Journal of Intelligent & Fuzzy Systems - Volume 41, issue 5

Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M. | Trajkovic, Ljiljana

Article Type: Editorial

DOI: 10.3233/JIFS-189845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5221-5234, 2021

Authors: Krishnan, Sajitha | Amudha, J. | Tejwani, Sushma

Article Type: Research Article

Abstract: It is quite alarming that the increase of glaucoma is due to the lack of awareness of the disease and the cost for glaucoma screening. The primary eye care centers need to include a comprehensive glaucoma screening and include machine learning models to elaborate it as decision support system. The proposed system considers the state of art of eye gaze features to understand cognitive processing, direction and restriction of visual field. There is no significant difference in global and local ratio and skewness value of fixation duration and saccade amplitude, which suggest that there is no difference in cognitive processing. …The significance value of saccadic extent along vertical axis, Horizontal Vertical ratio (HV ratio), convex hull area and saccadic direction show that there is restriction in vertical visual field. The statistical measures (p < 0.05) and Spearman correlation coefficient with class label validate the results. The proposed system compared the performance of seven classifiers: Naïve Bayes classifier, linear and kernel Support Vector classifiers, decision tree classifier, Adaboost, random forest and eXtreme Gradient Boosting (XGBoost) classifier. The discrimination of eye gaze features of glaucoma and normal is efficiently done by XGBoost with accuracy 1.0. The decision support system is cost-effective and portable. Show more

Keywords: Restriction, quality of life, decision support system, eye tracking, glaucoma

DOI: 10.3233/JIFS-189846

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5235-5242, 2021

Authors: Naik, Amrita | Edla, Damodar Reddy

Article Type: Research Article

Abstract: Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are …further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%. Show more

Keywords: CNN, GLCM, GLRLM, SVM

DOI: 10.3233/JIFS-189847

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5243-5251, 2021

Authors: Sethy, Prabira Kumar | Pandey, Chanki | Khan, Mohammad Rafique | Behera, Santi Kumari | Vijaykumar, K. | Panigrahi, Sibarama

Article Type: Research Article

Abstract: In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the …detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach. Show more

Keywords: Breast cancer, computer vision, mammography, support vector machine (SVM), HOG features

DOI: 10.3233/JIFS-189848

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5253-5263, 2021

Authors: Jaiseeli, C. | Raajan, N.R.

Article Type: Research Article

Abstract: The neurological disorders are developed in adults due to reduced visual perception. Opto Kinetic Nystagmus (OKN) is a clinical method to detect visual perception. For objective measurements, a computational algorithm based OKN detection is preferable rather than clinical practice. In this paper, a memory-efficient Subsampled Lucas-Kanade Optical Flow (SLKOF) is proposed. The proposal employs the Subsampling of images for various levels. The proposal deals with the computation of OKN gain for different image Subsampling factors using the MATLAB platform. The experimental set up to observe OKN is done using computer-based rotation control of the drum through a stepper motor. The …results are compared with the well established Lucas-Kanade (LK) method for Optical flow. It is observed that OKN gain corresponds to 1/4th of a subsampled image of the SLKOF method correlates with the LK method for the majority of the cases. This validation evidently elucidates that the proposal is computationally efficient. Show more

Keywords: Opto Kinetic Nystagmus, Lucas-Kanade Optical Flow, eye movements, subsampling, objective method

DOI: 10.3233/JIFS-189849

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5265-5274, 2021

Authors: Babu, Tina | Singh, Tripty | Gupta, Deepa | Hameed, Shahin

Article Type: Research Article

Abstract: Colon cancer is one of the highest cancer diagnosis mortality rates worldwide. However, relying on the expertise of pathologists is a demanding and time-consuming process for histopathological analysis. The automated diagnosis of colon cancer from biopsy examination played an important role for patients and prognosis. As conventional handcrafted feature extraction requires specialized experience to select realistic features, deep learning processes have been chosen as abstract high-level features may be extracted automatically. This paper presents the colon cancer detection system using transfer learning architectures to automatically extract high-level features from colon biopsy images for automated diagnosis of patients and prognosis. In …this study, the image features are extracted from a pre-trained convolutional neural network (CNN) and used to train the Bayesian optimized Support Vector Machine classifier. Moreover, Alexnet, VGG-16, and Inception-V3 pre-trained neural networks were used to analyze the best network for colon cancer detection. Furthermore, the proposed framework is evaluated using four datasets: two are collected from Indian hospitals (with different magnifications 4X, 10X, 20X, and 40X) and the other two are public colon image datasets. Compared with the existing classifiers and methods using public datasets, the test results evaluated the Inception-V3 network with the accuracy range from 96.5% - 99% as best suited for the proposed framework. Show more

Keywords: Transfer learning, features, CNN, colon cancer, classification

DOI: 10.3233/JIFS-189850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5275-5286, 2021

Authors: Agrawal, Rahul | Bajaj, Preeti

Article Type: Research Article

Abstract: Brain-Computer Interface provides and simplifies the communication channel for the physically disabled individuals suffering from severe brain injury related to brain stroke and lost ability to speak. It helps these patients to connect with the outside world. In the proposed work, the electroencephalogram signal is used as an input source taken from Bonn University database that is divided into three class of data consisting of 247 samples each. It is further processed by Tunable Q-Wavelet Transform signal decomposition technique where the signals are subdivided into various sub-bands depending on the value of Q-factor, redundancy factor, and number of sub-bands. A …novel custom technique uses Q-factor of 3, redundancy value of 3 & 12 number of sub-bands for high pass filtering as well as Q-factor of 1, redundancy value of 3 & 7 number of sub-bands for low pass filtering combined with nine statistical measures for feature extraction purpose. The classification is performed by using multi-class support vector machine giving the accuracy of 99.59%. The accuracy performs best when compared with the existing research results Furthermore, the comparative study has been performed on the same dataset by using deep neural network along with support vector machine giving an accuracy of 100%. Other evaluation parameters such as precision, sensitivity, specificity, and F1 score are also calculated. The classified data help transform the signal into three communication messages that will help solve the speech impairment of disabled individuals. Show more

