Merging the calibre of two well-fledged technologies will massively impact the momentum of social life. Decoding the possible links between promising technologies would employ solutions to various societal issues. Dr Ashok Kumar Pradhan, Associate Professor of the Department of Computer Science and Engineering, gave life to this thought by publishing a book titled Intelligent Systems for Social Good Theory and Practice. He published this work as an editor in a book series named Advanced Technologies and Social Change by Springer Nature.

The book highlights the connection between the two technologies: Artificial intelligence (AI) and the Internet of Things (IoT). It shows the better impact of the relation between these technologies in society, using real-world examples. Each chapter in the book proposes novel solutions to societal problems along with the challenges in the application of AI and IoT to solve them. The adverse attacks on Machine Learning models and how to protect sensitive data over the IoT network are discussed in the book.

The book is significant to Dr Ashok Kumar Pradhan as applying the two technologies mentioned helps resolve various social problems related to healthcare, agriculture, green environment, renewable energies, smart cities, etc.

Shyamapada Mukherjee and Naresh Babu Muppalaneni from NIT Silchar and Sukriti Bhattacharya from Luxembourg Institute of Science and Technology, Belvaux, have worked together with Dr Ashok for this work. The book’s target audience is undergraduate, master’s, and doctoral students from Science and Engineering backgrounds.

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Presenting papers at international research conferences helps hone the research questions. Students from the Department of Computer Science and Engineering have attended two international research conferences and presented their papers drafted under the supervision of Assistant Professor V M Manikandan.

The International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET- 2022), organised by NIT Patna, India, was held from June 24 to 25. At the conference, BTech students Nitesh Bharti and Mohit Kumar presented the paper A Hybrid System with Number Plate Recognition and Vehicle Type Identification for Vehicle Authentication at the Restricted Premises. The work was composed under the guidance of Assistant Professor Dr V M Manikandan. The paper will soon be published in IEEE Xplore Digital Library (Scopus indexed). In the future, they plan to integrate the proposed algorithms with proper hardware units to completely automate the authentication of vehicles in restricted areas. The proposed computer vision-based systems can be used in restricted areas to ensure the entry of authenticated vehicles.

Explanation of the research

Vehicle detection and number plate recognition approaches have been widely studied in recent years due to their wide applications. The research paper proposes a framework to ensure the entry of authorised vehicles in restricted areas such as University campuses, townships, etc., where the researchers are expecting the entry of a set of authorized vehicles. Certainly, unauthorised vehicles might be allowed to enter those areas after proper verification by the concerned people responsible for ensuring security. In the proposed approach, the admin should register all the authorised vehicles in a system with the essential attributes such as vehicle number, type, etc. A surveillance camera placed at the entrance will capture live videos. When a vehicle is in the camera view, the image frames will be passed to an automatic number plate recognition module. The number plate recognition module will identify the same and be matched with the details in the database to authorise the vehicle. This manuscript proposes a real-time and reliable approach for detecting and recognising license plates based on morphology and template matching. To ensure the system’s reliability, a frame selection module will select the image frames with high quality, and even to improve the number plate recognition accuracy, the image will be enhanced using image enhancement techniques such as histogram equalisation. The image enhancement techniques will help to provide better results even though the videos are taken in low lighting conditions. Further, we ensure that the vehicle type matches the number present in the database to prevent unauthorised access using fake number plates. The experimental study is conducted using videos taken under various environmental conditions such as lighting, slope, distance, and angle.

Jahnavi Kolli presented her research paper, An Efficient Face Recognition System for Person Authentication with Blur Detection and Image Enhancement, at the International Conference on Sustainable Technology for Power and Energy Systems (STPES). The conference was organised by NIT Srinagar and IIT Jammu, India, and was held from July 4 to 8, 2022. The research work was monitored by Assistant Professor V M Manikandan and done in collaboration with Professor Yu-Chen Hu, Providence University, Taiwan. The proposed computer face recognition systems can be used to record the attendance of students in class or employees in the office in an easy way. In the future, the researchers plan to improve the face recognition systems, which will perform better when the images are captured using low-resolution cameras or the face regions occluded for some reasons.

Explanation of the research

The recent advancements in technology widely help to substitute manpower with machines in a better way. Even though machines are increasingly replacing humans in various ways, there are still a few areas where the use of machines still needs to be explored much more efficiently. Facial recognition systems are one such field. Facial recognition systems are used with various motives, such as identification of suspects in public places, authentication of users on restricted premises, etc. In this work, we propose a facial recognition system to facilitate the authentication of students at the university entrance. The same scheme can also be utilised to authenticate the students before entering examination halls. As the strength of the students at universities increases in a more significant way, it becomes strenuous for the security people to record their attendance manually, which frequently results in erroneous data. This paper proposes a facial recognition system that will help to capture the live videos from an area of interest and identify the faces. Further, a face recognition scheme will detect whether the person is authorised or not. Several facial recognition systems are already available in the literature, and this scheme differs from them in many ways. The proposed method selects the frames with less blur for face detection and further face recognition. A blur detection scheme is used in the proposed system to analyse the amount of blur in the image. To overcome the challenges such as low accuracy during face recognition when the images are taken in low lighting conditions, we use a histogram equalization method to enhance the quality. The experimental study shows that the proposed approach works well in real-time.