Department of Computer Science and Engineering

Identity verification is essential in both an individual’s personal and professional life. It confirms a person’s identity for various services and establishes their legitimacy as an employee within an organization. As cybercrime evolves and becomes more sophisticated, ensuring robust, and secure personal authentication methods has become a critical challenge. Existing face-based authentication systems typically employ deep learning models for user verification. However, these systems are susceptible to various attacks, such as presentation attacks, 3D mask attacks, and adversarial attacks that exploit and deceive the models by manipulating digital representations of human faces. Although various liveness detection techniques have been proposed to combat face spoofing in face-based authentication systems. However, these systems remain vulnerable and can be exploited by sophisticated techniques. To counteract face spoofing in a face-based authentication system, we have proposed an advanced liveness detection technique using Visual Speech Recognition (VSR). The proposed VSR model is designed to integrate seamlessly with face-based authentication systems, forming a dual authentication framework for enhanced liveness detection. The VSR model decodes silently pronounced speech from video by analyzing unique, unforgeable lip motion patterns into textual representation. Although, various liveness detection techniques have been proposed to combat face spoofing in face-based authentication systems. However, these systems remain vulnerable and can be exploited by sophisticated techniques. To counteract face spoofing in a face-based authentication system, we have proposed an advanced liveness detection technique using VSR. The proposed VSR model is designed to integrate seamlessly with face-based authentication systems, forming a dual authentication framework for enhanced liveness detection. The VSR model decodes silently pronounced speech from video by analyzing unique, unforgeable lip motion patterns into textual representation. To achieve effective liveness detection using VSR, we need to enhance the accuracy of the VSR system. The proposed work employs an encoder-decoder technique to extract more robust features from lip motion. The encoder employs a three-dimensional convolution neural network (3D-CNN) combined with a fusion of bi-directional gated recurrent units and long short-term memory (BiGRU-BiLSTM) to effectively capture spatial-temporal patterns from lip movement. The decoder integrates Multi-Head Attention (MHA) with BiGRU-BiLSTM to effectively focus on relevant features and enhance contextual understanding for more accurate text prediction. The proposed VSR system achieved a word error rate (WER) of 0.79%, demonstrating a significant reduction in error rate and outperforming compared to the existing VSR models.

Facial biometric systems are extensively applied in diverse sectors for the purposes of person authentication and verification, primarily due to the distinctive nature of individual facial characteristics. Deep learning models are typically used in face authentication to validate people with excellent recognition accuracy. However, these systems are susceptible to a variety of cyber attacks that manipulate the digital representations of real-world faces to cheat the models. In the contemporary landscape of digital identity theft, liveness detection stands as a crucial technology. The need for enhanced security prompts the demand for a resilient system that can effectively counter face spoofing attempts and prevent unauthorized access. A Binary Authentication Protocol (BAP) technique is proposed to enhance facial biometric security in combination with Visual Speech Recognition (VSR). In the proposed method, the first verification step entails face authentication. Further, the authentication protocol involves a challenge-response-based method using VSR. The proposed method achieved a word error rate of 2.7% and a word recognition rate of 97.3%, surpassing existing state-of-the art methods in VSR. The proposed scheme offers practical and effective solutions to prevent face spoofing through active liveness detection in face-based authentication systems.

Deep learning techniques have demonstrated early advancements in addressing the challenges of complex Visual Speech Recognition (VSR) tasks. Nonetheless, a persistent issue arises when distinguishing characters or words with similar pronunciations, known as homophones, which results in ambiguity. Existing VSR systems also face technical constraints due to insufficient visual data for learning short-duration phonemes like “at”, “an”, “a”, and “eight”. Moreover, cutting-edge VSR techniques perform exceptionally well when interpreting overlapping speakers. However, extending these methods to unseen speakers leads to a significant performance decline due to the limited diversity in the training dataset and substantial variations in physical attributes, such as lip shape and color, across different speakers. To address the existing challenges in VSR, we propose a multi-modal approach that leverages visual and landmark information to capture complex spatio-temporal patterns for the model generalization capabilities. The model employs a multi-layered Three-Dimensional Convolutional Neural Network (3D-CNN) that extracts visual features, while a Graph Convolutional Network (GCN) captures precise landmark information for accurate lip shape localization. The extracted features are then fused for further processing using a Sequence-to-Sequence (Seq2Seq) model based on the attention mechanism. The proposed model achieved a WER of 0.53% and 8.21% for the overlap and unseen speakers category. Notably, these results surpass the performance of existing models, demonstrating remarkable accuracy for VSR on the GRID dataset in both the unseen and overlapping speaker scenarios.

