Volume 12 | Issue 5 |

 Abstract

This Research paper focuses on fractional calculus expands the idea of differentiation & integration to non-integer orders. Fractional derivatives provide extra modelling degrees of freedom while integer-order derivatives are well-known and often employed. Applications for fractional derivative operators can be found in many disciplines, including signal processing, image analysis, physics, and engineering. To fully utilise the potential of fractional derivative operators across a range of domains, it is crucial to comprehend their basic notions. The concept of fractional calculus was first introduced in a set of letters sent in 1695. Leibniz responded to L'Hopital's query about what would occur if the order of differentiation were assumed to be 1/2, and he said it seems that these contradictions will eventually have beneficial ramifications. The symbol d^n/(dx^n ) f(x), created by Leibniz in the late seventeenth century, represents a function's n^th derivative, with the conclusion that n?N. De l'Hospital was informed of this and in response, he questioned the importance of the operator if n = 1/ 2. Although n need not be restricted to Q, for the purposes of this paper, n ? R applies to all operators in the following text. This branch of mathematics is known as fractional calculus because of the specific questioning of Leibniz's operator in relation to n = 1/ 2 (a fraction).

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Keywords : Fractional Order Differential integrals, Fraction Differintegrals, Riemann-Liouville fractional integral; Riemann-Liouville fractional derivative;

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This project involves the creation of a robot capable of both writing text and drawing images on paper. The robot utilizes a combination of precise movements and writing/drawing tools to execute these tasks. The system is designed to accept input, convert it into readable text or drawable images, and then autonomously reproduce the input on paper. The project aims to explore the integration of robotics and artistic expression, providing a unique platform for automated creativity. The system accepts input in the form of text or image data. For text input, the robot utilizes natural language processing algorithms to interpret and understand the content. For image input, image recognition algorithms are employed to analyze and convert the visual information into a format suitable for drawing. This machine can draw both parallel and upstanding. Its single design structures a writing head that spreads beyond the machine, making it possible to draw on objects greater than the machine itself. The major benefit of the machine is that it can be located over the hardcover because the core XY extends the design of the machine. The purpose of this research paper is to present a comprehensive study on the design and development of a handwriting robot. The project aims to create a robotic system capable of accurately mimicking human handwriting, opening up possibilities for various applications in fields such as education, art, and automation. This paper explores the mechanical design, actuation mechanisms, control electronics, and sensors involved in building such a robot. By understanding the intricacies of these components and their interactions, we can achieve precise and fluid handwritten output.The research paper also delves into the challenges faced during the development process and proposes potential solutions for further improvement. Through this study, we hope to contribute to the advancement of robotics and its applications in the field of handwriting replication.

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servo motor, stepper motor

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In today's world there or lot of accidents and disasters are happening there is a need for use amount of money for Healthcare as the cost of treatment and medicines requirements are very high. There are many cases where people cannot offer that treatment and loss their life to prevent this government along with many private banks tied up with numerous hospitals to set up many insurance agencies.

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Insurance, ;Classification;Machine learning;SVM;ANN;Logistic regression;Decision tree

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Climate change poses serious challenges to agriculture, including an increase in the frequency and severity of extreme weather events that can damage crops and vegetables. Rice is not enough. Big data analytics holds promise for predicting climate events and managing agricultural disasters. This paper presents a data processing model that uses big data analytics (DHM-BDA) to investigate the role of big data in agricultural disaster management and inform the current state of affairs in providing good ideas and solutions. The DHM-BDA model highlights the importance of big data, including its implications for progress and development at different levels of climate drivers and disaster management. The model improves cost estimation, decision-making, information management, productivity and risk reduction compared to other existing methods.

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Data modeling for change-driven, data unpredictability, data management models for processing and interoperability, big data analytics (DHM-BDA).

