Two research papers from the Department of Computer Science and Engineering were presented at the 4th International Conference on Energy, Power, and Environment held from April 29 to May 1, 2022. Assistance professor V M Manikandan and three BTech students participated in the conference organised by NIT Meghalaya, India. The papers will be published in IEEE Xplore Digital Library (Scopus Indexed).

Research-paper-CSEThird-year BTech CSE student Harshad Dhane presented the paper A Novel High Capacity Reversible Data Hiding through Encryption Scheme by Permuting Encryption Key and Entropy Analysis, co-authored by Palak Agarwal, third-year BTech student, and Dr V M Manikandan. The reversible data hiding scheme proposed by the research can be used in the healthcare sector to transmit electronic patient reports along with medical images. Improving the embedding rate of the reversible data hiding is the further plan of the researchers.

Explanation of the research

A Reversible Data Hiding Through Encryption (RDTE) scheme will consider an original image and a sequence of bits as the input and generate an encrypted image as the output. This encrypted image will be able to transmit through the network securely, and the authorized receiver can take out the hidden details along with the restoration of the actual image. This paper proposes a new RDTE scheme with a good rate of embedding without any issues during the restoration of the original image. The researchers used the well-known RC4 pseudo-random generator for the image encryption and performed data hiding during block-wise image encryption. In the proposed scheme, the original image is considered non-overlapping blocks of size BXB pixels, and these blocks will be encrypted using a sequence of pseudo-random integers. During the RDTE process, all the possible unique permutations of the encryption key, K, will be generated, say (K0, K1,…, KN). Further, the sender will be capable of embedding one integer value from the set {0, 1, …,N} in a selected image block. A selected block will be encrypted using the pseudo-random sequence of integers using the key K_Q to embed the integer Q in the selected block. The proposed scheme prefers to select keys with unique characters with sufficient length to ensure the maximum embedding capacity. The message extraction and image restoration are performed by analysing the entropy measure from each block after attempting the decryption.

Research-srmapThe paper presented by second year BTech CSE student Sri Satya Maram is titled A Novel System for Automated Coloring of Neat Sketches and was co-authored by Dr V M Manikandan. The research introduces a new algorithm to colour a given neat sketch. The proposed algorithm can be used to colour drawings to create animated movies or to colour the designs developed by the designers. The researchers plan to develop artistic features on the coloured image for better visual appearance.

Explanation of the research

The process of colouring neat sketches is a significant activity when making animated movies or for better visualization in computer modelling. The colour filling tools are widely available in almost all the image/video editing software, which will help us pick a colour from a colour palette and can be filled in a selected region. This process is known as flat colouring. The flat colouring process has several challenges. One of the significant challenges is that the colour may leak from the selected regions to neighbouring regions if there are some small openings on the contours. The second concern while using flat colouring is that the designated areas will be filled entirely with the same colour, so the drawing will not have an artistic look. The research proposes a new software application that will take a neat sketch as the input, and the system will generate a coloured drawing as the output. In the proposed scheme, the researchers have converted the given sketch to a grayscale or binary image and applied image dilation operation to fill the small open spaces in the contours (if any). Further, the closed regions are identified and coloured with a predefined set of colours or random colour combinations. While colouring the regions, the proposed system will ensure that the adjacent regions will not be coloured with the same colours. A number of sketches have been considered during the experimental study, and the results are validated manually.

Chapter publication-CSE-transmission of medical imagesComputational and artificial intelligence is enjoying an unparalleled relevance in the modern world. They improve people’s lives and are highly anticipated in the healthcare industry. Research in this domain is hugely appreciated by the contemporary world, considering its potential to create revolutionary changes in the health care system. The Department of Computer Science and Engineering is delighted to inform you that the paper Robust, Reversible Medical Image Watermarking for Transmission of Medical Images over Cloud in Smart IoT Healthcare has been accepted for publishing as a chapter in the book Predictive Analysis in Cloud, Fog and Edge Computing and Practice of Blockchain, IoT and 5G.

The paper was submitted by Assistant Professor Dr Priyanka S, her PhD student Ms Jyothsna Devi, and MTech student Mr Jayant Krishna. The book chapter for the edited book is entitled Predictive Data Security using Ai – Insights and Issues of Blockchain, IoT, and DevOps and is published by Springer Nature. It is a part of the book series, Studies in Computational Intelligence, indexed by SCOPUS.

