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.

Assistant Professor Dr Ravi Kant Kumar of the Department of Computer Science and Engineering aspires to investigate and design the most effective face recognition system appropriate enough to recognise faces in various unconstraint environments. Recently, his patent application titled A Face Recognition System (Application number: 202241030009) got published. He collaborated with his BTech student Thota Venkata Saai Praneeth, for this project. Facial recognition systems have abundant applications in Face Identification, Automobile Security, Access Control, Immigration, Education, Retail, Healthcare, Image database investigations, Surveillance, and many more.

Abstract

A facial recognition system is used for matching a human face captured in an image or a video frame with a database of faces. The system generally authenticates users by measuring facial features from the stored images. The present disclosure relates to face recognition systems. The envisaged system comprises a repository comprising a set of facial images of a plurality of subjects, a set of threshold values, and a plurality of rules. The system further comprises an image capturing unit for capturing a plurality of images of a subject’s face. A segmentation unit divides each image into a plurality of frames. The system further comprises a classifier that receives the frames and classifies frames containing faces from frames containing non-faces from the other frames. The facial features of each frame are extracted by an extractor. An analyser receives the extracted facial features and computes eigenvector values of the facial features of the frame. A comparator compares the computed eigenvector values with threshold values using the comparison rules to identify the subject.

Explanation of the research

Conventional facial recognition systems are configured to identify the contours of a person’s image captured by a camera or in real-time and compare it with the stored image to identify the person. However, external factors such as position, light conditions, camera calibration, and unconstraint conditions, among others, affect the identification of the face of the subject. Hence, comparing the faces with the help of the contours is not reliable. The present disclosure envisages a face recognition system. The system comprises a repository, an image capturing unit, a segmentation unit, a classifier, an extractor, an analyser, and a comparator. The repository consists of a set of facial images of a plurality of subjects, wherein the images define the subject’s facial features in different facial poses. Each image is tagged with an identity, a threshold integral value, and threshold eigenvector values corresponding to the facial features. The repository further comprises a first set of deep learning rules for classifying frames of a captured image into frames containing faces and frames containing non-faces, a set of extraction rules for extracting facial features from the frames 20 containing faces, the second set of deep learning rules for extracting eigenvector values of facial features of the frames containing faces. The repository additionally comprises a set of comparison rules for comparing eigenvector values of the captured facial features with the stored threshold eigenvector values to identify a subject’s face in the captured image.

Publishing papers in Q1 journals is a remarkable accomplishment for research enthusiasts. Assistant Professor Dr Priyanka of the Department of Computer Science and Engineering published her latest research paper in the IEEE Access journal. The paper titled PRMS: Design and Development of Patients’ E-Healthcare Records Management System for Privacy Preservation in Third Party Cloud Platforms has an impact factor of 3.476. Kirtirajsinh Zala, Hiren Kumar Thakkar, Rajendrasinh Jadeja, Priyanka Singh, Ketan Kotecha, and Madhu Shukla co-authored the work with Dr Priyanka.

Abstract

In the digital era, personal data storage on public platforms is a significant cause of concern with severe security and privacy ramifications. This is true especially in e-health data management since patients’ health data must be managed following a slew of established standards. The Cloud Service Providers (CSPs) primarily provide computing and storage resources. However, data security in the cloud is still a major concern. To overcome e-healthcare records privacy issues in a third-party cloud, we designed a Patient’s E Healthcare Records Management System (PRMS) that focuses on latency and throughput. Moreover, the proposed PRMS system is compared with Blockchain platforms such as Hyperledger Fabric v0.6 and Etherium 1.5.8 against latency and throughput by adjusting the workload for each platform up to 10,000 transactions per second. The proposed PRMS is compared to the Secure and Robust Healthcare Based Blockchain (SRHB) approach using Yahoo Cloud Serving Benchmark (YCSB) and small bank datasets. The experimental results indicate that deploying PRMS on Amazon Web Services decreases System Execution Time (SET) and the Average Delay (AD) time by 2.4%, 8.33%, and 25.15%, 15.26%, respectively. Deploying PRMS on the Google Cloud Platform decreases System Execution Time (SET) and Average Delay (AD) by 2.27%, 2.4%, and 2.72%, 4.73% AD, respectively.

The research team from the Department of Computer Science and Engineering proposes a research scheme to address security concerns in the transmission of digital images of aerial Remote sensing images over the Internet. Assistant Professor Dr Priyanka, Assistant Professor Dr Jatindra Kumar Dash, research scholar Ms K Jyothsna Devi, and BTech student Mr. M V Jayanth Krishna, published the paper A New Robust and Secure 3-Level Digital Image Watermarking Based on G-BAT Hybrid Optimization in the Mathematics Journal SCI, a Q1 Journal with an Impact Factor of 2.9. The research project combats various threats in the transmission of Remote sensing images, such as copyright protection, copy control, and unauthorized access.

