Volume 11 | Issue 6 |

 Abstract

Gangrene is a severe medical condition characterized by the death of body tissues due to a lack of blood supply or infection. Timely and accurate prediction of gangrene can significantly impact patient outcomes and guide appropriate medical interventions. This paper provides an overview and analysis of machine learning-based prediction models for gangrene. We begin by discussing the clinical significance of gangrene, its causes, and the challenges associated with its prediction. We emphasize the need for early detection and intervention to prevent further tissue damage and potential complications.Various machine learning algorithms, including logistic regression, K-Nearest Neighbors, support vector machine, naive bayes, decision trees and random forests, are examined for their application in gangrene prediction. We explore the features and data sources commonly used in these models, such as patient Diabetis, blood pressure, heart rate, respiratory rate, body temperature, skin condition and line of demarkcation. Additionally, we discuss the importance of feature selection and extraction techniques to enhance the predictive performance of the models. Data preprocessing steps, including missing data imputation, outlier detection, and normalization, are addressed to ensure data quality and model robustness. We delve into the evaluation metrics employed to assess the performance of gangrene prediction models, such as accuracy.

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 Keywords

Logistic regression, K-Nearest Neighbors, SVM, Naive Bayes, Decision trees, Random forests.

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To emphasize the positive experience of using innovative teaching methodologies among healthcare providers and the effectiveness of CPR through the use of high-fidelity mannequins rather than standard once. The aim of the study was to analyze how high-fidelity simulation training may impact knowledge, clinical performance and confidence levels of undergraduate students and compare this with low fidelity simulation traditional teaching methods. "An Quasi experimental design, Purposive sampling technique was used to select 50 students of first year B.Sc. nursing. Pretest was taken for initial knowledge assessment which was followed by a structured teaching program on high and low fidelity CPR simulation. Furthermore, post-test was taken to assess the knowledge and comparison between pre and post-test scores which are analyzed by using descriptive and inferential statistics. The experimental group shows that overall knowledge mean score in pretest was 14.26 that was increased with 19.22 in post-test. The mean difference found 4.96 after completion of the structure teaching program that clearly indicates that the knowledge among students has been increased.

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 Keywords

Innovative teaching methodologies, CPR, Low-fidelity simulation, High- fidelity simulation, Simulator, Mannequins, knowledge, effectiveness, comparison.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Pregnancy is a kind of miracle by the God and a mother's joy begins when new life is stirring inside and a tiny heartbeat is heard for the very first time. Pregnancy is a unique and exciting time in a woman's life, as it highlights the woman's amazing creative and nurturing powers while providing a bridge to the future. The present study was to assess the knowledge and practices regarding Gestational Hypertension disease among antenatal mothers attending MCH centre, Tirupati, Chittoor (dist), A.P. Non experimental descriptive research approach was adopted. A total of 100 subjects were selected by purposive sampling technique. A structured questionnaire and check list were developed and standardized to collect the data. The results revealed that 78 per cent of antenatal mothers had inadequate knowledge, 16 per cent of antenatal mothers had moderate adequate knowledge and 6 per cent of antenatal mothers had adequate knowledge. Regarding practices 14 per cent of antenatal mothers had inadequate practices, 74 per cent of antenatal mothers had moderate adequate practices and 7 per cent of antenatal mothers had adequate practices. Significant association was found between level of knowledge and area of living, Religion of the mother, Education of the mother and Education of the father found significantly associated with practices. Keywords: Gestational Hypertension, Knowledge, Practices, * Research Scholar, Department of Home Science, Sri Padmavati Mahila Visvavidyalayam, Tirupati, Chittoor (dist), A.P, India. **Professor, Human Development and Family Studies, Department of Home Science, Sri Padmavati Mahila Visvavidyalayam, Tirupati, Chittoor (dist), A.P, India.