Keywords: Brain computer interface (BCI), Electroencephalogram (EEG), Tunable Q wavelet Transform (TQWT), Support vector machine (SVM), Deep Neural network (DNN) etc

DOI: 10.3233/JIFS-189851

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5287-5297, 2021

Authors: Rudhra, B | Malu, G | Sherly, Elizabeth | Mathew, Robert

Article Type: Research Article

Abstract:  Normal Pressure Hydrocephalus (NPH), an Atypical Parkinsonian syndrome, is a neurological syndrome that mainly affects elderly people. This syndrome shows the symptoms of Parkinson’s disease (PD), such as walking impairment, dementia, impaired bladder control, and mental impairment. The Magnetic Resonance Imaging (MRI) is the aptest modality for the detection of the abnormal build-up of cerebrospinal fluid in the brain’s cavities or ventricles, which is the major cause of NPH. This work aims to develop an automated biomarker for NPH segmentation and classification (NPH-SC) that efficiently detect hydrocephalus using a deep learning-based approach. Removal of non-cerebral tissues (skull, scalp, and dura) …and noise from brain images by skull stripping, unsharp-mask based edge sharpening, segmentation by marker-based watershed algorithm, and labelling are performed to improve the accuracy of the CNN based classification system. The brain ventricles are extracted using the external and internal markers and then fed into the convolutional neural networks (CNN) for classification. This automated NPH-SC model achieved a sensitivity of 96%, a specificity of 100%, and a validation accuracy of 97%. The prediction system, with the help of a CNN classifier, is used for the calculation of test accuracy of the system and obtained promising 98% accuracy. Show more

Keywords: Structural magnetic resonance imaging, normal pressure hydrocephalus, convolutional neural networks

DOI: 10.3233/JIFS-189852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5299-5307, 2021

Authors: Preethi, D. | Vimala, J. | Rajareega, S. | Al-Tahan, M.

Article Type: Research Article

Abstract:  This article deals with a fuzzy hypercompositional structure called fuzzy hyperlattice ordered δ - group ( FHLO δ - G ) , the extension of the fuzzy hypercompositional structure namely fuzzy hyperlattice ordered group (FHLOG ). Using FHLO δ - G , we can involve one additional non-empty set δ with FHLOG , which helps to develop new results and applications. The structural characteristics and properties of FHLO δ - G are analysed. Furthermore, an application of FHLO δ - G …for ABO blood group system is proposed. Show more

Keywords: Lattice ordered group, fuzzy lattice ordered group, fuzzy hyperlattice, fuzzy hyperlattice ordered group, 𝒜ℬ𝒪 blood group system

DOI: 10.3233/JIFS-189853

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5309-5315, 2021

Authors: Sukumaran, Poornima | Govardhanan, Kousalya

Article Type: Research Article

Abstract: Voice processing has proven to be an eminent way of recognizing the various emotions of the people. The objective of this research is to identify the presence of Autism Spectrum Disorder (ASD) and to analyze the emotions of autistic children through their voices. The presented automated voice-based system can detect and classify seven basic emotions (anger, disgust, neutral, happiness, calmness, fear and sadness) expressed by children through source parameters associated with their voices. Various prime voice features such as Mel-frequency Cepstral Coefficients (MFCC) and Spectrogram are extracted and utilized to train a Multi-layer Perceptron (MLP) Classifier to identify possible emotions …exhibited by the children thereby assessing their behavioral state. This proposed work therefore helps in the examination of emotions in autistic children that can be used to assess the kind of training and care required to enhance their lifestyle. Show more

Keywords: Emotion recognition, Autism Spectrum Disorder, voice processing, MFCC

DOI: 10.3233/JIFS-189854

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5317-5326, 2021

Authors: Jeena, R. S | Shiny, G. | Sukesh Kumar, A. | Mahadevan, K.

Article Type: Research Article

Abstract: Stroke is a major reason for disability and mortality in most of the developing nations. Early detection of stroke is highly significant in bio-medical research. Research illustrates that signs of stroke are reflected in the eye and may be analyzed from fundus images. A custom dataset of fundus images has been compiled for formulating an automated stroke detection algorithm. In this paper, a comparative study of hand-crafted texture features and convolutional neural network (CNN) has been recommended for stroke diagnosis. The custom CNN model has also been compared with five pre-trained models from ImageNet. Experimental results reveal that the recommended …custom CNN model gives the best performance by achieving an accuracy of 95.8 %. Show more

Keywords: Stroke, convolutional neural network (CNN)

DOI: 10.3233/JIFS-189855

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5327-5335, 2021

Authors: Nair, Sreelu P. | Abhinav Reddy, K. | Alluri, Prithvi Krishna | Lalitha, S.

Article Type: Research Article

Abstract: According to the National Crime Records Bureau, 63,407 children have gone missing in the year 2016, which makes almost 174 children go missing in India every day, out of which only 50% are ever found again. This brings up a need for an efficient solution to trace missing children. The proposed solution uses machine assistance during these search activities with face recognition technologies and can be used for essential development of applications which use CCTV footage across a camera network to identify the person lost. In our solution we use One Shot learning for face recognition to identify stranded people …in places such as mass gatherings. The same technology can be used for identification of criminals across the city. The paper also talks about the tracking of people across a network of multiple non-overlapping cameras, with a feature of shifting the target tovehicle, if the target gets into one. The experimentation is performed using mobile cameras and thus, helps in monitoring actions of criminals and finding their hideout. Show more

Keywords: Face recognition, person tracking, re-identification, non-overlapping cameras

DOI: 10.3233/JIFS-189856

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5337-5345, 2021

Authors: Remya Revi, K. | Wilscy, M. | Antony, Rahul

Article Type: Research Article

Abstract: Forged portraits of people are widely used for creating deceitful propaganda of individuals or events in social media, and even for cooking up fake pieces of evidence in court proceedings. Hence, it is very important to find the authenticity of the images, and image forgery detection is a significant research area now. This work proposes an ensemble learning technique by combining predictions of different Convolutional Neural Networks (CNNs) for detecting forged portrait photographs. In the proposed method seven different pretrained CNN architectures such as AlexNet, VGG-16, GoogLeNet, Res-Net-18, ResNet-101, Inception-v3, and Inception-ResNet-v2 are utilized. As an initial step, we fine-tune …the seven pretrained networks for portrait forgery detection with illuminant maps of images as input, and then uses a majority voting ensemble scheme to combine predictions from the fine-tuned networks. Ensemble methods had been found out to be good for improving the generalization capability of classification models. Experimental analysis is conducted using two publicly available portrait splicing datasets (DSO-1 and DSI-1). The results show that the proposed method outperforms the state-of-the-art methods using traditional machine learning techniques as well as the methods using single CNN classification models. Show more

