Volume 13 | Issue 3 |

 Abstract

ABSTRACT This study examines the crucial roles of internal and external auditors in maintaining financial integrity within private companies in coimbatore. With rising demands for transparency and accountability, auditors serve as key safeguards against financial mismanagement. Internal auditors focus on assessing internal controls, risk management, and compliance, while external auditors provide independent verification of financial statements. The research highlights the collaborative efforts between both audit functions and explores challenges faced in fraud detection and regulatory compliance. Using a descriptive research design, data was collected through surveys and analyzed using percentage analysis, Chi-square tests, and ANOVA. Findings indicate that experience and age do not significantly influence audit practices. The study suggests strengthening auditor collaboration, investing in technology, and enhancing training programs to improve financial oversight. By adopting proactive risk-based auditing and ethical practices, public organizations can ensure greater financial accountability and transparency. Keywords: Internal Auditors, External Auditors, Financial Integrity, Public Organizations,

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Internal auditors, external auditors

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Abstract -- Liver cirrhosis is a severe and chronic liver condition that often goes undiagnosed until advanced stages, leading to significant morbidity and mortality. Early detection is critical for effective treatment and improved patient outcomes. This project focuses on the development of a predictive model for liver cirrhosis using the Random Forest algorithm, a robust machine learning technique known for its high accuracy and interpretability. The system utilizes a dataset of patient information, including demographic details, liver function test results, and other relevant clinical factors. After preprocessing the data by handling missing values and encoding categorical variables, the Random Forest classifier is trained to predict whether a patient is at risk of liver cirrhosis. The model demonstrates its reliability in identifying potential cirrhosis cases. A Flask-based web application is integrated with the predictive model to provide an accessible interface for medical professionals and researchers

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Keywords: Liver Cirrhosis, Random Forest Classifier, Predictive Model, Flask Web Application

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Aim: This review article aims to evaluate the treatment outcomes and patient satisfaction after orthodontic treatment. Understanding these outcomes will help orthodontists better plan treatments for future patients. Materials and Methods A systematic literature search was conducted using electronic databases, including PubMed, Google Scholar, and relevant orthodontic journals. Studies focusing on patient perceptions of extraction versus non-extraction cases following orthodontic treatment were included in this review. Conclusion: According to the American Board of Orthodontics (ABO) and their Objective Grading System (OGS), also known as the ABO Grading System, the final occlusion of patients treated with extractions appeared more acceptable than those treated without extractions.

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extraction , ABO, OGS , Non extraction

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This work presents the automatic elucidation and categorization of red lesions in retinal images as a supporting mechanism to diagnose diabetic retinopathy (DR). Early identification of diabetic retinopathy is critical for preventing loss of vision, as it is among the most common causes of visual impairment in patients with diabetes. The critical indicators for DR grading and diagnosis are the red lesions, such as microaneurysms, hemorrhages, and exudates. The Proposed system uses CNN to detect and classify stages of the DR, from No_DR to DR using advanced deep learning on retinal fundus images especially in DR Detection. The methods include preprocessing to filter and improve input images, feature extraction using denoised retinal images with deep learning-based CNNs, followed by utilization of TensorFlow/Keras frameworks for training the model. Retinal images are trained by a standardized dataset to test and strengthen robustness. Thus, this work provides a viable solution to the design of automatic detection and categorization of diabetic retinopathy, which has a significant impact on the early diagnosis and blindness of diabetic patients.

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 Keywords

Deep Learning, CNN, TensorFlow

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Peer-to-peer (P2P) networks have become a popular architecture for large-scale distributed systems due to their scalability, fault tolerance, and decentralized nature. However, efficiently processing queries in such networks is challenging due to dynamic node availability, network latency, and resource constraints. Exact query processing can be computationally expensive, making approximate query processing (AQP) an attractive alternative for achieving faster and resource-efficient query execution. This paper presents an efficient AQP framework for P2P networks that leverages probabilistic data structures, sampling techniques, and distributed indexing mechanisms to provide high-accuracy query results with reduced communication overhead. The proposed approach dynamically adapts to network changes, ensuring robustness in highly dynamic environments. Through simulations and real-world experiments, we demonstrate that our method significantly improves query response times while maintaining acceptable accuracy levels compared to traditional exact query processing methods. Our findings highlight the effectiveness of AQP in P2P networks, making it a promising solution for large-scale data retrieval and analysis applications. Peer-to-peer (P2P) databases are increasingly used for distributing and sharing documents, applications, and other digital media over the Internet. Answering large-scale ad hoc analysis queries, such as aggregation queries, poses unique challenges due to the distributed and dynamic nature of P2P databases. Exact solutions can be time-consuming and difficult to implement. This paper presents novel sampling-based techniques for approximate answering of ad hoc aggregation queries in P2P databases. The proposed approach includes an adaptive two-phase sampling method based on random walks of the P2P graph and block-level sampling techniques. Extensive experimental evaluations demonstrate the feasibility of the proposed solution

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 Keywords

Peer-to-Peer Networks, Approximate Query Processing, Distributed Query Execution, Sampling, Sketching, Query Optimization, Scalability, Bandwidth Efficiency

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A crucial government program, the Chief Minister Girl Child Protection Scheme works to improve the welfare, education, and financial stability of girls, especially those from low-income households. This study assesses the program's efficacy critically in the Coimbatore district, concentrating on how it affects financial empowerment, socioeconomic advancement, and educational attainment. Structured questionnaires were used to survey a sample of 99 respondents in order to evaluate beneficiaries' awareness, accessibility, and satisfaction with the program. In order to comprehend more general implementation patterns, other data sources were also examined. While the program has improved female literacy rates, financial independence, and gender equality, the report points out that there are still major obstacles to overcome, such as a lack of knowledge, red tape, and delays in money disbursement.The results highlight the necessity of more effective awareness efforts, streamlined application processes, and better monitoring systems to guarantee the program successfully reaches its target audience. The report ends with legislative recommendations meant to increase the scheme's effectiveness and help Tamil Nadu's girls become more empowered overall.