This article presents a novel multiband, biocompatible MIMO nanoantenna forterahertz applications, designed to addressthe growing demand forfaster data transferratesin futurewireless nanocommunicationssystems. The design process beganwith a foundationalsingle-element antenna (157.12 × 184.40 × 11 μm3 ) constructed from a gold patch and ground on a PTFE substrate. Thisinitial element,resonating at 1.041, 1.602, 2.199, and 2.814 THz,wassubsequently expanded into a two-port MIMO structure (157.12 × 276.60 × 11 μm3 )to enhance channel capacity. The proposed MIMO nanoantenna operates nearly at the same frequencies asthe single-element nanoantenna. Still, it delivers better performance, achieving a significantly higher channel capacity (up to 1.163 Tbps at 1.044 THz) and gain (up to 9.06 dBi at 2.214 THz). Furthermore, the MIMO system demonstrates excellent diversity performance,with an ECCclose to zero and a consistently high DG of 9.999 dB—indicating effective signal fading mitigation and enhanced reliability. The proposed nanoantennas prove highly effective for multiband operations up to 3 THz, demonstrating significant potential for various advanced applications. These include low-power 6G communications, high-resolution terahertz imaging, in-vivo biomedicalsensing, and other high-speed nanoscale communication systems.

Abstract—This article presents a terahertz (THz) metamaterial-based nanosensor employing a split ring resonator (SRR) for the detection of N-terminal pro–B-type natriuretic peptide (NT-proBNP), a cardiac biomarker released in response to increased myocardial pressure and volume overload in the heart. The sensor is designed and simulated in CST Studio to enable real-time detection via changes in the refractive index of NT-proBNP associated with cardiac abnormalities. Validation is performed through equivalent circuit modeling (ECM) using the Advanced Design System (ADS). The nanosensor achieves a sensitivity of 1460 GHz/RIU, a Q-factor of 22.06, and a figure of merit (FOM) of 41.71. Assuming minimally invasive placement in the pericardium, signal attenuation is modeled using a path-loss framework that accounts for the serous and fibrous pericardial layers. Transmission line theory is applied to evaluate the intrinsic impedance, reflection coefficients, and attenuation characteristics of THz waves propagating through cardiac tissue. The model estimates the received power at a nanocontroller located at the fibrous layer and is validated using COMSOL Multiphysics. By leveraging refractive index variations induced by NT-proBNP, this nanosensor enables intrabody THz communication as a diagnostic modality. The platform is particularly suited for detecting conditions such as pericarditis, where biomarker fluctuations and pericardial thickening jointly modulate the THz signal.

As recommender systems shift from rating-based to interaction-based models, graph neural network-based collaborative filtering models are gaining popularity due to their powerful representation of user-item interactions. However, these models may not produce good item ranking since they focus on explicit preference predictions. Further, these models do not consider side information since they only capture latent feature information of user-item interactions. This study proposes an approach to overcome these two issues by employing preference relation in the graph neural network model for collaborative filtering. Using preference relation ensures the model will generate a good ranking of items. The item side information is integrated into the model through a trainable matrix, which is crucial when the data is highly sparse. The main advantage of this approach is that the model can be generalized to any recommendation scenario where a graph neural network is used for collaborative filtering. Experimental results obtained using the recent RS datasets show that the proposed model outperformed the related baselines. © 2024 Elsevier Ltd

