Shanthi Bala P - Academia.edu (original) (raw)

Conference Presentations by Shanthi Bala P

2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2018

A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton’s grav... more A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton’s gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.

IEEE, 2019

A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's grav... more A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.

Books by Shanthi Bala P

IGI Global, 2019

In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were... more In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.

Papers by Shanthi Bala P

International Journal of Scientific & Technology Research, Nov 25, 2019

arXiv (Cornell University), May 2, 2023

Vector-Borne Disease (VBD) is an infectious disease transmitted through the pathogenic female Aed... more Vector-Borne Disease (VBD) is an infectious disease transmitted through the pathogenic female Aedes mosquito to humans and animals. It is important to control dengue disease by reducing the spread of Aedes mosquito vectors. Community awareness plays a crucial role to ensure Aedes control programmes and encourages the communities to involve active participation. Identifying the species of mosquito will help to recognize the mosquito density in the locality and intensifying mosquito control efforts in particular areas. This will help in avoiding Aedes breeding sites around residential areas and reduce adult mosquitoes. To serve this purpose, an android application are developed to identify Aedes species that help the community to contribute in mosquito control events. Several Android applications have been developed to identify species like birds, plant species, and Anopheles mosquito species. In this work, a user-friendly mobile application 'mAedesID' is developed for identifying the Aedes mosquito species using a deep learning Convolutional Neural Network (CNN) algorithm which is best suited for species image classification and achieves better accuracy for voluminous images.

River Publishers eBooks, Aug 10, 2023

International Journal of Advanced Computer Science and Applications

The need for instantaneous processing for Internet of Things (IoT) has led to the notion of fog c... more The need for instantaneous processing for Internet of Things (IoT) has led to the notion of fog computing where computation is performed at the proximity of the data source. Though fog computing reduces the latency and bandwidth bottlenecks, the scarcity of fog nodes hampers its efficiency. Also, due to the heterogeneity and stochastic behavior of IoT, traditional resource allocation technique does not suffice the timesensitiveness of the applications. Therefore, adopting Artificial Intelligence (AI) based Reinforcement Learning approach that has the ability to self-learn and adapt to the dynamic environment is sought. The purpose of the work is to propose an Auto Centric Threshold (ACT) enabled Monte Carlo FogRA system that maximizes the utilization of Fog's limited resources with minimum termination time for time-critical IoT requests. FogRA is devised as a Reinforcement Learning (RL) problem, that obtains optimal solutions through continuous interaction with the uncertain environment. Experimental results show that the optimal value achieved by the proposed system is increased by 41% more than the baseline adaptive RA model. The efficiency of FogRA is evaluated under different performance metrics.

Journal of King Saud University - Computer and Information Sciences, Oct 1, 2019

Provably secure keyless hash function uses Random Oracle (RO) or Sponge principles for the design... more Provably secure keyless hash function uses Random Oracle (RO) or Sponge principles for the design and construction of security-centric hash algorithms. It capitalizes the aforesaid principles to produce outcomes like MD2, MD5, SHA-160, SHA-224/256, SHA-256, SHA-224/512, SHA-256/512, SHA-384/512, SHA-512, and SHA-3. These functions use bitwise AND, OR, XOR, and MOD operators to foresee randomness in their hash outputs. However, the partial breaking of SHA2 and SHA3 families and the breaking of MD5 and SHA-160 algorithms raise concerns on the use of bitwise operators at the block level. The proposed design tries to address this structural flaw through a polynomial function. A polynomial function of degree 128 demands arduous effort to be decoded in the opposing direction. The application of a polynomial on the blocks produces an unpredictable random response. It is a fact that the new design exhibits the merits of the polynomial function on subsets to achieve the avalanche response to a significant level. The output from experiments with more than 24 Million hash searches proves the proposed system is a provably secure hash function. The experiments on avalanche response and confusion and diffusion analysis prove it is an apt choice for security-centric cryptographic applications.

