Recent publications
In population biology, the interplay between prey and predators in the presence of infection can give rise to complex dynamics. On the flip side, implementing harvesting is an infection control measure. In the present work, we use the dynamical system theory to discuss the dynamics of the harvested prey–predator system in the presence of infection in prey species. Detailed mathematical and numerical evaluations have been presented to discuss the susceptible‐free state, infection‐free state, predator‐free state, species coexistence, stability, and occurrence of various bifurcations (saddle‐node, transcritical, and Hopf bifurcation). The study reveals the impact of harvesting parameters on the dynamics. Interestingly, we observe that an infection‐free state could be achieved by varying the harvesting parameter under all three harvesting schemes (linear, quadratic, and nonlinear). Moreover, with the help of reproduction number, we claim that linear harvesting is more effective in controlling the infection than quadratic and nonlinear harvesting provided the half‐saturation constant for nonlinear harvesting is greater than a threshold value (=1); otherwise, nonlinear harvesting is more effective. Also, the system can support more susceptible prey in the presence of harvesting. The present theoretical study suggests different threshold values of implemented harvesting to control the disease.
This work introduces a novel compact ultra-wideband (UWB) antenna designed for wearable applications, employing a bioinspired structure and machine learning (ML) techniques to achieve exceptional performance in the 3.10–10.42 GHz range. The antenna is fabricated by positioning conductive fabric on a polydimethylsiloxane polymer of 1 mm thickness to augment high flexibility and durability. Additionally, it pioneers integrating a complete ground plane to mitigate back radiation toward the human body, presenting a compact (35.5 × 30.5 × 1 mm ³ ) UWB antenna design compliant with IEEE 802.15.6 standards. The design methodology includes using bandwidth enhancement techniques such as chamfering edges, slots, and adding stubs in the feed, along with applying ML to optimize the antenna’s dimensions for desired return loss characteristics. The proposed antenna demonstrates exceptional resilience to human body loading and physical deformation. The simulation and measurement results have good agreement. The K-nearest neighbour method beat the other ML algorithms maximum accuracy of 99.62% to predict the S 11 . According to the author’s best knowledge, this is the first compact UWB antenna with full ground specified by IEEE.802.15.6 with ML reported.
A dual-band frequency reconfigurable cylindrical dielectric resonator antenna (DRA) for 5G New Radio (NR) application within a Sub-6 GHz is presented in the proposed work. In this work, nine n7, n30, n38, n40, n41, n46, n47, n53 and n79 5G NR bands are presented. A novel approach for 5G NR bands has been presented to provide dual-band capabilities and frequency reconfigurability with machine learning (ML). We achieve this reconfigurability by using two PIN diode switches that operate in various configurations, allowing for a maximum wide tuning range of 80.19%. In cylindrical DRA and modes are responsible for dual-band operation. The K-nearest neighbor (KNN) ML technique achieves an accuracy of more than 98%, as compared to artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), and decision tree (DT) across all configurations for the prediction.
Online sharing platforms offer countless choices and detailed product descriptions to consumers. In this study, we demonstrate the effect of choice and information overload on booking decisions using large-scale field data from Airbnb and observe an inverse U-shaped association. Furthermore, our results show that providing quality assurance of the product exacerbates the choice and information overload relationship. As a post-hoc analysis, we perform topic modeling to gain better insights into how product information influences booking decisions. Specifically, the post-hoc analyses show that the number of topics in the description has a positive association with the number of bookings. Furthermore, topic count moderates the information overload effect by intensifying the influence of product description on the number of bookings. Our findings have important implications for online sharing platforms, service providers, and travelers as they shed light on the detrimental effects of excessive variety and information on booking decisions.