The book Studies in Computational Intelligence targets to bring together researchers and practitioners in computational intelligence and AI technology, especially those related to the areas of Machine learning, blockchain, multimedia using AI, smart IoT environment and email spam and online surveys, and many more recent emerging fields. The research work mainly provides highly secure, robust medical image transmission over the cloud in a smart IoT healthcare environment to ensure high embedding capacity and integrity.

This book’s target audience comprises professionals and researchers working in the field of computational intelligence and AI for health services in a smart environment. The book will attract Engineers (computer, industrial, software, and others), health care scholars, and information scientists since it caters to their interests.

Omkar Mani Kanteswar -Bsc Integrative Biology Final year BSc (Hons) Integrative Biology student Mr Omkar Mani Kanteswar has received an admission offer for the Master of Science in Biology at the University of Memphis (UofM), USA. Omkar’s happiness has no bounds as he really worked hard to get into his dream university.

The University of Memphis is a public research university in Memphis, Tennessee. Since Omkar is from a Bsc Biology background, he wanted to continue in the same research field. While shortlisting the universities, he found this research-oriented college. He passed the Duolingo test and IELTS to get into this college.

The Master of Science (MS) in Biology is a research-centred degree programme with an intensive core and elective curriculum. Omkar opted for this course owing to the fact that he wanted to continue his research in the field of Biology. His message for the junior batches is to be confident and independent to make their own decisions and not run with the herd. He urges them to focus on holistic development and extracurricular activities. He is also ready to provide support to them if they need any.

He extended his gratitude to the faculty members who helped him a lot in the process by giving him confidence. He was glad to have them because they consistently and positively helped him in his academics and choosing various colleges. Faculty from the Department of Biological Sciences- Prof Jayaseelan Murugaiyan (HOD), Assistant Professor Sutharsan Govindarajan, and Prof Imran Pancha hold significant positions in Omkar’s career venture.

Admission to a reputed institute means a sense of pride, the joy of knowing you would study the best things with the best ones. Omkar believes that this is just the beginning, and he is yet to do a lot more things with his precious life.

The power of merging art with science is beyond our imagination. This amalgamation can pull off things that may seem insurmountable without the assistance of the other. Professor Hiren Deva Sarma, Guest faculty of the Department of Computer Science and Engineering, has developed a computational technique to find the similarity between the given songs in a pool. His paper titled An Approach to Discover Similar Musical Patterns has been published in IEEE ACCESS, a Q1 journal with an impact factor of 3.36.

Abstract

music recommendation systemAn algorithm has been developed to find the similarity between given songs. The song pattern similarity has been determined by knowing the note structures and the fundamental frequencies of each note of the two songs under consideration. The statistical concept, Correlation of Coefficient, is used in this work. The correlation of Coefficient is determined by applying the 16 Note-Measure Method. If the Correlation of Coefficient is near 1, it indicates that the patterns of the two songs under consideration are similar. Otherwise, there exists a certain percentage of similarity only. This basic principle is used in a set of Indian Classical Music (ICM) based songs. The proposed algorithm can determine the similarity between songs, so alternative songs in place of some well-known songs can be identified in terms of the embedded raga patterns. A digital music library has been constructed as a part of this work. The library consists of different songs, their raga name, and their corresponding healing capabilities in terms of music therapy. The proposed work may find application in the area of music therapy. Music therapy is an area of research that has been explored significantly in recent times. This work can also be exploited for developing an intelligent multimedia tool applicable in the healthcare domain. A multimedia-based mobile app has been developed encapsulating the abovementioned idea that can recommend alternative or similar songs to the existing ICM-based songs. This mobile app-based music recommendation system may be used for different purposes, including entertainment and healthcare. As a result of the applications of the proposed algorithm, similar songs in terms of raga patterns can be discovered from within the pool of a set of songs. A Music Recommendation System built on this algorithm can retrieve an alternative song from within the pool of songs as a replacement to a well-known song, which otherwise may be used for particular music therapy. Results are reported and analysed thoroughly. The future scope of the work is outlined.

