Volume 12 | Issue 5

 Abstract

This study explores how consumer perceptions influence the market demand for tribal products in the Nilgiris District, focusing on consumer awareness and perceived quality. Given the economic and cultural importance of these products, understanding the factors that drive consumer behavior is critical. The research employs a descriptive design and utilizes convenience sampling to collect data from 107 respondents through structured questionnaires, supplemented by secondary data for comprehensive coverage. Findings indicate a significant relationship between consumer perceptions and market demand, highlighted by the strong influence of perceived quality on purchasing decisions. Specifically, consumer awareness of tribal products, while high, showed a lesser direct impact on market demand compared to the perceived quality, which was strongly correlated with purchase behavior. This suggests that enhancing the perceived quality of tribal products could significantly boost market demand. Based on these findings, the study recommends intensifying marketing efforts to increase consumer awareness and highlight the unique aspects of tribal products through various media. Moreover, standardizing quality control measures could improve the consistency and overall perception of quality, further driving market demand. Ultimately, this research underscores the need for strategies that not only focus on enhancing product visibility but also on maintaining high quality to meet consumer expectations effectively. The conclusion drawn is that by addressing these key factors, there can be a substantial improvement in market demand for tribal products, benefiting the local economy and supporting the sustainability of tribal communities.

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Tribal Products, Consumer Perceptions, Market Demand, Nilgiris District, Cultural Significance

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ABSTRACT Weaning is the process of giving an infant other foods and liquids with breast milk after the age of 6 months as breast milk alone is not sufficient to meet the nutritional requirements of growing baby. It is the process by which the infant gradually becomes habituated to adult diet. Under nutrition remains one of the most common causes of morbidity and mortality among children globally, which is directly or indirectly related to knowledge of months regarding weaning. The present "A Descriptive study to assess the level of knowledge Regarding Weaning Among Nursing Mothers in Pediatric OPD of SMIH Patel Nagar, Dehradun, Uttarakhand with a view to develop informational booklet.'' AIM: To assess the level of knowledge Regarding Weaning Among Nursing Mothers in Pediatric OPD of SMIH Patel Nagar, Dehradun, Uttarakhand OBJECTIVE OF THE STUDY 1. To assess the level of knowledge regarding weaning among nursing mothers. 2. To find the association between level of knowledge of nursing mothers with their socio -demographic variables. METHODOLOGY The nature of the study was non-experimental. The study was conducted in Pediatric OPD SMIH, Patel Nagar, Dehradun; the research design use for the study was Non- Experimental descriptive research design. Total 49 nursing mothers were selected through purposive sampling technique and data was collected using self structured knowledge questionnaires in pediatric OPD in Shri Mahant Indiresh hospital Dehradun, Uttarakhand.The data collected for to assess the level of knowledge regarding Weaning among Nursing Mothers. RESULT The analysis revealed that mothers of infant (28.75%) had good knowledge, (67.57%) had average and (4.08%) had poor knowledge regarding weaning. There was no significant association found between knowledge score of nursing mothers regarding weaning with their socio demographic variables. CONCLUSION: It can concluded that most of the mothers had average level of knowledge regarding weaning.

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Knowledge,Weaning ,Nursing Mothers

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Abstract Indian English Literature paved way for many writers to create their own space of words and self expression. Self expression of emotions and feelings has created the genre of confession through the renowned female writer Kamala Das. Holding the ancestral traditional values and the modern equations of society Kamala Das made her writings more unique. This paper is an analysis of how Kamala portrayed herself being tackled in the clutches color in My Story.

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Keywords: Discrimination, color, humiliation, self expression, authority

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This study introduces an innovative approach to Flood Damage Assessment (FDA) by integrating advanced technologies, including Remote Sensing (RS), Geographic Information System (GIS), and a state-of-the-art neural network architecture. A custom-designed Convolutional Neural Network (CNN), incorporating a UNET with ResNet-34 as a backbone pretrained on the CIFAR-10 ImageNet model, plays a pivotal role in achieving accurate flood mapping. The utilization of transfer learning enhances the model's ability to discern intricate patterns from high-resolution satellite imagery, addressing challenges in complex landscapes. The proposed methodology, validated with diverse datasets from real-world flood events, demonstrates the effectiveness of the integrated neural network in improving disaster management strategies. This research significantly contributes to advancing flood damage assessment methodologies, showcasing the synergy of RS, GIS, and cutting-edge CNN architectures for timely and accurate disaster response and recovery.

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Flood Damage Assessment, Remote Sensing, Geographic Information System, Convolutional Neural Network, Transfer Learning, Satellite Imagery, Disaster Management.

