Volume 12 | Issue 4

 Abstract

With the rapid increase in digital transactions, credit card fraud has become a significant concern for financial institutions and consumers alike. Traditional rule-based fraud detection systems often struggle to keep pace with the evolving tactics of fraudsters. Machine learning (ML) techniques offer promising solutions for detecting fraudulent transactions due to their ability to adapt and learn from data patterns. In this paper, we present a comprehensive analysis of various ML algorithms for credit card fraud detection. Firstly, we review the existing literature on credit card fraud detection methodologies, highlighting the limitations of traditional approaches and the advantages of ML-based techniques. We then discuss the key challenges in building effective fraud detection models, including imbalanced datasets, feature selection, model interpretability, and scalability. Next, we provide an overview of different ML algorithms commonly used in credit card fraud detection, including supervised learning algorithms such as logistic regression, decision trees, random forests, support vector machines, and ensemble methods like gradient boosting and AdaBoost. We also explore the potential of unsupervised learning techniques, particularly clustering algorithms such as k-means and DBSCAN, and anomaly detection methods like isolation forest and autoencoders. Furthermore, we present a detailed analysis of feature engineering strategies essential for improving the performance of fraud detection models. Feature engineering techniques such as PCA (Principal Component Analysis), feature scaling, and synthetic minority oversampling technique (SMOTE) are discussed in detail. To evaluate the effectiveness of different ML algorithms, we conduct experiments on publicly available credit card transaction datasets, including the Kaggle Credit Card Fraud Detection dataset. We compare the performance of various algorithms in terms of metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Additionally, we investigate the interpretability of the models to understand the factors contributing to their decisions.

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The advancement of cloud computing technology has revolutionized various industries, including the transportation sector. Online bus ticket booking systems have become increasingly popular due to their convenience and accessibility. This research paper presents the design, implementation, and performance evaluation of a cloud-based online bus ticket booking system. Leveraging cloud computing infrastructure, the system offers scalability, reliability, and cost-effectiveness, catering to the dynamic demands of users and bus operators. The paper discusses the architectural components of the system, including frontend application, backend services, cloud infrastructure, database management, authentication, payment processing, bus schedule management, ticket booking, reservation, notifications, analytics, and monitoring. Furthermore, the paper evaluates the performance of the system in terms of scalability, availability, response time, and cost efficiency. Through extensive experimentation and analysis, the results demonstrate the effectiveness of cloud-based solutions in enhancing the scalability, reliability, and performance of online bus ticket booking systems. The findings of this research provide valuable insights for the design, development, and deployment of cloud-based applications in the transportation domain.

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Cloud computing, Online bus ticket booking, Scalability, Reliability, Performance evaluation.

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Abstract: Breast cancer detection and classification represent critical challenges in medical imaging, where accurate diagnosis is paramount for effective treatment planning and patient care. In this study, we leverage artificial intelligence (AI) techniques to address these challenges specifically in the context of elastography images. Elastography, coupled with B-mode ultrasound, offers valuable insights into the stiffness and geometric properties of breast lesions, aiding in the differentiation between benign and malignant tumors. Through the integration of AI algorithms, such as supervised learning with support vector machines (SVM), we develop a robust framework for the automated detection and classification of breast lesions. Our approach involves extensive preprocessing, feature extraction, and dimensionality reduction using techniques like principal component analysis (PCA). The proposed system undergoes rigorous validation using cross-validation methods to ensure its generalization and effectiveness. Our results demonstrate promising accuracy rates, highlighting the potential of AI-driven solutions in enhancing breast cancer diagnosis and patient outcomes.