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 Keywords

Gestational Hypertension, Knowkedge, Practices

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 Abstract

Customers are the foundation for any business benefit and that is why firms become conscious of the significance of acquiring satisfaction of customers. Customer churn is one of the major problems and it is regarded as one of the most essential concerns among companies because of increasing among firms, increased significance of marketing policies and customers awareness in present years. Organizations must develop different policies to solve the churn issues depending on the services they offer. Customer churn practice is essential in competitive and rapidly developing in telecom sector. The process of changing from one service provider to another telecom service provider occurs due to good services or rates or due to benefits to the customers which the competitor firm provides customers when signing up. Due to the greater cost related with acquiring new customers the prediction of customer churn has developed as an indispensable part of planning process and strategic decision making in telecom sector. The main aim of the study is to explore the customer churn prediction in telecom using in big machine learning data platform. Machine learning techniques have been used for estimating the customer probability to churn. This study makes use of logistic regression and KNN with big data for predicting consumer churn in the telecom sector. Logistic regression has been used widely to estimate the probability of churn as a function of variables set or features of customers. Similarly, for churn K-Nearest Neighbour is used to examine if a customer churns or not based on their feature's proximity to customers in every class. This study uses Kaggle website for dataset in predicting and analysing churn

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 Keywords

Churn, Telecom, Machine learning, Random Forest, logistic regression, decision tree, KNN.

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Aim: Assessment of pain and discomfort with different types of separators- Kansal, Kesling, Ni-Ti and Elastomeric separators. Materials and method: 100 adult subjects (50 Males and 50 females), aged 18-30 were chosen, four different types of separators (Kansal, Kesling, Ni-Ti and Elastomeric separator) were placed in the first permanent molar region in the same subjects and using a visual analogue scale [VAS] scoring system and Questionnaire Chart by the investigator, pain and discomfort recorded after 1st, 2nd, and 3rd day of separator placement over 24 hours intervals. Results: One-way ANOVA test depicts higher VAS score in elastomeric and less with Ni-Ti separators which was statistically highly significant (P<0.001). Presence of pain during chewing was statistically highly significant with Kesling and Kansal separator after day 1 while Ni-Ti and elastomeric after day 3 whereas at rest it was significant (P<0.05) for only elastomeric separator after day 3 in females Whereas in males, it was non-significant (P>0.05) for Ni-Ti separator but, highly significant difference(P<0.001) during chewing with Kesling, Kansal after day 1 while elastomeric separator after day 3 at rest and during chewing. Conclusion: VAS score was more with elastomeric and less with Ni-Ti at all durations whereas Kansal was less painful in females after day 1 and 2 and in males after day 3 while Kesling was less painful in males after day 1 and 3 which increased over 24 hrs of interval. So, Clinicians need to have preferred a suitable separator taking into account the amount of separation in conjunction with pain and discomfort. Clinical implication: A variety of separators are available in the market hence one should search for a separator that gives maximum separation and minimum discomfort to the patient.

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 Keywords

Separator, Visual Analogue Scale [VAS], Pain, Discomfort.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Our skin is one of the important parts of our body that covers and protects our internal parts. While doing so our skin can get affected by various allergies, irritants, or diseases which can lead to a skin condition. Skin disease is one of the conditions which affects our skin and causes various kinds of skin-related problems. We have come across various kind of skin diseases which involves a wide range of varieties while some of them can leave a permanent mark on our skin and some don't. If it's not cured early skin diseases can also cause a social impact on a patient. With the various advancement in the field of image processing and detection techniques, these diseases can be detected early and cured. In this paper, we have explored diseases like monkeypox, acne and detected them successfully using AlexNet, a CNN deep learning model with an accuracy of 97.2%, precision of 98.9%, F1 score of 97.24% and recall of 95.64%.

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 Keywords

Skin diseases, image processing, deep learning, CNN, AlexNet, Monkeypox, Acne

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Creative Commons Attribution 4.0 and The Open Definition

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The banking industry's technological advancements have made our lives easier. Today, it is very much a part of people's lives, particularly ATMs. The ATM has become a necessary element of our everyday lives and society. ATM has successfully reached a broad consumer base at a minimal cost. The purpose of this study is to determine the level of awareness among bank customers uses ATM services in Vapi. A self-administered questionnaire was used to obtain primary data from 238 bank customers for this study. To meet the study's objectives, data was tabulated and analysed using statistical methods.

