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Prof. Vijayakumar V
University of New South Wales (UNSW) Sydney, Australia

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0 Cloud Computing
0 Grid Computing
0 Network Security
0 IoT

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IoT

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Original article
Published: 01 July 2021 in International Journal of System Assurance Engineering and Management
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New York City taxi rides form the core of the traffic in the city of New York. The many rides taken every day by New Yorkers in the busy city can give us a great idea of traffic times, road blockages, and so on. Predicting the duration of a taxi trip is very important since a user would always like to know precisely how much time it would require of him to travel from one place to another. Given the rising popularity of app-based taxi usage through common vendors like Ola and Uber, competitive pricing has to be offered to ensure users choose them. Prediction of duration and price of trips can help users to plan their trips properly, thus keeping potential margins for traffic congestions. It can also help drivers to determine the correct route which in-turn will take lesser time as accordingly. Moreover, the transparency about pricing and trip duration will help to attract users at times when popular taxi app-based vendor services apply surge fares. Thus in this research study, we used real-time data which customers would provide at the start of a ride, or while booking a ride to predict the duration and fare. This data includes pickup and drop-off point coordinates, the distance of the trip, start time, number of passengers, and a rate code belonging to the different classes of cabs available such that the rate applied is based on a regular or airport basis. Hereafter, we applied XGBoost and Multi-Layer Perceptron models to find out which one of them provides better accuracy and relationships between real-time variables. At last, a comparison of the two mentioned algorithms facilitates us to decide that XGBoost is more fitter and efficient than Multi-Layer Perceptron for taxi trip duration-based predictions.

ACS Style

M Poongodi; Mohit Malviya; Chahat Kumar; Mounir Hamdi; V Vijayakumar; Jamel Nebhen; Hasan Alyamani. New York City taxi trip duration prediction using MLP and XGBoost. International Journal of System Assurance Engineering and Management 2021, 1 -12.

AMA Style

M Poongodi, Mohit Malviya, Chahat Kumar, Mounir Hamdi, V Vijayakumar, Jamel Nebhen, Hasan Alyamani. New York City taxi trip duration prediction using MLP and XGBoost. International Journal of System Assurance Engineering and Management. 2021; ():1-12.

Chicago/Turabian Style

M Poongodi; Mohit Malviya; Chahat Kumar; Mounir Hamdi; V Vijayakumar; Jamel Nebhen; Hasan Alyamani. 2021. "New York City taxi trip duration prediction using MLP and XGBoost." International Journal of System Assurance Engineering and Management , no. : 1-12.

Journal article
Published: 17 May 2021 in Electronics
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Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

ACS Style

Priya A G; Anitha K; Vijayakumar Varadarajan. Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach. Electronics 2021, 10, 1195 .

AMA Style

Priya A G, Anitha K, Vijayakumar Varadarajan. Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach. Electronics. 2021; 10 (10):1195.

Chicago/Turabian Style

Priya A G; Anitha K; Vijayakumar Varadarajan. 2021. "Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach." Electronics 10, no. 10: 1195.

Special issue paper
Published: 25 September 2020 in Multimedia Systems
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With time, AI technologies have matured well and resonated in various domains of applied sciences and engineering. The sub-domains of AI, machine learning (ML), deep learning (DL), and associated statistical tools are getting more attention. Therefore, various machine learning models are being created to take advantage of the data available and accomplish tasks, such as automatic prediction, classification, clustering, segmentation and anomaly detection, etc. Tasks like classification need labeled data used to train the models to achieve a reliable accuracy. This study shows the systematic review of promising research areas and applications of DL models in medical diagnosis and medical healthcare systems. The prevalent DL models, their architectures, and related pros, cons are discussed to clarify their prospects. Many deep learning networks have been useful in the field of medical image processing for prognosis and diagnosis of life-threatening ailments (e.g., breast cancer, lung cancer, and brain tumor, etc.), which stand as an error-prone and tedious task for doctors and specialists when performed manually. Medical images are processed using these DL methods to solve various tasks like prediction, segmentation, and classification with accuracy bypassing human abilities. However, the current DL models have some limitations that encourage the researchers to seek further improvement.

ACS Style

Chandradeep Bhatt; Indrajeet Kumar; V. Vijayakumar; Kamred Udham Singh; Abhishek Kumar. The state of the art of deep learning models in medical science and their challenges. Multimedia Systems 2020, 27, 599 -613.

AMA Style

Chandradeep Bhatt, Indrajeet Kumar, V. Vijayakumar, Kamred Udham Singh, Abhishek Kumar. The state of the art of deep learning models in medical science and their challenges. Multimedia Systems. 2020; 27 (4):599-613.