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Keywords: Breast cancer, Elastography, Artificial Intelligence, Supervised Learning, Support Vector Machines, Principal Component Analysis, Medical Imaging

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The "CREATING APP FOR BKS GARMENTS (CLASSY STORE)" project is in charge of handling garments product sale though online for the lending business BKS garments. This online helps the customers of particular Garments products purchase as well as to search for products through online. There will be Garments products catalo in this site which will give all the information about Garments products. In this economic world the trade in the business considers as in costly one, to reduce the economic cost and as well as the user work burden this project has been design. The main objective of the project is establishing the concept of the e business. This project give the advertisement in the website and its designing part. This project explains about its product details and its price details and this project helps to Garments products the orders in the online by referring the customer's credit card number. The customers who are placing the order are placed in the database for reference. And the details of the customer who are visiting the website is also maintained in the database. All the database details are maintained by the company administrator. Other users can only view the website and register their details and they cannot alter and details of the other viewers. It is the right of the administrator whether to save the particulars of the viewer's details or not. The Administrator will have additional functionalities when compared to the common user. He can add the old Garments products shop keepers into this system and create account for shop keepers to sale their old Garments products using this application. Once the shop keeper The "CREATING APP FOR BKS GARMENTS (CLASSY STORE)" project is in charge of handling garments product sale though online for the lending business BKS garments. This online helps the customers of particular Garments products purchase as well as to search for products through online. There will be Garments products catalo in this site which will give all the information about Garments products. In this economic world the trade in the business considers as in costly one, to reduce the economic cost and as well as the user work burden this project has been design. The main objective of the project is establishing the concept of the e business. This project give the advertisement in the website and its designing part. This project explains about its product details and its price details and this project helps to Garments products the orders in the online by referring the customer's credit card number. The customers who are placing the order are placed in the database for reference. And the details of the customer who are visiting the website is also maintained in the database. All the database details are maintained by the company administrator. Other users can only view the website and register their details and they cannot alter and details of the other viewers. It is the right of the administrator whether to save the particulars of the viewer's details or not. The Administrator will have additional functionalities when compared to the common user. He can add the old Garments products shop keepers into this system and create account for shop keepers to sale their old Garments products using this application. Once the shop keeper post their stock old Garments products into the system, the admin will get the notification and admin will fix the price for the old Garments products and finally publish the Garments products into the application.post their stock old Garments products into the system, the admin will get the notification and admin will fix the price for the old Garments products and finally publish the Garments products into the application.

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Garments, products, data, system.

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Creative Commons Attribution 4.0 and The Open Definition

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This study investigates the challenges encountered by women entrepreneurs operating small-scale businesses in Coimbatore City, Tamil Nadu, India. Despite the growing recognition of women's contribution to the entrepreneurial landscape, they continue to face unique obstacles that hinder their business growth and sustainability. Through qualitative research methods including interviews, surveys, and observations, this study aims to identify and analyze the multifaceted challenges experienced by women entrepreneurs in Coimbatore City.

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Women entrepreneurs, barriers, challenges.

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Creative Commons Attribution 4.0 and The Open Definition

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This study investigates the awareness and impact of government schemes among farmers in Coimbatore District, specifically focusing on Sulur Taluk. With agriculture being a vital sector in India, government schemes play a crucial role in supporting farmers and enhancing agricultural productivity. However, the effectiveness of these schemes relies heavily on farmers' awareness and their ability to access them.The research employs a mixed-methods approach, combining both qualitative and quantitative techniques. Surveys, interviews, and focus group discussions are conducted to gather data from a representative sample of farmers in Sulur Taluk. The study assesses farmers' awareness levels regarding various government schemes, their access to information channels, and the perceived impact of these schemes on their agricultural practices and livelihoods.Preliminary findings suggest that while there is a moderate level of awareness about government schemes among farmers in Sulur Taluk

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Government schemes, FarmersAwarenessImpactCoimbatore DistrictSulur TalukAgricultural developmentRural livelihoodsPolicy evaluationSustainable agriculture

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The proliferation for internet banking has transformed how customers interact with financial institutions. While convenience and cost-effectiveness are clear advantages, long-term adoption hinges on customer satisfaction with these services. This research examines the relationship between IB service quality and customer satisfaction.

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E-Banking, service quality, customer satisfaction, reliability, Factor analysis, Artificial neural network

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John Keats one of the eminent poet of all time belongs to the second generation of Romantic Poets. He was the youngest of the major romantic poets whose lifespan was very short. But within the short period of four productive years he reached the height of Shakespeare as a poet. His love for beauty made him a great poet. And he finds this beauty in the form of nature, women, art and literature. As a poet his works are relevant through ages and for all generations. His fame as a poet lies in his love her beauty, his sensuousness, negative capability, Hellenism and the blending of classicism with romanticism

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relevance, classicism, romanticism, nature, sensuousness, negative capability, Hellenism.

