Volume 11 | Issue 12 |

 Abstract

This study explores cryptocurrency sentiment using tweets, employing a deep learning ensemble (LSTMGRU) model. Analyzing emotions with Text Blob and Text2Emotion, it reveals predominant positive sentiments, especially happiness. Utilizing term frequency-inverse document frequency, word2vec, and bag of words features, the LSTM-GRU ensemble achieves high accuracy (0.99). Notably, machine learning models excel with bag of words features. The cryptocurrency market's rapid evolution prompts sentiment analysis, shedding light on public perceptions and emotions, crucial for predicting market trends.

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 Keywords

Cryptocurrency, Sentiment analysis, Machine learning, Deep learning & LSTM-GRU ensemble.

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 Abstract

The goal of this proposed work is to create an Android-based image text-to-speech (ITTS) application that enables users to translate text contained in photographs into spoken information in the language of their choice. The ability for users to customize the language in which the synthesized voice is produced is one of the application's standout features. Because of its user-friendly interface, a wide audience can access the Android application. Performance of an Android application, evaluating elements such as precision, reactivity, and ability to customize language. This proposed work can serve a variety of user demands, such as language learners, visually impaired people, and people looking for portable, effective tools for information consumption .

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 Keywords

Image, Text ,Speech ,Conversion ,Extraction, Image Processing

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Creative Commons Attribution 4.0 and The Open Definition

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In modern era psychological illnesses have grown quite common and depression remains one of the most prevalent forms of mental illness. Based on WHO statistics, depression is the second greatest cause of illness burden worldwide. The reality for those with mental diseases is significantly worse, especially in emerging and undeveloped nations because healthcare assets are brief. Depression is a form of psychological condition where an individual experiences constant despondency, demotivation, mood fluctuations and lack of interest in everyday mental, physical and social endeavors resulting in emotional harm and bodily modifications in the patient's physical condition. It has a particular impact on a person's learning ability, produces mood changes and frequently impairs job productivity. This paper deals with the various, techniques used by various researchers to predicts the different kind of depression.

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 Keywords

CNN, ANN, SVM, KN, Mental health, Depression

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 Abstract

Herbal drugs have been used in cosmetics from centuries. They are eventually used to treat skin disorders and to improve the skins external appearances. In 21st century the significant progress in herbal industry was begun. Herbal drugs prefferd over chemical substances because of their easy availability and lesser or no side effcts. Natural beauty is a boon and cosmetics help present and enhance the aesthetic and personality aspects of moral beings. Cosmetics alone arent able of takin care of skin and other body corridor it requires the association of active constituents to check skin damage and ageing. Herbal cosmetics gained great fashionability in population. Herbal plants have multifunctionality like antioxidant, antiinflammatory ,antiseptic and antimicrobial. The purpose of this review article is to improve herbal cosmetics knowledge in peoples and increase herbal cosmetics use to overcome skin conditions and they can clarify their skin by safe way.

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 Keywords

Herbal cosmetics, Antioxidants, Antiiflammatory, antiseptic, antimicrobial.

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 Abstract

Cloud data security refers to the set of procedures and technologies used to protect data that is stored, processed, and transmitted in cloud computing environments. Since data is stored on remote servers owned by third-party vendors, organizations must rely on cloud service providers (CSPs) to implement and maintain adequate security measures to protect their sensitive data . Some of the notable security challenges associated with cloud computing include unauthorized access, data breaches, data loss, data corruption, insider threats, and lack of visibility into cloud environments. To mitigate these risks, CSPs use a combination of encryption, authentication, access control, and monitoring tools to secure their clients' data . In addition to CSPs' efforts, organizations must also take measures to protect their data while it resides in the cloud. This includes implementing robust identity and access management (IAM) policies, data classification frameworks, and data retention policies. Organizations should also conduct regular audits and security assessments to identify vulnerabilities and mitigate risks that they may face . Overall, cloud data security requires a collaborative effort between CSPs and organizations to ensure that data is properly secured and protected in the cloud. With the right measures in place, organizations can reap the benefits of cloud computing without sacrificing security.

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Delirium is etiologically a nonspecific organic cerebral syndrome which is characterized by concurrent disturbances of consciousness and attention, perception, thinking, memory, psychomotor behavior, emotion, and the sleep-wake cycle. The duration may vary and the degree of severity ranges from mild to severe.1 The study was conducted by following quantitative research approach consisting of descriptive study design to identify prevalence of delirium patients in ICU, risk factors, it's severity and the challenges faced by nurses with delirium patients admitted in ICU. Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) tool was used to identify the prevalence & severity of delirium patient in ICU. In addition, a MCQ based questionnaire was used to evaluate the risk factors of delirium. The study was carried out among ICU patients who were diagnosed with delirium during the period of April to September 2023 at Apollo Hospitals, Guwahati. Out of 329 admitted patient in ICU, 67 patients had diagnosis of delirium, however, only 60 patients were considered for analysis since seven (7) patients have expired in mid of study period. The overall prevalence of patients with delirium was found as 20.36%. Risk factors for developing delirium were identified as sepsis and infections (23%), post operative patient (20%), history of alcohol and drug uses (20%), patient with cardiovascular diseases (17%), respiratory illnesses (8%), organ failure (2%), autoimmune disorder (2%) and 8% had other factors. 75%(majority) of delirious patients were hyperactive (RASS>+1), 13% were hypoactive (RASS<-1) and 12% had mixed type of delirium. While considering the degree of severity, 63% had severe delirium (score 6-7) and 47 % had mild to moderate (Score 3-5) as per CAM-ICU Scoring. The study could identify various nursing challenges as 71% patients had increased risk for fall and difficulty in mobilization, 55% had sleep disturbances, 40% had self-removal of tubings, 25% refused for feeding (food & drugs), 18% refused to commands/requests, 17% had bedwetting, 2% patient's family refused chemical restraint and another 2% could not be sedated for clinical reason. Moreover, study also found that majority (78.3%) of patients had more than one challenge for delivering effective nursing care. The study concluded that nurses encountered various challenges while caring for delirious patients with increased agitation in ICU. It is assumed that nurses working in such situation are unable to provide nursing care as desired. Therefore, efforts must be made for early detection of delirium, it's underlying cause, and treat the patients as early as possible. It is advisable to develop a nursing care pathway for managing delirium patients in ICU setting and researcher would like to continue further in this regard.