Advances in intelligent systems and computing, Dec 12, 2018

The prospect of applying the semantic relationships to the question generation system can revolut... more The prospect of applying the semantic relationships to the question generation system can revolutionize the learning experience. The task of generating questions from the existing information is a tedious task. In this paper, Question generation system based on semantic relationships (Q-Genesis) is proposed to generate more relevant knowledge level questions automatically. It will be useful for the trainer to assess the knowledge level of the learners. This paper also provides the importance of the semantic relationships when generating the questions from the ontology.

International journal in foundations of computer science & technology (IJFCST), Mar 31, 2013

Exploitation of semantic relationship in the represented knowledge is crucial to move the system ... more Exploitation of semantic relationship in the represented knowledge is crucial to move the system towards intelligent system. This paper brings a detailed survey about wide range of relations that has been exploited from literature to modern computational theory. The classification of the relations may be content dependent relationship, content independent relationship, foundation relations, spatial relations, temporal relations, participation relations, mereological relations, etc. The role of relations in various domains such as searching, auditing, ranking and reasoning clearly indicates the power of relationships to make the system more human like thinking. It is very complex to reason the relation between the concepts as it differs in context, semantics and properties. This paper provides a novel attempt to explore the significance of relations in reasoning and information retrieval.

International Journal of Applied Metaheuristic Computing, Mar 30, 2022

The gravitational search algorithm (GSA) is one of the highly regarded population-based algorithm... more The gravitational search algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on Levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of Levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on 23 well-known classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.

International Journal of Advanced Computer Science and Applications, 2021

The voluminous data produced and consumed by digitalization, need resources that offer compute, s... more The voluminous data produced and consumed by digitalization, need resources that offer compute, storage, and communication facility. To withstand such demands, Cloud and Fog computing architectures are the viable solutions, due to their utility kind and accessibility nature. The success of any computing architecture depends on how efficiently its resources are allocated to the service requests. Among the existing survey articles on Cloud and Fog, issues like scalability and time-critical requirements of the Internet of Things (IoT) are rarely focused on. The proliferation of IoT leads to energy crises too. The proposed survey is aimed to build a Resource Allocation and Service Placement (RASP) strategy that addresses these issues. The survey recommends techniques like Reinforcement Learning (RL) and Energy Efficient Computing (EEC) in Fog and Cloud to escalate the efficacy of RASP. While RL meets the time-critical requirements of IoT with high scalability, EEC empowers RASP by saving cost and energy. As most of the early works are carried out using reactive policy, it paves the way to build RASP solutions using alternate policies. The findings of the survey help the researchers, to focus their attention on the research gaps and devise a robust RASP strategy in Fog and Cloud environment.

International Journal of Applied Metaheuristic Computing, Dec 3, 2021

The chaotic gravitational search algorithm (CGSA) is a physics-based heuristic algorithm inspired... more The chaotic gravitational search algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's law of universal gravitation. It uses 10 chaotic maps for optimal global search and fast convergence rate. The advantages of CGSA have been incorporated in various mechanical and civil engineering design frameworks which include speed reducer design (SRD), gear train design (GTD), three bar truss design (TBTD), stepped cantilever beam design (SCBD), multiple disc clutch brake design (MDCBD), and hydrodynamic thrust bearing design (HTBD). The CGSA has been compared with 11 state-of-the-art stochastic algorithms. In addition, a non-parametric statistical test, namely the signed Wilcoxon rank-sum test, has been carried out at a 5% significance level to statistically validate the results. The simulation results indicate that CGSA shows efficient performance in terms of high convergence speed and minimization of the design parameter values as compared to other heuristic algorithms. The source codes are publicly available on Github (i.e., https://github.com/ SajadAHMAD1).