Keywords: Image splicing detection, deep learning, convolutional neural networks, transfer learning, ensemble classifier

DOI: 10.3233/JIFS-189857

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5347-5357, 2021

Authors: Rao, Adish | Mysore, Aniruddha | Ajri, Siddhanth | Guragol, Abhishek | Sarkar, Poulami | Srinivasa, Gowri

Article Type: Research Article

Abstract: We present an automated approach to segment key structures of the eye, viz., the iris, pupil and sclera in images obtained using an Augmented Reality (AR)/ Virtual Reality (VR) application. This is done using a two-step classifier: In the first step, we use an auto encoder-decoder network to obtain a pixel-wise classification of regions that comprise the iris, sclera and the background (image pixels that are outside the region of the eye). In the second step, we perform a pixel-wise classification of the iris region to delineate the pupil. The images in the study are from the OpenEDS challenge and …were used to evaluate both the accuracy and computational cost of the proposed segmentation method. Our approach achieved a score of 0.93 on the leaderboard, outperforming the baseline model by achieving a higher accuracy and using a smaller number of parameters. These results demonstrate the great promise pipelined models hold along with the benefit of using domain-specific processing and feature engineering in conjunction with deep-learning based approaches for segmentation tasks. Show more

Keywords: Augmented reality, computer vision, image segmentation, image processing, virtual reality

DOI: 10.3233/JIFS-189858

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5359-5365, 2021

Authors: Kartik, P. V. S. M. S. | Sumanth, Konjeti B. V. N. S. | Sri Ram, V. N. V. | Jeyakumar, G.

Article Type: Research Article

Abstract: The encoding of a message is the creation of the message. The decoding of a message is how people can comprehend, and decipher the message. It is a procedure of understanding and interpretation of coded data into a comprehensible form. In this paper, a self-created explicitly defined function for encoding numerical digits into graphical representation is proposed. The proposed system integrates deep learning methods to get the probabilities of digit occurrence and Edge detection techniques for decoding the graphically encoded numerical digits to numerical digits as text. The proposed system’s major objective is to take in an Image with digits …encoded in graphical format and give the decoded stream of digits corresponding to the graph. This system also employs relevant pre-processing techniques to convert RGB to text and image to Canny image. Techniques such as Multi-Label Classification of images and Segmentation are used for getting the probability of occurrence. The dataset is created, on our own, that consists of 1000 images. The dataset has the training data and testing data in the proportion of 9 : 1. The proposed system was trained on 900 images and the testing was performed on 100 images which were ordered in 10 classes. The model has created a precision of 89% for probability prediction. Show more

Keywords: Image processing, deep learning, convolutional neural network, multi-label classification, image segmentation, edge detection, contours, graphical encoding

DOI: 10.3233/JIFS-189859

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5367-5374, 2021

Authors: Nair, Rekha R. | Singh, Tripty | Sankar, Rashmi | Gunndu, Klement

Article Type: Research Article

Abstract:  The multi-sensor, multi-modal, composite design of medical images merged into a single image, contributes to identifying features that are relevant to medical diagnoses and treatments. Although, current image fusion technologies, including conventional and deep learning algorithms, can produce superior fused images, however, they will require huge volumes of images of various modalities. This solution may not be viable for some situations, where time efficiency is expected or the equipment is inadequate. This paper addressed a modified end-to-end Generative Adversarial Network(GAN), termed Loss Minimized Fusion Generative Adversarial Network (LMF-GAN), a triple ConvNet deep learning architecture for the fusion of medical images …with a limited sampling rate. The encoding network is combined with a convolutional neural network layer and a dense block called GAN, in contrast to conventional convolutional networks. The loss is minimized by training GAN’s discriminator with all the source images by learning more parameters to generate more features in the fused image. The LMF-GAN can produce fused images with clear textures through adversarial training of the generator and discriminator. The proposed fusion method has the ability to achieve state-of-the-art quality in objective and subjective evaluation, in comparison with current fusion methods. The model has experimented with standard data sets. Show more

Keywords: Medical image fusion, generative adversarial network, generator, discriminator, ADAM optimizer

DOI: 10.3233/JIFS-189860

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5375-5386, 2021

Authors: Chandra Sekhar, P. N. R. L. | Shankar, T. N.

Article Type: Research Article

Abstract: In the era of digital technology, it becomes easy to share photographs and videos using smartphones and social networking sites to their loved ones. On the other hand, many photo editing tools evolved to make it effortless to alter multimedia content. It makes people accustomed to modifying their photographs or videos either for fun or extracting attention from others. This altering brings a questionable validity and integrity to the kind of multimedia content shared over the internet when used as evidence in Journalism and Court of Law. In multimedia forensics, intense research work is underway over the past two decades …to bring trustworthiness to the multimedia content. This paper proposes an efficient way of identifying the manipulated region based on Noise Level inconsistencies of spliced mage. The spliced image segmented into irregular objects and extracts the noise features in both pixel and residual domains. The manipulated region is then exposed based on the cosine similarity of noise levels among pairs of individual objects. The experimental results reveal the effectiveness of the proposed method over other state-of-art methods. Show more

Keywords: Splicing localization, object segmentation, residual images, noise level inconsistencies, cosine dissimilarity

DOI: 10.3233/JIFS-189861

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5387-5397, 2021

Authors: Akila, K. | Indra Priyadharshini, S. | Ulaganathan, Pradheeba | Prempriya, P. | Yuvasri, B. | Suriya Praba, T. | Veeramuthuvenkatesh,

Article Type: Research Article

Abstract: The primary objective is to identify and segments the multiple, partly occluded objects in the image. The subsequent stage carry out our approach, primarily start with frame conversion. Next in the preprocessing stage, the Gaussian filter is employed for image smoothening. Then from the preprocessed image, Multi objects are segmented through modified ontology-based segmentation, and the edge is detected from the segmented images. After that, from the edge detected frames area is extracted, which results in object detected frames. In the feature extraction stage, attributes such as area, contrast, correlation, energy, homogeneity, color, perimeter, circularity are extorted from the detected …objects. The objects are categorized as human or other objects (bat/ball) through the feed-forward back propagation neural network classifier (FFBNN) based upon the extracted attributes. Show more

Keywords: Object segmentation, gaussian filtering, object classification, object detection, feature extraction, ontology

DOI: 10.3233/JIFS-189862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5399-5409, 2021

Authors: Aarthi, R. | Amudha, J.