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This project introduces a novel approach to forecasting dissolved oxygen levels in aquaculture settings, overcoming complexities in conventional methods. Integrating Light Gradient Boosting Machine (LightGBM) with Bidirectional Simple Recurrent Unit (BiSRU), the model effectively identifies pertinent parameters while minimizing irrelevant variables through linear interpolation and smoothing. LightGBM accurately predicts dissolved oxygen content, while the attention mechanism optimizes BiSRU's hidden states, enhancing predictive accuracy. Outperforming existing models, this hybrid model offers crucial insights for regulating aquaculture water quality. Additionally, an Ensemble model combining Bidirectional LSTM, GRU, Simple RNN, and Attention mechanisms demonstrates further improvement in Mean Squared Error (MSE) compared to individual algorithms, expanding the project's potential impact.

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Water Quality, LightGBM

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The exponential expansion of internet usage has profoundly reshaped global dynamics, ushering in a new era characterized by enhanced knowledge dissemination, streamlined goods movement, and interconnected interpersonal relationships. However, this unprecedented connectivity has also spawned a proliferation of malicious activities, particularly in the realm of client-side attacks targeting websites. Traditional mitigation approaches, such as blacklisting, have fallen short in effectively combating these sophisticated threats, prompting our project to embark on the development of a robust solution. Our endeavor involved the integration of host-based, content-based, and lexical features, culminating in the implementation of a random forest machine learning model. This powerful amalgamation yielded an impressive accuracy rate of 94.7 percentage underscoring the efficacy of advanced machine learning techniques in bolstering cybersecurity defenses. By leveraging these diverse features, our model exhibited enhanced discriminatory capabilities, adept at detecting anomalies within hosting environments, identifying malicious elements embedded within web content, and scrutinizing the linguistic attributes of URLs.

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URL , Breach , Phishing

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In India, the caste system is deeply ingrained in people's mentality and has been identified as a major obstacle to social justice and human rights. This article attempts to examine and analyze the sensitivity and rigidity of this system. Additionally, the paper makes an effort to examine the research of a few researchers, demonstrating how choosing a different religion allowed the victims to live honorably and with dignity.

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Social justice, Human Rights, Self-respect, Caste system, and people's psychology

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The aim of this paper is to introduce semiopen sets and semilocally closed sets in generalized topology and minimal structure spaces. Further we investigate some properties of these sets on these spaces.

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?_g m_X-semiopen, ?_g m_X-semilocally closed set

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E-commerce has come a long way since its humble beginnings and is now a major industry. It has revolutionized the way we do business and has made it possible for anyone to start their own online store. If you're thinking of starting an online business, then commerce is a great option. There are many different platforms to choose from, so you'll be sure to find one that suits your needs. And with the ease of payment options and delivery methods, there's no reason not to give it a try. So what are you waiting for? Start your own ecommerce days a week without having to worry about opening and closing times. This project is all about the E-commerce portfolio of VR cardmaker which provides Printing Card Services in different sectors such as business cards, visiting cards, and badges In addition, businesses can reach a larger audience with e- commerce than they would if they were only selling through brick and mortar stores.

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ecommerce, printing cards, business cards, printing press, online printing

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Predicting when it will rain is crucial for warning individuals of potential dangers and enabling them to take preventative measures for their own safety. This work aims to employ machine learning algorithms to accurately estimate rainfall, taking into account the substantial effects of little or excessive rainfall on both rural and urban life. Rainfall is a complicated phenomenon that is influenced by a wide range of meteorological, oceanic, and geographical factors, making it challenging to predict. This study makes use of a variety of machine learning algorithms, such as Support Vector Machine (SVM) and Random Forest classifier, as well as data pretreatment techniques, feature selection, model selection, and evaluation. The aim of the project is to develop the most accurate rainfall forecast model feasible by utilizing feature selection and machine learning techniques. While the Random Forest classifier obtained 84% accuracy by using ensemble learning and decision trees, which are excellent at capturing complicated correlations in data, the SVM only reached 83% accuracy by specifying a linear decision boundary, possibly restricting its ability to handle sophisticated data patterns.

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 Keywords

Rainfall prediction, Random Forest algorithms, Support Vector Machines, machine learning, weather data, hyperparameter tuning, model evaluation, comparative analysis

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