Forthcoming and Online First Articles

International Journal of Engineering Systems Modelling and Simulation

International Journal of Engineering Systems Modelling and Simulation (IJESMS)

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International Journal of Engineering Systems Modelling and Simulation (51 papers in press)

Regular Issues

  • Rainfall prediction using ensembled-LSTM and dense networks   Order a copy of this article
    by Ujjwal Sinha, Vishal Thakur, Sammed Jain, M. Parimala, S. Kaspar 
    Abstract: Rainfall prediction has been of utmost importance in any country. The amount of rainfall in a particular region has been known to affect the growth in that area, especially in an agriculture-based country like India. This paper proposes a model which performs one step rainfall forecasting in the regions Ranakpur and North-Eastern states of Assam and Meghalaya based on time series data acquired from 1 and 75 weather stations in both areas, respectively. This model was chosen to be based on the LSTM algorithm which has proven to be better than existing rainfall prediction models based on linear regression, support vector regressors, artificial neural network, random forest and decision tree algorithms. The RMSE score of the proposed architecture for Ranakpur and North-East were 1.948 and 2.654 respectively, better than the algorithms used in comparison. The factors taken into consideration for while predicting the weather are - max temperature, min temperature, precipitation, wind speed, relative humidity and solar radiation.
    Keywords: rainfall prediction; long short-term memory; LSTM; forecasting; weather; Root mean squared error; RMSE; precipitation; humidity; wind speed; time series.
    DOI: 10.1504/IJESMS.2021.10043810
  • Crowd management in public transport to ensure social distancing for prevention of spread of COVID-19   Order a copy of this article
    by K. Gerard Joe Nigel, J. Jenisha, R. Rajeswari, D. Pamela, P. Manimegalai 
    Abstract: Global shock from COVID-19 epidemic. Social isolation is becoming more crucial as this delicate condition spreads swiftly. Public transit must be enhanced to stop corona spread. An IoT method using LoRa technology might reduce overcrowding and disease transmission in public buses. Buses have LoRa transmitters and receivers. It is shown on the bus stop's LCD screen and announced over a speaker if the bus is within range of the receiver. An automatic door mechanism limits the number of people inside the vehicle. In the mobile app, the bus occupancy data is sent to Google Firebase. The app also indicates nearby buses, their occupancy, and their estimated arrival time. In certain cases, authorities may utilise this data to analyse and act. This simple technique would improve bus safety and contain COVID-19.
    Keywords: COVID-19; ESP32; crowd management; bus management; IoT.
    DOI: 10.1504/IJESMS.2021.10043863
  • Augmentation of predictive competence of non-small cell lung cancer datasets through feature pre-processing techniques   Order a copy of this article
    by M. Sumalatha, Latha Parthiban 
    Abstract: Non-small cell lung cancer (NSCLC) comprised of complex hidden and unknown data that is challenging for prediction at the earlier stage. The major objective of the research paper is to develop a novel preprocessing model based on minimisation of features and competency maximisation through feature pre-processing (FPP) to provide augmentation in predictive competence of NSCLC datasets. In Phase-I, the test for relevancy identified behavioural errors like null, empty and NAN values to reduce two features. In Phase-II, regression analysis was performed to find the relationship between features after which four features were removed. In Phase-III, cluster analysis is carried out to find the irrelevant features in the form of clusters and seven features are removed. The competency of NSCLC dataset before FPP showed more accuracy than after FPP with classifiers like simple tree, complex tree, linear SVM, Gaussian SVM, weighted KNN and boosted tree classifiers.
    Keywords: non-small cell lung cancer; NSCLC; competency of prediction; relevancy analysis; regression analysis; cluster analysis; feature pre-processing model; feature pre-processing; FPP; competency analytics.
    DOI: 10.1504/IJESMS.2022.10044031
  • A triple band MIMO slot antenna with enhanced resonance for WiMAX, Wi-Fi and WLAN applications   Order a copy of this article
    by Beulah Jackson, P. Pattunnarajam, S. Asha, M. Bindhu, J.C. Elizabeth 
    Abstract: A triple-band multiple input multiple output (MIMO) slot antenna with /4 wavelength slots of varying lengths that cover the operating frequency ranges of 1.6-1.8 GHz, 3-3.3 GHz and 4.54.8 GHz is presented in this paper. To achieve isolation of greater than -50 dB between the ports, a simple decoupler network is built for a large slot and a series of tapered slots. The antenna coupling is reduced by varying the ground plane. The designed antenna is fabricated using FR4 dielectric substrate with appropriate dimensions to achieve radiation across the frequency band from 1.6-4.8 GHz with more fractional bandwidth. Return loss of less than -25 dB and VSWR of 1.1 are obtained in this antenna.
    Keywords: multiple input multiple output; MIMO; defected ground structure; DGS; decoupler network; resonant frequency; VSWR.
    DOI: 10.1504/IJESMS.2022.10044364
  • XML document classification effectively using improved high-performance factor   Order a copy of this article
    by S. Sahunthala, Angelina Geetha, Latha Parthiban 
    Abstract: Nowadays, XML data plays in volume amount of business application. The real World Wide Web has more XML data in the website. The heterogeneous structure XML data classification is the challenging task in the research recently. Algorithms are available to classify the XML data by classification method. The performance is degraded in the classification XML document in the existing technique. In this paper, the machine learning technique tuning improved hyper parameter optimisation algorithm (TIHPOA) is proposed to classify the XML data. First the elements are extracted by using feature extraction vector space model. Then the XML data is classified using the algorithm of TIHPOA technique. The proposed model uses the improved hyper parameters to generate the better classifier than the existing classification approach. In existing approach, extreme machine learning (ELM), kernel principal component analysis (KPCA) and kernel extreme machine (KELM) and tuning swarm rapid swarm algorithm (TSRSA) methods are demonstrated. In this research the proposed model is compared with the existing model with various performance parameters.
    Keywords: XML data; classification; feature extraction; TIHPOA.
    DOI: 10.1504/IJESMS.2022.10044365
  • Intensity modulation based optical fibre humidity sensor using Agarose Chitoson composite   Order a copy of this article
    by Yogesh H. Patil, Amrit Ghosh 
    Abstract: Optical sensors are widely used in chemical industries to monitor humidity and hence assure machine, product, and device quality. The measurable quantity is the vaporised water (H2O) fraction. Optical fibre-based HS have recently been developed for particular applications, with benefits and drawbacks in RH detection. A thin coating of hygroscopic material (Agarose and Chitosan) is deposited on the humidity sensor’s core. agrose (100%); 2) Agarose + Chitosan (20%) (80%); 3) Agarose 40% + Chitosan (60%); 4) 60% Agarose + Chitosan (40%); 5) Agarose (80%) + Chitosan (20%) (100%). This indicates that both Agarose (40%) + Chitosan (60%) and Agarose (60%) + Chitosan (40%) composite proportions achieve the required sensitivity of 0.34 nm/% RH. The POF-HS with hygroscopic thin film coating design suggests a viable RH detection approach. A low installation cost, small size, and a wide range of visible light intensity.
    Keywords: relative humidity; RI; hygroscopic substance; Agarose and Chitosan; Abbe’s refractometer; plastic optical fibre; POF.
