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

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

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

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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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Herbal cosmetics, Antioxidants, Antiiflammatory, antiseptic, antimicrobial.

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

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A novel approach for design of low-power approximate multipliers by leveraging Long Short-Term Memory (LSTM) networks within a deep learning (DL)-based framework. Our proposed architecture, referred to as the DL -Based Approximate Multiplier (DLAM), exploits the sequence-to-sequence learning capabilities of LSTMs to enhance the efficiency of approximate multiplication in terms of both accuracy and power consumption. The DLAM model is trained on a diverse dataset, incorporating various input patterns and corresponding approximate multiplication outcomes. Through the integration of LSTM units, the model captures long-range dependencies within the input sequences, enabling more accurate predictions of approximate multiplication results. The trained DLAM exhibits superior performance in terms of both precision and energy efficiency when compared to traditional approximate multiplier designs. Furthermore, we explore optimization techniques to minimize power consumption without compromising the accuracy of multiplication results. Our test findings show that the DLAM accomplishes a significant reduction in power consumption while maintaining competitive levels of accuracy, making it a promising candidate for low-power applications in energy-constrained environments.

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low-power design, DL, LSTM networks, metaheuristics, Jellyfish Search Optimization algorithm, sequential data

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The one who appraises and comprehends the meaning will gain auspiciousness. The Vedas which are illimitable source of knowledge, are comprehended through the Vedangas. The role of Vedanga is not limited to the aid the Vedas in phonetics, recitation in a precise manner, comprehending the root of the words through etymology, appropriate time in the performance of rituals, etc.... they also have a deeper unintelligible meaning attached to it. This paper mainly focuses on bringing forth the hermetical meanings of the Vedangas and apprehending them in an indiscernible way. Descriptive research is conducted in a quantifiable manner considering both primary data (through Questioners, using percentage analysis) and secondary data, and through the research, it is ascertained that the Vedangas not only impose the rules on the Vedas but also impose directions on the Vaidikas. It provides the Guru and the Shishya with introspective knowledge of the minute meanings of the Veda Samhitas, when where and to whom the Veda Samhitas are to be taught, and also their limitations in parting with the knowledge and thus, they will be the reason of felicitation of those Vaidikas who have understood their deep meaning and not merely facilitate the Vedas Samhitas.

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Veda Samhita, Vedanga - Shiksha, Vyakarana, Chandas, Nirukta, Jyotisha, Kalpa Felicitator, facilitator.

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Identifying and correcting faults in IC design have become critical stages as the complexity of digital VLSI circuits continues to grow. Presented in this paper is a novel fault identification model based on deep learning (DL), utilizing a distinct type of artificial neural network (ANN) known as stacked sparse autoencoder (SSAE). The main goal of this proposed model is to tackle the challenge in the exploration domain by employing SSAE for identifying features and detecting anomalies in extensive electronic circuits. The model comprises three key stages: test pattern creation, feature reduction, and fault detection. Unsupervised learning using training data is implemented in the SSAE phase to enhance feature extraction. The evaluation of feature extraction effectiveness involves modifying the architecture of the SSAE network. The strategy achieves 99.3% fault coverage with ATALANTA and reduces features by 99.7% using SSAE for test patterns.

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Automatic Test Pattern Generation, ANN, Fault Detection, Digital Circuit, ML, SSAE, Test Pattern

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Mentha piperita L., a perennial herb, and Mentha arvensis var. piperascens, a member of the Labiatae family, are used to make peppermint oil. Numerous well-known essential oil species, including spearmint, basil, lavender, rosemary, sage, marjoram, and thyme, belong to this family. Known for its numerous therapeutic benefits, including analgesic, anesthetic, antiseptic, astringent, carminative, decongestant, expectorant, nervine, stimulant, stomachic, inflammatory diseases, ulcer, and stomach problems, this plant is highly valued in many indigenous medical systems. This review provides a thorough and current examination of the chemistry, pharmacology, analysis, and applications of peppermint oil. The culinary and pharmaceutical sectors have paid increased attention to the medicinal plant peppermint (Mentha piperita L.) due to its positive effects on human health. This article uses theoretical research to assess the molecular structure of peppermint molecules. Yes, an assessment of peppermint's health advantages was done. Two peppermints substances that appeared to bind to the active site of the aryl amine N-acetyltransferase enzyme were cineol and menthyl acetate, according to our molecular docking analysis. The inhibition of this enzyme by these substances is indicated by this kind of interaction. Popular plant Mentha piperita comes in a variety of forms (i.e., oil, leaf, leaf extract, and leaf water). The most versatile oil is peppermint oil, and usage statistics for this oil are also thought to be pertinent to compositions using leaf extracts. This herbal preparation is utilized for its taste and fragrance qualities in meals, pharmaceuticals, cosmeceuticals, and personal hygiene products. The aroma of peppermint oil is crisp and strong, and it tastes strongly before providing a cooling effect. It is also utilized in mouthwashes, toothpastes, aromatherapy products, bath preparations, and topical treatments due to its many medicinal qualities. Peppermint oil formulations applied topically have been used to reduce inflammation and itch.

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Peppermint, Antiviral, Antibacterial, Muscle pain

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

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

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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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Hyperthyroidism, Hypothyroidism, Thyroid cancer, Endocrine, India.

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

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