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Creative Commons Attribution 4.0 and The Open Definition

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The colonial era, spanning roughly from the 15th to the 20th centuries, fundamentally reshaped international relations. European powers embarked on a global quest for conquest and resource extraction, establishing vast empires that reshaped political, economic, and social structures across the globe. This paper explores the defining characteristics of international relations in colonial times, focusing on the motivations for European expansion, the systems of domination employed, and the impact on colonized societies. It also examines the seeds of resistance and the eventual dismantling of colonial empires

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1. European Expansion 2. Colonialism 3. Imperialism 4. Resource Extraction 5. Domination 6. Colonized Societies 7. Resistance Movements 8. Decolonization 9. Power Dynamics 10. Economic Exploitation 11. Social Structures 12. Cultural Assimilation 13. Global Conquest 14. International Power Shifts 15. Legacy of Colonialism

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The auto industry has made efforts to create safer vehicles, but traffic accidents are inevitable, as this review paper explains. If we develop accurate prediction models that are able to automatically classify the type of injury and severity of various traffic accidents, we might be able to identify patterns in dangerous crashes. These social and street mishap examples can be valuable to foster traffic security control approaches. We believe that measures should be based on scientific and objective surveys of the causes of accidents and the severity of injuries in order to achieve the greatest possible accident reduction effects with limited budgetary resources. The results of four machine learning paradigms used to model the severity of injuries sustained in traffic accidents are summarized in this paper. We considered brain networks prepared utilizing cross breed learning draws near, support vector machines, choice trees and a simultaneous half and half model including choice trees and brain organizations. The results of the experiment show that the hybrid decision tree-neural network approach performed better than the other individual machine learning paradigms.

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safer vehicles, traffic accidents, accurate prediction, identify patterns, machine learning.

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In this research paper there are many inventories in in automobile industries to design and build safety measures for automobiles, but traffic accidents are unavoidable. There is a huge number of accidents prevailing in all urban and rural areas. Patterns involved with different circumstances can be detected by developing an accurate prediction models which will be capable of automatic separation of various accidental scenarios .These cluster will be useful to prevent accidents and develop safety measures. We believe to acquire maximum possibilities of accident reduction using low budget resources by using some scientific measures. There is a huge impact on the society due to traffic accidents where there is a great costs of fatalities and injuries. In recent years, there is a increase in the researches attention to determine the significantly affect the severity of the drivers injuries which is caused due to the road accidents. Accurate and comprehensive accident records are the basis of accident analysis .the effective use of accident records depends on some factors, like the accuracy of the data, record retention, and data analysis.

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automobile industries, traffic accidents, safety measures, urban and rural areas, drivers injuries.

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Customer churn anticipation proves to majorly critical within subscription-based services like telecommunications, software, as a service (SaaS), plus online streaming platforms. Recognizing customers who might churn aids proactive retention strategies, thereby lessening revenue reduction and ameliorating customer gratification. In this research paper, a comparison assessment of varied machine learning techniques for predicting customer churn is introduced. A dataset involving historical customer interactions alongside churn events is utilized for training and assessing the efficacy of various models. The study encompasses conventional classifiers like logistic regression and decision trees, and advanced ensemble methods like random forests and gradient boosting. In addition, the efficiency of deep learning models like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in capturing multifaceted patterns intrinsic in customer behavior data is further explored. Assessment metrics encompassing accuracy, precision, recall, and F1-score are deployed to appraise the predictive performance of each model. Besides, the interpretability of the models is explored to gain insights into the factors impacting churn propensity. The results garnered from the experiments offer valuable insight into the strengths and constraints of different techniques for customer churn prediction, consequently guiding businesses in formulating effective retention strategies to alleviate churn and amplify customer loyalty.

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EDA, churn prediction

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Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is a key policy programme that aims to improve socioeconomic empowerment and financial sustainability for India's marginalised communities. With an emphasis on the effects on individuals with special needs, this study examines the MGNREGA's financial viability and impact evaluation in the Coimbatore area. The study uses a mixed-methods approach to assess people's financial sustainability using quantitative surveys and qualitative interviews.