Chicago/Turabian Style

Chandradeep Bhatt; Indrajeet Kumar; V. Vijayakumar; Kamred Udham Singh; Abhishek Kumar. 2020. "The state of the art of deep learning models in medical science and their challenges." Multimedia Systems 27, no. 4: 599-613.

Article
Published: 14 September 2020 in International Journal of Fuzzy Systems
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With the increasing use of wireless terminals, wireless sensor networks (WSNs) have received significant attention owing to their wide usage in monitoring harsh environments, crucial surveillance and security applications, and other real-life applications. Sensor nodes in operation are equipped with batteries that are not rechargeable in most of the cases. Attaining the lifetime maximization of these nodes has drawn the attention of researchers in recent years. The clustering mechanism is highly successful in conserving energy resources for network activities and has become a promising field for researches to overcome the issues of battery-constrained WSNs. This research work implements zone-based clustering with the fuzzy-logic approach for dynamic cluster head (CH) selection. It aims to resolve the problem of unbalanced energy dissipation among the CHs in the network. The experimental results show that the proposed protocol outperforms the existing protocols in terms of maximizing the network lifetime.

ACS Style

Thompson Stephan; Kushal Sharma; Achyut Shankar; S. Punitha; Vijayakumar Varadarajan; Peide Liu. Fuzzy-Logic-Inspired Zone-Based Clustering Algorithm for Wireless Sensor Networks. International Journal of Fuzzy Systems 2020, 23, 506 -517.

AMA Style

Thompson Stephan, Kushal Sharma, Achyut Shankar, S. Punitha, Vijayakumar Varadarajan, Peide Liu. Fuzzy-Logic-Inspired Zone-Based Clustering Algorithm for Wireless Sensor Networks. International Journal of Fuzzy Systems. 2020; 23 (2):506-517.

Chicago/Turabian Style

Thompson Stephan; Kushal Sharma; Achyut Shankar; S. Punitha; Vijayakumar Varadarajan; Peide Liu. 2020. "Fuzzy-Logic-Inspired Zone-Based Clustering Algorithm for Wireless Sensor Networks." International Journal of Fuzzy Systems 23, no. 2: 506-517.

Journal article
Published: 12 September 2020 in Journal of Systems Architecture
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Cellular networks are evolving to the era of 5th Generation (5G), where 5G new radio (NR) and Long-Term Evolution: Advanced (LTE-A) Pro technologies are being envisioned for enabling smart and innovative services of the Internet-of-Things (IoT). However, existing LTE-A Pro protocols such as the group paging approach is still heavily inclined towards human-to-human communications owing to the heterogeneous characteristics of a wide variety of IoT devices. Since most IoT devices are battery operated and their power consumption rate decides the battery lifetime, hence energy-efficient data transfer protocols are of paramount importance for next-generation IoT networks. Group paging is one such mechanism that has been widely accepted to improve energy efficiency of IoT networks. However, grouping approach for IoT devices is still not a much addressed topic, though a few novel group paging approaches have been studied that focus on varied IoT characteristics and mobility; though such approaches are not computationally efficient particularly for massive IoT deployments. Therefore, this paper proposes a novel multi-parameter evolutionary optimization, namely, genetic algorithm (GA) based grouping approach that also considers IoT features such as traffic patterns, delay requirements, and mobility patterns. Results obtained from simulations validate that our proposed method can significantly improve IoT devices’ energy efficiency over random grouping schemes and other approaches.

ACS Style

Buddhadeb Pradhan; V. Vijayakumar; Sanjoy Pratihar; Deepak Kumar; K. Hemant Kumar Reddy; Diptendu Sinha Roy. A genetic algorithm based energy efficient group paging approach for IoT over 5G. Journal of Systems Architecture 2020, 113, 101878 .

AMA Style

Buddhadeb Pradhan, V. Vijayakumar, Sanjoy Pratihar, Deepak Kumar, K. Hemant Kumar Reddy, Diptendu Sinha Roy. A genetic algorithm based energy efficient group paging approach for IoT over 5G. Journal of Systems Architecture. 2020; 113 ():101878.

Chicago/Turabian Style

Buddhadeb Pradhan; V. Vijayakumar; Sanjoy Pratihar; Deepak Kumar; K. Hemant Kumar Reddy; Diptendu Sinha Roy. 2020. "A genetic algorithm based energy efficient group paging approach for IoT over 5G." Journal of Systems Architecture 113, no. : 101878.