Article Type: Research Article

Abstract: Computer vision research aims at building models which mimic human systems. The recent development in visual information have been used to derive computational models which address a variety of applications. Biological models help to identify the salient objects in the image. But, the identification of non-salient objects in a heterogeneous environment is a challenging task that requires a better understanding of the visual system. In this work, a weight modulation based top-down model is proposed that integrates the visual features that depend on its importance for the target search application. The model is designed to learn the optimal weights such …that it biases the features of the target from the other surrounding regions. Experimental analysis is performed on various scenes on a standard dataset with the selected object in the scene. Metrics such as area under curve, average hit number and correlation reveal that the method is more suitable in target identification, by suppressing the other region. Show more

Keywords: Target search, visual attention, saliency, top down approach

DOI: 10.3233/JIFS-189863

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5411-5423, 2021

Authors: Indu, V. | Thampi, Sabu M.

Article Type: Research Article

Abstract: Social networks have emerged as a fertile ground for the spread of rumors and misinformation in recent times. The increased rate of social networking owes to the popularity of social networks among the common people and user personality has been considered as a principal component in predicting individuals’ social media usage patterns. Several studies have been conducted to study the psychological factors influencing the social network usage of people but only a few works have explored the relationship between the user’s personality and their orientation to spread rumors. This research aims to investigate the effect of personality on rumor spread …on social networks. In this work, we propose a psychologically-inspired fuzzy-based approach grounded on the Five-Factor Model of behavioral theory to analyze the behavior of people who are highly involved in rumor diffusion and categorize users into the susceptible and resistant group, based on their inclination towards rumor sharing. We conducted our experiments in almost 825 individuals who shared rumor tweets on Twitter related to five different events. Our study ratifies the truth that the personality traits of individuals play a significant role in rumor dissemination and the experimental results prove that users exhibiting a high degree of agreeableness trait are more engaged in rumor sharing activities and the users high in extraversion and openness trait restrain themselves from rumor propagation. Show more

Keywords: Social networks, rumor propagation, personality, five-factor model, user behavior analysis

DOI: 10.3233/JIFS-189864

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5425-5439, 2021

Authors: Priyanga, V.T | Sanjanasri, J.P | Menon, Vijay Krishna | Gopalakrishnan, E.A | Soman, K.P

Article Type: Research Article

Abstract: The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine …learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis. Show more

Keywords: Fake news, social media, word embedding, cosine similarity, Auto-encoders, network analysis

DOI: 10.3233/JIFS-189865

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5441-5448, 2021

Authors: Chirgaiya, Sachin | Sukheja, Deepak | Shrivastava, Niranjan | Rawat, Romil

Article Type: Research Article

Abstract: The decisions and approaches of renowned personality used to impress the real world are to a great extent adapted to how others have seen or assessed the world with opinion and sentiment. Examples could be any opinion and sentiment of people view about Movie audits, Movie surveys, web journals, smaller scale websites, and informal organizations. In this research classifies the movie review into its correct category, classifier model is proposed that has been trained by applying feature extraction and feature ranking. The focus is on how to examine the sentiment expression and classification of a given movie review on a …scale of (–) negative and (+) positive sentiments analysis for the IMDB movie review database. Due to the lack of grammatical structures to comments on movies, natural language processing (NLP) has been used to implement proposed model and experimentation is performed to compare the present study with existing learning models. At the outset, our approach to sentiment classification supplements the existing movie rating systems used across the web to an accuracy of 97.68%. Show more

Keywords: Machine learning, artificial intelligence, movie reviews, sentiment analysis

DOI: 10.3233/JIFS-189866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5449-5456, 2021

Authors: Sil, Riya | Alpana, | Roy, Abhishek | Dasmahapatra, Mili | Dhali, Debojit

Article Type: Research Article

Abstract: It is essential to provide a structured data feed to the computer to accomplish any task so that it can process flawlessly to generate the desired output within minimal computational time. Generally, computer programmers should provide a structured data feed to the computer program for its successful execution. The hardcopy document should be scanned to generate its corresponding computer-readable softcopy version of the file. This process also proves to be a budget-friendly approach to disengage human resources from the entire process of record maintenance. Due to this automation, the workload of existing manpower is reduced to a significant level. This …concept may prove beneficial for the delivery of any type of services to the ultimate beneficiary (i.e., citizen) in a minimal time frame. The administration has to deal with various issues of citizens due to the pressure of a huge population who seek legal help to resolve their issues, thereby leading to the filing of large numbers of pending legal cases at several courts of the country. To assist the victims with prompt delivery of justice and legal professionals in reducing their workload, this paper proposed a machine learning based automated legal model to enhance the efficiency of the legal support system with an accuracy of 94%. Show more

Keywords: Machine learning, image processing, document analysis, argument, text summarization