The size and complexity of software products have expanded significantly, prompting researchers to focus on quality assessment by quantitatively assessing software product’s reliability growth. Several time dependent software reliability growth models (SRGMs) have been produced and are available in this regard. The authors evaluated errors of varied severity, but they believed that low severity faults would be repaired first, followed by high severity faults. Furthermore, the authors assumed in the change point model that the team changed their strategy, i.e., the fault detection rate changed after a particular time but making a plan and executing that strategy takes a long time, and as a result, the industry confronts a delay in software launching. As a result, two fault categories (low and high severity) are considered in this study, and two types of testing effort function (TEF) related to fault detection rate are presented to detect and fix them. Because parameters have such a large impact on model performance, their sensitivity is also investigated.
This research introduces a proposed energy-efficient radiation-hardened and improved read stability (RHIRS)-12T SRAM cell with a polarity hardening technique, which lowers the vulnerable nodes and allows all single-event upset (SEU) techniques to recover from single-event multiple-effect (SEME). To evaluate the relative performance using UMC 65nm CMOS technology of the proposed cell, a comparison analysis is conducted with various radiation-hardened SRAM cells currently in use, such as RHPD-12T, RHWC12T, HPHS12T, RHBD12T, RHD-12T, and EDP12T. When compared to the existing 12T SRAM cells, the proposed RHIRS-12T radiation-hardened SRAM cell has the highest read and hold stability. Compared to EDP12T, RHPD-12T, and RHWC12T SRAM cells, the simulation results demonstrate improvements in write delay of 33.63%, 21.41%, and 9.58%, respectively. It exhibits the lowest read delay as compared to the other considered SRAM cells. It saves 76.34%, 43.68%, 2.77%, and 0.13% energy consumption compared with RHBD12T, HPHS12T, RHPD-12T, and RHWC12T, respectively, during the read operation and it also saves 55.59%, 21.44%, and 6.04% energy compared with EDP12T, RHD-12T, and RHWC12T, respectively, during the write operation. It also shows enhancements of 78.55%, 61.06%, 44.87%, 20.11%, and 0.12% in critical charge value compared to RHBD12T, HPHS12T, RHWC12T, RHPD-12T, and RHD-12T, respectively. Additionally, we evaluate the relative performance of the proposed RHIRS-12T, a comparison analysis is performed with RHD-12T radiation-hardened SRAM cells having low-voltage transistor (LVT), standard-voltage transistor (RVT or SVT), high-voltage transistor (HVT), and super-high voltage transistor (SHVT). It exhibits the highest improvement in read stability at LVT compared to RHD-12T SRAM cell. It can also be used for aerospace applications due to its improved performance and stability.
A ceramic (
Al
2
O
3
) material based dual-band high-tuning range frequency reconfigurable dielectric antenna for wireless applications with Machine Learning (ML) algorithm is presented in this article. The proposed antenna is a hybrid structure in which the antenna radiator is designed with a Dielectric Resonator (DR) (Alumina (
Al2
O
3
) ceramic material with a relative dielectric constant (∈
r
)=9.8. The presented work offers dual-band, compactness, and frequency reconfigurability (FR).FR is obtained through two PIN diode switches, operating in ON-ON, ON-OFF, OFF-ON and OFF-OFF configurations. It offers a total spectrum and a maximum wide tuning range of 71.49 % and 44.44 %, respectively. Dual-band is generated through the excitation of
HEM
11δ
, and
HEM
12δ
mode in cylindrical Dielectric Resonator (CDR). In contrast, compactness is obtained through the higher-order mode excitation and hybrid structure. The proposed antenna is designed on the ANSYS HFSS software and optimized through various ML algorithms such as K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Decision Tree (DT), Extreme Gradient Boosting (XGB), and Random Forest (RF). In all configurations, KNN achieved more than 99 % accuracy for the prediction of reflection coefficient (
s
11
). The proposed antenna is used for WiMAX, WLAN, and 5G wireless applications.