Infectious viruses are spread during human-to-human contact and can cause worldwide pandemics. We have witnessed worldwide disasters during the COVID-19 pandemic because of infectious viruses, and these incidents often unfold in various phases and waves. During this pandemic, so many deaths have occurred worldwide that they cannot even be counted accurately. The biggest issue that comes to the forefront is that health workers going to treat patients suffering from COVID-19 also may get infected. Many health workers have lost their lives to COVID-19 and are still losing their lives. The situation can worsen further by coinciding with other natural disasters like cyclones, earthquakes, and tsunamis. In these situations, an intelligent automated model is needed to provide contactless medical services such as ambulance facilities and primary health tests. In this paper, we explore these types of services safely with the help of an intelligent automated transportation model using a vehicular delay-tolerant network. To solve the scenario, we propose an intelligent transportation system for automated medical services to prevent healthcare workers from becoming infected during testing and collecting health data by collaborating with a delay-tolerant network of vehicles in intelligent transport systems. The proposed model automatically categorizes and filters infected patients, providing medical facilities based on their illnesses. Our mathematical evaluation and simulation results affirm the effectiveness and feasibility of the proposed model, highlighting its strength compared to existing state-of-the-art protocols. © 2024 Elsevier B.V.

In analytical modeling for biochemical pathways, precisely determining unknown parameters is paramount. Traditional methods, reliant on experimental time course data, often encounter roadblocks — limited accessibility and variable quality — that can significantly impact the algorithm's performance. In this study, we address these hurdles by unveiling a groundbreaking parameter estimation technique, Constrained Regularized Fuzzy Inferred Extended Kalman Filter (CRFIEKF). This innovative approach eliminates the need for experimental time-course measurements and capitalizes on the existing imprecise relationships among the molecules within the network. Our proposed framework integrates a Fuzzy Inference System (FIS) block to encapsulate these approximated relationships. To fine-tune the estimated parameter values, we employ Tikhonov regularization. The selection of Tikhonov regularization and Gaussian membership functions was based on the Mean Squared Error (MSE) values observed during the parameter estimation process, contrasting our results with those of previous studies. We rigorously tested the proposed approach across various pathways, from the glycolytic processes in mammalian erythrocytes and yeast cells to the intricate JAK/STAT and Ras signaling pathways. The results were impressive, showing a significant similarity (p-value < 0.001) to the outcomes of specific prior experiments. The dynamics of the biochemical networks normalized within the [0, 1] range mirrored the transient behavior (MSE < 0.5) of both in vivo and in silico results from previous studies. In conclusion, our findings highlight the effectiveness of CRFIEKF in estimating the kinetic parameter values without prior knowledge of experimental data within a biochemical pathway in the state-space model. The proposed method underscores its potential as a game-changer in biochemical pathway analysis. © 2024 Elsevier Ltd

We report the green synthesis of novel magnetically separable MgFe2O4 nanoparticles using Cajanus cajan (L.) Millsp leafs via combustion method. The MgFe2O4 were characterized by powder X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), and UV-diffuse reflectance (UV-DRS) spectroscopy. The crystalline structure of MgFe2O4 was confirmed via XRD studies and TEM showed that the MgFe2O4 NPs were distorted spherical particles with particle size ranging between 5 and 15 nm. UV-DRS study showed the optical band gap of MgFe2O4 NPs to be 1.8 eV. Microwave-assisted (MW) degradation of PCM-dolo drug using MgFe2O4 as catalyst was performed at different operating parameters such as time (30 min), drug concentration (PCM-dolo 50 mg/L), initial concentration of MgFe2O4 (0–110 mg/L), and microwave power (100–600 W) to obtained the degraded fragments of the drug. Experimental data was used to compute the degradation efficiency of PCM-dolo on MgFe2O4. The enhanced catalytic performance could be ascribed to the production of MW-induced active species, such as holes (h+), superoxide radicals (?O2?) and hydroxyl radicals (?OH) in the degradation process. A possible degradation mechanism and pathway was proposed. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