DOI: 10.3233/JIFS-189867

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5457-5466, 2021

Authors: Lalitha, S. | Gupta, Deepa

Article Type: Research Article

Abstract: Automatic recognition of human affective state using speech has been the focus of the research world for more than two decades. In the present day, with multi-lingual countries like India and Europe, population are communicating in various languages. However, majority of the existing works have put forth different strategies to recognize affect from various databases, with each comprising single language recordings. There exists a great demand for affective systems to serve the context of mixed-language scenario. Hence, this work focusses on an effective methodology to recognize human affective state using speech samples from a mixed language framework. A unique cepstral …and bi-spectral speech features derived from the speech samples classified using random forest (RF) are applied for the task. This work is first of its kind with the proposed approach validated and found to be effective on a self-recorded database with speech samples comprising from eleven various diverse Indian languages. Six different affective states of angry, fear, sad, neutral, surprise and happy are considered. Three affective models have been investigated in the work. The experimental results demonstrate the proposed feature combination in addition to data augmentation show enhanced affect recognition. Show more

Keywords: Affective state, cepstral, mixed-lingual, recognition, Indian languages

DOI: 10.3233/JIFS-189868

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5467-5476, 2021

Authors: Ojha, Chinmayee | Venugopalan, Manju | Gupta, Deepa

Article Type: Research Article

Abstract: Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers to make decisions but also helps companies to maintain sustainability in businesses. Sentiment analysis is an automated process to analyze the public opinion behind certain topics. Identifying targets of user’s opinion from text is referred to as aspect extraction task, which is the most crucial and important part of Sentiment Analysis. The proposed system is a rule-based approach to extract aspect …terms from reviews. A sequence of patterns is created based on the dependency relations between target and its nearby words. The system of rules works on a benchmark of dataset for Hindi shared by Akhtar et al., 2016. The evaluated results show that the proposed approach has significant improvement in extracting aspects over the baseline approach reported on the same dataset. Show more

Keywords: Sentiment analysis (SA), aspect term, sequential pattern, dependency parser (DP), part of speech (POS)

DOI: 10.3233/JIFS-189869

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5477-5485, 2021

Authors: Banerjee, Tulika | Yagnik, Niraj | Hegde, Anusha

Article Type: Research Article

Abstract: Human communication is not limited to verbal speech but is infinitely more complex, involving many non-verbal cues such as facial emotions and body language. This paper aims to quantitatively show the impact of non-verbal cues, with primary focus on facial emotions, on the results of multi-modal sentiment analysis. The paper works with a dataset of Spanish video reviews. The audio is available as Spanish text and is translated to English while visual features are extracted from the videos. Multiple classification models are made to analyze the sentiments at each modal stage i.e. for the Spanish and English textual datasets as …well as the datasets obtained upon coalescing the English and Spanish textual data with the corresponding visual cues. The results show that the analysis of Spanish textual features combined with the visual features outperforms its English counterpart with the highest accuracy difference, thereby indicating an inherent correlation between the Spanish visual cues and Spanish text which is lost upon translation to English text. Show more

Keywords: Multimodal analysis, natural language processing, non-verbal cues, classification algorithms, cultural-shift

DOI: 10.3233/JIFS-189870

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5487-5496, 2021

Authors: Dhanani, Jenish | Mehta, Rupa | Rana, Dipti

Article Type: Research Article

Abstract: Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O  (n 2 ). However, there is a limited number of pairs consisting of strong relevance among …the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems. Show more

Keywords: Legal document recommender systems, Pairwise similarity, Graph Clustering, Semantic similarity

DOI: 10.3233/JIFS-189871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5497-5509, 2021

Authors: Richa, | Bedi, Punam

Article Type: Research Article

Abstract: Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user …has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure. Show more

Keywords: Recommender system, cross domain, serendipity, trust, fuzzy sets

DOI: 10.3233/JIFS-189872

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5511-5523, 2021

Authors: Pathak, Vinay | Singh, Karan

Article Type: Research Article

Abstract: Due to the rapid growth in sensor technology and embedded technology, wireless body area network WBANs plays a vital role in monitoring the human body system and the surrounding environment. It supports many healthcare applications on the one hand and are very much help full in pandemic scenarios. It has become the most innovative health care area, which is intriguing to many researchers because of its vast future prospective and potential. Data collected by different wireless sensors or nodes is very personal, critical, and important because of human life involvement. WBANs can minimize human to human contact, which helps stop …the spread of severe infectious diseases. The biggest concern is the maintenance of privacy and accuracy of data is still a hot area of research due to nature of attacks, which are changing day by day and increasing, as well as for the sake of better performance. A suitable security mechanism is a way to address above issues, for achieving data security, it is expedient to propose a mechanism. It is essential to update the patient’s regular data. WBANs help to deliver truthful reports related to the patient’s health regularly and individually. This paper proposes an algorithm that shows a better result than the existing algorithm in their previous works. This work is all about proposing a mechanism which needs comparatively less resource. Only authentic entities can interact with the server, which has become obligatory for both sides, keeping data safe. Several authentication schemes have been proposed or discussed by different researchers. This paper has proposed a Secure and Efficient WBANs Authentication Mechanism (SEAM). This security framework will take care of the authentication and the security of transmitted data. Show more

Keywords: WBANs, wearable sensors, eHealth privacy & security, threat, WSN, security

DOI: 10.3233/JIFS-189873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5525-5534, 2021

Authors: Ayub, Mohammed | El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: The World-Wide Web technology has become an indispensable part in human’s life for almost all activities. On the other hand, the trend of cyberattacks is on the rise in today’s modern Web-driven world. Therefore, effective countermeasures for the analysis and detection of malicious websites is crucial to combat the rising threats to the cyber world security. In this paper, we systematically reviewed the state-of-the-art techniques and identified a total of about 230 features of malicious websites, which are classified as internal and external features. Moreover, we developed a toolkit for the analysis and modeling of malicious websites. The toolkit has …implemented several types of feature extraction methods and machine learning algorithms, which can be used to analyze and compare different approaches to detect malicious URLs. Moreover, the toolkit incorporates several other options such as feature selection and imbalanced learning with flexibility to be extended to include more functionality and generalization capabilities. Moreover, some use cases are demonstrated for different datasets. Show more

Keywords: Web security, malicious websites, malicious URL, machine learning, feature extraction, toolkits

DOI: 10.3233/JIFS-189874

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5535-5549, 2021

Authors: Jithendra, K.B. | Kassim, Shahana T.

