Volume 12 | Issue 5

 Abstract

Businesses operating on small to medium scale face a significant challenge known as the long-tail phenomenon. Each business vendor may have low individual demand, however, collectively it represents a substantial market share. As online markets continue to expand and diversify, navigating this long tail becomes increasingly complex and presents a growing obstacle for businesses aiming to reach their target audience effectively. This escalating problem underscores the pressing need for innovative solutions to facilitate the growth and success of small enterprises in the competitive landscape of online marketplaces. In navigating the multifaceted landscape of product catalogues, the study delves into the exploration of Machine Learning and Deep Learning based techniques that could help e-commerce collect data and use it to optimize its business operations, and increase profits and revenue. This study talks about Opinion Mining, Sales Prediction, Dynamic Pricing, Product Recommendation and Market Segmentation - aiming to develop a framework so versatile as it fits with any type of products catalogue, serving multiple businesses through a unified tool. The techniques used to achieve the aim are inclusive of but not limited to RoBERTa, K-Means Clustering, Regression, and Time-Series Analysis. This issue has been addressed by various familiar entities, and numerous solutions have been discussed. This study throws focus on the basic methodologies with minimal computational, development, and maintenance costs, further developments have been discussed, which leverage the publicly available AGIs - Artificial General Intelligence featuring GPT.

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Deep Learning, Machine Learning, E-Commerce, Artificial Intelligence, Sentiment Analysis, Opinion Mining, Product Recommendation, Dynamic Pricing, Sales Prediction, Market Segmentation, Customer Segmentation, Prescriptive AI, Business Intelligence, Neural Networks, RoBERTa, Support Vector Machines, Transformers, K-Means Clustering, Time-Series Forecasting, Time-Series Analysis, Linear Regression.

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Curcuma longa, commonly known as turmeric, has garnered significant attention in recent years due to its diverse pharmacological properties and potential health benefits. This review provides a comprehensive overview of the medicinal properties, bioactive compounds, pharmacokinetics, and therapeutic applications of Curcuma longa. Its principal bioactive component, curcumin, has been extensively studied for its anti-inflammatory, antioxidant, antimicrobial, and anticancer properties. Additionally, Curcuma longa demonstrates promising effects in the management of various chronic diseases such as cardiovascular disorders, neurodegenerative diseases, and metabolic syndromes. Despite its remarkable therapeutic potential, challenges such as poor bioavailability and limited clinical evidence remain. Strategies to enhance the bioavailability of curcumin, including the use of adjuvants and novel delivery systems, are discussed. Furthermore, the safety profile and potential adverse effects associated with Curcuma longa consumption are addressed. Overall, this review underscores the importance of Curcuma longa as a valuable natural remedy and highlights avenues for future research and clinical applications.

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Curcuma longa, Ayurveda, Anti-inflammatory, Antioxidant, Cancer Prevention, Immunomodulatory effect, Neuroprotection.

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The marketed medicine for cancer has various side effects, and the minimization of the side effects using a natural product is rising. In this study, Alangium salviifolium was for its constituents. The alkaloids, namely, Deoxytubulosine and Beta-carboline harmaline, present in plants, have anticancer properties. To understand the anticancer activity of the alkaloids, CADD, a computational-based designing method, was used. The anticancer properties of the compounds using the NPACT database, and the binding affinity study were carried out with six different receptors using the Auto Dock software, with results visualized in Discovery Studio. These results were evaluated with the standard marketed drug Paclitaxel. For the extra benefits, the compounds were modified with Zinc Oxide. After modification, the molecular docking study using the PyRx software results were visualized in Discovery Studio. The pharmacokinetics study using the pkCSM database needs in-vitro validation of Beta-carboline harmaline.

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Cancer, Colorectal, Bioinformatics, treatment

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Innovative platform Ensure revolutionizes insurance valuation by offering predictive analytics for a range of commodities. To reliably predict future valuations, our website uses cutting-edge machine learning algorithms, such as time series analysis and regression. Our platform takes in a wide variety of data, processes it using our unique algorithms, and produces accurate forecasts of future values. Users can price their goods more properly, evaluate their options well, and eventually increase profitability because of this predictive capability. To maintain the highest level of accuracy, our machine learning models are regularly updated with fresh data and trained on old data. Regression models allow us to quantify the relationship whereas time series analysis helps us analyze trends and patterns over time. It's a comprehensive solution for insurance companies looking to stay ahead in the competitive market. Join us in redefining insurance valuation and embarking on a journey toward data- driven decision-making.

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Predictive Analytics, Machine Learning, Regression, Time- Series Analysis, Valuation and Pricing.

