Volume 11 | Issue 5 |

 Abstract

Sapota (Manilkara zapota) is a tropical fruit tree that is native to Mexico, Central America, and parts of South America. This belongs to the family sapotaceace. The sapota fruit is a brownish, round or oval-shaped berry, about the size of a small apple, with a thin, rough, and grainy skin. The flesh is soft, creamy, and has a pleasant, musky flavor. The fruit contains many small, black, shiny seeds that are not usually eaten. It has the medicinal benefits like anti-inflammatory, helps in digestion, energy provider, source of antioxidants, it is good for bones, controls blood pressure. Sapota fruit is a good source of dietary fiber, vitamins A, C, and E, and minerals such as potassium, magnesium, and iron. This fruit is good for pregnant women The sapota tree is an evergreen tree that can grow up to 15-20 meters tall and has a dense, spreading crown of leaves.

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 Keywords

sapota, dietary fiber, phosporous

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 Abstract

Training and placement (TNP) are critical components of every educational establishment, where most of the work is done manually. Paper-based methods, databases, spreadsheets, and e-mail exchanges help to handle TNP activities. With paper and spreadsheet-based methods, it is difficult to manage the data and applications of all the students for every company in placement activity. In the traditional approach, the placement coordinators create forms and need to circulate them among the students to get applications. The application process for a company involves filling out student details forms which are repetitive as most details are static. The students face difficulty while tracking and managing all the applications they have made to various companies with a traditional spreadsheet-based approach. With the traditional approach, it is difficult for students to prepare for placement drives with vast content. The goal of the proposed work is to automate TNP activity. The main benefit of this proposed work is that it only requires a single registration. The proposed work will help the students in managing the applications. The chatbot is one of this system's essential components. This AI-assisted chatbot can help students with placement-related questions. In order to respond to and process the questions, this chatbot employs NLP and different ML models. The proposed work will provide a job posting facility that is much easier to work with. Filtering of students as per the company's eligibility criteria would also be achieved. Recommendations are made using the proposed work for the preparation of placement drives. Also, all the placed students can be tracked in a single place and statistics can be derived from the data of placed students.

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 Keywords

Automation, Artificial Intelligence, Chatbot, Machine Learning, NLP.

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 Abstract

The proposed work explores the potential of holographic projection technology and artificial intelligence (AI) to create a 3D holographic assistant that can be interacted with through speech-based input. The proposed system will assist users with a variety of tasks, such as communication, internet searches, video playback, weather updates, news, games, and reminders. Holographic projection technology, which allows for graphical interaction, is increasingly being used by multinational companies due to its complexity and versatility in various fields such as medicine, virtual reality, digital art, and security. The developed technique seeks to enhance the user experience by giving AI more control over the hardware, thereby making the virtual assistant more comfortable to talk to. The system uses a speaker, microphone, personal computer, extended display, and holographic projection setup to produce high-quality holograms and effectively transmit and receive video streams.

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 Keywords

Holograms, Holographic Artificial Intelligent Assistant, 3D Display, Interactive Display, Voice Assistant, pyttsx, Google API, Google Search.

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 Abstract

The integration of artificial intelligence in energy management has garnered significant attention due to its potential to revolutionize the way we consume, produce, and distribute energy. This paper presents a study on the impact of artificial intelligence on energy management, highlighting a revolutionary shift in the power industry. This study aims to shed light on the impact of artificial intelligence on energy management and pave the way for a more sustainable future. However, the objective of this study is to assess the potential of artificial intelligence and machine learning in transforming the energy industry. By leveraging artificial intelligence, energy management systems can optimize energy consumption, reduce waste, and enhance efficiency. The research delves into the ways in which AI is transforming the energy sector and the implications of this transformation for businesses and consumers alike. Through a comprehensive analysis of the latest trends and developments in AI-powered energy management, this study sheds light on the opportunities and challenges that lie ahead. By exploring the potential benefits of AI in optimizing energy consumption, reducing costs, and improving sustainability, this paper provides valuable insights for industry professionals and policymakers seeking to stay ahead of the curve.

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 Keywords

Artificial Intelligence, Energy Management, Power Sector, Smart Grid, Optimization

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 Abstract

This paper presents a flight delay prediction model using K-Nearest Neighbors (KNN) and Decision Tree algorithms. The model utilizes historical flight data to predict the likelihood of a flight being delayed. The KNN algorithm is used to identify similar flights in the past, while the Decision Tree algorithm is used to classify the flight based on its attributes. The model is trained and tested on a dataset containing flight information from a major airport over a period of several years. Results show that the KNN and Decision Tree algorithms are effective in predicting flight delays, with the Decision Tree algorithm outperforming the KNN algorithm in terms of accuracy. The proposed model has potential applications in the aviation industry, allowing airlines and airports to better anticipate and manage flight delays. This paper presents a flight delay prediction model using K-Nearest Neighbor (KNN) and Decision Tree algorithms. The model utilizes historical flight data, weather data, and airport information to predict the likelihood of a flight being delayed. The KNN algorithm is used to identify similar flights based on their characteristics, such as the airline, departure time, and destination, and predict the delay status based on the delays of the similar flights. The Decision Tree algorithm is used to create a rule-based model that predicts the delay status based on the most important factors contributing to delays. The model is evaluated using a dataset of flight information and weather data from a major airport, and achieves an accuracy of over 80% in predicting flight delays. The proposed model can assist airlines, airports, and passengers in making informed decisions and reducing the impact of flight delay.

