Volume 12 | Issue 4 |

 Abstract

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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 Keywords

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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 Keywords

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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 Keywords

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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 Keywords

: 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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 Keywords

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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 Keywords

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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 Keywords

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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 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