Vijaya Padmanabha
@mcbs.edu.om/en
Assistant Professor, Department of Mathematics and Computer Science
Modern College of Business and Science
EDUCATION
Ph.D in Computer Science and Engineering
RESEARCH INTERESTS
Machine Learning
Scopus Publications
Scopus Publications
P. Vijaya, Satish Chander, Roshan Fernandes, Anisha P. Rodrigues, and R. Maheswari
Springer Science and Business Media LLC
P. Vijaya, Rajeev Rajendran, Basant Kumar, and Joseph Mani
Springer Nature Switzerland
Safiya Al Salmi and P. Vijaya
Springer Nature Switzerland
Satish Chander, P. Vijaya, Roshan Fernandes, Anisha P Rodrigues, and Maheswari R
Elsevier BV
Kajol Rana, Ajay Vikram Singh, and P. Vijaya
IEEE
The Internet has undergone unimaginable advancements. Initially, progress was slow, but recently, innovation and development have occurred at an astonishing pace. IoT is a rapidly growing concept in modern communication networks, characterized by the widespread presence of objects such as mobile phones, sensors, smart devices, actuators, and other edge devices. These objects, equipped with unique addressing mechanisms, can communicate, and collaborate with each other to achieve specific goals. In this context, trust, security, and privacy play vital roles in the success of IoT implementation. Trust is defined as a crucial attribute for establishing reliability and connectivity among devices to ensure secure services and applications. Researchers have developed various trust calculation methods to address these challenges. This paper presents a survey of trust calculation methods for IoT systems, examining the available models and methods used by researchers. Additionally, a classification system is proposed to categorize trust calculation models based on parameters such as trust metric, trust source, trust algorithm, trust architecture, and trust propagation. The paper also highlights research challenges and future directions in the field of trust-based security in IoT.
Nikhil Kamath B, Roshan Fernandes, Anisha P. Rodrigues, Mufti Mahmud, P. Vijaya, Thippa Reddy Gadekallu, and M. Shamim Kaiser
MDPI AG
Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.
P. Vijaya and M. Selvi
World Scientific Pub Co Pte Ltd
The personalised learning is growing rapidly with the help of mobile and online technology. The e-learning recommendation scheme provides the suggestion concerning the courses to the students from numerous countries without past information of the courses online. The accuracy is an important issue in the e-learning course recommendation method. Hence, in this paper, Fuzzy-c-means clustering (FCM) and collaborative filtering are applied in the E-Khool user log data for effective e-learning recommendation system. The training phase and testing phase are the two phases of the devised method. During training, the relationship among the data in clustering is determined using the weighted cosine similarity and the data clustering is carried out with the help of FCM. During testing, the rating of the course is calculated using collaborative filtering. At last, the deep RNN classifier is used to evaluate prediction measure of the course recommendation. The devised e-learning recommendation method based on FCM and collaborative filtering offered a higher accuracy of 0.97 and less mean square error of 0.00115, respectively.
Girish Kumar Adari, Maheswari Raja, and P. Vijaya
Elsevier
A. Chaitanya Kumar, J. Andrew John, Maheswari Raja, and P. Vijaya
Elsevier
Anisha P Rodrigues, Roshan Fernandes, and P. Vijaya
IEEE
Due to its applicability in the actual world with problems such as meteorology, agricultural studies, and so on, weather prediction has become a very significant research topic. Weather forecasting is the process of forecasting the state of the atmosphere using several climatic characteristics. Present atmospheric condition is gathered and analyzed for weather forecasts. Meteorologists and academics have found accurate weather prediction to be a difficult endeavor. Weather data is critical in agriculture, tourism, airports, mining, and power generating are just a few examples. The rapid growth in the generation of meteorological data as well as the progress of climate observing technologies such as satellite meteorological observation led to the Big Data era. In this paper, we are analyzing weather data sets using different regressors. A detailed study on regression using machine learning regression models, namely, Linear Regressor, Polynomial Regressor, Decision Tree Regressor, Random Forest Regressor, Linear BayesianRidge Regressor, Linear Ridge Regressor, Linear Lasso Regressor, Linear ElasticNet Regressor, Support vector Regressor, and Artificial Neural Network(ANN) regressor are presented in this paper. Further, the performance of regressor models was measured through the error rate in the prediction with MSE, RMSE, MAE, and R-squared measures. Experimental results reveal that Random Forest regressors and Decision Tree regressors give better performance compared to other machine learning regressors. Regression using an Artificial Neural Network gives the best results compared to the machine learning approach in terms of prediction rate and execution time. This study helps in forecasting future weather conditions that farmer who grows crops by monitoring weather patterns and also arranging cricket, football matches, and open-ground events. In coastal areas, they anticipate tsunamis and natural calamities.