Article Type: Research Article

Abstract: Security of a recently proposed bitwise block cipher GIFT is evaluated in this paper. In order to mount full round attacks on the cipher, biclique cryptanalysis method is applied. Both variants of the block cipher are attacked using Independent biclique approach. For recovering the secret keys of GIFT-64, the proposed attack requires 2127.45 full GIFT-64 encryption and 28 chosen plain texts. For recovering the secret keys of GIFT-128, the proposed attack requires 2127.82 full GIFT-128 encryption and 218 chosen plain texts. The attack complexity is compared with that of other attacks proposed previously. The security level …of GIFT is also compared with that of the parent block cipher PRESENT, based on the analysis. Show more

Keywords: Block cipher, cryptanalysis, biclique, complexity

DOI: 10.3233/JIFS-189875

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5551-5560, 2021

Authors: Tripathi, Diwakar | Ramachandra Reddy, B. | Padmanabha Reddy, Y.C.A. | Shukla, Alok Kumar | Kumar, Ravi Kant | Sharma, Neeraj Kumar

Article Type: Research Article

Abstract: Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants’ credentials and directly affects to viability of issuing institutions. However, there may be a large number of irrelevant features in the credit scoring dataset. Due to irrelevant features, the credit scoring models may lead to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with large number of features. In this work, we emphasized on the role of feature selection to enhance the predictive …performance of credit scoring model. Towards to feature selection, Binary BAT optimization technique is utilized with a novel fitness function. Further, proposed approach aggregated with “Radial Basis Function Neural Network (RBFN)”, “Support Vector Machine (SVM)” and “Random Forest (RF)” for classification. Proposed approach is validated on four bench-marked credit scoring datasets obtained from UCI repository. Further, the comprehensive investigational results analysis are directed to show the comparative performance of the classification tasks with features selected by various approaches and other state-of-the-art approaches for credit scoring. Show more

Keywords: BAT algorithm, credit score, feature selection

DOI: 10.3233/JIFS-189876

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5561-5570, 2021

Authors: Biswas, Kusan

Article Type: Research Article

Abstract: In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems  (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy …channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature. Show more

Keywords: Data hiding, JPEG, ECC, SVD, Turbo codes, PSNR, SSIM

DOI: 10.3233/JIFS-189877

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5571-5581, 2021

Authors: Anushiadevi, R. | Amirtharajan, Rengarajan

Article Type: Research Article

Abstract: Reversible Data Hiding (RDH) schemes have recently gained much interest in protecting the secret information and sensitive cover images. For cloud security applications, the third party’s data embedding can be done (e.g., cloud service). In such a scenario, to protect the cover image from unauthorized access, it is essential to encrypt before embedding it. It can be overcome by combining the RDH scheme with encryption. However, the key challenge in integrating RDH with encryption is that the correlation between adjacent pixels begins to disappear after encryption, so reversibility cannot be accomplished. RDH with elliptic curve cryptography is proposed to overcome …this challenge. In this paper (ECC-RDH) by adopting additive homomorphism property; the proposed method, the stego image decryption gives the sum of the original image and confidential data. The significant advantages of this method are, the cover image is transferred with high security, the embedding capacity is 0.5 bpp with a smaller location map size of 0.05 bpp. The recovered image and secrets are the same as in the original, and thus 100% reversibility is proved. Show more

Keywords: Elliptic curve cryptography, reversible data hiding, additive homomorphism, lossless data hiding, reversible steganography

DOI: 10.3233/JIFS-189878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5583-5594, 2021

Authors: Sarraf, Gaurav | Srivatsa, Anirudh Ramesh | Swetha, MS

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-211111 .

DOI: 10.3233/JIFS-189879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5595-5606, 2021

Authors: Rajavel, Rajkumar | Ravichandran, Sathish Kumar | Nagappan, Partheeban | Venu, Sivakumar

Article Type: Research Article

Abstract: Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling …algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms. Show more

Keywords: Cloud computing, task scheduling, Qos aware task scheduling, cost enabled scheduling, cloud utilization, cloud utility

DOI: 10.3233/JIFS-189881

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5607-5615, 2021

Authors: Rajavel, Rajkumar | Ravichandran, Sathish Kumar | Nagappan, Partheeban | Ramasubramanian Gobichettipalayam, Kanagachidambaresan

Article Type: Research Article

Abstract: A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. …Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement. Show more

Keywords: Service level agreement, broker-based negotiation framework, game theory decision system, E-commerce application

DOI: 10.3233/JIFS-189882

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5617-5628, 2021

Authors: Sujatha, M. | Geetha, K. | Balakrishnan, P.

Article Type: Research Article

Abstract: The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference …system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model. Show more

Keywords: Cloud computing, IaaS selection, Sugeno Fuzzy inference system, CSP selection, compute service, MCDM

DOI: 10.3233/JIFS-189883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5629-5637, 2021

Authors: Dat, Nguyen Quang | Ngoc Anh, Nguyen Thi | Nhat Anh, Nguyen | Solanki, Vijender Kumar

Article Type: Research Article

Abstract: Short-term electricity load forecasting (STLF) plays a key role in operating the power system of a nation. A challenging problem in STLF is to deal with real-time data. This paper aims to address the problem using a hybrid online model. Online learning methods are becoming essential in STLF because load data often show complex seasonality (daily, weekly, annual) and changing patterns. Online models such as Online AutoRegressive Integrated Moving Average (Online ARIMA) and Online Recurrent neural network (Online RNN) can modify their parameters on the fly to adapt to the changes of real-time data. However, Online RNN alone cannot handle …seasonality directly and ARIMA can only handle a single seasonal pattern (Seasonal ARIMA). In this study, we propose a hybrid online model that combines Online ARIMA, Online RNN, and Multi-seasonal decomposition to forecast real-time time series with multiple seasonal patterns. First, we decompose the original time series into three components: trend, seasonality, and residual. The seasonal patterns are modeled using Fourier series. This approach is flexible, allowing us to incorporate multiple periods. For trend and residual components, we employ Online ARIMA and Online RNN respectively to obtain the predictions. We use hourly load data of Vietnam and daily load data of Australia as case studies to verify our proposed model. The experimental results show that our model has better performance than single online models. The proposed model is robust and can be applied in many other fields with real-time time series. Show more