In this article, a compact quad element coplanar waveguide (CPW) fed ultra‐wideband (UWB) multiple input multiple output (MIMO) antenna for future generation wireless communication system using a machine learning (ML) optimization approach is presented. The proposed antenna is used for 5G new radio (n46/n77/n47/n78/n48/n79), Wi‐Fi 5, Wi‐Fi 6, and dedicated short range communications (DSRC) services, vehicle to infrastructure (V2I), vehicle to vehicle (V2V), and vehicle to network (V2N) in the entire operating frequency band. It is operating from 3.2 to 11.85 GHz. The bandwidth is 8.65 GHz, and the percentage of impedance bandwidth is 115%. The comparative analysis between dual and quad elements are presented. It is optimized through the various ML model K‐nearest neighbor (KNN), extreme gradient boosting (XGB), artificial neural network (ANN), and random forest (RF). The KNN ML model achieved a higher accuracy of 93%, and it accurately predicted the S parameters of the suggested UWB antenna. The MIMO parameters are calculated and found within the acceptable limits. There is a strong correlation between the simulated and measured results. Hence, the suggested antenna is a suitable candidate for future wireless communication systems.
The present study investigates the dynamics of natural enemy (predator)-pest (prey) interactions in an agroecosystem. It focuses on the impacts of providing additional food to natural enemies and harvesting intervention on pest populations. A thorough mathematical and numerical analysis is performed to explore various existing equilibria as trivial, natural enemy free and species coexistence. The assessments also explore the emergence of different bifurcations such as saddle-node, transcritical, and Hopf bifurcation. The effct of harvesting rate and additional food on pests and natural enemy populations are highlighted through numerical simulations. The findings indicate that the system’s multiple equilibria, their stability, and various bifurcations are outcomes of changes in harvesting rates and the availability of additional food. This work offers theoretical insights into biological control initiatives, highlighting specific critical values for imposed harvesting. It also suggests optimal strategies for supplying additional food to effectively manage pest populations. This work concludes that making an arbitrary choice of additional food and harvesting could yield entirely contrary outcomes, potentially resulting in an escalation of pest concentrations and the elimination of the natural enemy.
p>In many countries, the public's ignorance and governmental restrictions provide significant obstacles for the e-waste management industry. As a result, maintaining the ecosystem presents several difficulties. Due to the limited lifespan of appliances like refrigerators, telephones, and televisions, there is a noticeable increase in e-waste when this equipment is replaced. This emphasizes how important it is to manage e-waste consistently and effectively.
India's increasing worldwide influence is consistent with its status as the world's largest manufacturer and user of technological products. This highlights how urgent it is to solve the issue of ewaste, which has a significant influence on resource preservation, public health, the environment, and India's economic situation.
This thorough analysis explores the world of e-waste in detail, emphasizing the dangerous components of e-waste and their negative impacts on the environment and human health. It covers the administration of electrical and electronic equipment in both developed and developing countries, emphasizing methods to promote the circular economy, encourage component reuse, and increase productivity.</p
This paper investigates a deterministic fractional-order epidemic model to study the dynamics of multidrug-resistant tuberculosis (MDR-TB) by considering control strategies, such as the treatment of TB individuals and the cost of fitness for exposed individuals. From the analysis of the model, conditions for the disease elimination or persistence of the disease, based on the basic reproduction number , are derived. Using data on MDR-TB cases reported in India from 2005 to 2022, the fractional-order (FO) and biological parameters of the model are estimated. The analysis in this paper demonstrates that various control measures, such as early detection of the disease and completion of the treatment, can effectively reduce the number of MDR-TB cases, mitigating the further spread of the disease.
The dynamics of the propagation and outspread of infectious diseases are eminently intricate, mainly due to the heterogeneity of the host individuals. In this paper, an age‐stratified SEIR (susceptible‐exposed‐infected‐recovered) epidemiological model incorporating saturated treatment function and heterogeneous contact rates is developed to study infectious disease transmission dynamics among various age groups. The expression for the basic reproduction number R0 and conditions for the global stability of the system have been derived by a recently developed graph‐theoretic (GT) approach. Digraph reduction creates a GT version of the Gauss elimination method for computing the R0. The global dynamics results have been formed by constructing the Lyapunov function using a GT approach. The endemic equilibrium exists uniquely if R0>1, whereas the disease‐free equilibrium is observed to be globally stable if R0≤1. The numerical simulations are demonstrated by extracting the daily reported COVID‐19 cases for the second wave in Italy. The age‐dependent contact matrix for the Republic of Italy (data sourced from the POLYMOD study) is computed via paper–diary methodology (PDM) grounded on a population‐prospective survey in European countries. Our numerical findings imply that (i) for the age group (20–49) years and (50–69) years, the number of infected persons is quite double as compared with the exposed cases; (ii) approximately 50% of positive cases lies in (20–69) years age group; (iii) for the (00–19) years age group, only half of the exposed individuals got infected; and (iv) a consistent graph is detected for the age group of (70–99) years in both cases; it shows that almost all the exposed got infected.