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MGNREGA, Job card, Objectives, Significance

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Abstract: Kilakarai, nestled along Tamil Nadu's southeastern coast, is a captivating blend of history, culture, and identity. This study delves into the intricate fabric of Kilakarai, unraveling its historical enigmas, cultural amalgamations, architectural nuances, maritime significance, and the urgent need for preservation. The paper explicitly addresses the pressing challenges faced by Kilakarai, including historical preservation, cultural identity, and environmental sustainability. Through a multidisciplinary approach, Kilakarai emerges not just as a geographic location, but as a microcosm of human existence, where the past shapes the present and the future holds both promise and challenge. As Kilakarai adapts to the passage of time, it serves as a testament to human resilience and the enduring allure of the unexplored.

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: historical preservation, cultural identity, environmental sustainability, human resilience

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Teratogenicity in pregnancy poses significant risk to fetal development and maternal health, necessitating a comprehensive understanding of its mechanisms, risk factors, and management strategies. This review article provides a thorough examination of teratogenicity in pregnancy, beginning with an introduction to the historical context and milestones in teratology. Mechanisms underlying teratogenic effects, including disruptions in developmental biology, are elucidated. Common teratogens are identified and classified, with an overview of their effects on fetal development. Maternal factors influencing teratogenic risk are discussed, along with strategies for risk assessment and management in clinical practice. Diagnostic challenges and considerations in the evaluation of teratogenicity are addressed, alongside prevention initiatives and public health policies. Finally, future directions in teratology research, including advances and innovations, are highlighted, aiming to enhance our understanding and management of teratogenic risks during pregnancy.

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Maternity, Pregnancy, Teratogenic effect, menstrual cycle, Birth defect, Abnormality

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As a prevalent gynaecological issue, dysmenorrhea affects women of all ages and ethnicities, impacting their quality of life. Various methods have been employed to alleviate dysmenorrhea, with herbal remedies commonly used in treatment. This article discusses the utilization of select herbal remedies for managing dysmenorrhea. Dysmenorrhea manifests as abnormal abdominal pain during menstruation. While it's typical for 50 to 70% of women to experience lower abdominal and uterine pain during menstruation, severe pain that hinders daily activities is termed dysmenorrhea or painful periods. Chemical treatments for dysmenorrhea often entail side effects such as gastrointestinal disturbances, nausea, blurred vision, headaches, and dizziness. Consequently, the use of herbal remedies and traditional medicine has gained popularity. This systematic review study highlights key medicinal plants utilized for dysmenorrhea. Keywords such as dysmenorrhea, medicinal plants, menstruation, pain, and primary dysmenorrhea were employed in this review. The medicinal herbs discussed in this study include Foeniculum Vulgare, Zingiber officinale, Matricaria Chamomilla, Cinnamomum Zeylanicum, and Zataria Multiflora.

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Dysmenorrhea, Foeniculum Vulgare, Zingiber officinale, Matricaria Chamomilla, Cinnamomum Zeylanicum, and Zataria Multiflora.

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ABSTRACT: In developing countries, especially in India where deep rooted beliefs, customs and superstitions regarding pregnancy. Child birth, health and the role of the mother are still widely prevalent and women with poor socio - economic back ground are more vulnerable to the health risks associated with child bearing in quick succession. These contribute to high rates of maternal morbidity and mortality. Family planning plays a crucial role in safe guarding the health of women. Study based on to assess the existing knowledge regarding Temporary Family planning methods among 30 primi gravida mothers at Birla Sarvajanik Hospital, Jhunjhunu, Rajasthan.. It is necessary that everyone should be aware of Temporary Family planning methods Method: An evaluative approach was adopted for the study with one group pretest posttest design. 30 samples were selected using non probability purposive sampling technique and the data was collected using a structured knowledge questionnaire on Temporary Family planning methods. The main study was done in Govt. Maternity Hospital, Rajasthan. Result: Comparison of overall knowledge score in pretest and posttest reveals that Temporary Family planning methods are having 36.00% more knowledge in posttest than pretest. which indicates the effectiveness of PTP. Before the administration of Intervention., pre-test knowledge of mothers was having a significant association with Mothers Education (?2=13.15 at p> 0.05). Whereas knowledge regarding Temporary Family Planning Methods. These results are obtained by Pearson test/Yates corrected chi-square test and were statistically significant. Pearson Chi square test was used to test statistical significance; it is statistically significant at 0.01 level. Conclusion: Descriptive and inferential statistics were employed to analyze the data. The data analysis was carried out on the basis of objectives and hypotheses of the study and has been presented on the sample characteristics with their knowledge. The overall pretest scores was 48.33 % and the posttest scores was 84.33%. There was association between posttest knowledge score and selected socio-demographic variables such as Mothers education, by mothers are significantly associated with posttest level of knowledge. The independent 't' value was 32.8 % which was greater than the table value at P<0.001 level of significance. This indicated that PTP was significantly effective in enhancing the knowledge of mothers of primi gravida mothers.

