Volume 11 | Issue 12 |

 Abstract

The Trauma Rapid application is a comprehensive healthcare management tool designed to provide critical assistance during emergencies and improve overall healthcare access. Its primary objective is to swiftly connect patients in distress with emergency services and the nearest hospitals through seamless integration with Google Maps. This integration allows the application to expedite the dispatch of ambulances and trigger SOS alerts when urgent assistance is needed. Users of the application include doctors, patients, and ambulance drivers. Key features of the app include its ability to pinpoint and direct users to the closest medical facilities, optimizing response times and saving crucial minutes during medical emergencies. Beyond emergency response, the application offers a convenient platform for scheduling non-emergency medical consultations, encouraging proactive health practices. Doctors can register, manage appointments. Additionally, the application integrates a blood bank contact and police station contact, broadening its utility to address healthcare and safety concerns. Users can seek blood bank services and report emergencies to local law enforcement, making the Trauma Rapid application an indispensable tool for ensuring the well-being and safety of individuals in critical situations and their ongoing healthcare management.

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 Keywords

doctors, patients, hospitals, ambulance services, blood banks, police station, Google maps.

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 Abstract

Manual colorization of black and white images is a laborious task and inefficient. It has been attempted using Photoshop editing, but it proves to be difficult as it requires extensive research and a picture can take up to one month to colorize. A pragmatic approach to the task is to implement sophisticated image colorization techniques. The literature on image colorization has been an area of interest in the last decade, as it stands at the confluence of two arcane disciplines, digital image processing and deep learning. Efforts have been made to use the ever-increasing accessibility of end-to-end deep learning models and leverage the benefits of transfer learning. Image features can be automatically extracted from the training data using deep learning models such as Convolutional Neural Networks (CNN). This can be expedited by human intervention and by using recently developed .implement image colorization using various CNN models while leveraging pre-trained models for better feature extraction and compare the performance of these models.

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 Keywords

Deep learning, Pre-trained model, CNN, image colorization.

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With an emphasis on the creation and effectiveness of a Hindi Articulation Therapy App, this study aims to address the need for language-specific resources in articulation therapy. Misarticulation is a major barrier to effective communication in the Indian culture, especially when it comes to young children. Language specificity is typically lacking in current treatments, which limits their applicability and efficacy. By providing a multifaceted mobile application designed specifically for Hindi speakers that includes Position, Phoneme, and Picture levels in therapeutic exercises, this study seeks to close this gap. The study's methodology includes the development and use of the Hindi Articulation Therapy App. Real-time feedback mechanisms and a wide range of therapeutic activities and procedures serve as the app's development guidelines. The Phoneme level unifies words across initial, medial, and final positions; the Position level shows how sounds are articulated in various mouth positions; and the Picture level offers visual cues to improve comprehension. As the app is being developed, input from parents, speech therapists, and therapy patients is taken into account to improve it. A pilot research is carried out to evaluate the app's efficacy. In therapy sessions led by the app, participants include parents, kids, and experts. Tracking right and wrong word output during data collection enables a quantitative examination of therapeutic outcomes. Initial results show that the software has the ability to greatly enhance Hindi speakers' articulation. The app's user-friendly layout, interesting therapeutic exercises, and importance of language-specific material are highlighted by participant feedback. According to the results, the Hindi Articulation Therapy App presents a viable substitute for conventional techniques, effectively tackling the distinct language obstacles inherent in the Indian setting. The study adds significant knowledge to the creation and use of articulation therapy instruments tailored to a certain language. It is recommended that additional study be conducted and the app be improved in order to increase its accessibility and wider influence inside India's heterogeneous linguistic landscape.

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 Keywords

Articulation Therapy, Therapeutic exercises, heterogeneous linguistic landscape, Misarticulation , therapeutic materials ,

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 Abstract

Thyroid conditions are common worldwide. In India too, there's a significant burden of thyroid conditions. According to a protuberance from colourful studies on thyroid complaint, it has been estimated that about 42 million people in India suffer from thyroid conditions. This review will concentrate on the epidemiology of five common thyroid conditions in India hypothyroidism, hyperthyroidism, thyroid cancer. (1) Endocrine diseases are common in India of which the thyroid diseases represent a major subset. Thyroid dysfunction frequencies is rising at an intimidating rate in Indian population. Hypothyroidism and hyperthyroidism constitute the maximum chance of thyroid conditions in India. Hormone relief remedy has been a standard approach to thyroid dysfunction.

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 Keywords

Hyperthyroidism, Hypothyroidism, Thyroid cancer, Endocrine, India.

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 Abstract

This article presents a new rule for automatic query construction that addresses the precise identification of syntactic and semantic patterns in sentences. The design and use of the method is carefully explained. Although the main goal is to create text-based questions, machine learning has shown great success in reading comprehension, especially focusing on creating questions from sentences. The system developed in human evaluation shows that it is more useful than other systems, that it creates problems like people and creates problems in general.

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 Keywords

Natural Language Processing, Machine Learning, Artificial Intelligence, Question Generation, T5 Model

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Natural products from plant sources have been the basis of the treatment of various human disease. The use of herbal medicine becoming popular due to toxicity and side effects of allopathic medicines. Chromolaena odorata (L) King & Robinson is considered as weed plant and it is a perennial shrub belonging to the family Asteraceae. Clinical studies using aqueous extracts from Chromolaena leaves have shown various properties such as antimicrobial, wound healing, anti-diarrheal, astringent, antispasmodic, antihypertensive, anti-inflammatory, and diuretic. In the present study, organoleptic and microscopic studies were done which is the basis for the identification and determination of adulterants for identify the potent crude drug. The fresh leaves of Chromolaena odorata were studied by organoleptic evaluation, proximate analysis, and florescence analysis of powdered drug. The present information on the pharmacognostic evaluation of the plant drug Chromolaena odorata serve the important information to the identity and to determine the quality and purity of the plant material in the future.