The ability to predict diseases is critical in healthcare for early intervention and better patient outcomes. Data security and privacy significantly classified medical data from several institutions is analyzed. Cooperative model training provided by Federated Learning (FL), preserves data privacy. In this study, we offer a fusion strategy for illness prediction, combining FL with Anova and Chi-Square Feature Selection (FS) and Linear Discriminate Analysis (LDA) Feature Extraction (FE) techniques. This research aims to use FS and FE techniques to improve prediction performance while using the beneficial aspects of FL. A comprehensive analysis of the distributed data is ensured by updating aggregate models with information from all participating institutions. Through collaboration, a robust disease prediction system excels in the limited possibilities of individual datasets. We assessed the fusion strategy on the Cleveland heart disease and diabetes datasets from the UCI repository. Comparing the fusion strategy to solo FL or conventional ML techniques, the prediction performance a unique fusion methodology for disease prediction. Our proposed models, Chi-Square with LDA and Anova with LDA leveraging FL, exhibited exceptional performance on the diabetes dataset, achieving identical accuracy, precision, recall, and f1-score of 92.3%, 94.36%, 94.36, and 94.36%, respectively. Similarly, on the Cleveland heart disease dataset, these models demonstrated significant performance, achieving accuracy, precision, recall, and f1-score of 88.52%, 87.87%, 90.62, and 89.23%, respectively. The results have the potential to revolutionize disease prediction, maintain privacy, advance healthcare, and outperform state-of-the-art models

Wireless Sensor Networks (WSNs) have become pivotal in numerous applications, including environmental monitoring, precision agriculture, and disaster response. In the context of urban flood monitoring, utilizing unmanned aerial vehicles (UAVs) presents unique challenges due to the dynamic and unpredictable nature of the environment. The primary challenges involve designing strategies that maximize data collection while minimizing the Age of Information (AoI) to ensure timely and accurate decision-making. Efficient data collection is crucial to capturing all relevant information and providing a comprehensive understanding of flood dynamics. Simultaneously, reducing AoI is essential, as outdated data can lead to delayed or incorrect responses, potentially worsening the situation. Addressing these challenges is critical for the effective use of WSNs in urban flood monitoring. Initially, we formulate the problem as a mixed integer non-linear programming (MINLP) problem. Further, it is solved using a Lagrangian-based branch and bound technique by converting it into an unconstrained problem. Then, for large-scale WSN, we propose a hybrid optimization technique which combines a genetic algorithm with a particle swarm optimization technique to simultaneously maximize the data collection and reduce the AoI of the collected data with the constraint of energy consumption of the UAVs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches in terms of both data collection and AoI.

Accidental falls and their consequences are critical concerns for elderly people. Fatal injuries, when delayed in treatment, can lead to severe outcomes. Fall detection systems are crucial for the timely treatment of such injuries. Although sensor-based fall detection approaches are effective, video-based approaches are more useful because they assist in analyzing the fall scene and identifying the cause of the fall. However, privacy preservation is a major concern in video-based fall detection. The proposed system introduces a privacy-preserving mechanism that masks the identified human with a silhouette. A custom dataset, including 80 activities of daily living and 70 fall activities, is introduced. An LSTM variational autoencoder architecture is designed with a gradient clipping mechanism and a smooth variant of Adaptive Moment Estimation with Stochastic Gradient Descent (AMSGrad) optimizer to enhance the accuracy of fall detection. The reconstruction error between normal and fall activities is clearly identified with the help of a dynamic threshold. This results in a system performance that achieves accuracy, precision, and sensitivity of 99%, 97%, and 99%, respectively