Bhuvaneshwari Shetty, Roshan Fernandes, Anisha P Rodrigues, and P. Vijaya
IEEE
When abnormal cells develop within the brain, a tumor is created. Depending on the size of the tumor and the section of the brain affected, symptoms from all forms of brain tumors might differ. Where symptoms are present, they may include vomiting, headaches, seizures, eyesight issues, and mental changes. Other signs might include the inability to speak or walk, feelings, or unconsciousness. A combination of surgery, radiation therapy, and chemotherapy may be used as treatment. The proposed work developed a binary classifier to detect MRI based brain tumors. The detection of MRI-based brain tumors will be covered in this research paper utilizing the machine learning model and convolutional neural network. An open dataset was used in this study. The dataset consists of 1500 brain MRI images with tumors and 1500 brain MRI images without tumors. The findings indicate that CNN model obtained better accuracy of 98.21% than the machine learning algorithms.
Kajol Rana, Ajay Vikram Singh, and P. Vijaya
Auricle Technologies, Pvt., Ltd.
Trust models have recently been proposed for Internet of Things (IoT) applications as a significant system of protection against external threats. This approach to IoT risk management is viable, trustworthy, and secure. At present, the trust security mechanism for immersion applications has not been specified for IoT systems. Several unfamiliar participants or machines share their resources through distributed systems to carry out a job or provide a service. One can have access to tools, network routes, connections, power processing, and storage space. This puts users of the IoT at much greater risk of, for example, anonymity, data leakage, and other safety violations. Trust measurement for new nodes has become crucial for unknown peer threats to be mitigated. Trust must be evaluated in the application sense using acceptable metrics based on the functional properties of nodes. The multifaceted confidence parameterization cannot be clarified explicitly by current stable models. In most current models, loss of confidence is inadequately modeled. Esteem ratings are frequently mis-weighted when previous confidence is taken into account, increasing the impact of harmful recommendations. 
 In this manuscript, a systematic method called Relationship History along with cumulative trust value (Distributed confidence management scheme model) has been proposed to evaluate interactive peers trust worthiness in a specific context. It includes estimating confidence decline, gathering & weighing trust parameters and calculating the cumulative trust value between nodes. Trust standards can rely on practical contextual resources, determining if a service provider is trustworthy or not and does it deliver effective service? The simulation results suggest that the proposed model outperforms other similar models in terms of security, routing and efficiency and further assesses its performance based on derived utility and trust precision, convergence, and longevity.
Satish Chander, P. Vijaya, and Praveen Dhyani
IGI Global
This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the Bhattacharya distance measure to newly propose the improved FLA (IMR-FLA). The proposed IMR-FLA is implemented in both the mapper and the reducer in the MapReduce framework to achieve the parallel clustering. The experimentation of the proposed IMR-FLA is done by using six standard databases, namely Pima Indian diabetes dataset, Heart disease dataset, Hepatitis dataset, localization dataset, breast cancer dataset, and skin segmentation dataset, from the UCI repository. The proposed IMR-FLA has the overall improved Jaccard coefficient value of 0.9357, 0.6572, 0.7462, 0.5944, 0.9418, and 0.8680, for each dataset. Similarly, the proposed IMR-FLA algorithm has outclassed other classifiers' performance with the clustering accuracy value of 0.9674, 0.9471, 0.9677, 0.777, 0.9023, and 0.9585, respectively, for the experimental databases.
Anisha P Rodrigues, Roshan Fernandes, P. Vijaya, and Satish Chander
World Scientific Pub Co Pte Ltd
Hadoop Distributed File System (HDFS) is developed to efficiently store and handle the vast quantity of files in a distributed environment over a cluster of computers. Various commodity hardware forms the Hadoop cluster, which is inexpensive and easily available. The large number of small files stored in HDFS consumed more memory which lags the performance because small files consumed heavy load on NameNode. Thus, the efficiency of indexing and accessing the small files on HDFS is improved by several techniques, such as archive files, New Hadoop Archive (New HAR), CombineFileInputFormat (CFIF), and Sequence file generation. The archive file combines the small files into single blocks. The new HAR file combines the smaller files into a single large file. The CFIF module merges the multiple files into a single split using NameNode, and the sequence file combines all the small files into a single sequence. The indexing and accessing of a small file in HDFS are evaluated using performance metrics, such as processing time and memory usage. The experiment shows that the sequence file generation approach is efficient when compared to other approaches concerning file access time is 1.5[Formula: see text]s, memory usage is 20 KB in multi-node, and the processing time is 0.1[Formula: see text]s.