    DOI: 10.1504/IJESMS.2022.10044366
  • Assessment of mental workload using XGBoost classifier from optimised EEG features   Order a copy of this article
    by R.K. Kapila Vani, Jayashree Padmanabhan 
    Abstract: Cognitive workload evaluation is vital in any critical working environment for assessing the users mental status. Despite the fact that there are many methods for evaluating cognitive strain, the model that uses electroencephalography (EEG) data remains the most promising one. Brain related activities can be used to assess various mental states and also help us to determine mental effort. This study calculates the cognitive workload of people while performing multitasking mental tasks. Here the 'STEW' dataset is used to measure mental effort. 'No task' and 'simultaneous capacity (SIMKAP)-based multitasking activity' are the two tasks in the dataset. For the study we have chosen only the SIMKAP task dataset. The cognitive workload assessment from optimised EEG features using XGBoost classifier (CWAOEX) framework is proposed, in which numerous features from EEG brain signals are retrieved and the grey wolf optimiser (GWO) is utilised to select the best ones. The data is then categorised according to the best feature set. The XGBoost algorithm is employed in the classification step. The recommended method has the classification accuracy of 94.25 in categorising the workload as low, moderate and high which is better than the current methods.
    Keywords: XGBoost; grey wolf optimiser; GWO; STEW dataset.
    DOI: 10.1504/IJESMS.2022.10044577
  • Study on generalisation of triple connected perfect dominating set in fuzzy graph   Order a copy of this article
    by T. Gunasekar, K. Elavarasan 
    Abstract: This study presents the idea of k-connected total perfect dominating set in fuzzy graphs in this study. We have generalised connected perfect dominating set to k-connected perfect dominating set. In fuzzy graphs, new concepts are compared with previous ideas. For a few of the important classes, such as cycles and trees are discussed. The vertex and edge k-connected perfect dominating number of fuzzy graphs are obtained. Maximal fuzzy bipartite part algorithm in fuzzy graph is also described.
    Keywords: perfect domination; connected perfect domination; k-connected perfect dominating set and number.
    DOI: 10.1504/IJESMS.2022.10044615
  • Optical fiber-based refractive index measurement sensor   Order a copy of this article
    by Jayprabha Vishal Terdale, Amrit Ghosh 
    Abstract: Optical devices are increasingly widely used in surveillance, particularly in the food, beverage, and healthcare sectors. A precise refractive index profile is required to support maximum light intensity on the fibre material. But unpolished optical fibre has a difficult RI profile. Changing the solutions concentration allows for precise RI measurement. We present an intensity modulated fibre optic refractive index measurement using macro-bending and side polishing. RI conditions and the dipping solution concentration in the detecting region are more sensitive to probes with a polished surface (sucrose, glycerin, and ethanol). With RI 1.34-1.42, the POF performance sensing capability increases. This allows for physical variations in intensity at visible wavelengths at cheap cost and compact size.
    Keywords: intensity modulation; macro-bending; transparent liquid; sucrose; glycerine; ethanol; U-shape polished optical fibre; refractive index sensor.
    DOI: 10.1504/IJESMS.2022.10044749
  • CAD-based automatic detection of tuberculosis in chest radiography using hybrid method   Order a copy of this article
    by M. Mercy Theresa, A. Jesudoss, P. Pattunnarajam, Sudha Rajesh, Jaanaa Rubavathy, A. Raja 
    Abstract: Automated processes are essential in medical imaging to identify anomalies. This study uses chest radiography (CXR) for CAD analysis, which is indicated for about 90% of TB patients. Even when it is cost effective, certain reasons are difficult to pinpoint. Input CXR lung field segmentation, highlights from the segmented lung region, and TB detection calculations. Plans call for three phases: To segment well, this step employs a deformable active contour model. These two parameters are used to assess the algorithm’s segmentation output. It’s now time to extract and optimise features Hybrid multiresolution extracts the features. Various transform coefficients were statistically analysed to obtain a feature collection. The final stage is to classify lung anomalies using MSVM and KNN for three publicly available datasets. The classification performance of the JSRT, Montgomery, and Shenzhen datasets is assessed. The recommended method identifies pulmonary TB 96.5% of the time.
    Keywords: computer-aided diagnosis; CAD; chest X-ray; CXR; lung segmentation; novel active contour model; hybrid multiresolution approach; feature extraction; classification.
    DOI: 10.1504/IJESMS.2022.10044924
  • Effective allocation of resources and task scheduling in heterogeneous parallel environment   Order a copy of this article
    by Kirankumar Kataraki, Sumana Maradithaya 
    Abstract: Scientific studies on a large-scale are being performed in cooperation with teams located around the world. Each team exchanges data and conducts experiments using dispersed resources. Therefore, scientific data is duplicated and stored in geographically dispersed places. These data are incorporated in application processes which ease the operation and maintenance of applications in distributed/parallel computing systems. A workflow management system must take advantage of the presence of different data sources and distributed/parallel computing resources provided by platforms such as grids and clouds to effectively execute these procedures. This paper thus expands an existing workflow architecture and provides better planning algorithms for resources management. It begins with a comprehensive study of the planning techniques used in the past as the basis for parallel systems. The work offers a method that includes information management components and assesses its practical viability by utilising resources to operate several real-world applications.
    Keywords: distributed; scientific; parallel; resources.
    DOI: 10.1504/IJESMS.2022.10044925
  • CFD analysis of direct-operated poppet relief valve under different parameters and its structure optimisation   Order a copy of this article
    by Yong Sang, Pengkun Liu, Xudong Wang 
    Abstract: This paper investigates the dynamic characteristics of the direct-operated poppet relief valve (DOPRV) with different parameters and improves its dynamic characteristics by modifying the core. First, a simplified schematic of the valve is introduced; mesh elements, SST k~w turbulence model and corresponding time step size are selected by comparing the simulation results. Then, simulation is conducted under different nominal diameters, and a suitable range of diameter is determined. Further, an improved structure of the DOPRV is introduced, and the dynamic characteristics of the DOPRV under improved structure are stimulated under different annular clearance, spring stiffness, and pressure jump. The simulation results show that adding annular clearance can largely enhance the dynamic performance of DOPRV and the optimal annular clearance for the 20 mm diameter DOPRV in this paper is 0.2 mm.
    Keywords: direct-operated poppet relief valve; dynamic characteristics; structure optimisation; computational fluid dynamics; CFD.
    DOI: 10.1504/IJESMS.2022.10045086
  • Design and evaluation of a deep CNN algorithm for detecting farm weeds   Order a copy of this article
    by Balachandra Pattanaik, Areej Malibari, M. Kumarasamy, V. Nagaraj, M. Gopikrishnan 
    Abstract: Weeds are unwanted plants that grow with crops and usually removed by spraying herbicides or by manual labour. Herbicides being sprayed mostly do not reach their target because of the focus on a very wide area. This also tends to harm the environment, and other living organisms. Manual labour is time-consuming and expensive and it is continuously managed and monitored. The autonomous robotics and image processing, tasks can be completed with precision and ease in agriculture. With image processing, plants and weeds can be classified. Methods like scale invariant feature transforms (SIFT), speeded-up robust features (SURF), and ensemble learning, neural networks can be incorporated into identifying the difference. We can easily classify weeds and crops from images of plantations leveraging machine learning algorithms, artificial vision analysis systems, among others. Deep learning methods like convolutional neural network (CNN), rectified linear units (ReLU) and SoftMax (for classification) are focussed in this paper.
