Volume 12 | Issue 5 |

 Abstract

This project presents an innovative solution combining IoT technology with a dual-axis solar tracking system to optimize energy management through load shedding. Load shedding is essential for effective power distribution, especially in regions susceptible to energy shortages. The dual-axis solar tracking system enhances solar panel efficiency by dynamically adjusting its orientation to maximize sunlight exposure. Leveraging IoT capabilities, the system enables real-time monitoring and intelligent decision-making for load shedding based on energy demand and availability. This report outlines the design, implementation, and evaluation of the integrated system, showcasing promising results in energy efficiency and load-shedding effectiveness. The fusion of IoT and solar tracking technologies holds significant promise for addressing energy challenges and promoting sustainable energy practices in various settings.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Arduion uno, Battery cells,Lcd display,Dc motor,.Matlab simulator.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In urban areas, electrical cables run underground instead of running over, because it does not affected by any adverse effect of weather such as heavy rainfall, snow, thunder storm. Whenever a fault occurs within the underground cable, it is difficult to detect the exact location of the fault for the repair process of particular cable. The proposed system found the point of the exact location of fault. This project is intended to detect the location of the fault in underground cable lines from the base station to exact location in kilometres using an Arduino micro controller kit. In the urban areas, the electrical cable runs in undergrounds instead of overhead lines. Whenever the fault occurs in underground cable it is difficult to detect the exact location of the fault for process of repairing that particular cable. The proposed system finds the exact location of the fault. This system uses an Arduino microcontroller kit and a rectified power supply. Here the current sensing circuits made with a combination of resistors are interfaced to Arduino microcontroller kit to help of the internal ADC device for providing digital data to the microcontroller representing the cable length in kilometres . The fault creation is made by the set of switches. The relays are controlled by the relay driver. A 16*2 LCD display connected to the microcontroller to display the information. In case of short circuit the voltage across series resistor changes accordingly, which is then fed to an ADC to develop precise digital data to a programmed Arduino microcontroller kit that further displays exact fault location from the base station in kilometres. In this project we used IOT thing speak for monitoring. We can monitor through our android phone the WIFI IOT.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Arduino microcontroller, LCD, ADC, Cable Fault, Relay, IOT.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Since the non-renewable resources we now use are going to run out soon, renewable energy is crucial for the modern world. Saving these non renewable energy sources is one step closer with the solar-powered vacuum cleaner. We are more aware of and affected by the effects of climate change now than ever before. The technology that can support us in both our everyday lives and in preserving the environment. We present the Smart Solar dust collection as one workable option that can perfectly alter our way of life, if only slightly. A solar vacuum cleaner can aid in reducing pollution. To capture solar radiation, offer an improved surface for collecting dust, which benefits the environment

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 Keywords

DESIGN AND FABRICATION OF SOLAR POWER VACUUM CLEANER

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Efficiency is one of major aspect of the software industry ever since its beginning, serving end-users quickly, and benefiting service providers cost-effetely. All parties involve getting an efficient system. A database management system is a essential part of all software systems effectively, so it makes sense to benchmark the performance of different DBMSs to find the most reliable one. This approach systematically synthesizes results and compare DBMS performance, providing suggestions for industry and research. Database management systems are today's most effective mean to organize data and collects that data which can be used for search and update operations. However, many database systems are available on the market each having their advantages and disadvantages in terms of reliability, usability, security, and performance.

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TRADITIONAL DATABASE AND ITS PAIN POINTS FOR IMAGE AND TEXT PROCESSING

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 Abstract

Accidents in India have emerged as a leading cause of fatalities, predominantly attributable to delayed assistance reaching victims rather than the accidents themselves. Particularly in areas with sparse and high-speed traffic like highways, victims often endure prolonged unattended periods, amplifying the risk of fatal outcomes due to delayed medical intervention. This paper introduces AcciVue, a proactive system designed to bolster road safety by employing a real-time accident detection mechanism. AcciVue integrates deep learning neural networks, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks[1], to analyze CCTV video feeds for timely accident detection. By promptly alerting nearby hospitals and police stations upon detection, AcciVue optimizes emergency response, mitigating accident repercussions and ultimately enhancing road user safety. This innovative approach showcases the transformative potential of deep learning technology in addressing the multifaceted challenges of road safety.

