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Hybrid area explorationbased mobility-assisted localization with sectored antenna in wireless sensor networks
In common practice, sensor nodes are randomly deployed in wireless sensor network (WSN); hence, location information of sensor node is crucial in WSN applications. Localization of sensor nodes performed using a fast area exploration mechanism facilitates precise location-based sensing and communication. In the proposed localization scheme, the mobile anchor (MA) nodes integrated with localization and directional antenna modules are employed to assist in localizing the static nodes. The use of directional antennas evades trilateration or multilateration techniques for localizing static nodes thereby resulting in lower communication and computational overhead. To facilitate faster area coverage, in this paper, we propose a hybrid of max-gain and cost-utilitybased frontier (HMF) area exploration method for MA node's mobility. The simulations for the proposed HMF area explorationbased localization scheme are carried out in the Cooja simulator. The paper also proposes additional enhancements to the Cooja simulator to provide directional and sectored antenna support. This additional support allows the user with the flexibility to feed radiation pattern of any antenna obtained either from simulated data of the antenna design simulator, ie, high frequency structure simulator (HFSS) or measured data of the vector network analyzer (VNA). The simulation results show that the proposed localization scheme exhibits minimal delay, energy consumption, and communication overhead compared with other area explorationbased localization schemes. The proof of concept for the proposed localization scheme is implemented using Berkeley motes and customized MA nodes mounted with indigenously designed radio frequency (RF) switch feed network and sectored antenna. 2019 John Wiley & Sons, Ltd. -
Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems
The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches. 2023 CRL Publishing. All rights reserved. -
Hybrid Bayesian and modified grey PROMETHEE-AL model-based trust estimation technique for thwarting malicious and selfish nodes in MANETs
Cooperation among mobile nodes during the routing process is indispensable for attaining reliable data delivery between the source and destination nodes in the Mobile ad hoc networks (MANETs). This cooperation between mobile nodes sustains the performance of the network especially when they are been deployed for handling an emergency scenario like forest fire, flooding, and military vehicle monitoring. In specific, the criteria considered for determining the cooperation degree of mobile nodes attributed towards the routing proves is dynamic and uncertain. In this paper, Hybrid Bayesian, and Modified Grey PROMETHEE-AL Model-based Trust Estimation (MGPALTE) technique is proposed for thwarting Malicious and Selfish Nodes for enforcing cooperation between the mobile nodes in MANETs. It specifically utilized Bayesian BestWorst Method method for generating the set of weights related to objective group criteria. It is also used for aggregating the judgements of cooperation determined during indirect monitoring process. Moreover, Grey theory is integrated with the classical PROMETHEE for improving its efficacy in terms of accuracy with respect to ranking of mobile nodes participating in the process of routing. This proposed MGPALTE technique isolated the malicious mobile nodes from the routing path depending on the threshold of detection. The simulation results of the proposed MGPALTE technique confirmed better packer delivery rate of 19.21%, improved throughput of 22.38%, minimized delay of 23.19%, and reduced end-to-end delay of 21.36%, better than the competitive cooperation enforcement strategies with different number of mobile nodes in the network. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Hybrid botnet detection using ensemble approach
Botnets are one of the most threatening cyber-attacks available today. This paper proposes a hybrid system which can effectively detect the presence of C&C, P2P and hybrid botnets in the network. The powerful machine learning algorithms like BayesNet, IBk, KStar, J48 and Random Tree have been deployed for detecting these malwares. The performance and accuracy of the individual classifiers are compared with the ensemble approach. Labelled dataset of botnet logs were collected from the Malware Facility. Secured data was collected from Christ university network and the combined dataset is tested using virtual test bed. The performance of the algorithms is studied in this paper. Ensemble approach out performed individual classifiers. 2005 ongoing JATIT & LLS. -
Hybrid cryptography security in public cloud using TwoFish and ECC algorithm