Special issue paper
Published: 01 July 2020 in Multimedia Systems
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Nowadays, highly sensitive medical images are vulnerable to data threats and privacy attacks. They must be kept secure while transmitting them across insecure channels precisely for this purpose. The robust image steganography is focused on this work by exploiting Redundant Integer Wavelet Transform (RIWT), Laplacian Pyramid, Arnold scrambling and Histogram shifting algorithm to facilitate secure communication of secret images in the context. Stego images thus generated are subjected to a deep learning approach to assess if it can be classified as a cover or not. If not, the HS parameter is modified to generate stego images in such a way to classify it as a cover image. Thus it is difficult to suspect the existence of a secret image by the Human Visual System (HVS). The efficiency of our method is analyzed by comparing it with related methods present in the literature. Average NCC values between the original secret image and the extracted secret image are 0.8917 which is higher than the schemes in the literature. Average PSNR values of the stego image are 36.375 even when the embedding rate is increased to 4 bits per pixel. The analysis was done on security and robustness also reveals better results. From the experimental analysis, it is proved that the proposed method is superior to the related methods of the literature.

ACS Style

ArunKumar Sukumar; V. Subramaniyaswamy; Logesh Ravi; V. Vijayakumar; V. Indragandhi. Robust image steganography approach based on RIWT-Laplacian pyramid and histogram shifting using deep learning. Multimedia Systems 2020, 27, 651 -666.

AMA Style

ArunKumar Sukumar, V. Subramaniyaswamy, Logesh Ravi, V. Vijayakumar, V. Indragandhi. Robust image steganography approach based on RIWT-Laplacian pyramid and histogram shifting using deep learning. Multimedia Systems. 2020; 27 (4):651-666.

Chicago/Turabian Style

ArunKumar Sukumar; V. Subramaniyaswamy; Logesh Ravi; V. Vijayakumar; V. Indragandhi. 2020. "Robust image steganography approach based on RIWT-Laplacian pyramid and histogram shifting using deep learning." Multimedia Systems 27, no. 4: 651-666.

Journal article
Published: 16 May 2020 in Sustainable Cities and Society
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Wind power generation is the clean energy source by which the pollution in the environment can be reduced to an extent and also it can be installed in and around the cities for electrical supply. The wind energy generated at the level of distributed generation also reduces the impact on the transmission system in terms of loss in the line and improves the efficiency of the power system. The increase in the level of penetration of wind power generation into an island power system network, causing severe threat in the system in terms of protection and security. The decrease in system inertia by means of increasing in Renewable Energy Sources (RES) causes an impact on the stability of the system and further may lead to a blackout of the system. The protection and control of this system are becoming more complicated due to the penetration of RES into the system. So in order to solve these issues, an intelligent control technique is implemented between the robust energy storage system and wind energy system. The Load Frequency Control (LFC) is required to emulate the virtual inertia into the island power system to stabilize the frequency variations and power variations. Furthermore, a digital frequency relay is implemented to protect the system from large power fluctuations for longer duration and a security system is implemented for protecting the whole island system from cyber-attacks. To verify the effectiveness of controller as well as protection system robustness, it is tested for different load disturbances and by increasing the penetration level of wind power, the digital relay has been tested for large frequency variation in the system by using MATLAB/Simulink.

ACS Style

Kirn Kumar N.; Indra Gandhi V.; Logesh Ravi; Vijayakumar V.; Subramaniyaswamy V.. Improving security for wind energy systems in smart grid applications using digital protection technique. Sustainable Cities and Society 2020, 60, 102265 .

AMA Style

Kirn Kumar N., Indra Gandhi V., Logesh Ravi, Vijayakumar V., Subramaniyaswamy V.. Improving security for wind energy systems in smart grid applications using digital protection technique. Sustainable Cities and Society. 2020; 60 ():102265.

Chicago/Turabian Style

Kirn Kumar N.; Indra Gandhi V.; Logesh Ravi; Vijayakumar V.; Subramaniyaswamy V.. 2020. "Improving security for wind energy systems in smart grid applications using digital protection technique." Sustainable Cities and Society 60, no. : 102265.

Journal article
Published: 02 April 2020 in Neural Computing and Applications
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A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.

ACS Style

N. Sivaramakrishnan; V. Subramaniyaswamy; Amelec Viloria; V. Vijayakumar; N. Senthilselvan. A deep learning-based hybrid model for recommendation generation and ranking. Neural Computing and Applications 2020, 1 -18.

AMA Style

N. Sivaramakrishnan, V. Subramaniyaswamy, Amelec Viloria, V. Vijayakumar, N. Senthilselvan. A deep learning-based hybrid model for recommendation generation and ranking. Neural Computing and Applications. 2020; ():1-18.