    Keywords: image processing; deep learning; convolutional neural networks; CNN; rectified linear units; ReLU; weed detection; automation.
    DOI: 10.1504/IJESMS.2022.10045350
  • Optimised dual hybrid energy storage systems for EV powertrain based on modified genetic algorithm   Order a copy of this article
    by Mukil Alagirisami, Balachandra Pattanaik, Ramesh Redrouthu, Chandu V.V. Muralee Gopi 
    Abstract: EV has generally been recognised as a viable substitute for internal combustion engine-powered vehicle. The EV is capacity and lifetime of the energy storage system, leading to decreased drive range of the vehicle and rise in price. To overcome these drawbacks, dual-HESS is introduced in the EV. In this work, batteries and ultracapacitors are utilised as HESS. In this work, fuzzy control is also implemented, which is accountable for splitting the energy among the front and rear wheels units in a far more appropriate manner in order to meet the needs of greater performance. Utilising the MATLAB/Simulink software, the entire framework was built with the FTP-75 (urban), US06 (maximum speed as well as required acceleration) and HWFET (highway) driving cycles. When contrasted to a corresponding existing EV deployed with a solo HESS unit, the suggested dual-HESS architecture enhanced the driving range by 145.15 km as well minimising the HESS mass by 23.93%.
    Keywords: electric vehicles; GOA; GA; HESS; power management control; PMC; SoC; FLC.
    DOI: 10.1504/IJESMS.2022.10045386
  • Robust control of frequency considering operations of AC microgrid in islanded mode   Order a copy of this article
    by Sanjiv Kumar Jain, Shweta Agrawal 
    Abstract: Modeling and control of an autonomous microgrid with both governable and non-governable sources is discussed in this paper. Power sources utilised in microgrid are solar, wind, fuel cell (FC) and diesel based generator (DG). Batteries, flywheels (FW), and aqua electrolyses (AE) are used as energy storage elements. Recently, due to the increased penetration of non-inertial distributed generations in the microgrid, frequency deviation has become a matter of concern. If the loading conditions and active power generation changes unexpectedly, then the power system frequency would deviate largely. The output of the flywheel, battery, diesel generator, FC, and aqua electrolyser is regulated using the proportional-integral-differential (PID) controller. In this paper, a novel mathematical framework is proposed to tune PID controllers. For zero frequency deviations in steady state, optimum values of proportional, differential, and integral gain coefficients are proposed. The importance of the proposed scheme is to achieve better steady state frequency response.
    Keywords: diesel generator; frequency regulation; microgrid; PID control; renewable energy sources.
    DOI: 10.1504/IJESMS.2022.10045754
  • An efficient fruit quality monitoring and classification using convolutional neural network and fuzzy system   Order a copy of this article
    by K. D. Mohana Sundaram, T. Shankar, N. Sudhakar Reddy 
    Abstract: Fruit quality monitoring in agro industries is carried out by people who may deviate from their responsibility due to tiredness, illness, or personal reasons. So, an automatic quality assessment system is proposed based on convolutional neural network (CNN) and Mamdani fuzzy logic that estimate quality of a Persian Lemon. The proposed CNN was trained with the transfer learning method and the results obtained were compared with previous works. The proposed CNN achieved 94.79% accuracy in the validation process which is 13% higher than the existing architecture. The proposed fuzzy logic classified each lemon in three ranges based on rules customised for the estimation of fruit quality standards.
    Keywords: fuzzy systems; transfer learning; convolutional neural network; CNN.
    DOI: 10.1504/IJESMS.2022.10045885
  • Machine learning-based financial analysis of merger and acquisitions   Order a copy of this article
    by S. Kalaivani, K. Sivakumar, J. Vijayarangam 
    Abstract: Stock market analysis and forecasting is one of the most sought-after areas of study. As anyone who has observed stock market movements even as an outsider knows very well the enormous amount of risk involved with numerous factors affecting it, its study is quite an intriguing and interesting one, let alone a profitable one. So, it is imperative we look for prediction tools to help us through the process. As we dwell into already available tools in the fields of economics and statistics, we can sense a need of innovation from other evolving domains and the immediate one is the field of machine learning. This paper is a stock market price forecasting one, using neural network model, employed on financial data concerning pre and post-mergers of companies. We have collected data of pre-merger and post-merger states, formed a neural network model to fit it and used the model to forecast. The predictions were reasonably accurate.
    Keywords: neural network; financial forecasting; merger; acquisitions.
    DOI: 10.1504/IJESMS.2022.10046049
  • Insights on future employment and required technical skills pertaining with Oman   Order a copy of this article
    by Sanjai Gupta, Mohammed Faisal 
    Abstract: Unemployment is one of the major problems of any country and a challenge that the graduates lack in required skills and knowledge that employer's need. Joblessness rate in Oman arrived to a stagnant state since 2017 at 3.1% (Oman Unemployment Rate-19912019 Data, 2019). Every year Oman is producing many graduates, which are lacking in required skills and knowledge to get suitable job. In this research, we have extracted the jobs and the skills from Oman job portals using RapidMiner, finding more demanding skills in the fields of engineering, information technology and sales, mapping the skillset with the college outcomes. The research possibly identifies the areas to enhance the employability skills and job profiles to meet the job requirements of in Oman. These inputs can further help the concern authorities to decide upon training the employable candidates to bridge the gap between employers expectations and the college produce (Rehman, 2014).
    Keywords: skills; skillset; employability skills; text-mining; RapidMiner; mapping skills; 4th industrial revolution; Oman jobs; Oman.
    DOI: 10.1504/IJESMS.2022.10046733
  • A modified hidden Markov model for outlier detection in multivariate datasets   Order a copy of this article
    by G. Manoharan, K. Sivakumar 
    Abstract: The processing of data is an essential part of any field. More than 80% of the study effort is focused on collecting meaningful information from the vast amounts of data available. However, in order to minimise calculation time and improve accuracy, it is necessary to keep track of any unused, redundant, or irrelevant data in the dataset. Because it’s tough to build up a data warehouse to separate homogeneous data, it will be inefficient and inappropriate in terms of deployment costs and performance metrics. Meanwhile, handling heterogeneous data consumes more time to process due to uneven data samples and missing data. Thus, identifying the data class and balancing the data is critical for improving the performance of classification models. Outlier detection is the process of detecting irrelevant, missing, or unequal data samples in a large database. The goal of this study is to employ a modified hidden Markov model to find such outliers in a big dataset. This method improves classification model performance while also reducing computation time and increasing classification accuracy. The proposed model is experimentally verified and compared with prominent existing technologies such as random forest and decision tree models.
    Keywords: outlier detection; hidden Markov model; HMM; classification; support vector machine; SVM; random forest; RF; decision tree; DT.
