Communication revolution and data security
The structure of human society is always profoundly affected by the developments happening in the domain of communication. Data security and privacy have always been a concern in the ongoing communication revolution. The Department of Computer Science and Engineering is glad to inform you that the paper, “An Efficient Spatial Transformation-based Entropy Retained Reversible Data Hiding Scheme in Encrypted Images,” has been published by Dr V M Manikandan, Assistant Professor, and his PhD student Mr Shaiju Panchikkil in “Optik Journal” with an impact factor of 2.443.
Abstract of the research
A critical issue with the current communication revolution is data security and privacy, which is an inevitable part of trustworthiness in the communication system. Hence, the applicability of the Reversible Data Hiding schemes (RDH) in this scenario is encouraging and critical, like medical image communication, satellite image transmission, etc. Earlier, we explored Arnold transform in one of our previous works to hide the secret data that uses the Convolutional Neural Network (CNN) model to design a complete RDH scheme. The proposed scheme follows a statistical approach to support recovering the cover image and the embedded information. This approach proves advantageous over the previous work following its computational capability. The scheme designed can retain the entropy of the encrypted images even after embedding the additional information, complementing the security of the encryption algorithm.
Explanation of the research
The research focuses on hiding information in an encrypted image and transmitting it to the receiver. Earlier, the researchers used the Arnold transform-based image scrambling algorithm to facilitate the data hiding. But at the receiver end, they have used a convolutional neural network model, which acts as a binary classifier to recover the image properly after extracting the hidden information. The researchers had a few overheads over there, like training the model and then sharing the same with the receiver to recover the original image efficiently. To overcome these overheads, they analysed the correlation of neighboring pixels and introduced a statistical measure at the receiver end to recover the exact image.
Social implications of the research
One of the various social implications of the research is an application concerning patient treatment. In a general scenario, during the covid 19 pandemic, people make an online consultation with the doctor by uploading their medical images. If the doctor wants to take a specialist’s opinion, he should send this image and the diagnosis report via a communication medium. The research team’s approach is meaningful in this aspect. The original image is initially encrypted, which makes it unreadable. The diagnosis report information is hidden over the encrypted image. Hence the doctor needs to send only a single file to the specialist. It is also difficult for an external agent or an unauthorized party to decode the report and the image as it is encrypted. Now it is essential to regain the original quality of the recovered image, as any degradation in the quality of the recovered image can lead to a wrong diagnosis. Hence, they have designed the recovery module carefully to extract all the hidden information and recover the original image without compromising its quality.
The researchers are in constant collaboration with Professor Yu-Dong Zhang from the University of Leicester, University Road, Leicester, LE1 7RH, UK, to introduce new strategies to elevate the embedding capacity from the current level without negotiating the quality of the recovered image.
- Published in CSE NEWS, Departmental News, News, Research News
Interdisciplinary research and the integration of perspectives
Assistant professors Dr Sabyasachi Mukhopadhyay and Dr Imran Pancha from the Department of Physics and the Department of Biological Sciences, respectively, along with Ms Ashwini Nawade, a PhD student of the Department of Physics, have developed a method to integrate plant proteins in the solid-state electronic circuits and utilize the biological functionality to produce a thin film, cost-effective photodetector. Their paper entitled Electron Transport across Phycobiliproteins Films and its’ Optoelectronic Properties has been published in the ECS Journal of Solid State Science and Technology with an Impact Factor of 2.07. It is an interdisciplinary research project between the Department of Biotechnology and Physics.
Explanation of the research
Biomolecules such as proteins, peptides being the most crucial life-forms, have an intimate relationship with various life activities for biological functions. The contemporary work with biomolecules mainly focuses on its evolving potential associated with nanoscale electronics where proteins and peptides are integrated as sensing materials. The researchers explored the optoelectronics functionality of combined proteins known as phycobiliproteins. They investigated electron transport behavior across the phycobiliproteins films under dark and white light illumination. The research affirms that the photochemical activity of the protein is more stable in a solid-state/ thin-film with tightly bonded water molecules than its presence in a buffer solution. Furthermore, the studies demonstrate that phycobiliproteins films modulate their electrical conductivity within their different conformation states. Researchers speculate that the electrical conductance variation could originate from the chemical alteration of cysteine-conjugated bilin chromophores to protein and the electrostatic environment around the chromophores.
The research explores the photochemical properties and electrical transport efficiency of phycobiliproteins (PBPs) films. In addition, it investigates the optoelectronics functionality of PBPs films by studying electron transport behavior across the protein films under a dark state and white light illumination. The researchers proposed to develop a photodetector with the protein film in the future.