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Girl Child Protection Scheme, Female Education, Socio-Economic Impact, Government Welfare Programs, Coimbatore, Policy Evaluation

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With the rapid growth of cloud computing, efficient resource allocation is a major challenge. This project introduces an optimized cloud resource management system using container technology to enhance scalability and cost-effectiveness.The model focuses on Cloud Service Providers (CSPs), tenants, and end-users, ensuring optimal resource allocation. It features a Profit/Loss Calculator to predict financial impacts, improving cost-efficiency for tenants and profitability for CSPs. A trust mechanism evaluates data transfer security, reliability, and performance to maintain data integrity.The system includes a Probabilistic Demand Allocation (PDA) model that predicts tenant demands based on historical data, optimizing server resources while maintaining Service Level Agreement (SLA) compliance. Built with Java and MySQL, the project uses Java Swing for the frontend and advanced backend data processing techniques.By integrating optimization, trust mechanisms, and predictive analytics, this solution improves cloud resource allocation, security, and efficiency, making cloud services more scalable and reliable.

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 Keywords

Cloud Computing, Resource Allocation, Container Technology, Predictive Analytics, Trust Mechanism

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India's rapid urbanization is transforming its cities, bringing both opportunities and challenges. With rising crime rates, increasing population density, and evolving urban conflicts, the task of maintaining law and order has become more complex. The traditional Superintendent of Police (SP) model, rooted in colonial-era governance, struggles to meet the demands of modern cities due to its dual-command structure, which often causes delays and inefficiencies. In contrast, the Commissionerate system offers a centralized approach, where a Police Commissioner wields both executive and policing powers, enabling quicker decision-making and more coordinated resource management. This paper examines the role of the Commissionerate system in addressing the challenges posed by India's growing urban landscape. Drawing from examples in Mumbai, Delhi, and Uttar Pradesh, it highlights the system's ability to enhance crime control, improve citizen safety, and increase public satisfaction. However, it also acknowledges the limitations of the model, including concerns over potential misuse of power and resource constraints faced by police departments. While the Commissionerate system holds promise, its success depends on broader reforms that modernize policing as a whole. Updating archaic laws like the Police Act of 1861, establishing independent oversight bodies to ensure accountability, and fostering community policing are vital steps toward building trust between law enforcement and the public. Together, these measures can address the authoritarian tendencies of traditional policing and create a framework focused on public service and transparency. As urbanization accelerates, reimagining India's urban policing structures is no longer optional but essential. The Commissionerate system, when paired with meaningful reforms, has the potential to transform Indian policing into an efficient, inclusive, and democratic institution capable of meeting the needs of modern cities.

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Urban Policing , Commissionerate system

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The escalating frequency of crimes involving victims highlights the urgent demand for technological advancements to enhance public safety. This research presents a Real-Time Victim Audio Detection System based on Machine Learning (ML) and Deep Learning (DL) techniques. The innovative technology is designed to recognize distress calls from audio inputs, enabling immediate alerts to law enforcement for swift response. The system integrates audio feature extraction techniques, such as Mel-Frequency Cepstral Coefficients (MFCC) and spectrogram analysis, to classify audio signals and identify distress situations. The deep learning model, trained on a comprehensive data set including genuine and simulated distress audio samples, demonstrates high accuracy and robustness across various environments. The system's real-time capabilities ensure rapid detection and reporting, reducing response times and potentially preventing criminal acts. This innovative approach exemplifies the application of artificial intelligence to improve public safety by addressing the limitations of traditional surveillance and monitoring systems.

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 Keywords

Audio Detection, Crime Prevention, Real-Time Systems, Machine Learning, Deep Learning, Distress Signal Recognition, Automated Alerts

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Education plays a vital role in creating values in a society and it is critical for future generations to uphold the value of skill education, which can generate capabilities, capacities, and a completely new experience of acquiring required skill sets that can lead to financial independence while also increasing economic growth. NEP 2020 encourages integrating traditional learning methods with vocational courses to enhance the employability of the students to meet the demands of the industry while keeping the quality of education intact. Integration of vocational education, multidisciplinary studies, apprenticeships and internships and entrepreneurship education were some of the key initiatives under NEP 2020. However, inspite of NEP 2020's elaborate measure to introduce skill based education in the Indian educational system, the following are the possible challenges skill-based education under NEP 2020 initiative could face- lack of necessary funding, inadequate infrastructure in most institutions, need for skilled trainers, institution and industry linkage need technological assistance.

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 Keywords

Skill based education, NEP 2020, employability, entrepreneurship.

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Creative Commons Attribution 4.0 and The Open Definition