Browse Items (14421 total)
Sort by:
-
A new lightweight data security system for data security in the cloud computing
In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture. 2023 The Authors -
A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience
A new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications. 2021, The Author(s). -
A new numerical investigation of fractional order susceptible-infected-recovered epidemic model of childhood disease
The susceptible-infected-recovered (SIR) epidemic model of childhood disease is analyzed in the present framework with the help of q-homotopy analysis transform method (q-HATM). The considered model consists the system of three differential equations having fractional derivative, and the non-linear system exemplifies the evolution of childhood disease in a population and its influence on the community with susceptible, infected and recovered compartment. The projected method is a mixture of q-homotopy analysis method and Laplace transform. Two distinct explanatory cases are considered, and corresponding simulations have been demonstrated in terms of plots for different value of the order. The present investigation elucidates that the projected both derivative and technique play a vital role in the analysis and illustrate the behaviour of diverse mathematical models described with differential equations in human disease. 2021 THE AUTHORS -
A new optimal design and analysis method based on MADM for MEMS products development
Abstract This paper presents an optimum design method to support the total micro-electromechanical systems (MEMS) product/device optimization, and its evaluation at the conceptual stage itself using the multiple attribute decision making method. In the traditional MEMS product development cycle, simulation and design using software tools are very important due to the knowledge limitation and complexity in design, fabrication, and packaging processes. The available tools are time consuming and relay on trial and error to achieve an optimum solution. The proposed method simplifies the relationship between parameters of design, fabrication, materials, packaging, and the performance of the MEMS product. The methodology is explained with the help of design flow diagram and time chart. A MEMS-based radio frequency (RF) power sensor is designed and the methodology is demonstrated. The proposed sensitivity analysis method is more effective and less time consuming than traditional techniques. Sensitivity analysis is carried out by varying the thickness of the signal conductor. The results of RF power sensor with insertion loss 0.428 dB, reflection loss 25.956 and voltage standing wave ratio of 1.106 at 1.5 GHz are reported. Springer-Verlag London Limited 2012. -
A new perspective on the genesis of the 2019/2020 Australian bushfire and its atmospheric radiative impacts
Extensive investigations of the genesis and atmospheric radiative impacts of the Australian bushfires of August 2019 to January 2020 (also known as the black summer event) have been carried out using in-situ, multi-satellite, and reanalysis data. We present the observational evidence for the role of total water storage in the initiation of this event. A strong correlation was found between the depletion of the total water storage (sum of surface and sub-surface water storage) caused by the hydrological drought and the burnt area in southeast Australia. Notably, a decadal low of Liquid Water Equivalent Thickness (LWET) going below ?5 cm in December 2019 strongly suggests the crucial role of hydrological drought in the genesis of the black summer event. The hydrological drought provided favorable conditions for intense fire activity during the black summer event and increased the aerosol loading across Australia. The assimilated Aerosol Optical Depth revealed that the impact of the black summer event on the aerosol loading is higher than previously reported. The amplified aerosol backscattering, coupled with the increased surface albedo due to the prevailing drought, led to a significant surge in outgoing shortwave flux and contributed to regional cooling. Along with the increased aerosol loading, it has also been observed that the co-emitted carbon monoxide enhanced the ozone production at 850 hPa, further degrading the air quality. These findings will offer crucial insights for predicting extreme bushfire events and their mitigation policies. 2025 Elsevier Ltd -
A New Series Solution Applicable to a Class of Boundary Layer Equations with Exponential Decay in Solution
A new series solution for the coupled nonlinear boundary value problem (BVP) of the FalknerSkan-type, resulting from the use of the boundary layer approximation to a mixed convective flow is obtained. The solution domain is transformed from the semi-infinite interval of physical interest to the unit interval by using the transformation z= 1 - e-?. The coupled nonlinear BVP is converted into an equivalent initial value problem (IVP) by supplying appropriate initial conditions. Series solution of the equivalent IVP is obtained in powers of 1 - e-? with assistance from the NewtonRaphson method. Convergence of the series is assured by the very design of the series as confirmed by the DombSykes plots and the plots of partial sums. The results of the series solution compare well with the numerical results obtained by the shooting method based on the RungeKuttaFehlberg45 and NewtonRaphson methods. The novelty of the methodology lies in the construction of a new series solution for a class of problems with exponential decay in their solution. Such a work has not been reported before. 2020, Springer Nature India Private Limited. -
A new shape of the supply chain during the COVID-19 pandemic