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Versatile phones are utilized all over in this present-day world. The mechanical progression has made everything conceivable beneath one touch. By utilizing the applications introduced within the versatile phones the clients can pay any bills and execute their cash to anybody at their comfort. Increment in utilize of versatile phones and web is the most reason for versatile wallet entrance. This think about was carried out to discover out the factors that influences shoppers inclination towards portable wallet. Employing a organized survey essential information was collected from 160 respondents. The calculate that influences customer inclination and Effect of statistic variables on customer inclination towards versatile wallet were too dissected.

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Versatile wallet, Advanced Instalments, Buyer inclination.

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The present study aims to find out the effect of continuous yoga practices on emotional maturity of students. It is a comparison study between yoga and non yoga Students in their emotional maturity level. The aim of this research is to find how yogic practices have positively affected emotional health of students. Regular practice of yoga including some basic asanas, pranayama helps students to control over their emotions, better adjustment within society. Study was to assess the impact of yogic practices that include Om chanting, some asanas and pranayama on emotional health of students. In the present study the sample selected by quota sampling method 60 subjects age ranged from (12-18) years. 30 yoga students from Gayatri Vidya Peeth, Gayatrikunj, Shantikung, Haridwar and 30 non-yoga students from Bhagirathi Public School, Haripur Kalan, Haridwar, Uttarakhand. Data collected by distributing questionnaire to respective schools and classes by the help of Ex-post facto research design. Data analysed with the help of t-test. Results revealed the significant high level of emotional maturity in yoga student as compare to non-yoga students.

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Emotional disorders, Adolescents, Yoga, Meditation.

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PROTECTION AND PROVISION FOR SMALL BUSINESS IN COMPANIES ACT ,2013:A STUDY

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PROTECTION AND PROVISION FOR SMALL BUSINESS IN COMPANIES ACT ,2013:A STUDY

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Female foeticide remains a persistent issue in India, reflecting deeply ingrained gender biases and social inequities. This abstract explores the multifaceted elements responsible for the prevalence of female foeticide in the country. At its core lies India's patriarchal social structure, which prioritizes male offspring and perpetuates cultural norms valuing sons over daughters. These entrenched beliefs are reinforced by religious and societal customs that celebrate the birth of a son while marginalizing the arrival of a daughter. Socio-economic factors exacerbate the gender imbalance, as poverty and dowry obligations contribute to the perceived burden of raising a daughter. The availability of prenatal sex determination technologies further enables sex-selective abortion, despite legislative efforts to regulate their use. The culmination of these cultural, socio-economic, and systemic factors underscores the complexity of the issue. Addressing female foeticide requires a holistic approach that challenges patriarchal norms, promotes gender equality, and empowers women and girls. Strengthening enforcement mechanisms, improving access to healthcare and education, and fostering economic opportunities for women are essential steps towards combating female foeticide in India. By confronting the root causes and investing in sustainable solutions, India can strive towards a future where every child, regardless of gender, is valued and afforded equal opportunities for a fulfilling life.

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Female foeticide India Gender biases Patriarchal social structure Cultural norms Socio-economic factors Poverty Dowry obligations Prenatal sex determination technologies Legislative efforts Gender equality Empowerment Enforcement mechanisms Healthcare access Education access Economic opportunities Sustainable solutions

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 Abstract

uttrati ke vyang sahitya me aalochya yugin aarthk paristhitiya

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uttrati ke vyang sahitya me aalochya yugin aarthk paristhitiya

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This paper offers a concise overview of tunnel field-effect transistor (TFET)-based biosensors, detailing their evolution and contemporary applications in biosensing. TFET-based biosensors exhibit notable characteristics such as high sensitivity, affordability, and the ability to detect biomolecules label-free, garnering significant interest. While conventional field-effect transistor biosensors possess advantages, limitations on subthreshold swing, particularly due to various short-channel effects (SS>60 mV/decade), constrain sensitivity, limiting their utility. Ongoing research endeavors aim to address these limitations by enhancing various aspects, with the ultimate aim of devising a device capable of surpassing the drawbacks of established biosensors constructed from multiple transistor devices. This initiative seeks to develop a biosensing platform that optimally combines sensitivity, affordability, and reliability, potentially revolutionizing biosensing applications across various fields.

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Keywords: tunnel field-effect transistor, TFET-based biosensors, biosensing applications, high sensitivity, label-free detection, affordability, subthreshold swing, short-channel effects, biosensor limitations, research, device enhancement, biomolecule detection, biosensing platform

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6G is the sixth-generation mobile system standard currently being developed for wireless communications over cellular data networks in telecommunications.6G, the upcoming sixth-generation mobile system, is being developed to revolutionize wireless communications. As the next step after 5G, it promises to be significantly faster. One of its ambitious goals is to achieve one microsecond latency, making it 1,000 times quicker than the current one millisecond latency. While 6G is still in the research and development phase, its potential to transform connectivity is already generating excitement.