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 Keywords

ATM Services, ATM Card, ATM Machine, Awareness, Technology

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Archetype (derived from Latin Archetypum and Greek Archetypon means arche + typos= root / origin +model or pattern) are the original objects, images or even characters, events, or situations from which other similar things are patterned or copied in various forms of art and literature. Archetypes often represent fundamental aspects of the human psyche and provide insight into the narrative's deeper meanings. In the present paper therefore, an attempt has been made to examine how Chitra Banerjee Divakaruni evokes the journey Archetype in her novel Oleander Girl by showcasing the evolution of her main protagonist Karobi. The emphasis in the study undertaken will primarily be on the exploration of the inner journey of the protagonist towards the completeness, wholeness and selfhood. With the inner journey the focus will also be laid on the outward physical journey which parallels the journey inward. Through the lens of archetypal criticism the paper will also examines recurring symbols, motifs, and character types that tap into universal human experiences and collective unconsciousness. Joseph Campbell's concept of journey contained in The Hero with Thousand Faces, Carl Jung's concept of 'individuation' and Carol. S.Pearson ideas about the archetypes or 'inner guides' will form the theoretical framework.

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Archetypes, Journey, Evolution, Wholeness, Selfhood,

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This dissertation explores the impact of homophobia on LGBTQIA+ individuals, focusing on lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other gender identities. Homophobia, defined as an irrational fear or hatred of LGBTQIA+ individuals, can manifest in various forms such as discrimination, bullying, harassment, violence, and denial of basic human rights. A sample of 100 college students was used to measure homophobia levels by the Italian version of the Homophobia Scale Questionnaire and ANOVA was conducted to examine if there were significant differences in the mean of the response variable across different treatment groups. The results indicated that there is a substantial difference in the mean of TREATMENT across the various treatment groups, with a significant F ratio. The rejection of the null hypothesis suggests that the impact of homophobia on the lives of LGBTQIA+ individuals varies significantly depending on the level of homophobia experienced. This study contributes to the existing literature by shedding light on the detrimental effects of homophobia on the lives of LGBTQIA+ individuals. The findings emphasize the urgent need for creating awareness, fostering inclusivity, and implementing measures to combat homophobia in order to ensure the well-being and equal rights of LGBTQIA+ individuals in society.

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 Keywords

Homophobia,internalized homophobia,health,mental health,LGBTQIA+,individuals

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Power systems are prone to faults that can result in substantial damage to expensive components, explosions, outages, and even fatalities. To mitigate these consequences, a robust power protection system is crucial for detecting, classifying, and locating faults. This technical description presents a comprehensive methodology that utilizes machine learning algorithms such as Convolutional Neural Networks (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM) to accurately detect and classify faults in electrical systems. The main objectives of this study are to identify and categorize various fault types occurring at different locations and resistance levels, gain insights into the causes of interruptions, restore power promptly, and minimize future occurrences. Additionally, the analysis aims to improve the understanding of protection system components to implement preventive measures and reduce the likelihood of service disruptions and equipment damage. The proposed solution integrates CNN as a machine learning algorithm to enhance fault detection and classification performance by leveraging its ability to extract relevant features from fault data.. The performance of various machine learning algorithms, including RF, KNN, Decision Tree, and SVM, is evaluated and compared. Among these algorithms, SVM demonstrates the highest performance in terms of fault detection and classification accuracy. It effectively identifies and categorizes different fault types, enabling swift fault location determination and subsequent mitigation actions. The experimental evaluation is conducted on a Test Network using MATLAB/SIMULINK, which provides a realistic representation of an electrical system. The results highlight the effectiveness of SVM in fault detection and classification, emphasizing its suitability for practical implementation in power system protection.

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 Keywords

Line fault, Ground fault, SVM, Linear Regression, CNN, Machine learning

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