Chicago/Turabian Style

N. Sivaramakrishnan; V. Subramaniyaswamy; Amelec Viloria; V. Vijayakumar; N. Senthilselvan. 2020. "A deep learning-based hybrid model for recommendation generation and ranking." Neural Computing and Applications , no. : 1-18.

Journal article
Published: 04 October 2019 in Computer Communications
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The primary sources for ecological degradation currently are the Forest Fires (FF). The present observation frameworks for FF absence need supporting in constant checking of each purpose of the location at all time and prime location of the fire dangers. This approach gives works on preparing UAV (Unmanned Aerial Vehicle) aeronautical picture information as indicated by the prerequisites of ranger service territory application on a UAV stage. It provides a continuous and remote watch on a flame in forests and mountains, all the while the UAV is flying and getting the elevated information, helping clients maintain the number and area of flame focuses. Observing programming spreads capacities, including Fire: source identification, area, choice estimation, and LCD module. This paper proposed includes (1) Color Code Identification, (2) Smoke Motion Recognition, and (3) Fire Classification algorithms. Strikingly, the use of a helicopter with visual cameras portrayed. The paper introduces the strategies utilized for flame division invisible cameras, and the systems to meld the information acquired the following: Correctly, the current FF location stays testing, given profoundly convoluted and non-organized conditions of the forest, smoke hindering the flame, the movement of cameras mounted on UAVs, and analogs of fire attributes. These unfavorable impacts can truly purpose either false alert. This work focuses on the improvement of trustworthy and exact FF recognition algorithms which apply to UAVs. To effectively execute missions and meet their relating execution criteria examinations on the best way to diminish false caution rates, increment the possibility of profitable recognition, and upgrade versatile abilities to different conditions are firmly requested to improve the unwavering quality and precision of FF location framework.

ACS Style

S. Sudhakar; V. Vijayakumar; C. Sathiya Kumar; V. Priya; Logesh Ravi; V. Subramaniyaswamy. Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires. Computer Communications 2019, 149, 1 -16.

AMA Style

S. Sudhakar, V. Vijayakumar, C. Sathiya Kumar, V. Priya, Logesh Ravi, V. Subramaniyaswamy. Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires. Computer Communications. 2019; 149 ():1-16.

Chicago/Turabian Style

S. Sudhakar; V. Vijayakumar; C. Sathiya Kumar; V. Priya; Logesh Ravi; V. Subramaniyaswamy. 2019. "Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires." Computer Communications 149, no. : 1-16.

Article
Published: 16 August 2019 in Wireless Networks
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Due to increased attraction in cloud computing, mobile devices could store or acquire private and confidential information from everywhere at any point in time. In parallel, the information safety issues over mobile computing become rigorous and retard increased advancements in the mobile cloud. Crucial analysis were performed to enhance the safety in cloud computing. Most of them are not appropriate for mobile cloud computing due to limited energy resource, thus mobile devices are unable to perform assessments and complex tasks. The crucial requirement of mobile cloud application is to provide solution with minimum computational overhead. Thus the aim of the research is to design a trivial information relaying scheme (TIRS) for mobile cloud computing. The proposed scheme implements Ciphertext Policy Attribute-based Encryption (CP-ABE) to alter the general framework of access governance hierarchy to make it appropriate for mobile cloud environment. The TIRS displaces immense segments of the assessment concentrated access governance hierarchy modifications in CP-ABE from smart devices to the peripheral proxy servers. Furthermore, TIRS initiates element portrayal field to plan indolent cancellation which is a thriving dispute for CP-ABE system. The experimental analysis depicts that TIRS successfully minimize the overheads during user relaying information over the mobile cloud environment.

ACS Style

N. Thillaiarasu; S. Chenthur Pandian; V. Vijayakumar; S. Prabaharan; Logesh Ravi; V. Subramaniyaswamy. Designing a trivial information relaying scheme for assuring safety in mobile cloud computing environment. Wireless Networks 2019, 1 -14.

AMA Style

N. Thillaiarasu, S. Chenthur Pandian, V. Vijayakumar, S. Prabaharan, Logesh Ravi, V. Subramaniyaswamy. Designing a trivial information relaying scheme for assuring safety in mobile cloud computing environment. Wireless Networks. 2019; ():1-14.

Chicago/Turabian Style

N. Thillaiarasu; S. Chenthur Pandian; V. Vijayakumar; S. Prabaharan; Logesh Ravi; V. Subramaniyaswamy. 2019. "Designing a trivial information relaying scheme for assuring safety in mobile cloud computing environment." Wireless Networks , no. : 1-14.