Volume 11 | Issue 12 |

 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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ICU Patient, Delirium, risk factors, Nursing challenges.

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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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Creative Commons Attribution 4.0 and The Open Definition

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Peppermint, Antiviral, Antibacterial, Muscle pain

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Creative Commons Attribution 4.0 and The Open Definition