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This article explores the concept of p-product cordial labeling of graphs, which generalizes the well-established notion of cordial labeling. Cordial labeling techniques have significant applications in various fields such as cryptography, neural networks, artificial intelligence, chemistry, and network modelling. In this study, we define p-product cordial labeling as a vertex labeling function ?: V (G) ? {0, 1, …, p ? 1}, where p ? N and p ? |V (G)|, with an induced edge labeling ??: E(G) ? {0, 1, …, p ? 1} given by ?? (uv) = ?(u) · ?(v) mod p. A labeling ? is said to be p-product cordial if, for all labels i and j, the number of vertices labeled i and j differ by at most one, and similarly, the number of edges labeled i and j differ by at most one. The paper specifically investigates the 2-product cordiality of k-regular bipartite graphs. It is shown that every 1-regular bipartite graph is 2-product cordial. Further, the study presents conditions under which general k-regular bipartite graphs are 2-product cordial and identifies cases where such cordiality does not hold. © 2025 KSCAM.

Two-dimensional (2D) materials, an electrifying family of innovative materials, have recently attracted wide attention due to their remarkable characteristics, primarily their high optical transparency, exceptional metallic conductivity, high mechanical strength, carrier mobility, tunable band gap values, and optimum work function. Interestingly, 2D-nanosheets/nanolayers (2D-NLs) might be synthesized into single/multi-layers using simple processes such as chemical vapor deposition (CVD), chemical bath deposition (CBD), and mechanical and liquid-phase exfoliation processes that simply enhance optoelectronic properties. However, the stability of 2D-NLs is one of the most significant challenges that limits their commercialization. Researchers have been focusing on the stability of 2D-NLs with the aim of developing next-generation solar cells. Easily tunable distinctive 2D-NLs that are based on the synthesis process, surface functional groups, and modification with other materials/hybrid materials thereby improve the stability of the 2D-NLs and their applicability to the hole transport layer (HTL) and the electron transport layer (ETL) in solar cells. Moreover, metal/non-metal-based dopants significantly enhance band gap ability and subsequently improve the efficacy of dye-sensitized solar cells (DSSCs). In this context, research has focused on 2D-NL-based photoanodes and working electrodes that improve the photoconversion efficiency (PCE) and stability of DSSCs. Herein, we mainly focus on synthesizing 2D-NLs, challenges during synthesis, stability, and high-performing DSSCs.

Precise identification of liver region from abdominal Computed Tomography-Angiography (CTA) plays an important role in the evaluation of donor for liver transplantation surgery. Nevertheless, the issues like intensity similarity of liver with neighbouring tissues and inter-intra patient liver shape variability; left the task of liver segmentation challenging. Here, we focus on improving the accuracy and reliability of liver donor evaluation system by customising its crucial step - liver segmentation and volume measurement. For achieving this, a Bayesian classifier is iteratively trained with salient features of liver, namely Haralick texture features and spatial information computed from the individual patient dataset. The proposed method is a combination of two techniques namely, advanced region growing and Bayesian classification. The agreement between the proposed method with the manual segmentation was satisfactory with Relative Volume Difference (RVD), Dice Similarity Coefficient (DSC), False-Positive Ratio (FPR), False-Negative Ratio (FNR) with values 8.98, 94.8 ± 1.5, 3.1 ± 2.8 and 5.67 ± 1.8, respectively. Copyright © 2015 Inderscience Enterprises Ltd.

Microwave imaging utilizes low-power Near-field electromagnetic fields at microwave frequencies to detect the internal structure of an object. Sufficient resolution through the thickness is crucial in biomedical applications to detect small objects of concern. Parameters such as the frequency of microwave signals, the design, and the material of the antenna are the most important factors to consider for microwave-based biomedical sensing. The proposed antenna falls under the good health and well-being goal, which is among the sustainable development goals (SDGs) that transform the world and yields merits of: compactness in size, ease of fabrication, wider impedance bandwidth, simple design, and good RF performance. An Asymmetric-fed Coupled Stripline (ACS) antenna is 3D-printed on an FR4 substrate with return loss measurements ranging from 2 GHz to 20 GHz. The impedance bandwidth is obtained between 6 GHz to 8 GHz and 15 GHz to 17 GHz. The proposed microwave antenna was simulated using Ansys HFSS. The parameters are designed to ensure optimum radiation efficiency. The radiation patterns obtained were omnidirectional in H-plane and bidirectional in E-plane. © The Electrochemical Society