Cloud computing is a structure for rendering service to the user for free or paid basis through internet facility where we can access to a bulk of shared resources which results in saving managing cost and time for large companies, The data which are stored in the data center may incur various security, damage and threat issues which may result in data leakage, insecure interface and inside attacks. This paper will demonstrate the implementation of hybrid cryptography security in public cloud by a combination of Elliptical Curve Cryptography and TwoFish algorithm, which provides an innovative solution to enhance the security features of the cloud so that we can improve the service thus results in increasing the trust overthe technology. 2019 Institute of Advanced Engineering and Science. -
Hybrid fruit-fly optimization algorithm with k-means for text document clustering
The fast-growing Internet results in massive amounts of text data. Due to the large volume of the unstructured format of text data, extracting relevant information and its analysis becomes very challenging. Text document clustering is a text-mining process that partitions the set of text-based documents into mutually exclusive clusters in such a way that documents within the same group are similar to each other, while documents from different clusters differ based on the content. One of the biggest challenges in text clustering is partitioning the collection of text data by measuring the relevance of the content in the documents. Addressing this issue, in this work a hybrid swarm intelligence algorithm with a K-means algorithm is proposed for text clustering. First, the hybrid fruit-fly optimization algorithm is tested on ten unconstrained CEC2019 benchmark functions. Next, the proposed method is evaluated on six standard benchmark text datasets. The experimental evaluation on the unconstrained functions, as well as on text-based documents, indicated that the proposed approach is robust and superior to other state-of-the-art methods. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Hybrid HOG-SVM encrypted face detection and recognition model
Security plays a major role in an individuals life to win this world with highly secure and authentic lifestyle with the digital equipments. The paper proposed an encryption based secure face detection and recognition model which can be implemented in daily life to generate a more robust and efficient security bubble around the world. The most crucial problem encountered during face recognition is due to the variation in face direction of an individual, the model solves the mentioned pose variation problem. The proposed model takes the help of face recognition library to recognize the face and use HOG (Histogram of Oriented Gradients) & SVM for checking the face authentication by performing an image match, the model also applies the concept of HOG to generate the encoded features from the image. The system is divided into two modules first is to detect a face and then match the detected face from the authentic persons dataset available. The system uses the concept of OpenCV library for giving a support system for the real time image. For data encryption, proposed model used the concept of DES3 and RSA algorithm. The proposed model gets 83.33% accuracy while tested for three different image types and states that the RSA algorithm performs encryption in less computational time. 2022 Taru Publications. -
Hybrid homomorphic-asymmetric lightweight cryptosystem for securing smart devices: A review
The Internet of Things (IoT) has emerged as a new concept in information and communication technology, and its structure depends on smart device communications. It was evolving as a significant factor of the Internet and made the interconnection of huge devices likely, accumulating huge amounts of information through innovative technologies. Thus, the requirement for IoT security is more significant. Scalable services and applications are susceptible to information leakage and attacks, demanding higher privacy and security. Cryptography is a technique to secure data integrity, confidentiality, authentication, and network access control. Owing to several limitations of IoT devices, the classical cryptographic protocols are not appropriate for all IoT smart devices like smart cities, smart homes and so forth. Consequently, researchers have introduced numerous lightweight cryptographic (LWC) protocols and algorithms for IoT security. Numerous solutions are available in the research field regarding security using cryptographic algorithms in IoT environments; however, such solutions have not attained satisfactory outcomes. So, finding a solution by examining the recent issues is open research. This article investigates the various LWC protocols for IoT devices and provides a reasonable enquiry into existing ubiquitous ciphers. Furthermore, the article appraises various recently presented lightweight (LW) block ciphers and hybrid homomorphic LWC regarding security. In addition, this article assists in comprehending the significance of security features and progression in cryptographic algorithms. Finally, the article reports on the necessary changes and recommends upcoming research focuses. Also, this article assists in realizing the importance of security and progressions in cryptographic algorithms. 2023 John Wiley & Sons, Ltd. -
Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms
Stock market prediction is one of the critical issues in fiscal market. It is important issue for the traders and investors. Artificial Neural Networks (ANNs) associated with nature inspired algorithms are playing an increasingly vital role in many areas including medical field, security systems and stock market. Several prediction models have been developed by researchers to forecast stock market trend. However, few studies have focused on improving stock market prediction accuracy especially when utilizing artificial neural networks to perform the analysis. This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Time Delay Neural Network (TDNN) and nature inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC).The developed models were named as GA-BPNN, PSO-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN. Nature inspired algorithms are employed for optimizing the parameters of ANNs. Technical indicators calculated from historical data are fed as input to developed models. Proposed hybrid models validated on four datasets representing different sectors in NSE. Four statistical metrics, Root Mean Square Error (RMSE), Hit Rate (HR), Error Rate (ER) and prediction accuracy were utilized to gauge the performance of the developed models. Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. The other models, GA-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN produced lower performance with mean prediction accuracy of 97.24%, 98.37%, 84.01%, 85.15%, 84.01%, 83.87%, 89.95% and 78.61% respectively. 2021 -
Hybrid nanofluid flow over a vertical rotating plate in the presence of hall current, nonlinear convection and heat absorption
An exact analysis has been carried out to study a problem of the nonlinear convective flow of hybrid nanoliquids over a vertical rotating plate with Hall current and heat absorption. Three different fluids namely CuAl2O3H2Ohybrid nanofluid, Al2O3H2O nanofluid and H2O basefluids are considered in the analysis. The simulation of the flow was carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e., sphere, hexahedron, tetrahedron, column and lamina). The governing PDEs with the corresponding boundary conditions are non-dimensionalised with the appropriate dimensionless variables and solved analytically by using LTM (Laplace transform technique). This investigation discusses the effects of governing parameters on velocity and temperature fields in addition to the rate of heat transfer. The numeric data of the density, thermal conductivity, dynamic viscosity, specific heat, Prandtl number and Nusselt number for twelve different hybrid nanofluids at 300 K is presented. The temperature profile of hybrid nanoliquid is larger than nanoliquid for same volume fraction of nanoparticles. Also, the glycerin-based nanoliquid has a high rate of heat transfer than engine oil, ethylene glycol and water-based nanoliquids in order. 2018 by American Scientific Publishers All rights reserved. -
Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy
Efficient traffic management solutions in 6G communication systems face challenges as the scale of the Internet of Things (IoT) grows. This paper aims to yield an all-inclusive framework ensuring reliable air pollution monitoring throughout smart cities, capitalizing on leading-edge techniques to encourage large coverage, high-accuracy data, and scalability. Dynamic sensors deployed to mobile ad-hoc pieces of fire networking sensors adapt to ambient changes. To address this issue, we proposed the Quantum-inspired Clustering Algorithm (QCA) and Quantum Entanglement and Mobility Metric (MoM) to enhance the efficiency and stability of clustering. Improved the sustainability and durability of the network by incorporating Dynamic CH selection employing Deep Reinforcement Learning (DRL). Data was successfully routed using a hybrid Quantum Genetic Algorithm and Ant Colony Optimization (QGA-ACO) approach. Simulation results were implemented using the ns-3 simulation tool, and the proposed model outperformed the traditional methods in deployment coverage (95%), cluster stability index (0.97), and CH selection efficiency (95%). This work is expected to study the 6G communication systems as a key enabler for IoT applications and as the title legible name explains, the solutions smartly done in a practical and scalable way gives a systematic approach towards solving the IoT traffic, and multi-routing challenges that are intended to be addressed in 6G era delivering a robust IoT ecosystem in securing the process. The Author(s) 2024. -
Hybrid scheme image compression using DWT and SVD
