S. No. Title Page No.

Abstract: In many fields, such as industry, commerce, government, and education, knowledge discovery and data mining can be immensely valuable to the subject of Artificial Intelligence. Because of the recent increase in demand for KDD techniques, such as those used in machine learning, databases, statistics, knowledge acquisition, data visualisation, and high performance computing, knowledge discovery and data mining have grown in importance. By employing standard formulas for computational correlations, we hope to create an integrated technique that can be used to filter web world social information and find parallels between similar tastes of diverse user information in a variety of settings


Abstract: A technology called Quantum Dot Cellular Automata (QCA) offers a far more effective computational platform than CMOS. Through the polarization of electrons, digital information is represented. In comparison to CMOS technology, it is more attractive because to its size, faster speed, feature, high degree of scalability, greater switching frequency, and low power consumption. This paper suggests structures of basic logic gates in the QCA technology. For the aim of verification, the produced circuits are simulated, and their results are then compared to those of their published counterparts. The comparison outcomes provide hope for adding the suggested structures to the collection of QCA gates.


Abstract : Fire incident is a disaster that results in the loss of life, damage to the property and endless disaster to the victim. Fire extinguishing is an exceptionally unsafe undertaking and it might likewise include death risk. Robotics is the answer to ensure the safeguarding of the surroundings and also the life of firefighters. Fire sensing and extinguishing robot is a model which can be used in extinguishing the fire with minimum human intervention. There is a threat to the life of the fire fighters in extinguishing the fire and there are some difficult areas where they cannot reach like that in the tunnels. At similar kind of places this automatic robot is veritably useful to perform the task. This robot can be controlled remotely by mobile phone using Bluetooth module. The robot is equipped with the flame sensors that automatically detects the fire and gives the further signal to the extinguisher units to start the pump and extinguish the fire by spraying water. Arduino uno is used as the microcontroller to operate the whole operation. The proposed robot has been used for various trials and proper evaluation has been done to check the proper functioning and to get the desired result.


Abstract: Detection of fake news based on deep learning techniques is a major issue used to mislead people. For the experiments, several types of datasets, models, and methodologies have been used to detect fake news. Also, most of the datasets contain text id, tweets id, and user-based id and user-based features. To get the proper results and accuracy various models like CNN (Convolution neural network), DEEP CNN, and LSTM (Long short-term memory) are used


Abstract : The project’s major goal is to create an intelligent trash can that would aid in maintaining a clean and environmentally friendly environment. The Swachh Bharat Mission motivates us. Since technology is becoming increasingly intelligent, we are utilising Arduino nano to develop an intelligent dustbin to help clean the environment. The ultrasonic sensors on the trashcan are part of the dustbin control and management system, which happens to be designed with a microcontroller-based platform. In the suggested method, we used an ARDUINO NANO, an ultrasonic sensor, a Mini servo motor, and jumper wire linked to a charger to construct an intelligent trash can. The Smart Dustbin application will launch when all hardware and software connections have been made. Dustbin lid will wait for the person to pass by at a distance of 60 cm.


Abstract—This paper provides a brief overview of the Intelligent Traffic Management System based on Artificial Neural Networks (ANN). It is being utilized to enhance the present traffic management system and human resource reliance. The most basic problem with the current traffic lights is their dependency on humans for their working. The technologies used in the making of this automated traffic lights are Internet of Things, Machine Learning and Artificial Intelligence. The basic steps used in Internet of Things are reported along with different ANN trainings. This ANN model can be used for the minimization of traffic on roads and less waiting time at traffic lights. As a result, we can make traffic lights more automated which in turn eventually deceases our dependency on human resources


ABSTRACT: In the field of computer science known as "machine learning," a computer makes predictions about the tasks it will perform next by examining the data that has been given to it. The computer can access data via interacting with the environment or by using digitalized training sets. In contrast to static programming algorithms, which require explicit human guidance, machine learning algorithms may learn from data and generate predictions on their own. Various supervised and unsupervised strategies, including rule-based techniques, logic-based techniques, instance-based techniques, and stochastic techniques, have been presented in order to solve problems. Our paper's main goal is to present a comprehensive comparison of various cutting-edge supervised machine learning techniques.


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