    DOI: 10.1504/IJESMS.2022.10046736
  • Effect of semi batch and fed batch addition of glucose on alkaline protease production: a multi-objective optimisation approach   Order a copy of this article
    by Anitha Mogilicharla, V. Swapna, Rajasri Yadavalli 
    Abstract: Alkaline protease is one of the important enzymes in many industries. In this effort, semi batch addition and fed batch addition of glucose have been considered for maximisation of protease concentration in minimum fermentation time. The kinetic model of the process is validated with the experimental batch and fed batch addition of glucose from the open literature. A theoretical study has been conducted with such a validated model to check the effect of protease concentration on the semi batch addition of glucose. Based on this, multi-objective optimisation studies have been done for the simultaneous minimisation of fermentation time and maximisation of protease concentration with the relevant constraints. The elitist non-dominated sorting genetic algorithm (NSGA II) has been utilised for this purpose. The additions of glucose in semi batch mode show the potential increasing of protease concentration in at a less fermentation time as compared to the batch experimental data.
    Keywords: protease; semi-batch addition; fed-batch; NSGA II; multi-objective optimisation.
    DOI: 10.1504/IJESMS.2022.10046792
  • HCP miner: an efficient heuristic-based clustering method for discovering colossal frequent patterns from high dimensional databases   Order a copy of this article
    by T. Sreenivasula Reddy, R. Sathya, Mallikhanjuna Rao Nuka 
    Abstract: This paper presents an efficient heuristic-based clustering method for colossal frequent patterns discovery from the high dimensional databases (HCP miner). The HCP miner avoids exhaustive level-wise pattern tree traversal and quickly mines colossal patterns from the high dimensional databases. To achieve this, our approach constructs the sub-patterns using a lattice array and applies the binary clustering over the sub-patterns initially. While constructing the sub-patterns using a lattice array, it uses the support values. These sub-patterns are explored as conditional patterns by estimating core patterns using heuristic measures to minimise the searching time during the database scan. Finally, colossal cluster is constructed from which colossal patterns are discovered. We perform the experiments on various high dimensional databases using different performance metrics. Our experiments shows that, the proposed HCP miner achieves prominent and efficient results for mining. In addition, these analysis of results reveals that the HCP miner algorithm outperforms with CoreFusion, colossal pattern miner (CPM) in diverse aspects.
    Keywords: data mining; big data; frequent pattern mining; FPM; high dimensional database; FIMI dataset; backtracking search.
    DOI: 10.1504/IJESMS.2022.10046794
  • Machine learning and image processing technique to describe outdoor scenes for visually impaired people   Order a copy of this article
    by S. Pavithra, V. Prabhakaran, T. Helan Vidhya, D. Gururaj, P. Shanmuga Priya 
    Abstract: It is estimated that there are about 280 million visually impaired individuals in the globe, who are unable to see and experience the world in the same manner that a normal human being does. Using digital image processing and voice processing, we want to assist visually impaired individuals in interacting with the actual world by narrating the descriptions of a scene in front of them, as described in our article. In addition to this, our gadget serves as a personal assistant by keeping the user up to speed on the latest developments.
    Keywords: blind; assistive device; image processing; speech processing; image recognition.
    DOI: 10.1504/IJESMS.2022.10046796
  • Artificial intelligence-based conference automation system involving image recognition   Order a copy of this article
    by Resmi R. Nair, R. Krishnapriya, R. Prineetha 
    Abstract: Conference hall automation system is very useful in the modern world primarily to reduce manpower required for monitoring the conference. From security point of view, the entry of only the authorised persons to the conference hall is of prime importance. Therefore, an artificial intelligence-based image recognition plays a vital role in the proposed automation system. Face is the essential part of the human physique that uniquely identifies a person. Utilising the face traits as biometric, the face recognition-based participants’ entry to the conference hall is implemented. Attendance database will be automatically updated whenever the participants enter and leave the conference hall. An attendance confirmation message will be sent to the participants registered e-mail ID once the participant enters the conference hall. The proposed conference hall automation system is superior to the conventional conference hall system in terms of man power requirement, automation, energy efficiency and security.
    Keywords: image processing; Python OpenCV; automation; artificial intelligence; Raspberry Pi.

  • Ultra-low latency communication technique for augmented reality application in mobile edge computing   Order a copy of this article
    by S. Narayanan, Rakesh Kumar Arora, Sanjeev Gangwar, J. Pradeep Kandhasamy, T. Ratheesh, K. Murali 
    Abstract: In wireless communication, ultra low-latency communications (ULLC) services offer short packets that may coincide alongside enhanced mobile broadband (eMBB) services which send lengthy packets. In a mobile edge computing architecture with eMBB and URLLC services, we explore how to evaluate latency and enhance shorter packet offloading methods. Using eMBB and URLLC functions, we examine how to evaluate latency and enhance shorter packet offloading methods in mobile edge computing (MEC) approach. In the MEC system, a server called processor sharing (PS) is utilised to reduce computation delay for short packets by distributing the server’s whole processing power equally to all packets. The server ignores long packets in favour of shorter ones. With a small packet size, a closed-form formula for the complementary latency distribution function may be developed. To minimise the short packet’s end-to-end (E2E) latency, offloading probabilities are adjusted based on the dependable demand. In tests with short and long packets, the processor sharing server outperforms 2 types of first-come, first-serve servers.
    Keywords: mobile edge computing; MEC; processor sharing server; ultra low latency communication augmented reality.
    DOI: 10.1504/IJESMS.2022.10046829
  • Image quality estimation based on visual perception using adversarial networks in autonomous vehicles   Order a copy of this article
    by D. Vijendra Babu, A. Umasankar, K. Somasundaram, C.M. Velu, A. Sahaya Anselin Nisha, C. Karthikeyan 
    Abstract: To improve autonomous cars, the dynamic systems method is re-enacted. Due to the unreality of the sensors employed in vehicles, human creation of the surrounding environment and objects is necessitated. We propose a novel efficient method for generating accurate scenario sensor data using limited LIDAR and video data from an autonomous vehicle. A new SurfelGAN network recreates realistic camera pictures to recognise the cars and moving objects in the scenario. The suggested approach uses real-world camera image data from Waymo Open Dataset to evaluate actual scenarios for autonomous vehicle movement. A new dataset allows for simultaneous analysis of two autonomous cars. This dataset is used to test and explain the proposed SurfelGAN model. GAN is the greatest technique for capturing realistic pictures. The machine generates precise sensor data that is used to identify obstacles, cars, and other moving objects in the route of an autonomous vehicle. The autonomous car approaches the destination by recreating a surfel scene. Pictures are collected using semantic and instance segmentation masks.
    Keywords: generative adversarial networks; GAN; visual perception; image quality assessment; IQA; autonomous vehicle; SurfelGAN.
    DOI: 10.1504/IJESMS.2022.10046831
  • Scalable image compression mechanism for surveillance video summary   Order a copy of this article
    by T. Venkata Satya Vivek, Manoj Kumar Gupta, J. Pradeep Kandhasamy, Renu Kachhoria, Santwana S. Gudadhe, S. Lakshmi Narayanan 
    Abstract: The use of large-scale video surveillance systems is widespread in important areas such as home and public safety. Recognising and evaluating appropriate security measures is critical since these systems are vulnerable. A clear movie requires good compression. Lossy image compression may decrease the amount of bandwidth needed for picture transmission and the amount of storage available to a device, improving network performance. Neural networks have thrived in image processing thanks to deep learning. We present an image reduction technique based on semantic analysis based on the degree of human attention to each region of the picture. After evaluating the semantic images using a convolutional neural network (CNN), a compression bit-allocation algorithmic technique is used. This technique enhances video surveillance visual quality while keeping the same compression ratio.