Satish Chander and P. Vijaya
Elsevier
P. Vijaya, Satish Chander, and S.L. Gupta
Inderscience Publishers
Raqiya Ahmed Al Hilali and P. Vijaya
Deanship of Scientific Research
Satish Chander and P Vijaya
IEEE
The corona-virus (COVID-19) pandemic outburst from China has infected number of peoples and causes many deaths. In addition, the count of infections and deaths rates augment quickly. The adaption of data mining for performing recognition of infectious patterns is utilized for analyzing he spread patterns of COVID-19 infection. The grouping of clinical information is major method for determining the concealed instances from large clinical dataset. The clustering assist to group the information obtained from different gatherings. This paper devises a novel methodology, namely Tunicate swarm algorithm-based Black-hole entropic fuzzy clustering (TSAbased BHEFC) for clustering the COVID data. The clustering is performed using a Black Hole Entropic Fuzzy Clustering (BHEFC) technique. The weighted coefficients equivalent to cluster centers is optimally found using Tunicate swarm algorithm (TSA). Here, the log transformation is adapted for transforming the data in order to make it suitable for further processing. In addition, significant features are selected using Pearson Correlation coefficient. Subsequently, the chosen features are fed to clustering phase, where the clustering of COVID patients are performed with proposed TSA-based BHEFC. The proposed TSA-based BHEFC algorithm outperformed with maximal accuracy of 95.061%, maximal jaccard coefficient of 90.852% and maximal dice coefficient of 90.420%.
Praveen Dhyani Vijaya and Binu D
Emerald
This special issue of the VLSI DESIGN: an International Journal of Custom-chip Design, Simulation and Testing is devoted to the special issue of "High-Performance Design Automation of VLSI Interconnects". The explosion in complexity of VLSI interconnections has made it increasingly difficult for designers to manually carry out the chip and systems design. Automated synthesis of interconnection behaviours is emerging as a most promising approach to the implementation of large scale systems. Current interconnection level design models lack the capability of capturing important physical design effects such as signal integrity, power, clock, crosstalk, and timing issues which are first order factors in chip performance (especially for large scale deep-submicron designs). Thus, in order to approximate a realistic layout model of signals, the designer must rely on efficient but accurate estimates of the various design matrics throughout the design process. This way, the physical effects can be properly accounted for without incurring an unacceptable penalty in runtime. In order to develop such models of layout, there is a need to address research problems both in synthesis and physical design of interconnects. The purpose of this special issue is to further investigate recent developments in algorithm design and combinatorial structures to VLSI interconnection applications. Those contributors in this special issue have either been applying algorithmic techniques to cope with the complexity of VLSI systems, or they have been working on algorithmic graph theory and combinatorics and have found a new source of problems in VLSI domain. Specifically, the topics of interest for this special issue include module clustering to improve interconnection delays, performance-driven interconnection, clock network synthesis and physical design, power and ground network synthesis, signal crosstalk minimization transmission line analysis, multilayer wirability and performance-driven FPGA layout designs. This kind of problems arisen in VLSI interconnection designs are described and their up-todate techniques are demonstrated in the following 8 papers. The first paper of this special issue discusses the recent progress in the problem of clustering with more than one implementation per module, which has practical importance. Karayiannis and Tragoudas propose an approach such that each module has a set of possible implementations with the area and delay of each implementation being different. The authors show the problem is NP-hard. They present a pseudo polynomial time algorithm for the
Vijaya P and Binu D
Emerald
Vijaya P and Binu D
Scalable Computing: Practice and Experience
The special issue has been focussed to overcome the challenges of scalability, which includes size scalability, geographical scalability, administrative scalability, network and synchronous communication limitation, etc.The challenges also emerge with the development of recent applications. Hence this proposal has been planned to handle the scalability issues in recent applications. This special issue invites researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing and artificial intelligence to submit original research papers and timely review articles on the theory, design, evaluation, and use of artificial intelligence and parallel and/or distributed computing systems for emerging applications. The ten papers in this special issue cover a range of aspects of theoretical and practical research development on scalable computing. The proposal provides an effective forum for communication among researchers and practitioners from various scientific areas working in a wide variety of problem areas, sharing a fundamental common interest in improving the ability of parallel and distributed computer systems, intelligent techniques, and deep learning mechanisms and advanced soft computing techniques. The issue covers wide range of applications, but with scalable problems that to be solved by perfect hybridization of distributed computing and artificial intelligence.The first paper is “CPU-Memory Aware