Networking and Communication Engineering, 2010

In modern world, innovation and growth on the mobile phones are astonishing. The foundation of a ... more In modern world, innovation and growth on the mobile phones are astonishing. The foundation of a strong democracy is an informed and engaged citizenry. In many developing countries, nearly 60% of the citizenry uses mobile phones. In future, all the citizens will use mobiles in their habitual life. The wide-spread use of mobile devices has made it possible to develop mobile voting system as a complement to the existing electronic voting system. However, due to limited resource, it is challenging to achieve both efficiency and security strength for mobile voting system. Conventional system uses many symmetric and asymmetric algorithms like DES, RSA, etc to provide secure data sharing between users. Though, it is not efficient to provide security in mobile voting process. This paper proposes secure mobile voting which supports biometric identification and cryptographic algorithms to provide authentication, integrity, confidentiality and non repudiation.

Evolving Systems, Aug 6, 2022

Advances in computer and electrical engineering book series, 2020

Internet of things (IoT) prevails in almost all the equipment of our daily lives including health... more Internet of things (IoT) prevails in almost all the equipment of our daily lives including healthcare units, industrial productions, vehicle, banking or insurance. The unconnected dumb objects have started communicating with each other, thus generating a voluminous amount of data at a greater velocity that are handled by cloud. The requirements of IoT applications like heterogeneity, mobility support, and low latency form a big challenge to the cloud ecosystem. Hence, a decentralized and low latency-oriented computing paradigm like fog computing along with cloud provide better solution. The service quality of any computing model depends on resource management. The resources need to be agile by nature, which clearly demarks virtual container as the best choice. This chapter presents the federation of Fog-Cloud and the way it relates to the IoT requirements. Further, the chapter deals with autonomic resource management with reinforcement learning (RL), which will forward the fog computing paradigm to the future generation expectations.

International Journal of Intelligent Systems and Applications in Engineering, Jan 30, 2019

Today most of the consumer services ranging from education to banking, hospital management to tic... more Today most of the consumer services ranging from education to banking, hospital management to ticket booking are made online. The online services are hosted in cloud and they are mostly time-critical applications. The cloud-based applications depend on datacenter (DC) resources for computation, communication, and storage. The resource utilization in the cloud needs to cope with the dynamic workload and stochastic request spikes. Virtualization is the key technology for effective resource utilization in the cloud data center. The type of virtualization technology (VT) that is adapted for the delivery of cloud application ensures the quality of service. The goal of this paper is to compare and contrast the performance measures of various virtualization technologies for heterogeneous workloads. The paper presents the impact of VT in the development of application in the cloud. Each virtualization technology outperforms the other in some or other performance metrics. In spite of the differences, certain virtualization technology dominates depending upon the application requirements in the software development sector.

Open Computer Science, 2021

The main aim of this article is to explore the real-life problem-solving potential of the propose... more The main aim of this article is to explore the real-life problem-solving potential of the proposed Lévy flight-based chaotic gravitational search algorithm (LCGSA) for the minimization of engineering design variables of speed reducer design (SRD), three bar truss design (TBTD), and hydrodynamic thrust bearing design (HTBD) problems. In LCGSA, the diversification of the search space is carried out by Lévy flight distribution. Simultaneously, chaotic maps have been utilized for the intensification of the candidate solutions towards the global optimum. Moreover, the penalty function method has been used to deal with the non-linear and fractional design constraints. The investigation of experimental outcomes has been performed through various performance metrics like statistical measures, run time analysis, convergence rate, and box plot analysis. Moreover, statistical verification of experimental results is carried out using a signed Wilcoxon rank-sum test. Furthermore, eleven heuristic algorithms were employed for comparative analysis of the simulation results. The simulation outcomes clearly show that LCGSA provides better values for TBTD and HTBD benchmarks than standard GSA and most of the competing algorithms. Besides, all the participating algorithms, including LCGSA, have the same results for the SRD problem. On the qualitative side, LCGSA has successfully resolved entrapment in local minima and convergence issues of standard GSA.

