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

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