Keywords: Hybrid online, RNN online, multi time series, multi seasonal decompose, Electricity forecasting

DOI: 10.3233/JIFS-189884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5639-5652, 2021

Authors: Kumar, Manish | Kumar, Bhavnesh | Rani, Asha

Article Type: Research Article

Abstract: The primary objective of this work is to optimize the induction motor rotor flux so that maximum efficiency is attained in the facets of parameter and load variations. The conventional approaches based on loss model are sensitive to modelling accuracy and parameter variations. The problem is further aggravated due to nonlinear motor parameters in different speed regions. Therefore, this work introduces an adaptive neuro-fuzzy inference system-based rotor flux estimator for electric vehicle. The proposed estimator is an amalgamation of fuzzy inference system and artificial neural network, in which fuzzy inference system is designed using artificial neural network. The training data …for neuro-fuzzy estimator is generated offline by acquiring rotor flux for different values of torque. The conventional fuzzy logic and differential calculation methods are also developed for comparative analysis. The efficacy of developed system is established by analyzing it under varying load conditions. It is revealed from the results that suggested methodology provides an improved efficiency i.e. 94.51% in comparison to 82.68% for constant flux operation. Show more

Keywords: Loss minimization, torque estimation, adaptive neuro fuzzy inference system (ANFIS), electrical vehicle (EV)

DOI: 10.3233/JIFS-189885

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5653-5663, 2021

Authors: Vasudevan, Nisha | Venkatraman, Vasudevan | Ramkumar, A | Sheela, A

Article Type: Research Article

Abstract: Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall …electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error. Show more

Keywords: Energy management, smart home, energy forecast, power management, efficiency

DOI: 10.3233/JIFS-189886

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5665-5676, 2021

Authors: Renugadevi, T. | Geetha, K.

Article Type: Research Article

Abstract: Management of IT services is rapidly adapting to the cloud computing environment due to optimized service delivery models. Geo distributed cloud data centers act as a backbone for providing fundamental infrastructure for cloud services delivery. Conversely, their high growing energy consumption rate is the major problem to be addressed. Cloud providers are in a hunger to identify different solutions to tackle energy management and carbon emission. In this work, a multi-cloud environment is modeled as geographically distributed data centers with varying solar power generation corresponding to its location, electricity price, carbon emission, and carbon tax. The energy management of the …workload allocation algorithm is strongly dependent on the nature of the application considered. The task deadline and brownout information is used to bring in variation in task types. The renewable energy-aware workload allocation algorithm adaptive to task nature is proposed with migration policy to explore its impact on carbon emission, total energy cost, brown and renewable power consumption. Show more

Keywords: Sustainable data centers, carbon footprint, brownout, migration, green energy, workload allocation

DOI: 10.3233/JIFS-189887

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5677-5689, 2021

Authors: Preethi, D. | Vimala, J.

Article Type: Research Article

Abstract: This paper introduces the concept of homomorphism on fuzzy hyperlattice ordered group ( FHLOG ) . It studies how the binary and the fuzzy hyperoperations of a FHLOG can be transformed into the binary and the fuzzy hyperoperations of another FHLOG . The notion of fuzzy hypercongruence relation on FHLOG is also defined. The paper also establishes the redox reaction of copper, gold and americium forms three FHLOG s. Besides, homomorphism and composition function of FHLOG …s using the redox reactions are developed. Therefore, the paper develops a relation among three different metal’s redox reactions in which the binary and the fuzzy hyperoperations, are preserved. Show more

Keywords: Lattice ordered group, fuzzy lattice ordered group, fuzzy hyperlattice, fuzzy hyperlattice ordered group, homomorphism, redox reactions

DOI: 10.3233/JIFS-189888

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5691-5699, 2021

Authors: Alpana, | Chand, Satish | Mohapatra, Subrajeet | Mishra, Vivek

Article Type: Research Article

Abstract: Coal is the mixture of organic matters, called as macerals, and inorganic matters. Macerals are categorized into three major groups, i.e., vitrinite, inertinite, and liptinite. The maceral group identification serves an important role in coking and non-coking coal processes that are used mainly in steel and iron industries. Hence, it becomes important to efficiently characterize these maceral groups. Currently, industries use the optical polarized microscope to distinguish the maceral groups. However, the microscopical analysis is a manual method which is time-consuming and provides subjective outcome due to human interference. Therefore, an automated approach that can identify the maceral groups accurately …in less processing time is strongly needed in industries. Computer-based image analysis methods are revolutionizing the industries because of its accuracy and efficacy. In this study, an intelligent maceral group identification system is proposed using markov-fuzzy clustering approach. This approach is an integration of fuzzy sets and the markov random field, which is employed towards maceral group identification in a clustering framework. The proposed model shows better results when compared with the standard cluster-based segmentation techniques. The results from the suggested model have also been validated against the outcome of manual methods, and the feasibility is tested using performance metrics. Show more

Keywords: Coal, macerals, image segmentation, clustering, fuzzy sets, markov random field

DOI: 10.3233/JIFS-189889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5701-5707, 2021

Authors: Rajareega, S. | Vimala, J.

Article Type: Research Article

Abstract: This paper introduces some new operations on complex intuitionistic fuzzy lattice ordered groups such as sum, product, bounded product, bounded difference and disjoint sum, and verifying its pertinent properties. The research exhibits the CIFS-COPRAS algorithm in a complex intuitionistic fuzzy soft set environment. This method was furthermore applied for the equipment selection process.

Keywords: Complex intuitionistic fuzzy soft sets, complex intuitionistic fuzzy soft lattice ordered group, COPRAS, C-COPRAS, equipment selection process

DOI: 10.3233/JIFS-189890

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5709-5718, 2021

Authors: Nagaballi, Srinivas | Kale, Vijay S.

