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A hybrid algorithm for face recognition using PCA, LDA and ANN
Face recognition is an evolving technique in the field of digital device security. The two procedures Principal Component Analysis and Linear Discriminant Analysis (LDA) are standard methodologies commonly used for feature extraction and dimension reduction techniques extensively used in the recognition of face system. This paper discourse, PCA trailed through a feed forward neural network (FFNN) called PCA-neural network and LDA trailed through feed forward neural network as LDA-neural network are considered for development of hybrid face recognition algorithm. In the current research work, a hybrid model of face recognition is presented with the integration of PCA, LDA, and FFNN. The proposed system experimental results indicate better performance compared to the state of the art literature methods. IAEME Publication. -
A Hybrid Approach Against Black Hole Attackers Using Dynamic Threshold Value and Node Credibility
Detecting black hole attackers is tedious in Vehicular Ad Hoc Networks due to vehicles' high mobility. The main consequence faced because of these attackers is an increase in the number of dropped packets which converts secure and fastest paths to compromised ones. Since these attackers can act individually and collaboratively as a group, early detection of these attackers must be feasible to preserve the network's performance. The majority of current methods rely on predetermined threshold and trust score values, which are ineffective in accurately identifying black hole attackers. Hence, this paper proposes a hybrid approach using dynamic threshold value and node credibility for early detection of black hole attackers. RSUs periodically compute the dynamic threshold value and categorize the vehicles into categories 1, 2, and 3. Vehicles classified as Category 1 are legitimate, whereas Category 3 vehicles are attackers. Vehicles in Category 2 are suspicious, requiring further analysis using node credibility values to identify attackers. It is protected against single, multiple, and collaborative black hole attackers. The NS2 simulation results demonstrate that the suggested method is optimal concerning PDR, Throughput, Delay, and Packet Loss Ratio compared to recent techniques. Since the proposed scheme efficiently identifies the attackers, it has 89.67% PDR, which is higher when compared to other schemes. 2013 IEEE. -
A hybrid approach for COVID-19 detection using biogeography-based optimization and deep learning
The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron emission tomography, lung ultrasound, and magnetic resonance imaging, as inputs. In the comparative analysis, the proposed deep learning model CNN is compared with other existing models, namely, VGG16, InceptionV3, ResNet50, and MobileNet. In the fitness function formation, classification accuracy is considered to enhance the prediction capability of the proposed model. Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50. 2022 Tech Science Press. All rights reserved. -
A HYBRID APPROACH FOR LANDMARK DETECTION OF 3D FACES FOR FORENSIC INVESTIGATION
Facial landmark detection is a key technology in many forensic applications, such as facial identification and facial reconstruction. However, the accuracy of facial landmark detection is often limited in 3D face images due to the challenges of occlusion, illumination, and pose variations. This paper proposes a hybrid approach for landmark detection of 3D faces for forensic investigation. A hybrid method of edge contour detection and Harris corner detection is proposed for feature extraction in face images for forensic investigation. Edge contour detection is used to detect the boundaries of the face, while Harris corner detection is used to detect the corners. The advantage of using a hybrid method of edge contour detection and Harris corner detection for feature extraction in face images is that it can capture both global and local features of the face. Edge contour detection can capture global features, such as the overall shape and outline of the face, while Harris corner detection can capture local features, such as the corners of the mouth, nose and eyes which are vital for facial reconstruction. Experimental results show that the proposed method outperforms existing landmark detection algorithms in terms of time complexity and minimum loss. 2023 Little Lion Scientific. -
A hybrid crypto-compression model for secure brain mri image transmission
Medical image encryption is a major issue in healthcare applications where memory, energy, and computational resources are constrained. The modern technological architecture of digital healthcare systems is, in fact, insufficient to handle both the current and future requirements for data. Security has been raised to the highest priority. By meeting these conditions, the hybrid crypto-compression technique introduced in this study can be used for securing the transfer of healthcare images. The approach consists of two components. In order to construct a cutting-edge generative lossy compression system, we first combine generative adversarial networks (GANs) with oearned compression. As a result, the second phase might address this problem by using highly effective picture cryptography techniques. A randomly generated public key is subjected to the DNA technique. In this application, pseudo-random bits are produced by using a logistic chaotic map algorithm. During the substitution process, an additional layer of security is provided to boost the techniques fault resilience. Our proposed system and security investigations show that the method provides trustworthy and long-lasting encryption and several multidimensional aspects that have been discovered in various public health and healthcare issues. As a result, the recommended hybrid crypto-compression technique may significantly reduce a photos size and remain safe enough to be used for medical image encryption. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