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 Keywords

Python, Machine Learning, KNN Algorithm, Decision tree algorithm, Flight Delay Prediction.

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Abstract:- Prakruti Mahila Vikas Kendra, Chandrapur (Maharashtra) is a reputed NGO working for women's sustainable development since 2003. The NGO is working in Chandrapur district's five talukas; Chandrapur, Rajura, Ballarpur, Jivti, and Korpana, and functioning for the development of women and the rural community. Women's economic empowerment, Health, Panchayat Raj, Self-Help Groups, Atrocities against women, Employment, and Family Counselling are the issues on which the NGO is striving endlessly. The vision of the NGO:- To establish a society based on equality, freedom, justice, and fraternity by removing oddities in class, caste, sex, color, religion, and gender. To work for women's financial, social, political, and cultural development. Objectives of the NGO:- 1. Creation of women's strong organization through Mahila mandals and their self-help groups. 2. Creating gender equality and social equality 3. Organizing various awareness programs for leadership development and personality development of women. 4. Economic, Social, Political, and Cultural development of women.

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 Keywords

economic empowerment, Leadership skills, upliftment, Sanjivani, gender equality

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The volatile nature of the stock market makes it very difficult to predict future market trends and where to invest. Hence, there is a need for cross-utilization supported by ultramodern architecture. With the recent advancement of LSTM & KNN, continuous practical problems can be modeled and solved with human-level accuracy. Apart from this, in dealing with interim trading strategy, the proposed architecture is designed as a continuous training pipeline so that the stored model is up-to-date with the latest market trends by providing high accuracy in prediction. The framework outperforms basic LSTM & KNN model algorithms and maximizes portfolio returns. Experimental results show how natural language processing and statistical inference can help us pick trending stocks based on news headlines and historical data. To evaluate the performance of the proposed method, a comparison of our portfolio results was made with various LSTM & KNN model algorithms keeping the same configuration.

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 Keywords

Machine Learning, Deep Learning, LSTM, KNN, Stock Market.

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 Abstract

Deep learning and image segmentation techniques are widely used in various sectors nowadays but, these methods play a predominant role in diagnosis of various health conditions. Automated and detailed medical image segmentation model have gained popularity since the advent of deep learning techniques specifically fully convolutional neural networks (FCNN). U-Net is one such fully convolutional neural network (FCNN) based image segmentation model, which has proven its efficiency in medical image segmentation over recent years. In this paper, we present the traditional U-Net model in comparison with a novel U-Net model with an additional dropout layer in each convolutional layer. Here, we compare these models considering the dice average values obtained for two different data inputs containing ultrasound images of a carotid artery in various patients. The proposed U-Net model with dropout layer is observed to have better dice average values in comparison with the traditional U-Net model.

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 Keywords

Medical Image Segmentation, FCNN, Deep learning, U-Net

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The stock market is a complex and dynamic system that can be difficult to predict accurately. Machine learning has emerged as a powerful tool for analyzing market data and making predictions about future trends. In this project, we explore the use of various machine learning techniques for predicting stock prices, including regression, classification, and deep learning models. We compare the performance of these models on historical data from a variety of stocks and evaluate their ability to make accurate predictions. Our results show that machine learning can be an effective approach for predicting stock prices, but the accuracy of the models varies depending on the specific market conditions and the type of data used. Overall, this study demonstrates the potential of machine learning for stock market prediction and provides insights into the strengths and limitations of different techniques.

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 Keywords

investor sentiment, stock market, big data

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper proposes an IoT-based system for predicting the energy production of wind turbines using NodeMCU, LCD display, and various sensors such as accelerometer, temperature and humidity, and rain detection sensors. The system also includes a voltage controller to regulate the output voltage of the wind mill. The data from these sensors is collected and transmitted to a central server using IoT protocols, where machine learning algorithms are applied to predict the energy production of the wind turbine. The system also features an LCD display that provides real-time data visualization for the user. Additionally, a suggestion mobile app is developed to provide recommendations for optimizing the energy production of the wind turbine based on the sensor data. The proposed system is expected to improve the efficiency of wind turbines and contribute to the growth of renewable energy.

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 Keywords

IoT mobile app, LCD Display, Rain Detection Sensor, Node MCU, Battery, Wind Mill , Temperature and Humidity Sensor, Accelerometer, Charging Port.

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Creative Commons Attribution 4.0 and The Open Definition