Software reliability growth models (SRGMs) are universally admitted and employed for reliability assessment. The process of software reliability analysis is separated into two components. The first component is model construction, and the second is parameter estimation. This study concentrates on the second segment parameter estimation. The past few decades of literature observance say that the parameter estimation was typically done by either maximum likelihood estimation (MLE) or least squares estimation (LSE). Increasing attention has been noted in stochastic optimization methods in the previous couple of decades. There are various limitations in the traditional optimization criteria; to overcome these obstacles metaheuristic optimization algorithms are used. Therefore, it requires a method of search space and local optima avoidance. To analyze the applicability of various developed meta-heuristic algorithms in SRGMs parameter estimation. The proposed approach compares the meta-heuristic methods for parameter estimation by various criteria. For parameter estimation, this study uses four meta-heuristics algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA). Four popular SRGMs did the comparative analysis of the parameter estimation power of these four algorithms on three actual-failure datasets. The estimated value of parameters through meta-heuristic algorithms are approximately near the LSE method values. The results show that RGA and GWO are better on a variety of real-world failure data, and they have excellent parameter estimation potential. Based on the convergence and R² distribution criteria, this study suggests that RGA and GWO are more appropriate for the parameter estimation of SRGMs. RGA could locate the optimal solution more correctly and faster than GWO and other optimization techniques.
Hardware Trojan (HT) is a significant threat to the integrity and security of the Integrated Circuit (IC). Hardware Trojans can be implanted in any stage of IC development. Detection of HT is difficult during testing due to its stealthy nature and IC’s complex design. Therefore, researchers have focused on developing HT detection methods to mitigate the threats of HT. In recent years, machine learning (ML) has emerged as a promising approach that uses various Trojan-related circuit features for detecting the hardware Trojans. The previously proposed ML-based methods either use all known features or use manual/random feature selection. Due to this, these methods are complex and provide low performance. This work proposes a new ML-based method that encompasses different automatic feature selection algorithms, such as Principal Component Analysis (PCA) and AutoEncoder, for effective Trojan detection in ICs. We also use algorithms like Lasso, Ridge, and Elastic-net to select the best features based on their coefficient values. Using feature selection enables us to identify the most influential features for accurately detecting hardware Trojans. Further, we develop different ML models by integrating these automatic feature selection algorithms to achieve high performance (in terms of accuracy and F1-score) during HT detection. The experimental evaluation shows that the proposed Autoencoder achieves 99.5% average accuracy and 99.28% F1 score, and PCA provides on average 98.6% accuracy and 98.35% F1-Score with different proposed machine learning models. The proposed XGBoost, Random forest-based ML model achieves 100% accuracy and F1-score with Autoencoder while detecting HT in different datasets prepared using Trust-hub benchmarks. The proposed model exhibits a notable improvement in the F1-score compared to the best-known existing ML-based model, ranging from 0.1% to 2.1%. The proposed method’s top-performing model (RF with Autoencoder) achieves significant enhancements in the F1-score, ranging from 2% to 53%, over the performance of existing ML-based hub trust detection methods.