Article Type: Research Article

Abstract: The advent of distributed energy resources is undoubtedly transforming the nature of the electric power system. The crisis of conventional energy sources and their environmental effects resulted in the integration of Distributed Generators (DGs) into the distribution system. Simultaneous application of optimum network reconfiguration, DGs, and Distribution Static Compensator (DSTATCOM) unit’s placement in the Radial Distribution Systems (RDS) comes with a raft of technical, economic, and environmental benefits. Benefits include improved power quality, reliability, stability, mitigation of power losses, and voltage profile improvement. In this paper, the combinational process of optimal deployment of DGs and DSTATCOM units in RDS with …suitable network reconfiguration to achieve positive benefits has been analyzed. A recent metaphor-less based Artificial Intelligence (AI) technique named the Rao-1 method is employed to overcome this combinational nonlinear optimization problem. The objective functions are to mitigate the power losses, enhance the voltage profile, and voltage stability index of the RDS considering the net economic cost-benefit to the distribution utility. The simulation study of this pragmatic approach problem is carried out on IEEE 33-bus RDS. The comparison of the results obtained by the Rao-1 method with other existing meta-heuristic optimization methods has been made to show its efficacy. Show more

Keywords: Artificial intelligence techniques, distributed generation, DSTATCOM, power losses, reconfiguration, voltage profile, stability, techno-economic benefits

DOI: 10.3233/JIFS-189891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5719-5729, 2021

Authors: Jeyanthi, R. | Sahithi, Madugula | Sireesha, N.V.L. | Srinivasan, Mangala Sneha | Devanathan, Sriram

Article Type: Research Article

Abstract: In process industries, measurements usually contain errors due to the improper instrumental variation, physical leakages in process streams and nodes, and inaccurate recording/reporting. Thus, these measurements violate the laws of conservation, and do not conform to process constraints. Data reconciliation (DR) is used to resolve the difference between measurements and constraints. DR is also used in reducing the effect of random errors and more accurately estimating the true values. A multivariate technique that is used to obtain estimates of true values while preserving the most significant inherent variation is Principal Component Analysis (PCA). PCA is used to reduce the dimensionality …of the data with minimum information loss. In this paper, two new DR techniques are proposed moving-average PCA (MA-PCA) and exponentially weighted moving average PCA (EWMA-PCA) to improve the performance of DR and obtain more accurate and consistent data. These DR techniques are compared based on RMSE. Further, these techniques are analyzed for different values of sample size, weighting factor, and variances. Show more

Keywords: Data reconciliation, MA-PCA, EWMA-PCA

DOI: 10.3233/JIFS-189892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5731-5736, 2021

Authors: Krishnamoorthy, Amrutha | Sindhura, Vijayasimha Reddy | Gowtham, Devarakonda | Jyotsna, C. | Amudha, J.

Article Type: Research Article

Abstract: Extraction of eye gaze events is highly dependent on automated powerful software that charges exorbitant prices. The proposed open-source intelligent tool StimulEye helps to detect and classify eye gaze events and analyse various metrics related to these events. The algorithms for eye event detection in use today heavily depend on hand-crafted signal features and thresholding, which are computed from the stream of raw gaze data. These algorithms leave most of their parametric decisions on the end user which might result in ambiguity and inaccuracy. StimulEye uses deep learning techniques to automate eye gaze event detection which neither requires manual decision …making nor parametric definitions. StimulEye provides an end to end solution which takes raw streams of data from an eye tracker in text form, analyses these to classify the inputs into the events, namely saccades, fixations, and blinks. It provides the user with insights such as scanpath, fixation duration, radii, etc. Show more

Keywords: Eye tracking, fixations, saccades, scanpath, deep learning

DOI: 10.3233/JIFS-189893

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5737-5745, 2021

Authors: Pradhan, Rosy | Khan, Mohammad Rafique | Sethy, Prabir Kumar | Majhi, Santosh Kumar

Article Type: Research Article

Abstract: The field of optimization science is proliferating that has made complex real-world problems easy to solve. Metaheuristics based algorithms inspired by nature or physical phenomena based methods have made its way in providing near-ideal (optimal) solutions to several complex real-world problems. Ant lion Optimization (ALO) has inspired by the hunting behavior of antlions for searching for food. Even with a unique idea, it has some limitations like a slower rate of convergence and sometimes confines itself into local solutions (optima). Therefore, to enhance its performance of classical ALO, quantum information theory is hybridized with classical ALO and named as QALO …or quantum theory based ALO. It can escape from the limitations of basic ALO and also produces stability between processes of explorations followed by exploitation. CEC2017 benchmark set is adopted to estimate the performance of QALO compared with state-of-the-art algorithms. Experimental and statistical results demonstrate that the proposed method is superior to the original ALO. The proposed QALO extends further to solve the model order reduction (MOR) problem. The QALO based MOR method performs preferably better than other compared techniques. The results from the simulation study illustrate that the proposed method effectively utilized for global optimization and model order reduction. Show more

Keywords: Antlion optimization, quantum information theory, model order reduction, metahueristic optimization, CEC benchmark

DOI: 10.3233/JIFS-189894

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5747-5757, 2021

Authors: George, Sheeja P. | Isaac, Johney | Philip, Jacob

Article Type: Research Article

Abstract: A higher operating frequency is desirable for Surface Acoustic Wave (SAW) based sensors as they become more sensitive at high frequencies. The acoustic wave gets more confined near the surface at high frequencies and become more sensitive to the external stimulations. This makes SAW devices a suitable device for sensing gaseous state chemicals. SAW devices have become the basic building block of wireless sensor networks with its advantages enabling remote sensing. In this paper, a SAW based Hydrogen sensor is realized through the Finite Element Analysis tool ANSYS. Hydrogen even though has a significant role in many industries, its explosive …nature demands constant monitoring. SAW delay line made up of XY-LiNbO3 as substrate with a thin layer of Palladium coated along the delay length as the sensing element is modeled. Palladium with its high affinity for Hydrogen absorbs the same and undergoes changes in properties like density and stiffness. This disturbs the surface wave propagation and in turn, affects the operating frequency which is the sensor response parameter. The frequency shift of 1.91 MHz for Hydrogen concentration of 0.3 a.f. as compared to 0.49 MHz with YZ- LiNbO3. The operating frequency also shifts to a higher range as the acoustic velocity of the substrate increases. Show more

Keywords: SAW, gas sensor, wireless sensor, FEM, ANSYS

DOI: 10.3233/JIFS-189895

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5759-5768, 2021