Ipsit Misra’s scientific contributions

The widespread usage of cars and other large, heavy vehicles necessitates the development of an effective parking infrastructure. Additionally, algorithms for detection and recognition of number plates are often used to identify automobiles all around the world where standardized plate sizes and fonts are enforced, making recognition a effortless task. As a result, both kinds of data can be combined to develop an intelligent parking system centered on ANPR technology. Extraction of the license plate characters from a photo is the primary objective of ANPR. Typically, this procedure is expensive. In this work, we introduce Chaurah, a low-cost Raspberry-Pi 3 based ANPR system designed especially for parking facilities. The system uses two stages of technique, the first of which is an ANPR system that uses two convolutional neural networks (CNNs). The first one uses a vehicle image to find and recognize license plates, while the second one uses optical character recognition to extract the licence plate numbers. The second step of the solution consists of a user-facing application made with Flutter and Firebase for database management in order to compare licence plate records. The app also functions as an interface for a payment system depending on the length of parking time, making it a full software embodiment of the concept.

p>The widespread usage of cars and other large, heavy vehicles necessitates the development of an effective parking infrastructure. Additionally, algorithms for detection and recognition of number plates are often used to identify automobiles all around the world where standardized plate sizes and fonts are enforced, making recognition a effortless task. As a result, both kinds of data can be combined to develop an intelligent parking system centered on ANPR technology. Extraction of the license plate characters from a photo is the primary objective of ANPR. Typically, this procedure is expensive. In this work, we introduce Chaurah, a low-cost Raspberry-Pi 3 based ANPR system designed especially for parking facilities. The system uses two stages of technique, the first of which is an ANPR system that uses two convolutional neural networks (CNNs). The first one uses a vehicle image to find and recognize license plates, while the second one uses optical character recognition to extract the licence plate numbers. The second step of the solution consists of a user-facing application made with Flutter and Firebase for database management in order to compare licence plate records. The app also functions as an interface for a payment system depending on the length of parking time, making it a full software embodiment of the concept.</p

p>The widespread usage of cars and other large, heavy vehicles necessitates the development of an effective parking infrastructure. Additionally, algorithms for detection and recognition of number plates are often used to identify automobiles all around the world where standardized plate sizes and fonts are enforced, making recognition a effortless task. As a result, both kinds of data can be combined to develop an intelligent parking system centered on ANPR technology. Extraction of the license plate characters from a photo is the primary objective of ANPR. Typically, this procedure is expensive. In this work, we introduce Chaurah, a low-cost Raspberry-Pi 3 based ANPR system designed especially for parking facilities. The system uses two stages of technique, the first of which is an ANPR system that uses two convolutional neural networks (CNNs). The first one uses a vehicle image to find and recognize license plates, while the second one uses optical character recognition to extract the licence plate numbers. The second step of the solution consists of a user-facing application made with Flutter and Firebase for database management in order to compare licence plate records. The app also functions as an interface for a payment system depending on the length of parking time, making it a full software embodiment of the concept.</p

p>The world around us has undergone a radical transformation due to rapid technological advancement in recent decades. The industry of the future generation is evolving, and artificial intelligence is the next change in the making popularly known as Industry 4.0. Indeed, experts predict that artificial intelligence will be the main force behind the following significant virtual shift in the way we stay, converse, and study, live, communicate, and conduct business (AI). All facets of our social connection are being transformed by this growing technology. One of the newest areas of educational technology is Artificial Intelligence in the field of Education (AIED). This study emphasis the different applications of Artificial Intelligence in education from both an industrial and academic standpoint. It highlights the most recent applications of AIED, with some of its main areas being the reduction of instructors' burden and students' contextualized learning novel transformative evaluations, and advancements in sophisticated tutoring systems. It analyses the AIED's ethical component and the influence of this transition on people, particularly students and instructors as well. Finally, the article touches on AIEd's potential future research and practices. The goal of this study is to introduce the present day applications of AIEd-based applications to its intended audience.</p

p>The world around us has undergone a radical transformation due to rapid technological advancement in recent decades. The industry of the future generation is evolving, and artificial intelligence is the next change in the making popularly known as Industry 4.0. Indeed, experts predict that artificial intelligence will be the main force behind the following significant virtual shift in the way we stay, converse, and study, live, communicate, and conduct business (AI). All facets of our social connection are being transformed by this growing technology. One of the newest areas of educational technology is Artificial Intelligence in the field of Education (AIED). This study emphasis the different applications of Artificial Intelligence in education from both an industrial and academic standpoint. It highlights the most recent applications of AIED, with some of its main areas being the reduction of instructors' burden and students' contextualized learning novel transformative evaluations, and advancements in sophisticated tutoring systems. It analyses the AIED's ethical component and the influence of this transition on people, particularly students and instructors as well. Finally, the article touches on AIEd's potential future research and practices. The goal of this study is to introduce the present day applications of AIEd-based applications to its intended audience.</p