The rapid advancement of mobile edge computing (MEC) has revolutionized the distributed computing landscape. With the help of MEC, the traditional centralized cloud computing architecture can be extended to the edge of networks, enabling real-time processing of resources and time-sensitive applications. Nevertheless, the problem of efficiently assigning the services to the computing resources is a challenging and prevalent issue due to the dynamic and distributed nature of the edge network's architecture. Thus, we require intelligent real-time decision-making and effective optimization algorithms to allocate resources, such as network bandwidth, memory, and CPU. This paper proposes an MEC architecture to allocate the resources in the network to optimize the quality of services (QoS). In this regard, the resource allocation problem is formulated as a bi-objective optimization problem, including minimizing cost and energy with quality and deadline constraints. A hybrid cascading-based meta-heuristic called GA-PSO is embedded with the proposed MEC architecture to achieve these objectives. Finally, it is compared with three existing approaches to establish its efficacy. The experimental results report statistically better cost and energy in all the considered instances, making it practical and validating its effectiveness.
The present research work reported the study of nanocomposites of undoped and Mg‐doped ZnO with CNT as electrode materials for supercapacitor application. The undoped ZnO/CNT and Mg‐doped ZnO/CNT nanocomposites (i.e., 10 % Mg‐doped and 20 % Mg‐doped) were synthesized using a cost‐effective blending‐assisted hydrothermal method. The morphological (FESEM & TEM) studies revealed that the diameter of CNT was ~7 nm and the ZnO nanoparticles were spherical in shape with an average particle size of ~5 nm. In addition, it was also found that 10 % Mg‐ZnO and 20 % Mg‐ZnO had a sheet‐like structure. XRD and FTIR studies further confirmed the successful doping of Mg in ZnO and CNT nanocomposites. BET analysis showed that the value of specific capacitance increased with the increase in surface area. Further, the electrochemical performance of these nanocomposites revealed that the higher doping percentage, 20 % Mg‐ZnO/CNT nanocomposite achieved the highest specific capacitance value i.e., 458.5 F/g at 0.1 A/g current density in 3M H2SO4 electrolytic solution, having a retention of 99.8 % after 12,00 long cycles. In addition, the charge storage mechanism revealed that the as‐synthesized nanocomposites showed both the diffusion‐controlled and capacitive‐controlled behaviors. Thus, a higher value of specific capacitance with excellent cyclic stability indicated the higher efficiency of Mg‐doped ZnO/CNT nanocomposite for future supercapacitor applications.
Data sparsity and new-user preference modeling are critical challenges in rating prediction for recommendation systems (RSs). Many review-based RS models use textual reviews along with rating data to address data sparsity, but they often fail to capture deeper context from reviews, overlooking word order and hidden pertinent information. Additionally, new-user preference modeling remains problematic due to these contextual limitations. To address these issues, we introduce a deep cognitive architecture using a convolution neural network (CNN) with an additional convolutional transform to filter out non-pertinent information in textual reviews. The proposed robust user and item deep cognitive CNN model, R-UIConvMF, addresses these challenges by leveraging users’ and items’ reviews through separate CNNs: User-wise CNN (UCNN) and Item-wise CNN (ICNN), which learn their respective latent profiles. These profiles are then integrated with probabilistic matrix factorization (PMF) through rating-count distribution to minimize irrelevant noise in the shared latent space. Experimental results on five benchmark Amazon datasets demonstrate that R-UIConvMF significantly outperforms current state-of-the-art RS models. Specifically, R-UIConvMF shows improvements in rating prediction MSE by 1.27%, 3.23%, 3.85%, 2.57%, and 4.03% on the musical instruments (MI), office products (OP), digital music (DM), video games (VG), and tools improvements (TI) datasets, respectively. Additionally, R-UIConvMF achieves superior performance in terms of recommendation F-score at a cutoff of the best 10 items, with improvements of 3.05%, 3.23%, 3.58%, 2.23%, and 2.37% on the MI, OP, DM, VG, and TI datasets, respectively.
In this paper, the boundedness and compactness of the Hankel wavelet multiplier associated with the unitary representation on [Formula: see text] space for [Formula: see text], are obtained by using the Hankel transform technique. The application of Hankel wavelet multipliers in form of the Landau–Pollak–Slepian operator is given. With the help of the Hankel wavelet multiplier, the Sobolev space associated with the Hankel transform is constructed and its various properties are studied.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
Information