    Keywords: convolutional neural network; CNN; image compression; recurrent neural network; scalable image; video surveillance.
    DOI: 10.1504/IJESMS.2022.10046832
  • Bayesian-based binary compression with bandwidth optimisation for UAV aerial images   Order a copy of this article
    by Pankaj Agarwal, Sapna Yadav, J. Pradeep Kandhasamy, A. Balaji, S. Markkandan, D. Vijendra Babu 
    Abstract: This article proposes a new Bayesian-based binary compression model for UAV aerial pictures. This technique utilises inter-signal correlations to extract several sparse signals simultaneously. BKF-based approach employs both intra- and inter-signal correlations. The Bessel K-form (BKF) also features a higher zero peak with longer tails. Consumers may use UAV-borne base stations for temporary or emergency services. The effectiveness of low-bandwidth wireless Bayesian UAV communication BS still a challenge. This study’s aim is to enhance UAV-BS spectrum usage while maintaining user fairness. Through aerial picture quality, we propose adjusting the distribution of bandwidth, power, and UAV-BS trajectory to capture the object image. The proposed method outperforms other approaches in aerial picture detection. To get high quality aerial images, Bayesian-based binary compression lowers picture size and minimises noise. The advantages of UAVs using the Bayesian approach have spurred research interest in novel communication systems.
    Keywords: Bayesian method; binary compression; bandwidth optimisation; UAV aerial images; Bessel K-form; BKF.
    DOI: 10.1504/IJESMS.2022.10046833
  • Hysteresis controlled single phase-VIENNA rectifier fed DC drive system with enhanced response   Order a copy of this article
    by B. Manimaran, R. Rani Hemamalini, Ramareddy Sathi 
    Abstract: This work deals with the forming, enquiry, strategy and simulation* of proportional resonant-proportional resonant (PR-PR) and hysteretic-controlled two loop single-phase VIENNA rectifier fed DC drive system (SPVRDDS) using MATLAB Simulink. VIENNA rectifier with low THD is proposed for the control of DC drive. HC is suggested for closed loop SPVRDDS. Simulation is done for PR (proportional resonant controller) and PI-HC (proportional integral-hysteresis controller) controlled two loop systems using VIENNA rectifier fed DC drive and the outcomes are evaluated. The assessment is done in terms of time domain parameters like settling time and steady state error. The endings of SPVRDDS represent grander concert of HC controlled two loops VIENNA.
    Keywords: THD; PIC; PRC; Swiss-rectifier; PWM-control.
    DOI: 10.1504/IJESMS.2022.10046834
  • Implementation of wearable device for upper limb rehabilitation using embedded IoT   Order a copy of this article
    by M. Veeresh Babu, V. Ramya, V. Senthil Murugan 
    Abstract: In this paper, designed a low-cost upper-limb rehabilitation device that includes sensors and in-built technology that allows for accurate movement evaluation and mussel force. The function modules designed encompasses a multiple mechanical structure, sensor in addition driver circuits, database, as well as interactive interface. In mechanical structure design, 3D printing is used for obtaining some key components. The proposed upper-limb rehabilitation has advantage of low step angle, holding torque, pressure and angle monitoring, assist or opposes upper limb mussel motion with low cost. The data storage and analysis is obtained using windows application, and control implementation is obtained by DSPIC30F4011 and stepper motor. The suggested upper-limb rehabilitation scheme is integrated and tested; the necessary parameters are verified.
    Keywords: wearable device; IoT; upper limb; 3D printing.
    DOI: 10.1504/IJESMS.2022.10046897
  • Privacy-preserving with data optimisation in social networks using ensemble algorithm and K-neural network   Order a copy of this article
    by P.S. Arun Karthi, S. Sathiyabama 
    Abstract: A huge collection of data is being produced for each second due to advance technology development and its innovation in the social media. Monitoring the system and network, securing lines and servers are come to end by using various mechanism. The data accuracy has been increased by the K-nearest neighbour (KNN) classification. A deep learning technique which is a neural network is used for the detection of attacks being done by hackers or fraudulent users. The proposed model uses a programming language called Python which has packages of Scikit-learn, Tensorflow and Seaborn. We have also developed and found the accuracy rate gets increased by the deep learning model and so the attacks made on the social networks have been avoided as much as feasible.
    Keywords: social networking; data mining; intrusion detection system; IDS; data optimisation; deep learning techniques; clustering-based IDS; ensemble algorithm; KNN classification.
    DOI: 10.1504/IJESMS.2022.10046898
  • 3D segmentation of brain tumour   Order a copy of this article
    by Rudresh Deepak Shirwaikar, Kuthika Ramesh, Abu Mohammed Faisal, M. Jeshwanth, Aditya Raghav 
    Abstract: Brain replacement is a grouping of abnormal cells in the brain. Brain tumours may be malignant or non-cancerous. Glioma, meningioma, and pituitary are common brain tumours. MRI scans may detect these cancers at various stages. There are many ways to classify and extract features from MRI brain tumour pictures. The CNN image classification approach accurately detects early stage tumours. Explore the 3D CNN using brain tumour segmentation. Training several CNN architectures to compare their performance and design collects local and contextual data. One of the drawbacks of 3D design is memory use. 3D convolutions are computationally intensive and have exponential parameters. However, if correctly done, automatic identification of crucial traits without human supervision is conceivable. It is tremendous computational efficiency makes it the most often used design. Our main objective is to optimise memory consumption and processing to detect brain cancers in 3D MRI data. The 3D CNN architecture removes brain tumours first, then feeds them to a pre-trained featured extraction for CNN model. Using these extracted features to choose the best features using correlation-based output. This is done through feed-forward neural networks.
    Keywords: tumour; CNN; segmentation; U-net architecture; accuracy; encoder; decoder; training; validation; dice score.
    DOI: 10.1504/IJESMS.2022.10047159
  • A scheme based on ECDSA and its implementation for information security   Order a copy of this article
    by G. Mallikharjuna Rao, K. Deergha Rao 
    Abstract: Cryptography methods are means of securing digital data on a network. Digital signature on a document today is trendy in the digital world for authentication, authorisation, integrity, and non-repudiation. The elliptical curve digital signature algorithm (ECDSA) has been implemented and proved that it requires a key size of small length as compared to the RSA. However, its implementation for image and audio security is lacking in the literature. Hence, this paper has proposed a scheme based on ECDSA for text, image, and audio security over networks. Further, the proposed method is implemented on text, image, and audio using both LabVIEW software and myRIO hardware and verified for authentication.
    Keywords: digital signature; ECC; elliptical curve digital signature algorithm; ECDSA; cryptography; security.