Purpose: The COVID-19 pandemic has created a new normal for international business (IB) activities, leaving them pondering their next steps. The decreasing effectiveness of current vaccines to protect individuals against new variants have created uncertainty on how to respond to the new waves of the COVID-19 infection. This study aims to empirically assesses how IBs perceive the unfolding challenges in the supply chain due to the pandemic and the solutions. Design/methodology/approach: The survey data is obtained from 166 logistics professionals in Hong Kong and India. Findings: The results reveal that returns on investment, logistics, delays and imports are the most affected areas. The most often recommended solutions for supply chain management (SCM) include using local manufacturing capabilities, analytics and automation, offering better customer service, providing more effective transportation means, ensuring diligence around optimization and focusing on sustainability. Originality/value: The findings of this study help to improve supply chain operations. This study also provides recommendations for changes to SCM in response to the new normal. 2022, Emerald Publishing Limited. -
A new stepwise method for selection of input and output variables in data envelopment analysis
Data envelopment analysis (DEA) is one of the widely accepted optimization technique uses to measure the relative efficiency of organizational units where multiple inputs and outputs are present. The significance of DEA results depends on the variables selected for DEA modelling. One of the main challenges in data envelopment analysis modelling is of identify the significant input and output variables for DEA modelling. In this study, we propose an enhanced stepwise method to identify the significant and insignificant input and output variable by reducing the iterations process in stepwise method. The statistical significance of the input and output variables evaluated using the statistical methods: Least significance difference (LSD), and Welchs statistics. The proposed method applied to the Indian banking sector and the results have shown that the proposed model significantly identified the significant and insignificant input and output variables with least loss of information. 2021 the author(s). -
A new trained ECG signal Classification method using Modified Spline Activated Neural Network
An ECG (Electrocardiogram) records the electrical activity of the heart and assess heart arrhythmia. Cardiac arrhythmia is an irregular heartbeat caused by unbalanced rhythm. In the past, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, a modified spline activated neural network, a new approach for cardiac arrhythmia classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. The MIT-BIH arrhythmia database was used and experimented for testing and training. 2018 IEEE. -
A New Versatile Discrete Distribution for Censored Data: Frequentist and Bayesian Methods With Real-Life Applications
This study introduces a novel and highly flexible class of discrete probability distributions tailored to model the diverse monotonic failure-rate patterns frequently observed in stock-market data. The proposed distribution accommodates outliers effectively and serves as a discrete analogue of the exponential law, enabling analysts to derive robust and interpretable insights into market dynamics. Fundamental mathematical characteristics of the distributionsuch as the probability-generating function, mean, and varianceare thoroughly derived. The model is further extended to handle Type-II censored data, enhancing its applicability to real-world scenarios where incomplete observations are common. Parameter estimation is performed using both maximum-likelihood and Bayesian approaches, with a special focus on techniques suitable for censored samples. The performance and reliability of the estimators are examined through extensive simulation studies. To validate the practical utility of the model, it is applied to five real stock-market datasets obtained from Indiastat. The results demonstrate a superior empirical fit, affirming the models relevance in capturing the underlying patterns of financial time series. This distribution provides a valuable tool for analysts and researchers in the fields of financial statistics, risk modeling, and market behavior analysis. 2013 IEEE. -
A node deployment mechanism for energy-efficient routing in heterogeneous wireless sensor networks
Military applications are the primary concern of the wireless sensor networks (WSNs). Efficient target object/event monitoring is a primary goal of military systems in unattended and unmanned areas. Heterogeneous wireless sensor network (HTWSN) is an emerging network for efficient enemy object monitoring in sensitive areas of low cost. The performance of HTWSN is mainly depends on the quality of data transmission and better network lifetime. However, after deployment of HTWSN, the network can experience a serious problem known as path failure. Path failure occurs due to high route overhead, which result poor-quality data transmission and increase the node energy consumption. Path failure results route rediscovery and data packet retransmission. The proposed node deployment mechanism for HTWSN has been minimized the route overhead and improved the path quality, quality data packet transmission by avoid the path failure. The proposed node deployment strategy has given better results in terms of 20 % low node energy consumption, 56 % lower route overhead, 22 % higher network lifetime and 17 % higher data packet delivery ratio than the existing node deployment mechanism of IMCC protocol. 2005 - 2016 JATIT & LLS. All rights reserved. -
A Non-Linear Approach to Predict the Salary of NBA Athletes using Machine Learning Technique
Every sportsman traded/drafted receives monetary compensation in accordance with their contract. In this study, we propose a nonlinear approach based on performance and other aspects to determine the salary of a basketball player. We estimate the salary based on four regressive models. Whilst predicting we also Figure out the important features impacting the salary. Comparatively speaking, random forest outperformed other algorithms. Furthermore, we consider that our findings might benefit discussions between basketball teams and players. This model can also help set a benchmark for salary expectations by the players in accordance. 2022 IEEE. -