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Radio Technology, Future, 4G,5G, 6G, Wireless Communication, Capacity, Latency.

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This research paper delves into the living conditions and overall welfare of stray dogs in the Delhi National Capital Region (NCR) and endeavours to comprehend their existing situations with the objective of proposing viable measures for improvements. The study employs a quantitative research method, with data collection carried out through the dissemination of a survey questionnaire among various localities. The analysis of the quantitative survey data yields a worrying reality, indicating that most regions within Delhi NCR exhibit insufficient living conditions for stray dogs. The revealed outcomes underscore the imperative need for immediate intervention to address the welfare challenges faced by these animals. The findings serve to shine a spotlight on the urgency of implementing measures that can improve the living conditions and overall well-being of stray dogs in Delhi NCR. As a result, this study makes a valuable contribution to the expanding body of knowledge on animal welfare, particularly shedding light on the plight of stray dogs in urban settings, and advocates for effective policies and actions to tackle the obstacles confronted by these animals across different regions. This research work is significant in its implications for policymaking and intervention strategies aimed at alleviating the welfare issues faced by stray dogs in urban environments. The findings call for the development and implementation of comprehensive measures such as targeted vaccination programs, widespread sterilization campaigns, robust adoption drives, and intensified public awareness initiatives to address the challenges faced by stray dogs in Delhi NCR and beyond. Furthermore, this study stresses the importance of collaborative efforts involving municipal authorities, animal welfare organizations, and the community to ensure the successful implementation of these proposed measures. In conclusion, this research paper fills a crucial gap by shedding light on the inadequate living conditions and pressing welfare challenges encountered by stray dogs in Delhi NCR. It advocates for the formulation and execution of proactive measures to protect and improve the well-being of these animals. The insights garnered from this study have the potential to inform and guide the development of effective policies and actions aimed at enhancing the living conditions and welfare of stray dogs not only within Delhi NCR but also in other urban regions facing similar challenges.

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Delhi NCR, Improvement, Stray Dogs, Welfare

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The BRIC concept, first termed by Jim O'Neill in 2001, initially included Brazil, Russia, India, and China, later expanded to BRICS with the addition of South Africa in 2010. After 2023 this block is progressed to BRICS Plus. This article investigates the BRICS presence as a global economic power, their collective de-dollarization efforts, and their impact on the global economy. This study utilizes a comprehensive analysis of theoretical aspects, statistical data, and empirical studies available from the past. The initiatives by the BRICS nations like, the New Development Bank, currency reserve pool, de-dollarization efforts on international trade among the members signify BRICS' rise as key actors in reshaping world economic governance. Despite internal gaps, BRICS nations are progressively collaborating in the issues of economy, global governance, and international diplomacy. The article emphasizes BRICS' potential to challenge traditional global economic foundations and offers visions into policy reforms that advance economic growth and regional integration. Moreover, the narrative of BRICS as a choice to the G7, with prospects of establishing its own exchange and norms, highlights its importance in current global geopolitics. The study also reviews BRICS' attempts towards de-dollarization, evident in commitments to promote local currency finance, and initiatives for starting an international reserve currency. Through the analysis of trends in GDP growth, foreign exchange markets, and de-dollarization strategies, this article highlights BRICS' fundamental role in shaping the changing global economic order.

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Brazil Russia India China South Africa (BRICS), gross domestic product, de-dollarization, foreign exchange

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Background: Watersoluble vitamin B12, also known as cobalamin CBL, is necessary for the formation of hematopoietic cells, which include red blood cells, white blood cells, and platelets. It has a role in the synthesis of the myelin sheath and DNA. Megaloblastic anemia, characterized by defective cell division leading to macrocytic anemia with other characteristics, can be caused by deficiencies in vitamin B12 and/or folate. A less common exordium of severe vitamin B12 deficiency is pancreatopenia. Neuropsychiatric symptoms might also result from a vitamin B12 shortage. Ascertaining the underlying reason is crucial to management, not only to rectify the deficiency but also because it may affect the necessity of further tests, the length of therapy,and the mode of administration. Methods: Four patients with megaloblastic anemia (MA) associated with pancytopenia are shown here. Every patient with a diagnosis of MA had their clinic-hematological and etiological profiles examined. Results : Megaloblastic anemia and pancytopenia were found in every case. A vitamin B12 deficiency was found in every single case. There was no relationship found between the degree of anemia and vitamin deficiencies. In one case of MA, subclinical neuropathy was observed, but none of the cases had overt clinical neuropathy. In two cases, pernicious anemia was the cause of the vitamin B12 shortage, while in the other cases, poor food intake was the cause. Conclusion: This case study emphasizes the role of vitamin B12 deficiency as a leading cause of pancytopenia among adults.