A hybrid deep learning and quantum computing approach for optimized encryption algorithms in secure communications
As online dangers get worse, there is a greater need for strong encryption methods to protect private conversations. Utilizing the strengths of both deep learning and quantum computing, this study suggests a new mixed method for improving the security of communication systems by making encryption algorithms work better. When it comes to keeping up with new online threats, traditional security methods often fall behind. Deep learning techniques could be a good way to improve encryption algorithms because they let the system learn and change to new attack methods. In the meantime, quantum computing offers unmatched computing power that can completely change how cryptography works by using quantum events like superposition and entanglement. Our suggested method combines the flexibility of deep learning with the computing power of quantum computing to get around the problems with current encryption methods. This will make safe communication systems more resistant to attacks from smart people. Through tests and models, we show that our mixed approach works better and more effectively than current encryption methods. This shows that it has the ability to solve the growing safety problems in a world that is becoming more and more linked. 2024, Taru Publications. All rights reserved. -
A hybrid level set based approach for surface water delineation using landsat-8 multispectral images
The detection and delineation of surface water is a crucial step in change detection studies on water bodies using satellite images. Single band methods, spectral index methods, classification using machine learning and spectral un-mixing methods are the widely used strategies for surface water mapping from multi-spectral images. Level set theory based algorithms have been successfully employed in image segmentation problems and are proven to be effective. This study presents a hybrid level set theory based segmentation algorithm which is a combination of edge based and region based approaches to detect and delineate surface water bodies in Landsat 8 images. Level set algorithms were applied in combination with Modified Normalized Difference Water Index (MNDWI) to further improve the delineation accuracy. Robustness of the proposed approach was established by successfully applying the algorithm to delineate water bodies of different sizes, ranging from 0.5 km2 to 298 km2 in surface area. The proposed algorithm was also compared with established machine learning based delineation methods and found to be faster than the algorithms those produced comparable delineation outputs. As the ground truth was not available for accuracy measurement, the output image of the proposed method was compared with the outputs of the machine learning algorithms using Pearsons correlation co-efficient, Structural Similarity Index (SSIM) and Dice Similarity Index. The proposed algorithm was subsequently applied to multi-temporal Landsat data for water body change detection and analysis. 2021, International Association of Engineers. All rights reserved. -
A hybrid level shifted carrier-based PWM technique for modular multilevel converters
This paper presents a hybrid level shifted carrier-based pulse width modulation (HLSC-PWM) technique for modular multilevel converters (MMCs). The concept of the proposed HLSC-PWM method is developed by combining the principles of phase disposition PWM (PD-PWM), phase opposition disposition PWM (POD-PWM), and alternate phase opposition disposition PWM (APOD-PWM) methods. The main aim of the proposed HLSC-PWM method is to generate an output voltage with half-wave and quarter-wave symmetries. The generated symmetrical PWM output voltage based on the proposed HLSC-PWM method provides less total harmonic distortion (THD) and enhances the DC-Link voltage utilization (DCLVU). A generalized mathematical model is formulated to develop a single HLSC for MMC with an N number of submodules (SMs) per arm. Theoretical analysis of DCLVU for the proposed method is described. The functionality and performance of the HLSC-PWM method are carried out on a three-phase five-level MMC in MATLAB/Simulink. A hardware prototype of a single-phase five-level MMC is designed for experimental validation. The proposed HLSC-PWM method is implemented on an Altera/Cyclone I series (EP1C12Q240C8N) field-programmable gate array (FPGA), simulation and experimental results are presented. 2021 The Authors. IET Power Electronics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology -
A hybrid scheme of image compression employing wavelets and 2D-PCA