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Orange peel, Osmotic dehydration, Water loss, Solid gain .

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As a foundational technology in the security net environment, detection of network intrusions has a great deal of attention and use. Network Intrusion Detection (NID) still has difficulties when it comes to installing on devices with limited resources, despite the tremendous efforts of research & advanced technologies. We provide a lightweight detection of intrusions technique based on knowledge of extraction is called Lightweight Neural Network (LNet), which strikes the balance between efficiency and accuracy by concurrently lowering computing costs and model storage. To be more precise, we stack DeepMax blocks to create the LNet after carefully designing the DeepMax blocks to extract compressed representation effectively. Additionally, in order to compensate for the lightweight network's performance decrease, we apply batchwise knowledge of oneself distillation to regularise training consistency. Our suggested Lightweight Neural Network (LNN) and XGboost methodology is shown to be successful on Allflowmeter_Hikari datasets through experiments

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Explainable Artificial Intelligence for Cyber Security

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Logistics especially cargo handling and import exports highly depend on internal and external transportation chains which are usually handled by human drivers and overseers or in recent developments by partially automated vehicles which has disadvantage of free thinking. Our project "intelligent carrier" is primarily aimed to automate cargo and luggage handling in airports at an affordable price such that it can also be implemented by storage handlers etc. This carrier follows prefixed and subsidiary paths for intelligent path selection and added ability to avoid obstacles. This robot is completely developed to be controlled by an external processing centre which is able to control multiple robot units. Its complete function is to position an airplane on its arrival and allot a robot carrier which travels along the mapped path with intelligent obstacle avoidance to reach predestined position based on closeness to the anchor location.

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carrier, anchor, processing center

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The Film-forming gels containing povidone iodine can create a protective barrier over the wound, promoting a moist environment conducive to healing while preventing infection. The experimental work done to carry out organoleptic properties, UV spectroscopy, FTIR study and melting point determination for drug determination and then trial batches were prepared for determining the concentration and selection various polymers. Based on that factorial batches were prepared and evaluation is performed. The povidone iodine gel was prepared using different ratios of PVA and PVP K-30, PEG 400 used as a plasticizer. Formulations (F1-F9) were prepared and evaluated for their physicochemical properties. Based on that 32 full factorial designs were applied to optimize the final formulation and check the impact of independent variables. The optimized (F6) Formulation (Ratio of PVA: PVP K-30 and PEG 400) exhibited % drug release 93.59% in 360 minutes. F6 formulation found stable for 1 month during stability study.

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Film forming gel, povidone iodine, antiseptic, wound healing.

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A wide spectrum of vitamin deficiencies can show one or more visually distinguishable symptoms and indications that appear in multiple locations in the human body. The application provides individuals with the capability to diagnose their possible vitamin deficiencies without the need to provide blood samples through the analysis of photos taken of their eyes, lips, tongue, and nails. This process is implemented using the deep learning based CNN algorithm. Here we have considered the dataset of eyes, lips, tongue and lips. Once after the consideration of dataset, the pre-processing is performed and then CNN algorithm is used to train the data. Once after the training, model is saved and the testing is performed using the dataset.

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-Vitamin deficiency, deep learning, CNN, dataset

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In the contemporary landscape, both insurance companies and customers (insurers) are increasingly motivated to secure the most advantageous deals, particularly in the realm of health insurance where the need for coverage is of paramount concern. Customers, in particular, exhibit a heightened sense of caution when evaluating health insurance options. Fuzzy logic is a computational approach that deals with uncertainty and imprecision, has emerged as a versatile solution across various real-world domains such as electronics, computer science, finance and medical science. This study offers a comprehensive exploration of the application of fuzzy sets methodologies within the field of actuarial science, focusing on health insurance risk assessment. It delves into the foundational mathematics underpinning fuzzy sets, providing a quick overview of their development and utilization. Moreover, the study demonstrates how fuzzy logic models can be effectively constructed and refined to adapt to changing rules and incorporate supplementary data, thus enhancing their predictive accuracy and practical utility. Here a model is presented, which is based on fuzzy expert system that will help the insurance companies to find out predictive modeling of health insurance premiums for cardiac patients using Fuzzy Inference Techniques. Through the application of fuzzy logic techniques, the study aims to derive actionable insights that can inform more informed decision-making processes in the realm of health insurance.

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Health insurance, Decision-making, Risk assessment, Predictive modeling, Policy evaluation.