Image compression is process of reducing data size to represent an image by removing redundant data. Hybrid scheme image compression is combination of methods performed in order or as an amalgam to form a new technique. In this paper, we proposed a new approach to compress the image by collaborating Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD). Image is decomposed into wavelets using DWT and approximate wavelet is subsequently transformed into four bands. Different wavelet filters are implemented for transformation namely Haar, Daubechies, Biorthogonal and Coiflets. Apart from approximate image, SVD is applied on remaining wavelets (Horizontal, Vertical and Diagonal Details) at each decomposition level. On reconstruction, various singular values are selected depending on the level transformation. The performance of the proposed method is compared and evaluated with SVD, DCT-SVD and DWT-DCT-SVD. Evaluation is carried out based on Compression Ratio (CR), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. From the experimental results, it is observed that proposed method yields better MSE, PSNR and SSIM compared to state-of-the-art methods. 2017, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Hybrid shuffled frog leaping and improved biogeography-based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks
Wireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography-based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes. 2021 John Wiley & Sons Ltd. -
Hycons Renewable Private Limited: decision to accept or reject an equity investment
Learning outcomes: This study will help students determine the economic value of a firm particularly in case of a small business. The crux of the case is to help students estimate an enterprise value for a company and figure the actual worth of the company to aid in decision-making. Case overview/Synopsis: This case is about a decision dilemma faced by Shashi Hegde, Director, Hycons Renewable Private Ltd, a company ventured into the production of Bio-CNG. It is about a recent proposal received by the firm from APL Ltd for equity investment with 40% stake in the firm. The case reflects the dilemma faced by small businesses to choose between investment or loss of control. Accepting the proposal will bring in additional funds, whereas the Board loss control on the firm. The case revolves around this dilemma. To help Hegde in this task, he seeks advice from his CFO and his confidant Kumar. Complexity academic level: This case is most appropriate for a core finance class for both under-graduate and graduate programs. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 1: Accounting and Finance. 2022, Emerald Publishing Limited. -
Hycons Renewable Private Limited: Viable Biogas Production from Paddy Straws: A Capital Budgeting Decision
The case revolves around the decision to be taken by Mr Sashikant Hegde, Managing Director, Hycons Renewable Private Ltd, on the project viability of a business proposal. The proposal was to start a manufacturing plant in Punjab to produce compressed biogas using paddy straws. Hegde firmly believed that the company would do well considering the growth of the CNG market in India, as the oil and natural gas sector in India is among the top 10 core industries in the country and plays an important role in the existence of other important sectors as well. The proposal would benefit Hycons to establish its presence in northern India, but the project viability and funding of the investment remained an unanswered question. 2023 Lahore University of Management Sciences. -
Hydrogen Sulfide-Induced Activatable Photodynamic Therapy Adjunct to Disruption of Subcellular Glycolysis in Cancer Cells by a Fluorescence-SERS Bimodal Iridium Metal-Organic Hybrid
The practical application of photodynamic therapy (PDT) demands targeted and activatable photosensitizers to mitigate off-target phototoxicity common in always on photosensitizers during light exposure. Herein, a cyclometalated iridium complex-based activatable photodynamic molecular hybrid, Cy-Ir-7-nitrobenzofurazan (NBD), is demonstrated as a biomedicine for molecular precision. This design integrates a hydrogen sulfide (H2S)-responsive NBD unit with a hydroxy-appended iridium complex, Cy-Ir-OH. In normal physiological conditions, the electron-rich Ir metal center exerts electron transfer to the NBD unit, quenches the excited state dynamics, and establishes a PDT-off state. Upon exposure to H2S, Cy-Ir-NBD activates into the potent photosensitizer Cy-Ir-OH through nucleophilic substitution. This mechanism ensures exceptional specificity, enabling targeted phototherapy in H2S-rich cancer cells. Additionally, we observed that Cy-Ir-NBD-induced H2S depletion disrupts S-sulfhydration of the glyceraldehyde-3-phosphate dehydrogenase enzyme, impairing glycolysis and ATP production in the cellular milieu. This sequential therapeutic process of Cy-Ir-NBD is governed by the positively charged central iridium ion that ensures mitochondria-mediated apoptosis in cancer cells. Dual-modality SERS and fluorescence imaging validate apoptotic events, highlighting Cy-Ir-NBD as an advanced theranostic molecular entity for activatable PDT. Finally, as a proof of concept, clinical assessment is evaluated with the blood samples of breast cancer patients and healthy volunteers, based on their H2S overexpression capability through SERS and fluorescence, revealing Cy-Ir-NBD to be a promising predictor for PDT activation in advanced cancer phototherapy. 2024 American Chemical Society. -
Hydrothermally synthesized mesoporous Co3O4 nanorods as effective supercapacitor material