In this paper, we have presented a method of compressing 2D grey-scale images employing wavelets and two-dimensional principal component analysis (2D-PCA). Principal component analysis (PCA) is an already established technique for image compression which primarily aims at exploiting inter pixel redundancies present in the image, while wavelet is a tool widely used in multi-resolution image processing. In the proposed method the image is subjected to a multi-resolution decomposition using wavelet. Subsequently, 2D-PCA is applied on the set of detail images at each level of resolution. The compressed form of the image is constituted by representative pairs of principal components and projection vectors from each level of resolution along with the approximate image at the coarsest resolution. The proposed method requires relatively few number of principal components (of varied dimension) to produce improved compression ratio with acceptable peak signal to noise ratio (PSNR). The method has been implemented and tested on a set of real 2D grey-scale images and the results have been assessed on both qualitative and quantitative basis by measuring parameters like compression ratio (CR), PSNR, structural similarity index measurement (SSIM) and the overall performance is found to be satisfactory. Copyright 2017 Inderscience Enterprises Ltd. -
A hybrid technique linked FOPID for a nonlinear system based on closed-loop settling time of plant
Wind and hydroelectric systems are more cost-effective and environmentally beneficial. A hybrid technique is proposed for the fractional-order proportional-integral-derivative (FOPID) controller to regulate the wind and hydro system. The proposed hybrid technique combines the feedback-artificial-tree (FAT), and atomic-orbital-search (AOS); together known as FAT-AOS approach. The proposed technique is utilized to decide the optimum controller parameters, and it guarantees system constancy in large disturbances using less computation and overshoot by restraining the parameter variation. The FAT is used to predict the optimum gain parameter of FOPID, and minimizing the system error is accomplished with the AOS approach. The performance metrics are peak time, rise time, settling time, and peak overshoot, are analyzed. The performance of the proposed method is done in the MATLAB platform. The simulation result of proposed approach for the rise time as 0.001 sec, settling time is 0.012 sec, and the overshoot percentage is 0.02 %. By comparing the existing methods, like Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Particle swarm optimization (PSO), the proposed approach rise time and settling time overshoot, is less. The comparison proves that the proposed system delivers improved outcome than existing systems. 2024 -
A hybridized semantic trust-based framework for personalized web page recommendation
The World Wide Web is constantly evolving and is the most dynamic information repository in the world that has ever existed. Since the information on the web is changing continuously and owing to the presence of a large number of similar web pages, it is very challenging to retrieve the most relevant information. With a large number of malicious and fake web pages, it is required to retrieve Web Pages that are trustworthy. Personalization of the recommendation of web pages is certainly necessary to estimate the user interests for suggesting web pages as per their choices. Moreover, the Web is tending towards a more organized Semantic Web which primarily requires semantic techniques for recommending the Web Pages. In this paper, a framework for personalized web page recommendation based on a hybridized strategy is proposed. Web Pages are recommended based on the user query by analyzing the Web Usage Data of the users. An array of strategies is intelligently integrated together to achieve an efficient Web Page Recommendation system. Latent Semantic Analysis is applied to the User-Term Matrix and the Term-Frequency Matrix that are built from the Web Usage Information to form a Term Prioritization Vector. Further, techniques like Latent Dirichlet Allocation for Topic-based Segregation of the URLs and Normalized Pointwise Mutual Information strategies are used for recommending web pages based on users queries. The Personalization is achieved by prioritizing the Web pages based on the Prioritization Vector. Also, a unique methodology is incorporated into the system to retrieve trustworthy websites. An overall Accuracy of 0.84 is achieved which is better than the existing strategies. 2018 Informa UK Limited, trading as Taylor & Francis Group. -
A Legal Analysis of Cyber-Enabled Wildlife Offences in India: A Qualitative Case Study of Sea Fans (Gorgonia spp.) on YouTube
With the advent of the Internet, offences against threatened species have transitioned online. Such species are directly or indirectly traded on social media despite being protected under Indian wildlife law. A qualitative case study was undertaken to assess the preparedness of national law and policy in prohibiting such offences. Sixty-three YouTube links on sea fans in the Hindi language were accessed over 8 weeks, and the information generated by both content creators and audiences was gathered and categorized for analysis. The legal provisions were then interpreted and applied to assess the extent to which the parties involved could be held liable. Our investigation shows that of these video links, the content creators directly offered specimens for sale in 15.87% of instances, demonstrated physical possession of wild specimens in 23.81% of these posts, and were involved in both activities in 20.63% of the links, which in our analysis is explicitly prohibited under national law. The remaining 39.68% of video links merely disseminated information on the relevance or usage of species in occult or religious practices, for which no express legal provision currently exists. Certain indirect legal provisions were found to be relevant; however, there were challenges associated with their implementation. Even the liability of a social media company was found to be limited if it can be demonstrated that the company exercised due diligence. Therefore, there is a need to explicitly regulate online content that has the potential to drive an unlawful demand for protected species alongside the imposition of enhanced liability on social media companies. Such measures, coupled with community awareness, can reduce cyber-enabled wildlife offences committed through social media channels. 2024 Taylor & Francis Group, LLC. -