    DOI: 10.1504/IJESMS.2022.10047195
  • Study on optimisation of seismic performance of special-shaped column structure in residential buildings based on BIM technology   Order a copy of this article
    by Peng Zhang, Li Wang, Jing Zhong 
    Abstract: In order to solve the problems of poor seismic performance of special-shaped column structure of residential buildings after traditional optimisation methods, a seismic performance optimisation method of special-shaped column structure of residential buildings based on BIM technology is proposed. Building visual information base model is constructed by BIM technology; The variation of parameters of special-shaped column structure in residential buildings is analysed; considering the economic benefits of special-shaped structure of residential buildings, build a comprehensive two-way driving structure; obtain the displacement parameters of the special-shaped structure of residential buildings, analyse its multi-degree of freedom system and equivalent single degree of freedom, determine the displacement degree of the special-shaped column structure of residential buildings, and complete the optimisation of the special-shaped column structure of residential buildings. The results show that the maximum displacement curvature ductility coefficient is about 4.5 and variation range of energy dissipation coefficient is 0.03~0.04.
    Keywords: BIM technology; residential building; non-standard pillar; Rhino parametric model; seismic performance optimisation; special-shaped column structure.
    DOI: 10.1504/IJESMS.2022.10047414
  • Review of artificial intelligence techniques used in IoT networks   Order a copy of this article
    by Mujahid Tabassum, Kartinah Bt Zen, Sundresan Perumal, Veena Raj 
    Abstract: Artificial intelligence (AI) is an effective and efficient solution to manage and analyse data flow in any network. Internet of things (IoT) has quickly attracted significant global attention as an innovative, progressively growing technology. It has shown a rapid and successful involvement in many fields. Thus, IoT applications evolve exorbitantly and produce vast amounts of data required for intelligent data processing. It is approximately calculated that by 2025, IoT could make significant traffic of 79 zettabytes, and by 2030 around 25 billion active smart gadgets would be linked and woven through a single massive information network. It creates hurdles for the end-user to effectively evaluate and analyse the collected information. Therefore, IoT networks utilise robust and effective AI techniques such as machine learning (ML) and data analytics (DA), which examine large amounts of data and generate meaningful information promptly. ML is a self-learning process, and DA is another effective method for predicting the future behaviour of object or activities, using past data to improve productivity in different industries such as agriculture, transportation, online gaming, eHealth, etc. This paper discussed AI techniques such as ML and DA used in IoT networks and their impacts on productivity. Furthermore, we have discussed the future trends and challenges of IoT networks.
    Keywords: internet of things; IoT; artificial intelligence; data analytics; machine learning; internet.
    DOI: 10.1504/IJESMS.2022.10047758
  • Neural network-based optimisation of smart odometry classification in a self-governing robot for precise position and location estimation   Order a copy of this article
    by Shaik Mohammad Rafi, A. Prakash, Firdouse Banu, P. Muthu Krishnammal, K. Bhavana Raj, J.E. Anusha Linda Kostka 
    Abstract: The Verdino self-governing robot’s intelligent dummy device will benefit greatly from this study’s findings. An odometric mathematical model based on the robot’s trajectory equations determines the robot’s position. Odometer devices are system inputs, and a model is constructed using the wheel diameter and distance. This model determines the optimal nominal parameters by trying to conduct a restricted squares reduction. This model is computed using the current wheel diameter to assure the accuracy of the findings. A neural network model is used to train an odometric model using data. There is no doubt that the neural network works.
    Keywords: neural network; autonomous robot; position; and orientation estimate; odometry system.
    DOI: 10.1504/IJESMS.2022.10047953
  • Ensemble approach of GP, ACOT, PSO, and SNN for predicting software reliability   Order a copy of this article
    by D. Shanthi, Narla Swapna, Ajmeera Kiran, Shaga Anoosha 
    Abstract: In recent decades, software has grown in importance. More and more computing systems are being intefted into modern society, increasing the necessity for rigorous software development methodologies. Software crises are issues that create delays, increased expenses, or failure to meet user needs. This difficult endeavour can be made easier by enhancing the software development process. We proposed GP, ACOT, PSO, SNN, and a mixture of GP, ACOT, PSO, and SNN to predict software reliability. Our results were compared to existing machine learning algorithms like neural networks and decision trees. We collected three software failure datasets using RMSE and NRMSE to support the need.
    Keywords: decision tree; ant colony optimisation techniques; particle swarm optimisation techniques; spiking neural networks; soft computing techniques; software reliability; machine learning.
    DOI: 10.1504/IJESMS.2022.10048112
  • A novel hybrid supervised machine learning model for real-time risk assessment of floods using concepts of big data   Order a copy of this article
    by Tegil J. John, R. Nagaraj 
    Abstract: Risk assessment (RA) modelling refers to combinatorial development of identification and assessment of the potential for the occurrence of an event that causes a negative impact on an entity of interest. With recent advances in data acquisition and archival methods, concepts of big data have been a great boon to RA development. It is primarily due to the fact that the accuracy of RA relies on the volume of historical data analysed. Based on this, a RA model is designed as a hybrid model using differential evolution and an adaptive neuro-fuzzy inference system to assess risk in real-time. The performance ability of the proposed hybrid model is compared with conventional ANFIS and neural network models by analysing the rainfall status in India. Data from the expert systems are collected by analysing various case study areas from India to validate the performance of the proposed hybrid system. The proposed model performance is validated through parameters like precision, recall, f1-score and accuracy. With maximum accuracy of 94.65% proposed model attains better performance than conventional approaches.
    Keywords: neural network; autonomous robot; position and orientation estimate; odometry system.
    DOI: 10.1504/IJESMS.2022.10048225
  • Improved performance for Alzheimer's disease earlier detection and diagnosis using deep learning algorithms   Order a copy of this article
    by T. Deenadayalan, S.P. Shantharajah 
    Abstract: As our society ages, cognitive impairment and dementia in the elderly, particularly Alzheimer’s disease (AD), have become more prevalent. After clinical symptoms arise, senile cognitive impairment will advance to irreversible dementia, finally leading to death as a multifactor, multistage, and clinical syndrome with concomitant disorders. Alzheimer’s disease is currently irreversible, and scientific trials for effective treatments are lacking. The progression of a patient’s condition will go through numerous stages, so early detection is critical. Early Alzheimer’s disease treatments can effectively decrease disease development while also reducing the burden on patients’ families and society. The research presents a deep learning-based strategy for early detection and screening of Alzheimer’s disease. The method involves slicing a three-dimensional magnetic resonance picture of the human brain into a two-dimensional image, then using an object recognition network called faster region with convolutional neural networks (R-CNN) to detect shrinkage in the hippocampal region of the brain to make an AD diagnosis. To get feature maps and to get 100% high-precision detection of AD samples, a new network is constructed and optimised based on VGG16, which is the basic network of faster R-CNN. At the same time, the validation set achieves 97.67% of the detected picture correctness.
    Keywords: disease detection; feature extraction; CNN; deep learning algorithm; high-precision.