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People in today's era usually have the tendency of using their own private vehicles for commutation rather than using public transit and this result in large number of private vehicles on road. It leads to traffic congestion at every roads. In such scenario one cannot restrict individual to limit the usage of their private vehicles but we can able to manage traffic flow in a way that it doesn't alleviate congestion issues. The traditional traffic management approach works efficiently only if the traffic is less, but if the density of vehicles on a particular side of road increases on one side than other side, this approach fails. Hence, we aim to redesign the traffic signal system from static switching to dynamic signal switching, which can perform instant-time signal monitoring and handling. There are many projects emerging in order to convert the current transport system of cities to 'Smart system', by introducing Intelligent Transport System. Many initiatives are taken to design a system that can perform instant monitoring of traffic signals i.e., the traffic signal switching time will depend on the count of vehicles on each side of the road instead of predefined switching time. The switching time of signal will be decided based on vehicle detection in day-to-day traffic scenarios with good accuracy. This practice can prove its effectiveness in releasing the congested traffic at an efficient and faster rate

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Intelligent traffic management system

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Diabetes mellitus is defined as the metabolic disorder of multiple etiology characterized by chronic hyperglycaemia with the alterations in carbohydrates, protein and fat metabolism resulting from defects in insulin secretion, action or both. Type 1 diabetes mellitus develops when the insulin secreting pancreatic beta cells of islets of Langerhans are destroyed. This is an auto immune type of system. The type II diabetes is due to insulin resistance, i.e a disorder in which the cells within the liver, muscles and fat tissues do not use insulin properly. WHO projects that from the total diabetes patients in the world, more than half will live in Asia by 2030 and type II diabetes alone particularly increases up to 55 % by 2035. The people in developing countries still depend on the traditional practitioners and herbal medicine for their primary care. The interest for the medicinal plants with anti-diabetic properties has been increased worldwide for the treatment of diabetes mellitus than the conventional anti-diabetic drugs such as biguanides, sulfonylurea and thiazolidine dione, which are presently available. The main aim of the work is to formulate and evaluate poly herbal anti diabetic tablets for medicinal purpose by using Gurmar and Neem leaf were collected from the local area, dried, powdered and extracted with ethanol separately and stored for further use.

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Diabetes mellitus, herbal plant, Gurmar, Neem.

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: Background and objective: Karate is one of the most participated sports in the world. It has rich history tracing back to China and India. Combat sports are characterized by high-intensity and long trainings, and require a high degree of mental concentration, which combined with the frequent shortening of the time needed for tissue regeneration, can place a significant overload on the lumbar spine. Low back pain is extremely common, not only among the general population but also among athletes. The most common causes of lumbar pain include inadequate biochemical patterns, muscle imbalance, improper warm ups, inadequate training leading to repeated micro injuries. Therefore, the objectives of this research were to assess the level of disability of low back pain in Karate practitioners using Oswestry Low Back Pain Disability Questionnaire. Materials and Methods: The study was conducted on a group of karate practitioners whose age ranged from 18 to 35 years with years of experience between 2 and more years. Oswestry low back pain disability questionnaire was used to assess level of disability for low back pain. Results: We had 46 males and 14 females out of 60 participants in our study. The study showed 10 participants had moderate disability and 50 participants had minimal disability. Conclusions: Low Back Pain commonly occurs in karate practitioners. Based on the study's results it can be concluded that low back pain has minimal disability in karate practitioners. Index Terms - Karate, Low back pain (LBP), Oswestry low back pain disability questionnaire.

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- Karate, Low back pain (LBP), Oswestry low back pain disability questionnaire.

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Rooted in a rich historical tapestry, Kannada, a language that has evolved over time, presents shifts in pronunciation, sentence structure, words, and meanings. This project aimed at rendering old Kannada more accessible by offering written and spoken interpretations. The project introduces a "smart translator" converting old Kannada into the modern variant, facilitating comprehension through word and sentence mapping. Additionally, it employs Optical Character Recognition (OCR) to recognize handwritten characters within scanned images. The system also integrates Text-to-Speech (TTS) technology, enhancing accessibility for users. By digitizing handwritten text and accurately mapping translations, the project bridges the gap between old and modern Kannada. This comprehensive approach seeks to understand and preserve the linguistic and cultural heritage of old Kannada, ensuring its continued enrichment of modern Kannada and the broader cultural landscape. However, challenges remain in effectively translating and comprehending old Kannada text, hindering access to valuable historical and literary resources. Through ongoing development and refinement, the proposed system aims to overcome these obstacles and contribute to the preservation and accessibility of Kannada heritage.