A Lightweight Multi-Chaos-Based Image Encryption Scheme for IoT Networks
The swift development of the Internet of Things (IoT) has accelerated digitalization across several industries, offering networked applications in fields such as security, home automation, logistics, and quality control. The growth of connected devices, on the other hand, raises worries about data breaches and security hazards. Because of IoT devices' computational and energy limits, traditional cryptographic methods face issues. In this context, we emphasize the importance of our contribution to image encryption in IoT environments through the proposal of Multiple Map Chaos Based Image Encryption (MMCBIE), a novel method that leverages the power of multiple chaotic maps. MMCBIE uses multiple chaotic maps to construct a strong encryption framework that considers the inherent features of digital images. Our proposed method, MMCBIE, distinguishes itself by integrating multiple chaotic maps like Henon Chaotic Transform and 2D-Logistic Chaotic Transform in a novel combination, a unique approach that sets it apart from existing schemes. Compared to other chaotic-based encryption systems, this feature renders them practically indistinguishable from pure visual noise. Security evaluations and cryptanalysis confirm MMCBIE's high-level security properties, indicating its superiority over existing image encryption techniques. MMCBIE demonstrated superior performance with NPCR (Number of Pixel Changing Rate) score of 99.603, UACI (Unified Average Changing Intensity) score of 32.8828, MSE (Mean Square Error) score of 6625.4198, RMSE (Root Mean Square Error) score of 80.0063, PSNR (Peak Signal to Noise Ratio) score of 10.2114, and other security analyses. 2013 IEEE. -
A longitudinal examination of the relation between academic stress and anxiety symptoms among adolescents in India: The role of physiological hyperarousal and social acceptance
Academic stress is a critical aspect of adolescent experience around the world, but particularly in countries with dense populations that lead to highly competitive college admissions. With a population of over one billion people, the competition for higher education in India is significantly high. Although research has shown that academic pressures are associated with anxiety in adolescents, this work is primarily cross-sectional. The current study examined academic stress and anxiety symptoms over time and assessed physiological hyperarousal as a mediator and social acceptance as a moderator of this relation in a sample of adolescents from India (N= 282, 1318 years, 84% female). Adolescents completed measures of academic stress, physiological hyperarousal, social acceptance and anxiety symptoms at two-time points, 5 months apart. Findings demonstrate direct effects of academic stress on changes in symptoms of generalised anxiety and panic, as well as indirect effects through physiological hyperarousal. Social acceptance did not moderate the relation, although it uniquely predicted changes in panic disorder symptoms over time. The findings of this study contribute to the scientific understanding of a potential mechanism through which academic stress leads to anxiety among adolescents in India. 2021 International Union of Psychological Science. -
A Low-Complexity Multiplier-Less Filter Bank Based on Modified IFIR for the SDR Channelizer
Digital filter banks are extensively used in an SDR channelizer for channelization. The objective of this research work is to design a low computational complexity FIR filter bank for generating sharp transition width channels for SDR. The design of unified and variable bandwidth channels for SDR using the proposed structure is based on the modified IFIR filter structure and cosine modulation technique (CMT). The performance of the proposed structure is demonstrated with the help of an example. The results show that the multiplier complexity of the proposed structure is less than those of other state-of-the art methods. The optimization techniques are incorporated in this work to further reduce the complexity of the proposed structure. With the help of canonical signed digit (CSD), multi-objective artificial bee colony (MOABC) and shift inclusive differential coefficients (SIDC) common sub-expression elimination (CSE) optimization, the filter used in this structure is made multiplier-less. 2024 IETE. -
A machine learning model for population analysis among different states in India which influences the socio, demographic and economic needs of society
In this work Data from 2011 census is taken to identify the state which influences more in Population census among the different states identified. The data is considered from Madhya Pradesh, followed with Utter Pradesh, then to Bihar, Bengal and Orissa. Similarly other case studies are also done for Southern Indian states and North Eastern States. Genetic algorithm will be tried to find the optimal location for the given study. A fitting function is calculated for the population data of 2011 using Lagrange Interpolation technique. This fitting function is given as input to Genetic algorithm to find the optimal state which have maximum influence in the population growth among different states of India as per the Case studies done. BEIESP. -
A Malicious Botnet Traffic Detection Using Machine Learning