Explanation of the research

A computational technique has been developed to identify a particular song similar to another in terms of its embedded raga pattern. Indian Classical Music (ICM) based songs are considered in this work. As a result of the application of the proposed technique, it is possible to identify similar songs in terms of their raga patterns from within a pool of songs. Subsequently, a similar alternative song can be recommended for different applications, including music therapy. If we consider music therapy, an alternative medicine (note: here, medicine is the song) is possible to recommend due to the proposed technique. This algorithm will find many applications in the domain of music information retrieval (MIR) and music recommendation systems (MRS).

Practical implementation of the research

music retrieval systemThis algorithm may be applied in recommending music in music recommendation systems. Moreover, music information retrieval based on raga patterns can be an important domain where the proposed algorithm may be exploited. Considering the social implications, music therapy has been the intended area of the research; therefore, this algorithm has been developed considering numerous applications of music therapy based on Indian Classical Music. The music therapy community will be benefited from the proposed algorithm.

In this research project, Professor Hiren Deva Sarma has collaborated with; Assistant Professor Sudipta Chakrabarty, Techno India, Salt Lake at the Department of Master of Computer Application; Mr Ruhul Islam, IT Consultant, Cloud Shine Global LLP; and Emil Pricop, Associate Professor in the Department of Automatic Control, Computers and Electronics, Petroleum-Gas University of Ploiesti, Romania.

In the future, the researchers look forward to exploring the music therapy capabilities of Indian Folk Music (IFM) like Kamrupia Lokgeet, Goalparia Lokgeet, and Baul Geet. Understanding the similarity and dissimilarity of the above-mentioned folk songs with Indian Classical Music (ICM) from computational musicology perspectives is another objective of the proposed research work. The researchers also aim to develop Music Recommendation Systems (i.e., applications) considering the songs mentioned above (ICM + IFM) and the different requirements of the users.

The research paper, An under complete autoencoder for denoising computational 3D sectional images from the Department of Electronics and Communication Engineering has been accepted in a prestigious conference called Imaging and Applied Optics Congress to be held in Vancouver, Canada 2022. Assistant Professors; Dr Sunil Chinnadurai, Dr Karthikeyan Elumalai, Dr Inbarasan Muiraj, and the PhD students; Ms Vineela Chandra Dodda and Ms Lakshmi Kuruguntla are the authors who contributed to composing the paper.

Abstract

computational 3D sectional images-research-srmapThis paper proposes to use a deep-stacked under complete autoencoder to denoise the noisy 3D integral (sectional) images with a patch-based approach. In this process, the noisy input 3D sectional image is divided into multiple patches, which are then used to train the neural network. By using the patch-based approach, the time required to prepare the labeled training data is greatly reduced. Results demonstrate the feasibility of our proposed model in terms of the peak-signal-to-noise ratio.

computational 3D sectional images-research-srmapExplanation of the research

Denoising is one of the preliminary processes in image processing that removes noise from an image of interest and restores a clean image. The noise which was generated during the image acquisition process is attenuated using deep learning techniques. The denoised image is further used in various tasks of image processing.

In any image acquisition system, noise is inevitable and needs to be attenuated before further processing for qualitative results. The medical field is an example of this (images acquired through CT, MRI, PET, etc.). The researchers further investigate various techniques in deep learning to improve the denoising performance along with the applicability of deep learning in various tasks such as object recognition etc.

The Electrochemical Society Transactions (ECST) is the official conference proceedings publication of The Electrochemical Society. Recently, a research paper was published in ECST by  Mr Vasudeva Bevara, a PhD scholar of the Department of Electronics and Communication Engineering, under the supervision of Assistant professor Dr Pradyut Kumar Sanki. The paper is titled VLSI Architecture of Decision Based Adaptive Denoising Filter for Removing Salt & Pepper Noise and proposes an innovative concept to restore a highly corrupted digital image.