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Analysis, investigation, research, Kannada, OCR, TTS.

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order to offer a thorough evaluation on the International Society for Krishna Consciousness's (ISKCON) financial performance and sustainability, this study examines the organization's financial statements. This study intends to assess ISKCON's financial health, effectiveness, as well as efficiency in resource allocation using financial statement analytic methodologies, among which are analysis of trends, along with comparative analysis. Through an analysis of critical financial metrics like liquidity, profitability, solvency, as well as efficiency, this study aims to shed light on ISKCON's financial management strategies, pinpoint its strong and weak points, and offer suggestions for improving sustainability and financial performance. The study's conclusions add to our knowledge regarding the financial dynamics that exist within religious institutions and provide insightful information to ISKCON's management, donors, and stakeholders.

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ISKCON, Financial performance, financial statements, comparative analysis, trend analysis

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Thanks to Edwin Hubble's observations we know that universal expansion unfolds the cosmos. Going back in time, we think the universe was more concentrated. Here comes the amalgam between concentration and volume: because the universe was more concentrated, we think it was also smaller. But the size of the universe is a big unknown. Its limits could never be established. What is established are human errors: at first, it was thought that the universe was limited to Earth. Then it was the sun that became the center of the cosmos. Then we thought the universe was limited to our galaxy. Today, despite James Webb, the limits of the universe are still not found! In short, what makes us think that the universe has limits? Arbitrariness: the human has its limits. Territorial boundaries, among other things. Like its home, its garden or its country. So, when humans interpret their environment, they automatically look for limits. However, it is not the human who created space. He just interprets it... in his own way: with automatic limits! Limits that could never be established. What is established, on the contrary, is the immensity of space!

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Big Bang, inflation, gravitational singularity, quantum mechanics, exclusion of Pauli, EPR paradox

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Rasa Shastra - mainly deals with mercurial ,mineral and metal based medicines indicated in treatment of various diseases. Rasa Shastra , clearly explained about various standard operating procedures in preparation of Ayurvedic medicines. Vanga(Tin), is a Putiloha, which is used in form of Bhasma therapeutically in different type of diseases. There are many hidden properties of Vanga which have ability to the cure most of the diseases. This paper explains about the therapeutic uses of Vanga in detail.

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Rasa Shastra, Vanga , Putiloha , Vanga Bhasma therapeutic uses.

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A number of linguistic traits are common to the fake news reports that are first spread over social media platforms, including an overuse of unfounded hyperbole and unattributed cited information. The performance of a fake news classifier is documented in a study on fake news identification, the results of which are provided in this publication. A innovative fake news detector that leverages quoted attribution in a Bayesian machine learning system as a major feature to evaluate the likelihood that a news story is fraudulent was developed using the Text blob, Natural Language, and SciPy Toolkits. The method precision as a result is 63.333% effective in determining the probability of forgery in an article containing quotes, Influence mining is the term for this procedure, which is described as a unique tool that can be used to detect propaganda and even fake news.

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? Natural Language,Fake News,Fake NewsStories,Machine Learning,Social Media,Classification Performance,Twitter,Social Media Platforms,Integration Problems,Radio News,Research Team,Training Set, News Quality

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In recent years, numerous efforts have been made to shorten the time needed for doing manual work by automating with the help of machine learning and Artificial Intelligence, which are related to postcard, making tables and many other aspects. Esports is growing at a rocket speed and tournaments are organised for which result of matches needs to be made to evaluate points of individual players or teams. Our research focuses on automating table making through feature extraction for which the model extracts the score of players from the screenshot of current most popular game in India, BGMI. We prepared the dataset related to the games having 400 screenshots from the players results. We extracted ROI from the screen shot using Yolo. Once we separate the needed part out, we utilize the OCR that is also based with YOLO. Once we get the needed digits from the image, we will get it as a table having 20 team's score. The project hence would be able to generated a result in 98.3% times the manual labor.

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BGMI, OCR, Segmentation and Yolo

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Abstract: This research paper explores the integration of image captioning and face recognition using generative AI techniques. Image captioning involves generating textual descriptions of images, while face recognition identifies and verifies individuals in images. Combining these tasks can enhance various applications, such as accessibility tools for the visually impaired, surveillance systems, and social media platforms. We propose a generative AI framework that leverages deep learning models to accomplish both tasks simultaneously. The framework extracts features from images, detects faces, recognizes individuals, and generates captions using a single model. Experimental results demonstrate the effectiveness of the proposed approach in generating accurate image descriptions and recognizing faces in various scenarios

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generative ai, image captioning, face recognition

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