Volume 11 | Issue 5 |

 Abstract

India is the world's largest milk patron and is known as the oyster of the dairy assiduity. The assiduity offers numerous openings for entrepreneurs around the world who want to subsidize on one of the world's largest and fastest growing milk and milk product requests. All journal companies, like other businesses, hold colorful means similar as supplies, debtors, and fixed means. still, it's veritably important to manage all these means. In this paper, different effectiveness rates have been used in" Comparison of effectiveness in Indian Dairy Assiduity". thus, data from the three major milk producing enterprises Amul, Sudha, and Mother Diary from 2011 to 2018 are used for these comparisons. EVOVA is used to determine the relationship between the three, and it's derived that only the force timeover rate.

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Analysis of Indian Dairy Industry

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Image processing technique is one of the crucial technologies in the modern world problem solving systems. Various application of image processing has been introduced and also are being utilized in the present era. The classification, analysis and approaches of this technique are available in abundant, but the difference between such techniques are still known only to few. It is essential that proper distinctions between such techniques should be interpreted and they should be analyzed. The main aim in our project is to control mini aerial vehicles or micro drones by recognizing and classifying hand gestures to correct directions or instructions for the movement of drones with maximum accuracy possible. This will be done with use of certain hardware components and image processing scripts with a dataset of different hand gestures for different movements or directions.

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Gesture Control:Drone

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Diabetes mellitus is a chronic disease that occurs as a result of the pancreas not producing enough insulin or the body's inability to use the insulin it produces effectively. Insulin is a drug that controls blood sugar. A fasting blood sugar level of 70-110 mg/dL is considered normal, 100-125 mg/dL is considered diabetes, and 126 mg/dL and above is considered diabetes. The number of people with diabetes increased from 108 million in 1980 to 422 million in 2014. The rate of increase was faster in low- and middle-income countries than in high-income countries. There are two types of diabetes - type1 diabetes and type2 diabetes. This section explains the causes of both types of diabetes. Diagnosis of diabetes: Many scientists and doctors are now developing artificial intelligence-based diagnostic methods to better solve problems caused by human error. KNN, Support Vector Machine (SVM), decision trees, Random Forest etc. Various types of machine learning are discussed and compared

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 Keywords

Diabetes mellitus, Pancreas, Insulin, Blood sugar, Fasting blood sugar level, Type1 diabetes, Type2 diabetes, Causes of diabetes, Diagnosis of diabetes, Artificial intelligence, KNN, Support Vector Machine (SVM), Decision trees, Random Forest, Machine learning

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Online social networks (OSN) have greatly improved communication, information exchange, and enjoyment in modern society. However, because of their accessibility and anonymity, OSNs have provided a favorable environment for a variety of harmful practices like spamming, trolling, fake news, and astroturfing. One of the primary threats to OSNs is socialbots, which are computer programs that perform various illicit activities. To address the threat of socialbots, researchers have been developing various detection methods. One of the latest and most advanced methods is SBRidAPI, which stands for SocialBot RID (Rapid Identification) using deep API learning. The goal of SBRidAPI is to detect socialbots by analyzing a user's behavior on OSNs.SBRidAPI models a broad range of profile, temporal, activity, and content information for user behavior representation using deep learning techniques. Profile information includes user's name, age, location, and other similar data, whereas temporal information is about the frequency and timing of user activities. Activity information includes the type of activities, such as likes, comments, and shares. Content information includes the text, images, and videos shared by the user. SBRidAPI represents profile, temporal, and activity information as sequences in order to analyze the sequential nature of this information that is supplied to a two-layers stacked BiLSTM. Deep CNN is fed content data in order to analyze the text content and learn the visual characteristics of images and videos. Once SBRidAPI has analyzed a user's behavior, it assigns a score that reflects the likelihood of the user being a socialbot. The user is labeled as a socialbot if their score is higher than a threshold that is then used to compare scores. SBRidAPI is the first method that jointly models a complete collection of profile, temporal, activity, and content information for user behavior representation, making it an effective tool for identifying socialbots on OSNs.