VM Consolidation for Cloud Data Centers” introduced a CPU Memory aware VM placement algorithm is proposed for selecting suitable destination host for migration. The Virtual Machines are selected using Fuzzy Soft Set (FSS) method VM selection algorithm. The proposed placement algorithm considers CPU, Memory, and combination of CPU-Memory utilization of VMs on the source host.In “Bird Swarm Optimization-based stacked autoencoder deep learning for umpire detection and classification”, presented the umpire detection and classification by proposing an optimization algorithm. The overall procedure of the proposed approach involves three steps, like segmentation, feature extraction, and classification. Here, the classification is done using the proposed Bird Swarm Optimization-based stacked autoencoder deep learning classifier (BSO-Stacked Autoencoders), that categories into umpire or others.In “Enhanced DBSCAN with Hierarchical tree for Web Rule Mining”, proposed an enhanced web mining model based on two contributions. At first, the hierarchical tree is framed, which produces different categories of the searching queries (different web pages). Next, to hierarchical tree model, enhanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique model is developed by modifying the traditional DBSCAN. This technique results in proper session identification from raw data. Moreover, this technique offers the optimal level of clusters necessitated for hierarchical clustering. After hierarchical clustering, the rule mining is adopted. The traditional rule mining technique is generally based on the frequency; however, this paper intends to enhance the traditional rule mining based on utility factor as the second contribution. Hence the proposed model for web rule mining is termed as Enhanced DBSCAN-based Hierarchical Tree (EDBHT).In “A comprehensive survey of the Routing Schemes for IoT applications”, this review article provides a detailed review of 52 research papers presenting the suggested routing protocols based on the content-based, clustering-based, fuzzy-based, Routing Protocol for Low power (RPL) and Lossy Networks, tree-based and soon. Also, a detailed analysis and discussion are made by concerning the parameters, simulation tool, and year of publication, network size, evaluation metrics, and utilized protocols. In “Chicken-Moth Search Optimization-Based Deep Convolutional Neural Network For Image Steganography”, proposed an effective pixel prediction based on image stegonography is developed, which employs error dependent Deep Convolutional Neural Network (DCNN) classifier for pixel identification. Here, the best pixels are identified from the medical image based on DCNN classifier using pixel features, like texture, wavelet energy, Gabor, scattering features, and so on. The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). The CMSO is designed by integrating Chicken Swarm Optimization (CSO) and Moth Search Optimization (MSO) algorithm based on limited error.In “An Efficient Dynamic Slot Scheduling Algorithm for WSN MAC: A Distributed Approach”, an effective TDMA based slot scheduling algorithm needs to be designed. In this paper, we propose a TDMA based algorithm named DYSS that meets both the timeliness and energy efficiency in handling the collision. This algorithm finds an effective way of preparing the initial schedule by using the average two-hop neighbors count. Finally, the remaining un-allotted nodes are dynamically assigned to slots using a novel approach.In “Artefacts removal from ECG Signal: Dragonfly optimization-based learning algorithm for neural network-enhanced adaptive filtering”, proposed a method utilizes the adaptive filter termed as the (Dragonfly optimization + Levenberg Marqueret learning algorithm) DLM-based Nonlinear Autoregressive with eXogenous input (NARX) neural network for the removal of the artefacts from the ECG signals. Once the artefact signal is identified using the adaptive filter, the identified signal is subtracted from the primary signal that is composed of the ECG signal and the artefacts through an adaptive subtraction procedure.In “A Comprehensive Review on State-of-the-Art Image Inpainting Techniques”, this survey makes a critical analysis of diverse techniques regarding various image inpainting schemes. This paper goes under (i) Analyzing various image inpainting techniques that are contributed in different papers. (ii) Makes the comprehensive study regarding the performance measures and the corresponding maximum achievements in each contribution. (iii) Analytical review concerning the chronological review and various tools exploited in each of the reviewed works.In “An Efficient Way of Finding Polarity of Roman Urdu Reviews by Using Boolean Rules”, proposed a novel approach by using Boolean rules for the identification of the related and non-related comments. Related reviews are those which show the behavior of a customer about a particular product. Lexicons are built for the identification of noise, positive and negative reviews.The final paper is “Forecasting the Impact of Social Media Advertising among College Students using Higher Order Statistical Functions”, this research work plans to develop a statistical review that concerns on social media advertising among college students from diverse universities. The review analysis on social media advertising is given under six sections such as: (i) Personal Profile; (ii) Usage; (iii) Assessment; (iv) Higher Order statistics like Community, Connectedness, Openness, Dependence, and Participation; (v) Trustworthiness such as Trust, Perceived value and Perceived risk; and (vi) Towards advertisement which involves attitude towards advertisement, response towards advertisement and purchase intension.