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License Plate Recognition (LPR) is one of the tough tasks in the field of computer vision. Although it has been around for quite a while, there still lies the challenges when we have to deal with; the harsh environmental conditions like snowy, rainfall, windy, low light conditions etc. as well as the condition of the plates which includes the bent, rotated, broken plates. The performance of the recognition and detection frameworks take a significant hit when it is concerned with these conditional effects on the license plate. In this paper, we introduced a model to improve our accuracy based on the Chinese Car Parking Dataset (CCPD) using 2 separate convolutional neural networks. The first CNN will be able to detect the bounding boxes for the license plate detection using Non-Maximus Suppression (NMS) to find the most probable bounding area whereas the second one will take these bounding boxes and use the spatial attenuation network and character recognition model to successfully recognize the license plate. First, we train the CNN to detect the license plates, then use the second CNN to recognize the characters. The overall recognition accuracy was found to be 89% in the CCPD dataset. [ABSTRACT FROM AUTHOR]

Cyber-attacks which is known as Computer Network Attack (CAN). It is a threat created by cybercriminals by more than one computer against networks. There are various types of cyberattacks among them Phishing Attack is one of them. Phishing is a technique of gathering sensitive information of a target such as username, password, bank details etc. There are various ways to perform phishing attacks, among them URL PHISHING attack is one way to gather user's information. Mainly hackers create a fake website regarding bank details, shopping websites, etc. using social engineering tool which looks like legitimate website. This fake URL will be sent to user via email or through some other resources. In this paper, we will be performing data analysis, data preprocessing, data exploring, training and predicting through machine learning and optimization techniques on dataset which contains two attributes (URL, Label). We will be introducing performance metrics like confusion matrix accuracy, precision, recall and F1 score to know the performance of the model. An optimization technique which is Stochastic gradient descent performed better than logistic regression. [ABSTRACT FROM AUTHOR]

With the world digitizing and moving at a fast pace, framing questions for examinations or learning is a time-consuming process and requires a lot of critical thinking. Questions we solve in the exams, for instance, school and college level examinations, are similar to the last year papers and contain repeated questions with little or no paraphrasing or modifications. Educators spend a significant amount of time in preparing question papers to come up with creative brainstorming questions. Automation has become a vital aspect of life. New technologies are coming up every day to minimize manual work and make everything automated with just a click. Considering the present pandemic scenario, education is now internet based and exams are being conducted online. Most of the examinations are based on multiple choice questions and these questions are, in most cases, taken from popular quizzing websites. This practice makes it easier for students to find the correct answer without even studying the subject and increases malpractices. We propose an automatic solution to the issue of making questions that will save time and energy and also promote proper learning with our model "A.Q.U.A - Automated Questions Unique Arrangement. It is a machine learning model that uses transformers for natural language processing and generating meaningful and understandable questions from the given context. A.Q.U.A will be of great use in online assessments, school level and university level exams, as well as competitive exams. It'll be also helpful for students and learners to take practise tests for a topic and evaluate their knowledge in it. [ABSTRACT FROM AUTHOR]

Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in researchers due to its wide area of applications. To extract the data from underwater and transmit to watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of clustering and data aggregation schemes are to decrease the consumption of energy and prolong the lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm. The simulation results indicate that the proposed scheme performs better in maximizing network lifetime and minimizing energy consumption. [ABSTRACT FROM AUTHOR]