A note on ?(k)-colouring of the Cartesian product of some graphs
The chromatic number, x(G) of a graph G is the minimum number of colours used in a proper colouring of G. In an improper colouring, an edge uv is bad if the colours assigned to the end vertices of the edge is the same. Now, if the available colours are less than that of the chromatic number of graph G, then colouring the graph with the available colours lead to bad edges in G. The number of bad edges resulting from a ? (k)-colouring of G is denoted by bk(G). In this paper, we use the concept of (k)-colouring and determine the number of bad edges in Cartesian product of some graphs. 2022 by the authors. -
A Note on J-colouring of Jahangir Graphs
In this paper, we discuss J-colouring of the family of Jahangir graphs.Note that the family of Jahangir graphs is a wide ranging family of graphs which by a generalised definition includes wheel graphs. We characterise the subset of Jahangir graphs which admit a J-colouring. 2019, The National Academy of Sciences, India. -
A note on perfect lucky k-colourable graphs
This paper presents the notion of perfect Lucky k-colouring. Basic conditions for a perfect Lucky k-colourable graph are presented. Application thereof is then presented by obtaining the Lucky 4-polynomials for all connected graphs G on six vertices with ten edges. The chromatic number of these connected graphs is ?(G) = 3 or 4. For k = max{?(G): 3 or 4g = 4, it is possible to find Lucky 4-polynomials for all graphs on six vertices and ten edges. The methodology improves substantially on the fundamental methodology such that, vertex partitions begin with Lucky partition forms immediately. Finally, further problems for research related to this study are presented. 2020, International Scientific Research Publications. All rights reserved. -
A Note on the Rainbow Neighbourhood Number of Certain Graph Classes
A rainbow neighbourhood of a graph G is the closed neighbourhood N[v] of a vertex v? V(G) which contains at least one colored vertex of each color in the chromatic coloring C of G. Let G be a graph with a chromatic coloring C defined on it. The number of vertices in G yielding rainbow neighbourhoods is called the rainbow neighbourhood number of the graph G, denoted by r ? (G). In this paper, rainbow neighbourhood number of certain graph classes are discussed. 2018, The National Academy of Sciences, India. -
A Note on the Significance of Quartic Autocatalysis Chemical Reaction on the Motion of Air Conveying Dust Particles
Little is known on the significance of fluid-particle interaction for velocity and temperature as in the case of centrifuge for separating particles of different types, launching of rockets, and motion of space shuttle through the air when there exist chemical reactions between the flow and the wall. The aim of this study is not only to explore the significance of quartic autocatalytic chemical reaction on the flow of dusty fluid in which the transmission of energy in form of electromagnetic is nonlinear but also to unravel the effects of buoyancy on the velocity of the dust and temperature of the dust. The nonlinear partial differential equations that model the transport phenomenon was transformed, nondimensionalised, and parameterised using suitable variables. The corresponding boundary value problems were converted to an initial value problem using the method of superposition and solved numerically. The outcome of the study indicates that enhancement of buoyancy is a yardstick to increase the vertical velocity, horizontal velocity, and shear stress within the fluid domain; increase the velocity of the dust particles; increase the temperature distribution across the flow of dusty fluid; increase the concentration of dusty fluid; and decrease the concentration of the catalyst. It is worth noticing that utmost velocity of the dust occurs at a minimum value of fluid-particle interaction for velocity in the flow over a larger thickness of paraboloid of revolution. 2019 Walter de Gruyter GmbH, Berlin/Boston 2019. -
A novel African buffalo based greedy routing technique for infrastructure and cluster based communication in vehicular ad-hoc network
In this modern era, the wire free replica is utilized Vehicular Ad hoc Networks (VANETs) to converse each other. Also, the VANET paradigm not required any specific fixed infrastructure. Furthermore, the vehicle in VANET framework is movable like as mobile nodes. Also, the wireless connectivity between the vehicular nodes is not stable in all cases, it often changes their structure. Research have recommended various responses to control these issues and furthermore to lessen blockage in VANET environment. Therefore, the infrastructure of a network changes frequently which results in communication overheads, energy consumption and lifetime of the nodes. Consequently, in this paper a novel African Buffalo based Greedy Routing (ABGR) technique is to improve the performance of infrastructure and cluster based communication of the node. Moreover, the routing overhead and infrastructure communication can be enhanced by this proposed protocol. Consequently, the energy consumption solution is enhanced based on the CH. Sequentially, the proposed routing protocol is compared with existing protocols in terms of end-to-end delay, throughput, Data transmission Ratio (DTR), and energy consumption and so on. Therefore, it shows that the energy utilization and lifetime of the nodes in the proposed network has been enhanced. 2021 Little Lion Scientific. -
A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP. 2025 Institute of Advanced Engineering and Science. All rights reserved.