A major difficulty in 5G-based mobile communications is to guarantee robust selecting routes for adaptive packet transmission in a dynamic environment. Traditional routing protocols struggle to adapt to the fluctuating wireless channel conditions inherent in 5G networks. To address this, our study introduces a novel system that integrates Deep Q-Networks (DQN) techniques with the Zone routing protocol (ZRP). Leveraging real-time network data including channel quality, traffic load, and congestion levels, the system employs machine learning algorithms to predict route stability. This predictive capability enables dynamic identification of the most stable route for packet transmission, with continuous monitoring and adjustment in response to evolving network conditions. Our proposed system follows a multi-step flow, starting from data collection and culminating in route selection based on machine learning predictions. Extensive simulations and real-world experiments validate the efficacy of our approach, demonstrating significant improvements in packet delivery ratio, latency, and overall network stability compared to conventional methods. Notably, our system exhibits resilience against varying network conditions and maintains scalability with increasing network size and traffic load. Through the fusion of machine learning and routing protocols, our study offers a promising solution to the critical challenge of stable route selection in 5G-based mobile communications, addressing the diverse demands of emerging applications and services. © 2024, Scibulcom Ltd.. All rights reserved.

Autonomous Vehicles (AVs) rely on secure neighbors and proactive infrastructures for robust communications. The heterogeneous communication scenario exploits concealed media for secure information exchange. Concealed communications assist precise navigation, information exchange, object detection, etc. In this process, the end-to-end computations are complex in converting a vehicle communication stream to an encrypted stream. This article introduces a Secure Module for Concealed Navigation Communication (SM-CNC) in AV environments. The information identified is transmitted by attaching volatile authentication that disintegrates if a communication source (vehicle or access point, etc.) is intercepted. Contrarily, the information is dropped in the communication medium if adverse or unauthorized vehicles intercept. This process is monitored during the relaying and end-to-end validation processes. The proposed secure module relies on quantum computing and recurrent learning for multiple recommendations from the information relay points. The information points are modeled as qubits for low-complex processing to administer security and ensure reliable communication. This prevents security breaches in the concealed medium through authentication failures and unauthorized access detection based on vehicle information allocated to individual qubits. The learning paradigm analyzes the qubits for their availability for secure information exchange throughout the navigation. The performance of the proposed module is verified using the metrics of communication failure, complexity, and authentication rate. The proposed secure module improves the authentication rate by 9.29% and reduces the communication failure and computation complexity by 11.87% and 12.13% respectively, for the maximum communication requests/ interval. © The Author(s) 2025.