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 Keywords

Accidents, Road Safety, Deep Learning, CNN, LSTM

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the increasing volume of digital resumes, efficient and accurate classification is essential for effective talent acquisition.This research delves into the innovative application of gradient boosting algorithms, a subset of machine learning techniques, for the intricate task of resume classification in the domain of talent acquisition. Gradient boosting methodologies, renowned for their adeptness in iteratively refining predictive models by combining weak learners, present a compelling avenue for bolstering the precision and efficiency of resume categorization systems.Moreover, the research endeavors to unravel the interpretability of gradient boosting models, shedding light on their role in fostering transparency and equity in the recruitment ecosystem. Through this multifaceted inquiry, this study not only advances the frontier of machine learning applications in talent acquisition but also underscores the transformative potential of gradient boosting in revolutionizing resume classification practices, thereby empowering organizations to make data-driven and equitable hiring decisions.

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 Keywords

Resume, Classification, Machine Learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Social distancing strategies are crucial for halting the development of various air born disease and maintain the distance for various causes. To disrupt the cycle of dissemination, social Distancing is often adhered to carefully. This study presents a technique that may be used to detect instances of social distance breaches in public spaces such as ATMs, malls, and hospitals. By using the suggested approach, it would be easy to keep an eye on people to make sure they are keeping their social distance in the monitored area and to notify them when someone does not adhere to the established boundaries. Installing the suggested deep learning technology-based system will allow coverage up to a predetermined, restricted distance. To complete the task, the algorithm uses real-time IP camera footage. The simulated model employs a YOLO model trained on the COCO dataset to detect individuals in the frame, then deep learning methods with the OpenCV library to estimate the distance between them.

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 Keywords

Deep learning, OpenCV YOLO model, COCO dataset, Image processing,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Machine learning and feature extraction play a very importance in the Internet and health department. The traffic environment consists of everything that can affect traffic on the road, be it traffic lights, accidents, rallies, even road repairs that can cause a large amount of congestion. If we somewhat have imprecise prior information on all of the above and many other everyday situations that can affect traffic, then the driver or rider can make somewhat of an informative decision. Needless to say, it also helps in contributing to the future of autonomous vehicles! In the current decades, traffic data is significantly generated exponentially and we slightly have moved towards embracing big data concepts for transportation. This interesting fact really inspired us to somewhat work on the issue of traffic flow prediction, sort of based on traffic data and models.

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 Keywords

Traffic prediction, Intelligent Transportation Systems, Random Forest and KNN

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This final-year project aims to analyse and detect Breast Cancer . Womens are highly suffered from breast cancer,with huge medical problems caused by a treatment(Morbidity) and destined to die(Mortality). Due to lack of prediction models the accuracy of prediction or detection of breast cancer if difficulty. Because of this the prediction time and the patient duration for sustaing time need to be prolonged. Hence to predict early the technique or model is designed to give prediction accuracy exactly. In this SVM, DT, GaussianNB and KNN are the four algorithms used to predict breast cancer results compared with large and different datasets. The proposed model is selected to predict the result of many techniques and correct technique is used depending upon the treatment. This model is based on getting and future studies can be done to predict other methods it can be categorised on basisi of other methods.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Breast Cancer, Machine learning, Support Vector Machine, Decision Tree, KNN and GaussianNB.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid developments in machine learning and computer vision have revolutionized many industries, most notably the gaming business. This project makes use of OpenCV in conjunction with Tkinter, a popular Python GUI toolkit, to create an aesthetically pleasing and user-friendly menu system that enables simple and quick game selection. This project includes a menu that enhances interactivity inside four bespoke games (Quiz, Eat the Fruit, Rock-Paper-Scissors, and Number Guessing) by combining OpenCV's image recognition and gesture detection. These games strive to surpass user expectations and showcase the potential of computer vision in gaming by utilizing OpenCV's real-time responsiveness and dynamic interaction features. This invention raises the bar for interactive, user-centered game.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Computer Vision, OpenCV, Tkinter, Interactive Gaming, Game Selection Menu, Real-Time Interaction, Gesture Detection, Image Recognition, Gaming Innovation.

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Creative Commons Attribution 4.0 and The Open Definition