Volume 11 | Issue 5 |

 Abstract

In this ever digitizing world, maintaining integrity of data is of utmost importance to ensure that this digital world remains safe for the consumption of the masses. However, as technology advances, the ease of protecting this data decreases as stronger passwords and encryption keys need to be used. In order to keep up with this technological advancement, Biometrics are employed in order to secure these systems and technologies however in the current state of Surveillance, Biometrics is being employed in combination with Mass Surveillance technology. This paper will be talking about the implications of combining the usage of Biometrics and Mass Surveillance technology on the world as a whole.

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 Keywords

Biometrics, Mass Surveillance, Privacy, Artificial Intelligence

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 Abstract

With the increasing demand for transportation and the rapid urbanization of India, innovative solutions and methods are being developed and imported from other nations. Numerous Maestro Rapid Transit systems have emerged in numerous cities over the past decade, including Metro Rail, Bus Rapid Transit systems, and Monorails. The interconnection of these public transportation modes and several other means is currently a crucial issue from the users' and planners' points of view, for simple travel between the numerous modes and sustainability of the city's overall public transportation network. This paper attempts to comprehend the integrated multi-modal urban transportation system for Indian cities. With the use few case studies and taking references of a few previous studies by researchers, efforts are made to identify the characteristics, benefits, and challenges of integrated multi-modal urban transportation for Indian cities. The Urban Transportation Planning Concepts regarding the development of Interchange Hubs are explored.

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 Keywords

Multimodal Transportation, Content analysis, City Planning, Integrated Public Transport

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Abstract :Increase in the demand of conventional construction materials and the need for providing a sustainable growth in the construction field has prompted the developers to opt for `Alternative materials. For this objective, agricultural by-products like rice husk ash can be replaced instead of cement. Rice Husk ash provides good compressive strength to the concrete, it is a by product, hence, it helps in cutting down the environmental pollution. The high silica content makes it is good supplementary Cementous material. It is highly porous and light weight, with a very high specific surface area . Now we are going to measure and compare with 0%,5%,10%,15%,20% of replacement of rice husk ash with cement on strength aspects with some tests like compressive strength ,split tensile , flexural strength and workability tests (slump flow) were carried out on Cubes of size 150mm*150mm*150mm , cylinders of size 150mm*300mm,beams of size 100mm*100mm*500mm.These specimens were tested for compressive strength , split tensile strength , flexural strength respectively for 7 days and 28 days of curing period.

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Rice husk ash, Compressive Strength, Split Tensile Strength, Flexural Strength.

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Think about the scenario when you are seeking to forecast the marketing performance of a particular product. Customers could be categorised into many groups based on how they behave in the market. Recognise that our five senses cannot possibly process the tremendous amount of information that is available. Algorithms for machine learning and processing power will be used. One of the many important uses of unsupervised learning is customer segmentation. Make use of clustering algorithms (K- means, Agglomerative, and Mean Shift) to pinpoint client segments where the potential user base is most likely to be. As a result, groupings of customers are created based on shared traits like gender, age, and spending habits. Age and gender-related trends can also be found using K-means clustering. Examine their yearly revenue and after that, expense totals. Compared to the existing model, which makes use of Mini Batch K-means, the results from the projected model are more accurate.

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clustering, algorithms, segmentation

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Child obesity has emerged as a global public health concern, with a significant increase in prevalence over the past few decades. This review article aims to provide an in-depth analysis of the multifactorial causes, detrimental consequences, and potential interventions for child obesity. We explore the complex interplay of genetic, environmental, social, and behavioral factors contributing to the development of obesity in children. Additionally, we examine the short- and long-term physical and psychological health consequences, emphasizing the importance of early intervention and prevention strategies. Finally, we discuss evidence-based interventions, including lifestyle modification, school-based programs, and community-driven efforts, in order to address this growing epidemic. By synthesizing current knowledge and highlighting gaps in research, this review underscores the urgent need for collaborative efforts among stakeholders to promote healthy lifestyles and prevent child obesity.

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 Keywords

Childhood obesity, Health consequences, Prevention strategies

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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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 Abstract

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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 Keywords

Logistic Regression, Decision Tree Classifier, Random Forest Classifier

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Creative Commons Attribution 4.0 and The Open Definition