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 Keywords

Deep learning, LSTM, CNN, SBRidAPI

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Agriculture is the key point for survival. For agriculture, rainfall is most important. These days rainfall prediction has become a major problem. Prediction of rainfall gives awareness to people and they can get to know in advance about rainfall to take certain precautions to protect their crop from rainfall. Machine Learning algorithms are mostly useful in predicting rainfall. Some of the major Machine Learning algorithms are ARIMA Model(Auto-Regressive Integrated Moving Average), Artificial Neural Network, Logistic Regression, Support Vector Machine and Self Organizing Map. Two commonly used models predict seasonal rainfall such as Linear and Non-Linear models. The first models are the ARIMA Model. While using Artificial Neural Network(ANN) predicting rainfall can be done using Back Propagation NN, Cascade NN or Layer Recurrent Network. Artificial NN is the same as Biological Neural Networks. We will use a kaggle dataset to train and test a Decision Tree Classifier, Logistic Regression & Random Forest Classifier model to predict whether there is going to be rainfall tomorrow or not. We will split the dataset into training and testing data and will be using the training dataset to train the model and testing dataset to observe the accuracy of the model.

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Logistic Regression, Decision Tree Classifier, Random Forest Classifier

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The connection between religious affiliation and individuals' spiritual, psychological, and overall health has been established for a long time. The purpose of this study is to investigate the influence of religiosity on the health and well-being of young adults. Those with higher levels of religiosity tended to have higher levels of spiritual well-being, including a sense of purpose and connection to something greater than themselves, and reported lower stress, anxiety, and sadness, as well as improved self-esteem and resilience. Religious practices were linked to lower rates of substance abuse, higher adherence to healthy lifestyles, and lower rates of chronic diseases. However, religiosity can also have negative consequences, such as religious guilt and shame, as well as strict adherence to religious norms. Additionally, the impact of religiosity varies by religious tradition and cultural context. Healthcare providers and policymakers should consider the role of religiosity in young adults' health and well-being and offer resources to those struggling with negative aspects of religiosity.

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 Keywords

Religion, Spirituality, Wellbeing, Health, Young Adults

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 Abstract

IOT PRISon BREAK MONITORING AND ALTERING SYSTEM

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IOT PRISon BREAK MONITORING AND ALTERING SYSTEM

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Now a days, documentary has become very famous to know and feel the poets. The most important components of documentary are poems of the poet, prose, evaluation of the poet by the other poets and so on. In making scene in the light of the poet's life symbols, detailing, cinematic presentation make documentary artistic. Although the inspiration of documentary is the life of poet but it is actually the director's own realization.

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documentary, life, literature, poet

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In order to generate good revenue, every business needs to make proper data-oriented decisions. These decisions lead to the emergence of successful business strategies, which themselves are data driven. This is even more relevant in the world of banking, where the stakes are quite high. The huge number of customers increase the risk of a massive default on credit cards if banks are not being careful. Further, it becomes difficult for banks to cater to the individual needs of such a large number of customers. Therefore, we propose the usage of these machine learning practices to predict the next step, sort your data, and apply multiple algorithms to your dataset to find your target audience. Using these practices, we will be able to predict whether a customer will default their next credit card bill or will be able to repay their loan and organising the customers so that bank can manage their resources to cater to the right group of people who will buy their different schemes.

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Machine learning, Datasets, Customer Segmentation, Loan predictions, Credit card defaulters

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This review article explores the effects of nocebic language in patient communication, especially how it relates to the nocebo effect, in which patients' unfavourable perceptions of their therapies result in subpar results. Nocebic language, which is characterised as words or expressions that might stir up or exacerbate these unfavourable expectations, has been linked to serious negative effects on patient outcomes, altering elements like pain perception and side effect experiences. Research on the nocebo effect, the impacts of nocebic language, and mitigation techniques are examined in this review. Nocebic language's ethical conundrums and how they affect clinical practise are also covered. In order to minimise negative effects while preserving patients' access to information and reducing the nocebo effect, the review emphasises the need for further study on ethical and effective communication tactics.

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 Keywords

Nocebic Language, Nocebo Effect, Patient Communication, Pain Perception, Side Effects, Ethical Dilemmas, Clinical Practice, Communication Strategies

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