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Aging;vitaminB12;megaloblasticanemia; pancytopenia

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The next era of communication is IoT. The emergence and rapid growth of Internet of Things (IoT) focus on automating different task and trying to empower the physical objects to act without human interaction. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users.IoT devices and application increase operational efficiency by enabling centralized monitoring and management of manufacturing equipment, critical infrastructure, or remote sites. However, IoT based systems and applications are vulnerable to various security threats and attacks which leads to other cyber security threats. Moreover, due to the lack of standardization due to heterogeneity of devices and technologies implementing security in IoT is real challenge. In this paper, detailed review of sources of threats in IoT and challenges in IoT and their solutions are presented.

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IoT, IoT security, Threats, Security Solutions

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This study has been undertaken to investigate the potential impact of the work-from-home model on the physical health of new entrant employees in corporate settings. With the increasing prevalence of remote work arrangements, understanding their effects on employee well-being is crucial. Utilising a quantitative approach, the study has examined various factors including ergonomic setup, work-life balance, commute patterns, nicotine consumption, and physical activity levels. Data is collected through surveys to assess the physical health outcomes of new entrant employees. The findings of this study suggest that Newer professionals, with 1-3 years of experience, face more challenges, including ergonomic issues, unhealthy habits, and disrupted sleep patterns. This leads to increased junk food consumption, weight gain, nicotine urges, and sleep difficulties. Seasoned professionals, with 5-10 years and over 10 years of experience, adapt better, showing improved physical fitness and fewer health issues. Both groups recognize the need for targeted interventions to manage ergonomic setups, maintain physical health, and optimize productivity while working remotely.

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Work from Home, Remote work, New entrant employees, Corporate employees, Physical health, Ergonomics issues

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This project report explores the development of a contactless EV charging system in static mode. The system aims to provide a convenient and efficient way to charge electric vehicles without the need for physical connections. The report discusses the design and implementation of the system, including the use of wireless charging technology and smart grid integration. The project also evaluates the performance and reliability of the contactless EV charging system in static mode. Overall, this report provides valuable insights into the potential benefits and challenges of contactless EV charging technology for future transportation infrastructure.

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EV charging ,Static , batteries

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Potato plant disease classification using convolutional neural networks (CNN) is an important application of deep learning in agriculture. Potatoes are a staple food worldwide and diseases can seriously affect their yield and quality. With CNN, we can automate the process of disease detection and diagnosis and help farmers act quickly to avoid crop failures.

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ML, CNN, Agriculture, Diseases

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The optimization of power management and extension of battery life have become critical in the advancement of wireless technologies. Accurate profiling and characterization of lithium-ion batteries are essential for optimizing battery performance, enhancing user experience, and improving overall device efficiency. Traditional methods for battery profiling, which often rely on manual processes, struggle to manage the complexity of data associated with battery parameters and device performance metrics. In contrast, machine learning (ML) models offer a robust solution by excelling at handling complex data and uncovering hidden insights. This paper introduces a novel approach that leverages the Random Forest algorithm, an ensemble learning method known for its robustness in handling both classification and regression tasks. By combining multiple decision trees trained on diverse data subsets, the Random Forest method provides enhanced predictive performance and reduces the risk of overfitting. This approach enables more accurate predictions of battery health, under various conditions. Implementing machine learning in battery profiling not only improves the accuracy of power management systems but also facilitates the development of smarter charging protocols tailored to user habits. This results in extended battery life, reduced frequency of charging, and overall better user satisfaction. Furthermore, this method has broader implications for the industry, contributing to more sustainable technology practices by reducing electronic waste through prolonged device lifespans.

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SoH, Random Forest, Arduino WeMOS D1 Wi-Fi UNO

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This study focuses on the detection of Acute Lymphoblastic Leukemia (ALL) using an improved YOLOv5 model. Leukemia, a common form of blood cancer, disrupts normal blood cell production, emphasizing the need for accurate detection methods for early diagnosis and treatment. The study explores the use of deep learning techniques, specifically YOLOv5, for classifying leukemia and healthy cells from microscopic blood cell images. Data augmentation techniques were employed to enhance the dataset, and the model's performance was compared with traditional classifiers. Results show that the YOLOv5 model enhanced with SENet achieved the highest accuracy of 90% in classifying leukemia cells. The study highlights the potential of deep learning in improving leukemia diagnosis and treatment outcomes.

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Acute Lymphoblastic Leukemia, YOLOv5, Deep Learning, Data Augmentation, Microscopic Blood Cell Images, SENet, Classification, Diagnosis, Treatment, Accuracy

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