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Experimental study, Planned teaching programme, Knowledge, family planning methods, primi gravida mothers

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Recruitment necessitates that candidates start working on the first day to ensure companies achieve their goals. Job candidates with technical competence possess both soft and hard skills, consisting of measurable and personal attributes. They are often essential to companies in the information technology (IT) sector, but many other fields also require employees with technical competency skills. In this article, we'll discuss the various types of technical proficiency you should consider acquiring before sending future job applications

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Company, Job, Technology, Soft and hard Skills etc.

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Creative Commons Attribution 4.0 and The Open Definition

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More than 277 distinct forms of cancer disease are referred to as cancer in the broadest sense. Researchers have discovered various cancer stages, suggesting that a number of gene alterations have a role in the genesis of cancer. Anomalous cell proliferation results from these gene alterations. Millions of people worldwide are still impacted by the complicated and common health issue that is breast cancer. In this paper, the complex field of breast cancer is thoroughly examined, with a focus on the wide range of risk factors that contribute to the disease's occurrence and the developments in diagnostic techniques. Many risk variables, including genetic predispositions such BRCA mutations, hormone effects, lifestyle factors, and reproductive patterns, have been found by a thorough review of the literature published recently. The complex web of elements that contribute to breast cancer etiology also includes age, family history, and environmental factors. Furthermore, the analysis highlights the critical role that diagnostic technologies play in the early identification and treatment of breast cancer. New technologies such as magnetic resonance imaging and molecular diagnostics complement mammography, the gold standard for breast cancer screening. New technologies such as genetic testing and magnetic resonance imaging complement mammography, the mainstay of breast cancer screening, to improve the sensitivity and specificity of breast cancer diagnosis. Even with these developments, it is still difficult to guarantee that screening programs are widely accessible, especially in environments with low resources. To sum up, this review highlights the significance of comprehending many risk variables in the emergence of breast cancer and stresses the vital role that developing diagnostic methods have in improving early detection. The goal of this review's synthesis of the available data is to further our understanding of the complex nature of breast cancer and provide guidance for future investigations, screening methods, and preventative measures.

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Cancer, mutation, hormones, mammography, biopsy

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Creative Commons Attribution 4.0 and The Open Definition

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In the contemporary automotive sector, the precise evaluation of used cars' resale value stands as a pivotal task for diverse stakeholders, encompassing car dealerships, insurance entities, and individual car owners. Conventional methods of car valuation often hinge on manual data analysis and subjective judgment, fostering inconsistencies and inaccuracies. To surmount these challenges, the presented project introduces CarValueML: an AI-driven car price estimation system bolstered by Gradio. CarValueML leverages machine learning algorithms to scrutinize an extensive dataset of car attributes, including make, model, year, mileage, condition, and features. The system employs varied data preprocessing techniques to meticulously cleanse and prepare the data for both training and inference. Subsequently, sophisticated machine learning models, such as random forests and gradient boosting trees, undergo training on the refined data, yielding predictions of car prices with remarkable precision. Gradio, an accessible web framework, serves as the bedrock for CarValueML's user interface. Facilitating the creation of interactive and user-friendly interfaces for machine learning models, Gradio extends CarValueML's accessibility to a broad spectrum of users, encompassing those without technical expertise.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Gradio, Machine Learning, Mean Squared Error, Root Mean Error

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Creative Commons Attribution 4.0 and The Open Definition