International Journal of Bio-inspired Computation, 2021

2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2018

A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton’s grav... more A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton’s gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.

IEEE, 2019

A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's grav... more A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.

IGI Global, 2019

In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were... more In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.

International Journal of Scientific & Technology Research, Nov 25, 2019

arXiv (Cornell University), May 2, 2023

Vector-Borne Disease (VBD) is an infectious disease transmitted through the pathogenic female Aed... more Vector-Borne Disease (VBD) is an infectious disease transmitted through the pathogenic female Aedes mosquito to humans and animals. It is important to control dengue disease by reducing the spread of Aedes mosquito vectors. Community awareness plays a crucial role to ensure Aedes control programmes and encourages the communities to involve active participation. Identifying the species of mosquito will help to recognize the mosquito density in the locality and intensifying mosquito control efforts in particular areas. This will help in avoiding Aedes breeding sites around residential areas and reduce adult mosquitoes. To serve this purpose, an android application are developed to identify Aedes species that help the community to contribute in mosquito control events. Several Android applications have been developed to identify species like birds, plant species, and Anopheles mosquito species. In this work, a user-friendly mobile application 'mAedesID' is developed for identifying the Aedes mosquito species using a deep learning Convolutional Neural Network (CNN) algorithm which is best suited for species image classification and achieves better accuracy for voluminous images.

River Publishers eBooks, Aug 10, 2023

International Journal of Advanced Computer Science and Applications

The need for instantaneous processing for Internet of Things (IoT) has led to the notion of fog c... more The need for instantaneous processing for Internet of Things (IoT) has led to the notion of fog computing where computation is performed at the proximity of the data source. Though fog computing reduces the latency and bandwidth bottlenecks, the scarcity of fog nodes hampers its efficiency. Also, due to the heterogeneity and stochastic behavior of IoT, traditional resource allocation technique does not suffice the timesensitiveness of the applications. Therefore, adopting Artificial Intelligence (AI) based Reinforcement Learning approach that has the ability to self-learn and adapt to the dynamic environment is sought. The purpose of the work is to propose an Auto Centric Threshold (ACT) enabled Monte Carlo FogRA system that maximizes the utilization of Fog's limited resources with minimum termination time for time-critical IoT requests. FogRA is devised as a Reinforcement Learning (RL) problem, that obtains optimal solutions through continuous interaction with the uncertain environment. Experimental results show that the optimal value achieved by the proposed system is increased by 41% more than the baseline adaptive RA model. The efficiency of FogRA is evaluated under different performance metrics.

Journal of King Saud University - Computer and Information Sciences, Oct 1, 2019

Provably secure keyless hash function uses Random Oracle (RO) or Sponge principles for the design... more Provably secure keyless hash function uses Random Oracle (RO) or Sponge principles for the design and construction of security-centric hash algorithms. It capitalizes the aforesaid principles to produce outcomes like MD2, MD5, SHA-160, SHA-224/256, SHA-256, SHA-224/512, SHA-256/512, SHA-384/512, SHA-512, and SHA-3. These functions use bitwise AND, OR, XOR, and MOD operators to foresee randomness in their hash outputs. However, the partial breaking of SHA2 and SHA3 families and the breaking of MD5 and SHA-160 algorithms raise concerns on the use of bitwise operators at the block level. The proposed design tries to address this structural flaw through a polynomial function. A polynomial function of degree 128 demands arduous effort to be decoded in the opposing direction. The application of a polynomial on the blocks produces an unpredictable random response. It is a fact that the new design exhibits the merits of the polynomial function on subsets to achieve the avalanche response to a significant level. The output from experiments with more than 24 Million hash searches proves the proposed system is a provably secure hash function. The experiments on avalanche response and confusion and diffusion analysis prove it is an apt choice for security-centric cryptographic applications.

