluctuated with increasing soil depth. In the lowlands, sand and silt content decreased down the profile, while clay content increased down the profile. The clay content had an inverse relationship with saturated hydraulic conductivity, decreasing the profile. In the midslope, sand content increased, and silt content decreased, while clay content was high in the topsoil, fluctuating with increasing depth. This result shows that physiographic positions critically influence the nature, properties, and variation of soils across the landscape. The soil properties, in turn, influenced the growth response of the rice plants, with particular reference to varietal variations (FARO 44, 52, 58, 59). Due to its high sand content, high runoff rate, and saturated hydraulic conductivity, the upland yielded rice plants with a lower number of tillers and height, while the reverse was the case in the lowlands. This may be attributed to Rice thriving best under swampy conditions, which are prevalent in lowland areas. The relatively low hydraulic conductivity, and higher organic matter, clay content, and water accumulation in the lowlands consequently ensure a higher yield. Generally, the swamp rice varieties (FARO 44 and 52) performed best in the lowlands, while the upland varieties (FARO 58 and 59) performed best in the midslopes.