Abstract

Paper publicationA new Decision Based Adaptive Denoising Filter (DBADF) algorithm and hardware architecture are proposed for restoring the digital image that is highly corrupted with impulse noise. The proposed DBADF detects only the corrupted pixels, and that pixel is restored by the noise-free median value or previous value based upon the noise density in the image. The proposed DBADF uses a 3×3 window initially and adaptively goes up to a 7×7 window based on the noise corruption of more than 50% by impulse noise in the current processing window. The proposed architecture was found to exhibit better visual qualitative and quantitative evaluation based on PSNR, IEF, EKI, SSIM, FOM, and error rate. The DBAMF architecture also preserves the original information of digital image with a high density of salt and pepper noise compared to many standard conventional algorithms. The proposed architecture has been simulated using the VIRTEX7 FPGA device, and the reported maximum post place and route frequency are 149.995MHz, and the dynamic power consumption is 179mW.

We make a life by what we give

blood donor day

Miracles do happen if we are willing to believe in the power of giving. The World Blood Donor Day 2022 calls attention to the significance of voluntary blood donations in saving lives and enhancing solidarity within communities. The day aims to spread gratitude toward blood donors worldwide and create a broader understanding of the need for regular, unpaid blood donation. The day is an opportunity to highlight the urgency of committed and year-round blood donation to ensure adequate resources and achieve universal access.

This World Blood Donor Day, the Department of Student Affairs, in collaboration with Indian Red Cross Society and STEP, Swasakthi, Guntur celebrates the value of voluntary blood donations in improving social cohesion. Smt Vidadala Rajani, Hon’ble Minister for Health and Family Welfare, Andhra Pradesh, will be the chief guest of the day. She is one of the young and dynamic individuals who became an MLA in her first election and created a record as the youngest ever MLA from Chilakaluripet Constituency. Guest of Honour, Shri Alla Ramakrishna Reddy, MLA Mangalagiri, will also grace the occasion with his esteemed presence.

Date: June 14, 2022

Time: 10.30 AM IST

This World Blood Donor Day, donate blood for a reason and be the reason behind someone else’s life. Blood donation is an act of solidarity. Join the effort and save lives!

In recent years, human activity recognition has gained significant attention inside the scientific community. The enhanced spotlight is on the ground of its direct application in multiple domains. The latest research at the Department of Computer Science validates this assumption. Assistant Professor Dr V M Manikandan, and the 4th year B Tech Student Chaitanya Krishna Pasula have published a chapter titled An analysis of human activity recognition systems and their importance in the current era in the book Computational Intelligence Based Solutions for Vision Systems. The book is published by IOP Publishing Ltd.

Explanation of the chapter

Human activity recognition systemHuman activity recognition is one of the most interesting and active research areas in computer vision. More research efforts are being put towards automatically identifying and analysing human activities due to their emerging importance in everyday applications. It serves applications in various areas like security video surveillance, smart homes, healthcare, human-computer interaction, virtual reality, robotics, and digital entertainment. Numerous papers have been published in the domain of human activity recognition. This book chapter discusses the various applications of human activity recognition, different methods available for automatic activity detection from videos, and the advantages of the human activity recognition system. It also describes the challenges in designing and implementing human activity detection schemes. Researchers further explain the publicly available datasets used for training and evaluating the systems for human activity recognition. The efficiency parameters used to evaluate the human activity recognition systems are also briefed in this chapter. The chapter is concluded by comparing the methodologies and speculating the possibilities of future research in this field.

In the future, the researchers are planning to design and implement an activity recognition system to identify abnormal activities in public places for safety purposes. This book chapter will be a helpful reference for UG/PG/Ph.D students who aspire to research in the domain of activity detection from video.

Research at the Department of Physics is currently exploring the potential applications of NdNiO3. Recently, Professor Ranjit Thapa, and his Ph D student, Mr Deepak S Gavali published the paper, Low-Temperature Spin-Canted Magnetism and Bipolaron Freezing Electrical Transition in Potential Electron Field Emitter NdNiO3 in the journal ACS Applied Electronic Materials, with an Impact Factor of 3.314. This work is done in collaboration with the Department of Physics and Astronomy, National Institute of Technology Rourkela, Rourkela, Odisha, India.