Advances in intelligent systems and computing, Dec 12, 2018

The prospect of applying the semantic relationships to the question generation system can revolut... more The prospect of applying the semantic relationships to the question generation system can revolutionize the learning experience. The task of generating questions from the existing information is a tedious task. In this paper, Question generation system based on semantic relationships (Q-Genesis) is proposed to generate more relevant knowledge level questions automatically. It will be useful for the trainer to assess the knowledge level of the learners. This paper also provides the importance of the semantic relationships when generating the questions from the ontology.

International journal in foundations of computer science & technology (IJFCST), Mar 31, 2013

Exploitation of semantic relationship in the represented knowledge is crucial to move the system ... more Exploitation of semantic relationship in the represented knowledge is crucial to move the system towards intelligent system. This paper brings a detailed survey about wide range of relations that has been exploited from literature to modern computational theory. The classification of the relations may be content dependent relationship, content independent relationship, foundation relations, spatial relations, temporal relations, participation relations, mereological relations, etc. The role of relations in various domains such as searching, auditing, ranking and reasoning clearly indicates the power of relationships to make the system more human like thinking. It is very complex to reason the relation between the concepts as it differs in context, semantics and properties. This paper provides a novel attempt to explore the significance of relations in reasoning and information retrieval.

International Journal of Applied Metaheuristic Computing, Mar 30, 2022

The gravitational search algorithm (GSA) is one of the highly regarded population-based algorithm... more The gravitational search algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on Levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of Levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on 23 well-known classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.

International Journal of Advanced Computer Science and Applications, 2021

The voluminous data produced and consumed by digitalization, need resources that offer compute, s... more The voluminous data produced and consumed by digitalization, need resources that offer compute, storage, and communication facility. To withstand such demands, Cloud and Fog computing architectures are the viable solutions, due to their utility kind and accessibility nature. The success of any computing architecture depends on how efficiently its resources are allocated to the service requests. Among the existing survey articles on Cloud and Fog, issues like scalability and time-critical requirements of the Internet of Things (IoT) are rarely focused on. The proliferation of IoT leads to energy crises too. The proposed survey is aimed to build a Resource Allocation and Service Placement (RASP) strategy that addresses these issues. The survey recommends techniques like Reinforcement Learning (RL) and Energy Efficient Computing (EEC) in Fog and Cloud to escalate the efficacy of RASP. While RL meets the time-critical requirements of IoT with high scalability, EEC empowers RASP by saving cost and energy. As most of the early works are carried out using reactive policy, it paves the way to build RASP solutions using alternate policies. The findings of the survey help the researchers, to focus their attention on the research gaps and devise a robust RASP strategy in Fog and Cloud environment.

International Journal of Applied Metaheuristic Computing, Dec 3, 2021

The chaotic gravitational search algorithm (CGSA) is a physics-based heuristic algorithm inspired... more The chaotic gravitational search algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's law of universal gravitation. It uses 10 chaotic maps for optimal global search and fast convergence rate. The advantages of CGSA have been incorporated in various mechanical and civil engineering design frameworks which include speed reducer design (SRD), gear train design (GTD), three bar truss design (TBTD), stepped cantilever beam design (SCBD), multiple disc clutch brake design (MDCBD), and hydrodynamic thrust bearing design (HTBD). The CGSA has been compared with 11 state-of-the-art stochastic algorithms. In addition, a non-parametric statistical test, namely the signed Wilcoxon rank-sum test, has been carried out at a 5% significance level to statistically validate the results. The simulation results indicate that CGSA shows efficient performance in terms of high convergence speed and minimization of the design parameter values as compared to other heuristic algorithms. The source codes are publicly available on Github (i.e., https://github.com/ SajadAHMAD1).