- Published in Biology News, Departmental News, News, Physics News, Research News
Metaheuristics attribute selection model for efficient diagnosis of chronic disorders
The technological advancements in the medical domain have aided in the effective collection of data such as personal information, clinical history, and disease symptoms of patients. However, the accumulation of massive quantity of data may cause errors in the diagnosis of the disease. A chronic disease dataset may be comprised of numerous symptoms and attributes where not all of them are of equal importance in disease diagnosis. A few of those attributes may be less relevant or redundant.
Through her research, Dr Priyanka from the Department of Computer Science and Engineering proposes metaheuristics driven attribute optimization techniques that can be implemented in optimizing chronic disease datasets to achieve optimal efficiency in disease risk prediction which can help in proper medical diagnosis. Her paper titled “A Decisive Metaheuristics Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risks Assessment” was published in the Q1 journal Computational Intelligence and Neuroscience.
This research can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner. The system model may be used to assist medical experts in the efficient diagnosis of chronic disease risks. In future, the research study can be further enhanced to validate the model on more complex heterogeneous datasets with varying sizes and structures. Also, deep learning methods can be employed using image-based real-time datasets.
Abstract of the Research
Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in precise assessment of chronic disorders risks in patients. In this paper, a novel buffer enabled heuristic a Memory based Metaheuristics Attribute Selection (MMAS) model is proposed, which performs local neighbourhood search for optimizing chronic disorders data. Heart disease, breast cancer, diabetes and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall and f-score metric computed were 96.05%, 94.07% and 95.06% respectively. The model also has a least latency of 0.8 sec. Thus it is demonstrated that chronic disease diagnosis can be significantly improved by heuristics based attribute selection coupled with clustering followed by classification.
Figure 1: The proposed Metaheuristics attribute selector based classification model for chronic disorders detection
Figure 2: Accuracy analysis of MMAS method on different disease datasets
- Published in Computer Science News, Departmental News, News, Research News
Ultra-stable perovskite nanocrystals for light-emitting applications
Cesium lead halide perovskite nanocrystals (PNCs) belong to the flourishing research area in the field of photovoltaic and optoelectronic applications because of their excellent optical and electronic properties. Mainly, Cesium lead bromide (CsPbBr3) NCs with bright green photoluminescence (PL) and narrow full-width at half-maximum (FWHM) of <25 nm are the most desirable for television displays and green-emitting LEDs. However, challenges with respect to CsPbBr3 PNCs‘ stability, limit their usage in practical applications. The recent findings of Dr Nimai Mishra and his research team assert that surface passivation with an additional ligand could be an excellent, easy, and facile approach to enhancing the photoluminescence and stability of PNCs.
Dr Nimai Mishra, Assistant Professor, Department of Chemistry, along with his research group comprising of students pursuing PhD under him, Dr V G Vasavi Dutt, Mr Syed Akhil, Mr Rahul Singh, and Mr Manoj Palabathuni have published their research article titled “Year-Long Stability and Near-Unity Photoluminescence Quantum Yield of CsPbBr3 Perovskite Nanocrystals by Benzoic Acid Post-treatment“ in The Journal of Physical Chemistry C (A Q1 journal published by ‘The American Chemical Society’) having an impact factor of ~4.2.
In this article, the research group addresses the stability issues of green-emitting CsPbBr3 PNCs with simple post-treatment using benzoic acid (BA). A remarkable improvement in PLQY from 69.8% to 97% (near unity) was observed in benzoic acid-treated CsPbBr3 PNCs. The effective surface passivation by benzoic acid is also apparent from PL decay profiles of BA-CsPbBr3 PNCs. The long-term ambient stability and stability against ethanol of BA-CsPbBr3 PNCs are also well presented in the research. The PL intensity of untreated CsPbBr3 PNCs is completely lost within five months since the synthesis date, while ̴ 65% of initial PL intensity is preserved for BA-CsPbBr3 PNCs even after one year.
Furthermore, BA-CsPbBr3 PNCs exhibits excellent photo-stability where 36% of PL is retained while PL is completely quenched when the PNCs are exposed to 24 hours of continuous UV irradiation. Importantly, BA-CsPbBr3 PNCs show excellent stability against ethanol treatment as well. Finally, green, emitting diodes using BA-CsPbBr3 PNCs are fabricated by drop-casting NCs onto blue-emitting LED lights. Thus a simple benzoic acid posttreatment further presents the scope of use of these materials display technologies.
- Published in Chemistry-news, Departmental News, News, Research News