Mesoporous Co3O4 nanomaterial in rod-shape morphology has been synthesized via a hydrothermal method, and heat treated at 350 C for 2 h to develop a phase. Phase purity, morphology, specific surface area and chemical composition of as-obtained Co3O4 material were studied using XRD, Raman, TEM, N2-adsoprtion/desorption and XPS techniques. XRD and Raman analyses indicate single phase material formation with nano-structure, and cubic normal spinel-type structure with a cell parameter of 8.123 The spinel particles are of rod-shape morphology and the specific surface area, estimated through BET studies, is obtained as 47 m2/g. Cyclic voltammogram (CV) recorded at different scan rates evidently demonstrate pseudocapacitance nature of the synthesized material. Maximum specific capacitance (CS) is computed and the value is 261 F/g at 0.25 A/g. These materials have shown longer cycle stability at lower KOH concentration and lower current density. Synthesized Co3O4 nanomaterial could be used as electrode material for energy storage applications. 2023 Elsevier B.V. -
Hyperspectral multi-level image thresholding using qutrit genetic algorithm
Hyperspectral images contain rich spectral information about the captured area. Exploiting the vast and redundant information, makes segmentation a difficult task. In this paper, a Qutrit Genetic Algorithm is proposed which exploits qutrit based chromosomes for optimization. Ternary quantum logic based selection and crossover operators are introduced in this paper. A new qutrit based mutation operator is also introduced to bring diversity in the off-springs. In the preprocessing stage two methods, called Interactive Information method and Band Selection Convolutional Neural Network are used for band selection. The modified Otsu Criterion and Masi entropy are employed as the fitness functions to obtain optimum thresholds. A quantum based disaster operation is applied to prevent the quantum population from getting stuck in local optima. The proposed algorithm is applied on the Salinas Dataset, the Pavia Centre Dataset and the Indian Pines dataset for experimental purpose. It is compared with classical Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Gray Wolf Optimizer, Harris Hawk Optimization, Qubit Genetic Algorithm and Qubit Particle Swarm Optimization to establish its effectiveness. The peak signal-to-noise ratio and Sensen-Dice Similarity Index are applied to the thresholded images to determine the segmentation accuracy. The segmented images obtained from the proposed method are also compared with those obtained by two supervised methods, viz., U-Net and Hybrid Spectral Convolutional Neural Network. In addition to this, a statistical superiority test, called the one-way ANOVA test, is also conducted to judge the efficacy of the proposed algorithm. Finally, the proposed algorithm is also tested on various real life images to establish its diversity and efficiency. 2021 Elsevier Ltd -
I am lost in that reality, and I'm just playing the game: a qualitative study exploring gaming behaviour and its effect on young adults in India
The current study aims to explore gaming behaviour among young adults in India and its effects on their physical and emotional health, productivity, interactions, and social life. Data were collected from 12 avid gamers through in-depth semi-structured interviews. A thematic analysis was conducted to identify the global theme and organise themes from the data. The global theme was the exploration of contemporary gaming behaviour. The habit theme included the origin of the game, practice, and change over time, while the effects theme focused on the physical and emotional health, productivity, interactions, and social life of young adults who engaged in gaming. The findings suggest that gaming behaviour has become an established habit among young adults in India, significantly affecting various aspects of their lives. The study highlights the need for increased awareness of the potential negative consequences of excessive gaming and emphasises the importance of moderation in gaming. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
I Can Live Without Banks, but Not Without Banking: Role of Trust on Loyalty and Evangelism
The purpose of this paper is to examine the antecedents of e-banking loyalty and evangelism via threefold construct of WEQUAL (usability, information quality, and service interaction) of public sector banks operating in India. Moreover, it also investigates the mediating role of consumers' trust on the website quality of these banks and their impact on e-banking loyalty and evangelism. The data was collected from 243 respondents through online questionnaire. In order to develop the model and test the hypotheses, partial least square structural equation modeling (PLS-SEM) was done through Smart PLS version 3.2.9. Results assert that website quality of banks positively influences the trust of consumers via usability, information quality, and service interaction. Also, consumer trust plays a mediation role between WEBQUAL constructs and e-banking loyalty and evangelism. 2021 IGI Global. All rights reserved.