Networking and Communication Engineering, 2010

In modern world, innovation and growth on the mobile phones are astonishing. The foundation of a ... more In modern world, innovation and growth on the mobile phones are astonishing. The foundation of a strong democracy is an informed and engaged citizenry. In many developing countries, nearly 60% of the citizenry uses mobile phones. In future, all the citizens will use mobiles in their habitual life. The wide-spread use of mobile devices has made it possible to develop mobile voting system as a complement to the existing electronic voting system. However, due to limited resource, it is challenging to achieve both efficiency and security strength for mobile voting system. Conventional system uses many symmetric and asymmetric algorithms like DES, RSA, etc to provide secure data sharing between users. Though, it is not efficient to provide security in mobile voting process. This paper proposes secure mobile voting which supports biometric identification and cryptographic algorithms to provide authentication, integrity, confidentiality and non repudiation.

Evolving Systems, Aug 6, 2022

Advances in computer and electrical engineering book series, 2020

Internet of things (IoT) prevails in almost all the equipment of our daily lives including health... more Internet of things (IoT) prevails in almost all the equipment of our daily lives including healthcare units, industrial productions, vehicle, banking or insurance. The unconnected dumb objects have started communicating with each other, thus generating a voluminous amount of data at a greater velocity that are handled by cloud. The requirements of IoT applications like heterogeneity, mobility support, and low latency form a big challenge to the cloud ecosystem. Hence, a decentralized and low latency-oriented computing paradigm like fog computing along with cloud provide better solution. The service quality of any computing model depends on resource management. The resources need to be agile by nature, which clearly demarks virtual container as the best choice. This chapter presents the federation of Fog-Cloud and the way it relates to the IoT requirements. Further, the chapter deals with autonomic resource management with reinforcement learning (RL), which will forward the fog computing paradigm to the future generation expectations.

International Journal of Intelligent Systems and Applications in Engineering, Jan 30, 2019

Today most of the consumer services ranging from education to banking, hospital management to tic... more Today most of the consumer services ranging from education to banking, hospital management to ticket booking are made online. The online services are hosted in cloud and they are mostly time-critical applications. The cloud-based applications depend on datacenter (DC) resources for computation, communication, and storage. The resource utilization in the cloud needs to cope with the dynamic workload and stochastic request spikes. Virtualization is the key technology for effective resource utilization in the cloud data center. The type of virtualization technology (VT) that is adapted for the delivery of cloud application ensures the quality of service. The goal of this paper is to compare and contrast the performance measures of various virtualization technologies for heterogeneous workloads. The paper presents the impact of VT in the development of application in the cloud. Each virtualization technology outperforms the other in some or other performance metrics. In spite of the differences, certain virtualization technology dominates depending upon the application requirements in the software development sector.

Open Computer Science, 2021

The main aim of this article is to explore the real-life problem-solving potential of the propose... more The main aim of this article is to explore the real-life problem-solving potential of the proposed Lévy flight-based chaotic gravitational search algorithm (LCGSA) for the minimization of engineering design variables of speed reducer design (SRD), three bar truss design (TBTD), and hydrodynamic thrust bearing design (HTBD) problems. In LCGSA, the diversification of the search space is carried out by Lévy flight distribution. Simultaneously, chaotic maps have been utilized for the intensification of the candidate solutions towards the global optimum. Moreover, the penalty function method has been used to deal with the non-linear and fractional design constraints. The investigation of experimental outcomes has been performed through various performance metrics like statistical measures, run time analysis, convergence rate, and box plot analysis. Moreover, statistical verification of experimental results is carried out using a signed Wilcoxon rank-sum test. Furthermore, eleven heuristic algorithms were employed for comparative analysis of the simulation results. The simulation outcomes clearly show that LCGSA provides better values for TBTD and HTBD benchmarks than standard GSA and most of the competing algorithms. Besides, all the participating algorithms, including LCGSA, have the same results for the SRD problem. On the qualitative side, LCGSA has successfully resolved entrapment in local minima and convergence issues of standard GSA.

International Journal of Bio-inspired Computation, 2021

CRC Press eBooks, Aug 18, 2022

AIJR Abstracts, Jul 29, 2020

Expert Systems, May 15, 2021

Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the p... more Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient‐based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC‐SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.