    DOI: 10.1504/IJESMS.2022.10048226
  • Effect of sliding friction on torsional vibration of wind turbine drivetrain system under constant wind load   Order a copy of this article
    by Rishi Kumar, Sankar Kumar Roy 
    Abstract: Sliding friction is an unavoidable phenomenon that occurs between two contacting bodies under motion. In this paper, dynamic analysis of wind turbine drivetrain system (WTDS) is performed and sliding friction is incorporated in the model. Key tooth meshing points along path of contact like pitch point, single and double pair region are identified; reversal of force due to sliding friction at pitch point is explained. The drivetrain system contains three stages of gear drive. To study the dynamic characteristics of the system, governing equations of motion of lumped parameter model is derived using Lagrange’s formulation. Time varying mesh stiffness (TVMS) is estimated using the analytical method and frictional torque are incorporated in the drivetrain model. Finally, the governing equations are numerically solved using the Houbolt discretisation method in MatLab. Torsional vibration signals and frictional torque are obtained in MatLab. The simulation results with and without friction are fast Fourier transformed. The dynamic effect of sliding friction on the WTDS is investigated in the frequency domain and comparison is made with no friction condition.
    Keywords: wind turbine drivetrain system; WTDS; mathematical modelling; sliding friction; pitch point; time varying mesh stiffness; TVMS; fast fourier transform.
    DOI: 10.1504/IJESMS.2022.10048371
  • Experimental study on frost heave deformation characteristics of rock and soil based on pore distribution model   Order a copy of this article
    by Guo-xing Pang, Lin-ping Fu, Diandian Ding 
    Abstract: The traditional rock and soil frost heave deformation characteristics analysis test is time-consuming and expensive. Therefore, an experimental study method of rock and soil frost heave deformation characteristics based on the pore distribution model is proposed. The pore distribution model was constructed to calculate the stress intensity factor formed by the point force and the distributed gravity, and then the frost heave displacement was obtained and the frost heave deformation characteristics were analysed. The test results show that the running time of this study is saved by at least 2 min, and the average test cost is 14,951 million Yuan. Through the results of soil strain energy release rate and soil moisture content obtained, the effectiveness of the frost heave deformation characteristic test of rock and soil is fully verified.
    Keywords: pore distribution model; frost heave of rock and soil; deformation characteristics; experimental study.
    DOI: 10.1504/IJESMS.2022.10048586
  • Numerical simulation based radial laser cladding process optimisation for annular thin-walled parts   Order a copy of this article
    by Xuhui Xia, Yuding Gao, Lei Wang, Zelin Zhang, Ping Yi, Tong Wang, Baotong Chen 
    Abstract: In order to effectively reduce the deformation and improve the forming quality, the radial laser cladding process optimisation for annular thin-walled parts with numerical simulation and orthogonal experiment is carried out in this paper. A three-dimensional thermal-mechanical coupling finite element model for laser cladding of the annular thin-walled part is established. Based on the orthogonal experiment, the influence of laser power, scan speed and laser spot radius on the formation quality of radial single-layer cladding layer is investigated. The residual stress result shows that the maximum value of the residual stress in each direction appears at the junction of cladding layer and substrate. The optimal process parameters combination is laser powder of 1,400 W, scan speed of 21 mm/s, laser spot radius of 2.5 mm with smaller deformation and well forming quality. The results can provide some scientific and theoretical guidance for actual laser cladding of annular thin-walled parts.
    Keywords: radial laser cladding; thermal-mechanical coupling model; annular thin-walled part; process optimisation.
    DOI: 10.1504/IJESMS.2022.10048707
  • Performance analysis of doubly-fed induction generator using PID controller optimised by whale optimisation   Order a copy of this article
    by Ashutosh Kashiv, H.K. Verma 
    Abstract: The large-scale installation of wind turbines equipped with a doubly powered induction generator (DFIG) has been promoting the carrying out of several studies related to potential solutions for their integration into the electrical grid. In this paper, a control technique is presented that allows to regulate the active and reactive powers of DFIG in a stable and independent way. Its feasibility is supported by simulation results of models developed using MATLAB/Simulink software. To optimise the gains of the proportional, integral, derivative (PID) controller, a metaheuristic optimisation methodology called whale optimisation is used, where the operation of the system will be considered in the design stage to increase the robustness of the control. The behaviour of the control loops is evaluated after the occurrence of three-phase short circuits in the distribution network.
    Keywords: doubly-fed induction generator; DFIG; GSC; PID; rotor-side controller; RSC; wind turbine; whale optimisation algorithm; WOA.
    DOI: 10.1504/IJESMS.2022.10048733
  • A comprehensive review on different optimisation components for hybrid renewable energy sources   Order a copy of this article
    by Muleta Negasa, Altaf Q.H. Badar 
    Abstract: Renewable energy sources (RES) are natural energy sources that do not deplete with time. These energy sources are characterised as decentralised, modular, flexible technologies, closer to the load, and having smaller production capability. RES suffers from some drawbacks like reliability owing to its intermittent nature and high initial investment costs. To mitigate these drawbacks, hybrid renewable energy systems (HRES) are proposed in research studies. Different methodologies that operate these HRES at the optimal with their mathematical modelling, different algorithms (artificial intelligence and meta-heuristic) are reviewed in this paper. According to the literature survey, more researchers are giving attention to adjusting reliability and cost of energy separately or in the combination of both. On the other hand, environmental effects are critical issues for the globe in this century but have not given more attention.
    Keywords: hybrid renewable energy system; HRES; optimisation techniques; reliability; cost; environmental impacts.
    DOI: 10.1504/IJESMS.2022.10048772
  • Polarisation maintaining square shape photonic crystal fibre with high nonlinearity   Order a copy of this article
    by Monika Kiroriwal, Poonam Singal 
    Abstract: A highly birefringent square shape photonic crystal fibre (S-PCF) with high nonlinearity has been simulated and studied. AlGaAs infiltrated slotted elliptical and rectangular cores are considered to identify the impact of core shape on optical properties. The light managing behaviour of the triangular meshed S-PCF is studied by employing the finite element method (FEM). Simulated results indicate that the slotted elliptical core is more compelling than the slotted rectangular core. Proposed PCF with high birefringence nearly to 0.46, high nonlinearity of 1.2 x 105 W-1km-1, and high numerical aperture of 0.873 at 2 um can be a prominent contender for a wide span of uses such as in nonlinear optics, polarisation-maintaining, sensing, and medical imaging.
    Keywords: photonic crystal fibre; semiconductor nonlinear material; birefringence; nonlinearity; nonlinear optics; shape photonic crystal fibre; S-PCF; finite element method; FEM.
    DOI: 10.1504/IJESMS.2022.10048773
  • Elephant sound classification using machine learning algorithms for mitigation strategy   Order a copy of this article
    by T. Thomas Leonid, R. Jayaparvathy 
    Abstract: Conflicts between humans and elephants have become a wide problem in the agricultural and forest sectors, posing a threat to human lives and inflicting significant resource loss. This paper presents and compares the results of feature extraction techniques for detecting elephant voice signal. Support vector machine (SVM) classifiers, K-nearest neighbour (KNN) classifiers, nave Bayes classifiers and convolutional neural network (CNN) classifiers all use the recovered features as inputs. The performance of all feature extraction techniques are validated and compared on elephant voice signals. The experimental results have confirmed that highest testing classification accuracy of 84% is resulted from CNN classifier with discriminatory features from the voice. This signifies that the different techniques of feature extraction technique have immense potential than other techniques in Identifying elephant voice signal.