Sub-Pixel Motion Estimation and compensation is a high-precision algorithm with very high complexity in HEVC/MPEGH/H.265 Video Codec's. This is because moving objects do not move by integer pixel locations between successive video frames. Typically, fractional pixel accuracy is obtained by means of bilinear interpolation producing a spatially blurred predicted signal. The motion estimation and compensation is improved in this paper by means of the filtering effect using a very effective spatial digital low pass FIR filter. This filter allows the motion to be detected, at very high precision using the fractional motion estimation. The fractional pixel accuracy was achieved using a total of 112 8-tap digital FIR filter for one-eighth pixel precision, which includes half and quarter pixel accuracy. The design has been implemented on a 28nm foundry process, with a speed of 1.101 GHz and it has achieved 2262 GOPS at this speed, outputting data at the rate of 1.8 Tera bits per second, for one-eighth pixel accuracy. Computational complexity, Memory & I/O Bandwidth has been reduced by inputting the Mean Square Error Map of the pixels to the Fractional Pixel Estimator and then searching in the sub-pixel grid. This design is targeted for 8K Ultra High Definition Television (UHDTV).8K HDTV format is 8192 x 4320 pixels. This amounts to 35.4 Million Pixels per Frame. The incoming Video Pixel Rate for 8K HDTV at 60 Frames per Second(fps) is 2.12 Billion Pixels per Second or 2.12 Giga Pixels/second. This amounts to an incoming Video Data Rate of 51 Billion bits per second or 51 Gbps. At 120 frames per second the Incoming Video Pixel rate is 4.24 Billion Pixels per Second or 4.24 Giga Pixels/Second. This amounts to incoming Video Data Rate of 102 Gbps. For Quarter Pixel Motion Estimation, we are adding 3 Sub-Pixels for every Integer Pixel. Pixel Count increases by a factor of 16. For 4K HDTV this becomes 142 Million Pixels per Frame. At 60fps, the Pixel rate is 8.5 Billion Pixels per Second or 8.5 Giga Pixels/Second, with a Video Processing Data Rate of 195 Gbps. At 120fps the Pixel rate 17 Billion Pixels Per Second or 17 Giga Pixels/Second, with a Video Processing Data Rate of 390 Gbps. For 1/8th Pixel Motion Estimation, we are adding 7 Sub-Pixels for every Integer Pixel. Pixel Count increases by a factor of 64. For 4K HDTV -570 Million Pixels per Frame. At 60fps, the Pixel rate is 34 Billion Pixels per Second or 34 Giga Pixels/Second, Video Processing Data Rate of 780 Gbps. At 120fps the Pixel rate 70 Billion Pixels Per Second or 70 Giga Pixels/Second, Video Processing Data Rate of 1560 Gbps / 1.56 Tbps. © 2016 IEEE.

In this present work, a review of fatigue strength on bio-nano composites was presented. Generally, the biocomposites possessed higher fatigue strength than the conventional materials. Because the propagation in biocomposites can be arrested due to their internal structure, whereas the damages occur in the matrix and/or at the fiber/matrix interface region. In the case of conventional materials, the cracks would rapidly grow until a catastrophic failure occurs. Thus, the S–N curves of composites show a flatter curve when compared to the conventional materials. In terms of fatigue properties, conventional fiber-reinforced composites (e.g., glass and carbon-reinforced composites) were more extensively studied than bio-based reinforced composites. Examining the fatigue properties of materials is significant for all engineering-based applications. Because the fatigue failures can occur below the ultimate tensile strength of materials. However, the fatigue performance of the composites can be enhanced by improving the fiber-matrix interfacial bonding resulting from surface treatment of fibers and incorporating nanofillers within the matrix, and so forth. Since the nanocomposites have a larger surface area and aspect ratio, they are preferred to use in many applications: aerospace, automotive, biotechnology, construction, and building, electronics, marine, and packing industries. Thus, this chapter starts with the unique advantages of bio-nano composites. Then, the fatigue properties of bio-nano composites, the significance of the S–N diagram, and the pattern of fatigue testing were discussed. In order to understand the fatigue behavior, several factors such as structure, fillers, fabrication techniques, and so forth were included. Challenges of fatigue strength of biocomposites were also summarized.

The increase in human population has triggered the need to increase the agriculture production worldwide. At the same time climatic conditions, water scarcity and population growth are decreasing the arable land. Hence there is a need to evolve novel ways to improve agricultural produce while utilizing lesser resources. Precision Agriculture combines temporal, spatial, remote, and individual data along with decisions to enable specific automated actions on fields. Big Data is one of the central technologies used in precision farming to store, retrieve and process abstract information. In the article we propose a system, method to efficiently collate, store and process data from multiple sources on a Big Data system and validate the approach. The proposed system will build on the Hadoop framework. © 2022 IEEE.

Dynamic data stream processing using real time ETL techniques is currently a high concern as the amount of data generated is increasing day by day with the emergence of Internet of Things, Big Data and Cloud. Data streams are characterized by huge volume that can arrive with a high velocity and in different formats from multiple sources. Therefore, real time ETL techniques should be capable of processing the data to extract value out of it by addressing the issues related to these characteristics that are associated with data streams. In this work, we asses and analyze the capability of existing ETL techniques to handle dynamic data streams and we present whether the existing techniques are relevant in the present situation. © 2016 IEEE.