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 Keywords

C. odorata, organoleptic, macroscopical, microscopical, fluorescence, adulteration.

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 Abstract

Technological development is unavoidable over time. That phenomenon has created a new trend, which is online transportation services. Online transportation services provide road transports that track and exploit the development of technology-based online application. Indonesia has the highest market share in Southeast Asia with an estimated transaction value of 18 million dollars by 2025. Indonesia has many online transportation service companies. Gojek, Grab, Maxim, and InDriver are the companies with the most user in Indonesia. Gojek leads an online transportation market in Indonesia, followed by Grab in second rank, Maxim in the third rank, and InDriver in the fourth. Online transportation services companies retain their customer by making customers repurchase. Customer repurchase intention us affected by customer satisfaction while using online transportation services. Customer satisfaction is affected by e-service quality and customer's trust to the brand. Hence, this study aims to find out how much e-service quality and trust affect customer satisfaction, and how much customer satisfaction affects customer's repurchase intention. This study uses quantitative method with descriptive analysis way. Data collection will be done by using questionnaire as a tool which will be spreaded out through Google Forms. Respondence's population is a sum of Grab's user in Indonesia, with a purposive sampling technique. Datas that already collected will be processed using SMART-PLS software.

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 Keywords

consumer behaviour, customer satisfaction, electronic service quality, online transportation services, price, repurchase intention, trust

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Creative Commons Attribution 4.0 and The Open Definition

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The surge in online reviews has revolutionized consumer decision-making, accompanied by the pervasive issue of fake online reviews. This research paper delves into a robust machine learning approach for combating this challenge. The paper addresses the crucial need for dependable methods to distinguish genuine user feedback from deceptive ones, fostering trust in online commerce and informed consumer decisions. Leveraging a diverse dataset of reviews, the study employs a multi-faceted methodology encompassing data preprocessing, feature extraction, and machine learning classification algorithms. Techniques such as text processing, sentiment analysis, and advanced classification models are utilized to assess their effectiveness in identifying fake reviews across diverse industries and platforms. The paper's outcomes highlight the relative performance of various machine learning algorithms, including Random Forests, Support Vector Machines, Logistic Regression, and more. The evaluation employs precision, recall, F1-score, and accuracy metrics to gauge predictive accuracy. These findings provide insights into the strengths and limitations of each algorithm, aiding practitioners in selecting the most appropriate model for their specific context. Concluding with the broader implications, the research underscores the significance of a machine learning-driven approach in countering fake reviews and elevating the credibility of online platforms. Serving as a valuable contribution to academia and industry, this research paper equips stakeholders with insights to promote more authentic and trustworthy digital interactions.

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 Keywords

Fake reviews, Online reviews, Machine learning, Classification algorithms, Text processing, Sentiment analysis, Trustworthiness, Deceptive content, E-commerce, Consumer trust, Data preprocessing, Feature extraction, Predictive accuracy, Robust detection, Data-driven approach, Decision-making, Information integrity, Online platforms, Performance evaluation, Algorithm comparison.

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In the realm of computational science, quantum computing stands as a beacon of unparalleled potential, offering a glimpse into solving the unsolvable for classical computers.In my research paper, I examine and blend insights from four important papers about quantum computing. These papers each offer unique perspectives: Stolze and Suter's "Quantum Computing: A Short Course from Theory to Experiment," Bertels' "Quantum computing: How far away is it?," Paler and Devitt's "An introduction into fault-tolerant quantum computing," and Wu's "Qubits or Symbolic Substitutions for General- Purpose Quantum Computing."My paper aims to make a big impact in the world of quantum computing. By combining the key points from these papers, I hope to highlight the main challenges in quantum computing, such as making sure the data is accurate and the systems work properly. I want to suggest some ways to make the technology better, improving how long data can be stored and fixing any mistakes that might happen.This work is essential because it paves the way for future developments in quantum computing. I want to help solve the big problems in this field, hoping to push quantum computing into a brighter future by finding solutions to these challenges.

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 Keywords

Quantum computing, Evolution, India, Quantum technology, Historical overview, Challenges, Collaborations, National initiatives, Commercial interest, Quantum algorithms, Quantum error correction.

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The words unemployment and employment are most oftenly and frequently used in youth's group. The meaning of these words are very big than its literal meaning in youths life. Other people associated with youth also know the importance of these two words and how these two words affect the future of them. Country like India has faced the problem of unemployment because there is no job for such a huge number of youths. But unemployment is not only a national problem it is a family and social problem also. The youth, who is unemployed, himself faced so many psychological problems. The family and society expects them to work and earn money and when he failed to earn money and manage his expenses they think that he is useless. Unemployment, which has proved a bane for the society, is a universal phenomenon and is the negative aspect of the economic process as it is a condition of being without gain full work. It is generally considered as an involuntary failure to get income yielding work. Unemployed person can be a big problem for any nation. Unemployment is a concern for youths mental health.

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 Keywords

Employment, Unemployment, Job, Youth, Mental Health, Economy

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