Detection of incorrect and malign data transfers in the Internet of Things (IoT) network is important for IoT safety to observe an eye on and prevent unwelcomed traffic flow to the network of IoT. For it, Machine Learning (ML) strategic methods are produced by several researchers to prevent malign data flows through the network of IoT. Nonetheless, because of the wrong choice of feature, a few malign Machine Learning models differentiate especially the movement of malign traffic. Still, what matters is the problem that needs to be deliberated in-depth to select the best features for better malign traffic acquisition in the network of IoT. Dealing with the challenge, a new process was proposed. 1st, the metric method of selecting a novel feature called the proposed CorrAUC, and hinged on CorrAUC, a new highlight for choosing the Corrauc algorithm name is also being developed, designed hinged on the system folding filter features precisely and select the active features of the choose ML method using AUC metric. After that, we apply a combined application Order of Preference by Similarity to Ideal Solution Using Shannon Entropy (TOPSIS) built on a bijective set which is soft to verify selected features for identification of malign 1traffic in IoT network. We test our method using data set of Bot-IoT and 4 dissimilar ML classifiers. Practical outcomeanalysis showed that our proposed approach works as well and can achieve greater than 96% results on average. 2022 Wolters Kluwer Medknow Publications. All rights reserved. -
A Markovian risk model with possible by-claims and dividend barrier
A MAP/PH risk model with possible by-claims and a dividend barrier is considered. Along with the main claim, a by-claim also can occur with a certain probability but by-claims are settled only after an inquiry and hence delayed. The model is analysed considering associated Markovian fluid models under the original timeline and an auxiliary timeline. Systems of integro differential equations (IDE) are developed for the Gerber-Shiu function (GSF) and the total dividends paid until ruin. Explicit expressions are obtained for the GSF of the models without and then with the barrier. Expressions are also provided for the moments of the total dividends paid until ruin. A dividends-penalty identity is given. The method is numerically illustrated with a two-phase model and sensitivity analysis of the model is done by varying some of the parameters involved. 2023 Inderscience Enterprises Ltd.. All rights reserved. -
A mathematical model that describes the relation of low-density lipoprotein and oxygen concentrations in a stenosed artery
The cellular activities of the endothelium layer between lumen and intima are significantly linked to the origin of the disease atherosclerosis. Three stages of atherosclerosis were investigated in this study (40%-mild, 50%-modest, and 60%-acute) concerning the coronary arterial segment. The essence of the hemodynamic factors like flow velocity, pressure, and wall shear stress has been investigated, as well as the interrelationships between them. At all degrees of stenosis, the biophysical relationship between convection-diffusion of low-density lipoproteins (LDL) and convection-diffusion of oxygen in the bloodstream is investigated. The Finite Element Methods (FEM) are used to solve the modeled partial differential equation systems. The method adopted is numerical in nature providing accurate graphical solutions to the framed systems. The physical effects of the deposition of LDL on the arterial wall, like a decrease in the diameter of the lumen, and toughening of the walls, are analyzed through the evaluation of the physical parameters. The study revealed that the deposition of LDL molecules in the post stenotic region leads to the depletion of oxygen in the region leading to the rapid dysfunctioning of the endothelial layer of the lumen-intima boundary. 2022 World Scientific Publishing Company. -
A Meta-Analysis on the Determinants of Online Product Reviews with Moderating Effect of Product Type
The technological advances in digital space have provided a renewed impetus for business to expand their footprint across digital modes. The growth of the internet and the ease of its access to the masses has encouraged many businesses to go online. Online e-commerce platforms make it easy to search, locate and place orders. Technology-assisted supply chains and fast delivery mechanisms ensure that users don't have to go elsewhere to fulfill their needs. To earn loyalty and customer satisfaction, e-commerce platforms have evolved into a sophisticated recommender system. It has evolved from just an informational source to a participative mode where users can share their experiences about their purchases. Customer values other user experiences more than the information provided by the seller. The presence of many conflicting and contradicting reviews can make the task of making rational decisions difficult for many users. Many studies were performed to understand what constitutes a review helpful and came up with different or mixed outcomes. The present study reviews the factors that influence online customer reviews helpful. Meta-analysis was performed to reconcile the mixed findings of different factors of online review helpfulness. The meta-analysis found that with the moderating effect of product type, factors like review length, readability, rating, reputation, and expertise positively correlate with helpfulness. Further, the customer finds moderate reviews more helpful in terms of polarity. Meta-analysis has a mix of findings for the selected data points in the study. The mixed findings include product type (search, experience, or other) and helpfulness measurement criteria. 2022 Kavita Rawat and Sunita Kumar. This is an open access article licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.