About the research

NdNiO3.In this work, NdNiO3 nanoparticles are synthesized by sol-gel auto-combustion techniques, and its primary characterization related to structural and surface morphological analysis is carried out by X-Ray Diffraction (XRD), Fourier Transforms Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive X-ray spectroscopy (EDX), and Transmission Electron Microscopy (TEM) techniques. The research is focused on magnetic phase transition below Curie temperature (TN) ∼176 K, and the magnetic susceptibility indicates a weak antiferromagnetic ordering at low temperature. Different ac conduction mechanisms, that is, Correlated Barrier Hopping (CBH), Continuous-Time Random Walk (CTRW) conduction model, and Non-overlapping Small Polaron Tunneling (NSPT), are introduced to interpret its electrical transport behavior near, above, and below TMI ∼178 K. Using first principles and Density of States (DOS) calculation, the researchers have characterized the electronic and magnetic ground state of NdNiO3 at room temperature. It exposed the overlapping of conduction and valence band at room temperature, and the degree of hybridization between Ni 3d and O 2p is very high compared to Nd 5d states. The work function is also calculated for a few-layer NdNiO3 to estimate the field enhancement factor (β), which plays a crucial role in the practical application of a field emitter.

Practical implications

The additional novelty of the present work is to explore the potential application of NdNiO3 as an efficient field emitter and controlled electron/X-ray sources in a flat panel display, microwave vacuum electronic devices, electron microscopy/ lithography, and so forth. To eject conducting electrons from the metal/semiconducting surface by a quantum mechanical tunneling process, sufficient energy is required in terms of the applied electric field (∼106 to 107 V/cm) to overcome the potential barrier at the vacuum−metal interface. The potential difference between the Fermi level (Ef ) of the metal surface to vacuum is known as the work function (Φ). It depends on material characteristics and plays an essential role in field enhancement capability. The primary requirement for efficient field emitters is high aspect ratios (i.e., field enhancement factor), inferior turn-in field, low work, function, etc. Researchers have examined various classes of materials for efficient field emitter electrodes, such as (i) carbonaceous materials like graphene and carbon nanotube, (ii) various 1D and 2D metal oxide and transition metal dichalcogenides like ZnO, MnO2, In2O3, WS2, WSe2, MoS2, PdSe2, etc., (iii) inorganic semiconductors like SiC and Si, and (iv) wide band gap semiconducting compounds GaN, AIN, and so on. The field emission properties of rare earth nickelates (RNiO3; R = La, Gd, Nd, Sm, etc.) with an exciting room temperature metallic nature have not been examined.

With its vast applications in the industry, computing influential nodes is becoming a popular research field in recent days. The Department of Computer Science and Engineering is delighted to inform you that the paper, Computing Influential Nodes Using Nearest Neighborhood Trust Value and Pagerank in Complex Networks have been published by Dr Murali Krishna Enduri, Assistant Professor, Dr Satish Anamalamudi, Associate Professor, and the PhD students; Koduru Hazarathaiah, Ms Srilatha Tokala in the Entropy Journal (Q2 Journal), with an impact factor 2.587.

Abstract

Influential nodesComputing influential nodes attract many researchers’ attention for spreading information in complex networks. It has vast applications such as viral marketing, social leaders, rumour control, and opinion monitoring. The information spreading ability of influential nodes is more compared with other nodes in the network. Several researchers proposed centrality measures to compute the influential nodes in the complex network, such as degree, betweenness, closeness, semi-local centralities, PageRank, etc. These centrality methods are defined based on the local and/or global information of nodes in the network. However, due to the high time complexity, centrality measures based on the global information of nodes have become unsuitable for large-scale networks. Very few centrality measures exist that are based on the attributes between nodes and the structure of the network. We propose the Nearest Neighbourhood Trust PageRank (NTPR) based on the structural attributes of neighbours and nearest neighbours of nodes. We define the measure based on the degree ratio, the similarity between nodes, the trust value of neighbours, and the nearest neighbours.

Explanation of the research

The research computes the influential nodes on the various real-world networks by using the proposed centrality method NTPR. The researchers find the maximum influence by using influential nodes with SIR and independent cascade methods. They also compare the maximum influence of our centrality measure with the existing basic centrality measures.

Social implications

Viral Marketing is a business strategy that uses existing social networks to promote products. The influential nodes in complex networks can be found using the centrality measure and can be used as the seed nodes for promoting products in the social networks. A rumour is a statement being said without knowing if it is true or not. The rumours can be easily controlled by discovering influential nodes. The researchers look forward to finding a centrality measure to detect the influential nodes efficiently.