Within the context of the Internet of Things (IoT), this study presents a novel approach that makes use of blockchain technology to guarantee the safety of data that is involved in the environment. The internet of things (IoT) trend has become more widespread, which has resulted in an increased demand for robust security methods. When it comes to providing the necessary position of security and sequester protection, traditional centralized approaches regularly and unexpectedly fail to meet expectations. In order to facilitate secure data sharing among Internet of Things bias, our proposed solution makes use of blockchain technology as a decentralized tally. The utilization of operation keys, the restriction of allowed access, and the preservation of data integrity are the means by which essential operations are accomplished. Through the implementation of blockchain technology, our strategy further improves the transparency, invariability, and responsibility of the data. In a similar vein, it reduces the risks that are associated with centralized control points and single points of failure. Using theoretical analysis and simulation experiments, we establish the efficacy and efficiency of our suggested technique in ensuring the secure sharing of data inside Internet of Things ecosystems. This paves the way for the abandonment of blockchain-based solutions in Internet of Things security. © 2024 IEEE.

The blockchain skillset is one of the most emerging skill sets that brings the world into the hands of the self. The number of industrial applications depends on this new technology just because of its decentralized, transparent, and secure nature. This enables a new way for the next generation of computing environments like cloud computing and edge computing. By keeping this in mind, this work develops a new disruptive method using adaptive learning model to address the security issues in a data sharing environment with decentralized access control. The developed framework has been executed and tested utilizing Python, and the results have been presented. A performance study comparison between the existing RSA algorithm, AES algorithm, and the proposed algorithm (ALM) has been done, and the various parameters taken for the study and their values are presented in this paper. Results obtained show that the algorithm presented is proven to be efficient in terms of security, scalability and time. © Mohanambal. B. et al.

In the realm of medical visual scrutiny, the accurate identification of prostate cancer holds paramount significance for early diagnosis and effective treatment. This work presents a pioneering method for prostate cancer identification, harnessing the power of deep learning and Migration Learning strategies. Leveraging the VGG-16 Convolutional Neural Network (CNN) framework as the cornerstone, the proposed approach capitalizes on its ability to extract intricate features from medical images. By incorporating Migration Learning, the model is enriched with knowledge gleaned from diverse datasets, enabling it to achieve exceptional performance even with limited medical image data. The methodology entails meticulous dataset curation and preprocessing, ensuring the quality and representativeness of the images. The VGG-16 model undergoes a meticulous finetuning process, accommodating the unique characteristics of prostate cancer images. Performance evaluation is conducted rigorously, utilizing established metrics to gauge the approach's effectiveness. Comparative analysis with contemporary methods showcases the breakthrough potential of the proposed approach. The model gave 93.97% testing accuracy. © 2024 IEEE.

Machine learning models have been successfully applied in numerous fields. Training a model is the most important aspect of machine learning for its successful application for the problem. To improve the training of a machine learning model thereby improve the performance, the selection of features and setting optimal parameters is crucial. Mainly two kinds of parameters are required to deal with, namely internal and external parameters. Internal parameters are model parameters and configurable such as weights of neural networks and their estimation can be done using data set. The hyperparameters such as learning rate, size of layers, number of layers, loss function etc, are external parameters and its values cannot be determined using the data set and it is not the part of the model. Its estimation can be done by the domain expert or using some trialanderror techniques until it achieves some acceptable values. However, these techniques are highly timeconsuming and cannot ensure the optimal values for these hyperparameters. In recent years different metaheuristic techniques have been applied to determine the optimal values of hyper parameters for machine learning models. In this paper we have conducted a brief comparative study of a few popular metaheuristic approaches applied for the hyperparameter optimization for various machine learning models. In this paper various evaluation measures have been considered for comparative analysis of metaheuristic approaches for hyperparameter optimization for deep learning model. © 2023 IEEE.