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 Keywords

ICU Patient, Delirium, risk factors, Nursing challenges.

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 Abstract

India is the country of villages and the people in rural area depends on agriculture for their livelihood. In rural area educated youths are not getting the job opportunities according to their educational qualification.In prevailing situation, it is necessary for the educated rural youths to undertake the entrepreneurship in rural area as the career option to overcome from the problem of poverty and unemployment. Economic development has direct link with entrepreneurial development and government has been promoting various programmes and schemes to boost rural economy through entrepreneurship development and income generation at non-farm sector.Rural procurement and supply enterprise (RPSE) model is an option which help the educated youths to overcome from the problem of unemployment by engaging themselves in entrepreneurial activities in non-farm sector. There are many hindrances for the youths to undertake the entrepreneurship as a career option. The researcher has used the focus group discussion to understand the problems in implementation and working of RPSE model.The researcher found out the various problems faced during the implementation of the model like lack of self-esteem, innovation, personal control, etc. The researcher tried to give the possible solutions to the above said problems.

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Unemployment, Poverty, Rural entrepreneurship, etc.

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Big data's role in decision-making has transformed industries and disciplines in recent years. This comprehensive review paper aims to provide an overview of the current landscape of research and applications of big data in decision-making processes across various domains. Drawing on a systematic review of literature published in the past decade, we identify emerging trends and patterns in the utilization of big data analytics for informed decision-making. Our findings reveal that big data has led to significant advancements in predictive analytics, optimization, and real- time decision support systems. However, challenges related to data privacy, security, and ethical considerations persist. This review paper contributes to the field by consolidating current knowledge, pinpointing research gaps, and offering insights for practitioners and researchers. Our analysis underscores the transformative potential of big data in decision-making and highlights the need for ongoing interdisciplinary collaboration to address its associated challenges. The implications of this review extend to industries ranging from healthcare and finance to marketing and logistics. This work aids in framing the future trajectory of big data's role in decision-making processes.

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Big Data, Big Data Analytics, Decision-making

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Creative Commons Attribution 4.0 and The Open Definition

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In recent times, sustainability has become a prominent and widely discussed topic. It is being considered across various industries on a global scale. Therefore, if someone wishes to study sports technology in India, it is imperative to possess a comprehensive understanding of sustainability and its impact on the field. Sustainability encompasses the ability to thrive and succeed in the future without exhausting or depleting natural resources. According to the Brundtland report, a United Nations publication, sustainable development involves meeting the needs of the present generation while ensuring that future generations can meet their own needs. In the context of sports, sustainability entails adopting environmentally friendly practices when organizing sporting events, with the aim of minimizing harm to the environment and reducing the carbon footprint of organizers. Presently, sustainability holds significant importance as it intersects with a wide range of social, environmental, and economic issues. There is a global concern regarding matters such as climate change, economic inequality, and social injustice, which affect people worldwide. In the world of sports, there exist significant challenges that pertain to both the daily functioning and the accountability towards children and future generations. However, it is also important to recognize that sports have a unique ability to uplift and inspire a large number of individuals.

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 Keywords

sports, outdoor activities, natural environment, sustainability, globalization, climate change.

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Image recognition, a crucial facet of computer vision, involves the automated identification and categorization of visual content within images, playing a pivotal role in diverse applications such as medical diagnostics, autonomous vehicles, security systems, and augmented reality, significantly enhancing efficiency and accuracy in various domains. This study explores and compares various object detection and classification methods, incorporating LBP,Haar Cascade, HOG for detection, and for classification CNN, DNN. Hybrid methodologies, including Haar Cascade with CNN, Haar Cascade with DNN, LBP with CNN, LBP with DNN, HOG with CNN, HOG with DNN, were rigorously tested on different embedded systems utilizing the Microsoft COCO dataset. Results revealed that the Haar Cascade with CNN methodachieved the highest recognition success rate at 78.60%, surpassing other methods. These outcomes highlight the efficacy of the Haar Cascade with CNN approach, especially on powerful embedded systems, showcasing its potential for real-time object recognition applications

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 Keywords

Haar Cascade algorithm, Image recognition, Embedded systems, Binary pattern

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