Abstract

This contribution applies tools from the information theory and soft computing (SC) paradigms to the embedding and extraction of watermarks in aerial remote sensing (RS) images to protect copyright. By the time 5G came along, Internet usage had already grown exponentially. Regarding copyright protection, the most important responsibility of the digital image watermarking (DIW) approach is to provide authentication and security for digital content. The main goal of the paper is to provide authentication and security to aerial RS images transmitted over the Internet by the proposal of a hybrid approach using both the redundant discrete wavelet transform (RDWT) and the singular value decomposition (SVD) schemes for DIW. Specifically, SC is adopted in this work for the numerical optimisation of critical parameters. Moreover, 1-level RDWT and SVD are applied to digital cover images and singular matrices of LH and HL sub-bands are selected for watermark embedding. Further selected singular matrices S LH and S HL are split into 3 × 3 non-overlapping blocks, and diagonal positions are used for watermark embedding. Three-level symmetric encryption with a low computational cost is used to ensure higher watermark security. A hybrid grasshopper–BAT (G- BAT) SC-based optimization algorithm is also proposed to achieve high-quality DIW outcomes, and a broad comparison against other methods in the state-of-the-art is provided. The experimental results have demonstrated that the proposal provides high levels of imperceptibility, robustness, embedding capacity, and security when dealing with DIW of aerial RS images, even higher than the state-of-the-art methods.

The proposed scheme is easily dumped into the sender and receiver machines to work readily. MATLAB platform is the only requirement. Researchers aspire to design new image watermarking schemes using machine learning and deep learning techniques. For this project, they have collaborated with Professor José Santamaría from the Department of Computer Science, University of Jaén, and Professor Antonio Romero-Manchado from the Department of Cartographic Engineering, Geodesy, and Photogrammetry, University of Jaén.

The Department of Computer Science and Engineering is glad to announce that Assistant Professor Dr Saleti Sumalatha and her students got two of their patent applications published in a row. The patent titled “System and method for mining of constraint based high utility time interval sequential patterns” (Application number: 202241044001) was published in collaboration with the BTech students; K Rasagna, N Naga Sahithya, K Hemalatha, B Sai Charan, and Upendra Karthik.

The main intention of the proposed system is to discover the sequences that include the time period between the purchases of each item. For example, if we consider a shop which sells some groceries like Grains, Milk, Yogurt, Bread and Eggs as the set of items in the database. Now, the main aim is to find the time period between the purchases of particular items that are being sold. From this, the shop owner can easily maintain the stock of completed items according to the time period.

For example, an output sequential pattern including time intervals of the form indicates that a customer who purchased item x also bought item y after three months and visited the store again after five months to buy item z. It considers the same utility threshold for each of the items in the database, which shows that each item is assumed to have the same unit profit. This is not convincing as each item is different in real-time applications and should not be treated equally. For example, the sales of” Gold bangles” will produce more profit than the sales of” Cotton Jeans”. In view of this, the research proposes UIPrefixSpan-MMU.

The other patent titled “A system and a method for automatic essay grading” (Application number: 202241043045) was published in collaboration with M Purnima, G Haveela, K U Meghana, and S Deepthi Reddy. Essay grading systems are being adopted by different organisations to reduce the hectic workload from a teacher’s point of view. They made a model which is trained with datasets containing different essay topics and numerous essays with scores varying in a wide range.

Essay grading systems will not only save the time for evaluation but also give accurate results. The output of the system will be quick such that it could evaluate many essays and get trained. This system benefits both the student and the teacher as well. Their model predicts the scores of the essay by comparing them with the features extracted from the trained data. This model can be used to reduce the effort of teachers to grade many essays in a limited time. The work of grading will be fastened and accurate.

The Department of Computer Science and Engineering is delighted to announce that Assistant Professors, Dr Sambit Kumar Mishra and Dr Tapas Kumar Mishra, and the final year BTech students – Kotipalli Sindhu, Mogaparthi Surya Teja, Vutukuri Akhil, Ravella Hari Krishna, and Pakalapati Praveen – published a chapter titled Applications of Federated Learning in Computing Technologies in the scopus-indexed book titled Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation, a Wiley publication.

The book chapter describes the application of federated learning to various computing technologies. The federated learning concept is similar to the client-server model, where the client sends data to the server for processing and the processed data is again sent to the client. But, in federated learning, the clients are allowed separately to teach the deep neural network models with the local data combined with the deep neural network model at the central server.

Federated learning is a machine learning technique that trains the knowledge across different decentralized devices holding samples of information without exchanging them. The concept is additionally called collaborative learning. Researchers have used large frameworks for all the computations in the past years, and then they have moved to client-server frameworks.

It is also like a traditional centralised machine learning technique. All the local datasets are uploaded to a minimum of one server, so it assumes that local data samples are identically distributed. Because of its security and privacy concerns, it’s widely utilised in many applications like IoT, cloud computing; Edge computing, Vehicular edge computing, and many more. In the chapter, different applications of federated learning, their privacy concerns, and their definition in various fields of computing technologies like IoT, Edge Computing, Cloud Computing, Vehicular edge computing, etc. are presented. It will be of advantage to graduate students, researchers, academicians, institutions, and professionals that are interested in exploring the areas of intelligent computing systems.