With the rapid growth of Internet technology and social networks, the generation of text-based information on the web is increased. To ease the Natural Language Processing (NLP) tasks, analyzing the sentiments behind the provided input text is highly important. To effectively analyze the polarities of sentiments (positive, negative and neutral), categorizing the aspects in the text is an essential task. Several existing studies have attempted to accurately classify aspects based on sentiments in text inputs. However, the existing methods attained limited performance because of reduced aspect coverage, inefficiency in handling ambiguous language, inappropriate feature extraction, lack of contextual understanding and overfitting issues. Thus, the proposed study intends to develop an effective word embedding scheme with a novel hybrid deep learning technique for performing aspect-based sentimental analysis in a social media text. Initially, the collected raw input text data are pre-processed to reduce the undesirable data by initiating tokenization, stemming, lemmatization, duplicate removal, stop words removal, empty sets removal and empty rows removal. The required information from the pre-processed text is extracted using three varied word-level embedding methods: Scored-Lexicon based Word2Vec, Glove modelling and Extended Bidirectional Encoder Representation from Transformers (E-BERT). After extracting sufficient features, the aspects are analyzed, and the exact sentimental polarities are classified through a novel Positional-Attention-based Bidirectional Deep Stacked AutoEncoder (PA_BiDSAE) model. In this proposed classification, the BiLSTM network is hybridized with a deep stacked autoencoder (DSAE) model to categorize sentiment. The experimental analysis is done by using Python software, and the proposed model is simulated with three publicly available datasets: SemEval Challenge 2014 (Restaurant), SemEval Challenge 2014 (Laptop) and SemEval Challenge 2015 (Restaurant). The performance analysis proves that the proposed hybrid deep learning model obtains improved classification performance in accuracy, precision, recall, specificity, F1 score and kappa measure.

The ring oscillator (RO) is widely used to address different hardware security issues. For example, the RO-based physical unclonable function (PUF) generates a secure and reliable key for the cryptographic application, and the RO-based aging sensor is used for the efficient detection of recycled ICs. In this paper, a CMOS inverter with two voltage control signals is used to design a configurable RO (CRO). With its control signals, the proposed CRO can both accelerate and lower the impact of aging on the oscillation frequency. This vital feature of the proposed CRO makes it suitable for use in PUFs and RO-based sensors. The performance of both the proposed modified architecture, i.e., CRO PUF and CRO sensor, is evaluated in 90 nm CMOS technology. The aging tolerant feature of the proposed CRO enhances the reliability of CRO PUF. Similarly, the aging acceleration property of CRO improves the rate of detection of recycled ICs. Finally, both the proposed architectures are area and power-efficient compared to standard architectures

Globally, cancer stands as the second leading cause of mortality. Various strategies have been proposed to address this issue, with a strong emphasis on utilizing gene expression data to enhance cancer detection methods. However, challenges arise due to the high dimensionality, limited sample size relative to its dimensions, and the inherent redundancy and noise in many genes. Consequently, it is advisable to employ a subset of genes rather than the entire set for classifying gene expression data. This research introduces a model that incorporates Ranked-based Filter (RF) techniques for extracting significant features and employs Extreme Learning Machine (ELM) for data classification. The computational cost of using RF technique over high dimensional data is low. However extraction of significant genes using one or two stage of reduction is not effective. Thus, a 4-stage feature reduction strategy is applied. The reduced data is then utilized for classification using few variants of ELM model and activation function. Subsequently, a two-stage grading approach is implemented to determine the most suitable classifier for data classification. This analysis is conducted over four microarray gene expression data using four activation function with seven learning based classifiers, from which it is shown that II-ELM classifier outperforms in terms of performance matrix and ROC graph

In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often leading to consequential outcomes such as dementia, which can be mitigated through early detection and treatment of cardiovascular issues. Continued research into preventing strokes and heart attacks is crucial. Utilizing the wealth of healthcare data related to cardiac ailments, a two-stage medical data classification and prediction model is proposed in this study. Initially, Binary Grey Wolf Optimization (BGWO) is used to cluster features, with the grouped information then utilized as input for the prediction model. An innovative 6-layered deep convolutional neural network (6LDCNNet) is designed for the classification of cardiac conditions. Hyper-parameter tuning for 6LDCNNet is achieved through an improved optimization method. The resulting model demonstrates promising performance on both the Cleveland dataset, achieving a convergence of 96% for assessing severity, and the echocardiography imaging dataset, with an impressive 98% convergence. This approach has the potential to aid physicians in diagnosing the severity of cardiac diseases, facilitating early interventions that can significantly reduce mortality associated with cardiovascular conditions