    Keywords: classification; convolutional neural network; CNN; accuracy; elephant; feature extraction.
    DOI: 10.1504/IJESMS.2022.10049166
  • Performance and analysis of solar to vehicle and vehicle to grid integrated energy conversion technologies   Order a copy of this article
    by Shreashtha Varun, Sanjiv Kumar Jain, Sandeep Bhongade, Shweta Agrawal 
    Abstract: In last few decades, the transportation sector is putting a lot of efforts to reduce the CO2 emission levels by introducing technologically advanced electric vehicles. The rapidly increasing numbers of EVs in the market have encouraged the concept of involving EVs in the smart grid technologies by utilising the bidirectional capabilities of EVs batteries. This paper presents the solar to vehicle (S2V) and vehicle to grid (V2G) performance analysis for the efficient and reliable operations. In S2V operation mode, charging of EV battery from photovoltaic systems through bidirectional DC to DC converter is analysed. In V2G operation mode, the energy stored in the EV battery is fed to the grid with the help of DC to DC bidirectional converter and DC to AC inverter. The performance of the proposed scheme is shown through the simulation results in terms of battery voltage, current and battery state of charge, while charging and discharging process.
    Keywords: DC to DC converter; electric vehicle; PI controller; solar to vehicle; S2V; vehicle to grid; V2G.
    DOI: 10.1504/IJESMS.2022.10049476
  • Review on sentiment analysis of movie reviews using machine learning techniques based on data available on Twitter   Order a copy of this article
    by Dharmendra Dangi, Amit Bhagat, Jeetendra Kumar Gupta 
    Abstract: Opinion mining or sentiment analysis is the study to extracted useful information from the given datasets like tweets on Twitter or opinions of people on other social blogs or portals related to a particular topic. Sentiment analysis aims to predict the type of opinion like positive, somewhat positive, or negative somewhat negative and neutral. Sentiment analysis based on machine learning techniques has more importance as it gives better outputs. The study of these kinds of datasets with the help of machine learning techniques can be used in many different forms like to make predictions, to study the patterns, to analyse the sentiments, to study the reviews the movies, to predict the way stock market may behave. Data available on microblogging sites like Twitter have certain hidden indications which are useful to solve many research problems. This article is the review article that will highlight some recent studies in the field of sentiment analysis based on the movie review available on websites like Twitter.
    Keywords: machine learning; sentiment analysis; positive; negative; Twitter.
    DOI: 10.1504/IJESMS.2022.10049764
  • Design and experimental analysis of novel window mill vertical axis wind turbine   Order a copy of this article
    by N. Suthanthira Vanitha, L. Manivannan, A. Karthikeyan, K. Radhika, T. Meenakshi 
    Abstract: A novel window mill vertical axis wind turbine (VAWT) is introduced to utilise the maximum wind power to produce the electricity. The novel design improves the conversion ratio by overcoming the pressure imbalance on the existing blade design with maximum utilisation of wind energy and is capable of generating power which is three times greater than the existing windmill design. The proposed window mill is enveloped by metal case with subways and huge walls on both sides to run the turbine even during low wind head for ensuring higher efficiency than existing windmills. A complete layout of VAWT blade design is presented including the calculation of theoretical maximum efficiency, practical efficiency, propulsion and blade loads. The ANSYS simulation and experimental results are presented. These results encourage and reinforce the conviction that vertical axis wind energy conversion systems are practical and potentially very contributive renewable energy system to produce the electricity. In addition, artificial intelligence-based vertical axis wind turbine is found to provide higher performance for wind speed with economical. The proposed window mill with metal case is capable of improving the efficiency by 52% even for low heads of wind speed.
    Keywords: vertical axis wind turbine; VAWT; computational fluid dynamics; finite element analysis anemometer; blower; rotor; analysis system; solid works.
    DOI: 10.1504/IJESMS.2022.10051749
  • An extensive review on use of CFD simulation technique for assessment of performance of various rib geometries and arrangements in heated fluid ducts   Order a copy of this article
    by Harshad N. Deshpande, Vaijanath N. Raibhole 
    Abstract: The use of ribs is one of the vital methods of improvement of the performance of thermal systems, for example, the cooling channel of the heat exchanger, solar air heater, etc. There are many benefits of performing CFD simulation before experimentation to select rib parameters that will give the highest magnitude of the rate of heat transfer with the least magnitude of friction penalty. Therefore, considering the large scope of research for a thermal and fluid flow simulation of ribbed surfaces this review article is presented. Numerical studies performed by the researchers on rib geometrical parameters, and different rib arrangements affecting thermal performance have been reviewed and explored. The details of flow governing equations, turbulence model, discretisation scheme, grid size; the type used for CFD simulation have been discussed. This review article will certainly provide a vision to the researchers for fluid flow modification and thermal performance intensification using ribs in thermal systems.
    Keywords: CFD; discretisation scheme; grid; geometry of rib; model of turbulence; rib arrangements; thermo- hydraulic performance; simulation.
    DOI: 10.1504/IJESMS.2022.10051123
  • Seismic performance analysis of auxiliary pier of long-span cable-stayed bridge under seismic excitation   Order a copy of this article
    by Mengqiang Cao 
    Abstract: In order to overcome the problems of low accuracy and long-time, the seismic performance evaluation method of auxiliary pier of long-span cable-stayed bridge based on structural dynamics theory is designed considering the seismic excitation effect. The seismic performance evaluation standard is set and the structure and dynamic characteristics of the auxiliary pier are analysed by using OpenSees finite element analysis software and structural dynamics theory, and the natural vibration frequency and mode characteristics of the auxiliary pier are obtained. The seismic excitation is simulated to determine the damage degree corresponding to different earthquake damage levels, and the response of auxiliary pier is analysed. The evaluation index of seismic performance is set, and the evaluation result is obtained by comparing with the evaluation standard. The experimental results show that the proposed method has high evaluation accuracy and short evaluation time, which shows that this method has high practical application value.
    Keywords: seismic excitation; long span bridge; cable stayed bridge; auxiliary pier; seismic performance evaluation.
    DOI: 10.1504/IJESMS.2022.10051355
  • A stacking ensemble for credit card fraud detection using SMOTE technique   Order a copy of this article
    by Kaithekuzhical Leena Kurien, Ajeet A. Chikkamannur 
    Abstract: In present times online shopping is on rise as more people stay at home and credit card do the walking resulting in increase in credit card fraud. COVID-19 outbreak pushed digital payments exponentially, making gateway for online frauds even more common to this dependence on digital payment platforms. Empirical evidence on ensemble techniques of machine learning algorithms for fraud prediction has exhibited superior performance in identifying fraud patterns in usage of credit card data through technique of stacking and voting. The ensemble techniques like AdaBoost, gradient boost and XGBoost are applied along with stacking classifier on the credit card dataset. The stacking classifier using heterogeneous classifiers of random forest, K-nearest neighbours and logistic regression to learn the fraud patterns effectively in credit card data. The proposed methods are analysed using F1-score and recall metrics due to skewness of data.
    Keywords: ensemble methods; random forest; fraud detection; XGBoost; AdaBoost; anomaly detection.
    DOI: 10.1504/IJESMS.2023.10053233