Journal of Intelligent & Fuzzy Systems - Volume 43, issue 5

Authors: Mishra, Anju | Singh, Laxman | Pandey, Mrinal | Lakra, Sachin

Article Type: Research Article

Abstract: Diabetic Retinopathy (DR) is a disease that damages the retina of the human eye due to diabetic complications, resulting in a loss of vision. Blindness may be avoided If the DR disease is detected at an early stage. Unfortunately, DR is irreversible process, however, early detection and treatment of DR can significantly reduce the risk of vision loss. The manual diagnosis done by ophthalmologists on DR retina fundus images is time consuming, and error prone process. Nowadays, machine learning and deep learning have become one of the most effective approaches, which have even surpassed the human performance as well as …performance of traditional image processing-based algorithms and other computer aided diagnosis systems in the analysis and classification of medical images. This paper addressed and evaluated the various recent state-of-the-art methodologies that have been used for detection and classification of Diabetic Retinopathy disease using machine learning and deep learning approaches in the past decade. Furthermore, this study also provides the authors observation and performance evaluation of available research using several parameters, such as accuracy, disease status, and sensitivity. Finally, we conclude with limitations, remedies, and future directions in DR detection. In addition, various challenging issues that need further study are also discussed. Show more

Keywords: Retinal fundus images, machine learning, deep learning, classification, Diabetic retinopathy

DOI: 10.3233/JIFS-220772

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6709-6741, 2022

Authors: Niu, Guo | Ma, Zhengming | Liu, Xi

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-220785

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6743-6754, 2022

Authors: Aras, Cigdem G. | Al-shami, Tareq M. | Mhemdi, Abdelwaheb | Bayramov, Sadi

Article Type: Research Article

Abstract: A bipolar soft set is given by helping not only a chosen set of “parameters” but also a set of oppositely meaning parameters called “not set of parameters”. It is known that a structure of bipolar soft set is consisted of two mappings such that F  : E  → P  (X ) and G  :⌉ E  → P  (X ), where F explains positive information and G explains opposite approximation. In this study, we first introduce a new definition of bipolar soft points to overcome the drawbacks of the previous definition of bipolar soft points given in [34]. Then, we explore …the structures of bipolar soft locally compact and bipolar soft paracompact spaces. We investigate their main properties and illuminate the relationships between them. Also, we define the concept of a bipolar soft compactification and investigate under what condition a bipolar soft topology forms a bipolar soft compactification for another bipolar soft topology. To elucidate the presented concepts and obtained results, we provide some illustrative examples. Show more

Keywords: bipolar soft set, bipolar soft topology, bipolar soft locally compactness, bipolar soft paracompactness

DOI: 10.3233/JIFS-220834

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6755-6763, 2022

Authors: Xu, Qin | Xu, Shumeng | Wang, Dongyue | Yang, Chao | Liu, Jinpei | Luo, Bin

Article Type: Research Article

Abstract: Representing features at multiple scales is of great significance for hyperspectral image classification. However, the most existing methods improve the feature representation ability by extracting features with different resolutions. Moreover, the existing attention methods have not taken full advantage of the HSI data, and their receptive field sizes of artificial neurons in each layer are identical, while in neuroscience, the receptive field sizes of visual cortical neurons adapt to the neural stimulation. Therefore, in this paper, we propose a Res2Net with spectral-spatial and channel attention (SSCAR2N) for hyperspectral image classification. To effectively extract multi-scale features of HSI image at a …more granular level while ensuring a small amount of calculation and low parameter redundancy, the Res2Net block is adopted. To further recalibrate the features from spectral, spatial and channel dimensions simultaneously, we propose a visual threefold (spectral, spatial and channel) attention mechanism, where a dynamic neuron selection mechanism that allows each neuron to adaptively adjust the size of its receptive fields based on the multiple scales of the input information is designed. The comparison experiments on three benchmark hyperspectral image data sets demonstrate that the proposed SSCAR2N outperforms several state-of-the-art deep learning based HSI classification methods. Show more

Keywords: Hyperspectral image classification, deep learning, convolutional neural networks (CNNs), Res2Net, visual attention mechanism

DOI: 10.3233/JIFS-220863

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6765-6781, 2022

Authors: Sanjana, R. | Ramesh, G.

Article Type: Research Article

Abstract: This paper is concerned with the solution mechanism to solve the transportation problem under unpredictability by using interval valued intuitionistic fuzzy parameters. The parameters are chosen as intervals in which costs are modeled by intuitionistic fuzzy numbers, whereas source and destination are taken as crisp values. Various methods of transportation problem like VAM, Monalisha’s Approximation method, Zero point method are used to illustrate the cost in interval numbers by using the interval arithmetic operations. For each method, a solution is derived without converting into crisp expression followed by a graphical representation.

Keywords: Interval valued intuitionistic fuzzy numbers, inteval valued intuitionistic fuzzy transportation problem, interval arithmetic, interval VAM, interval ZPM

DOI: 10.3233/JIFS-220946

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6783-6792, 2022

Authors: Lakkshmanan, Ajanthaa | Anbu Ananth, C. | Tiroumalmouroughane, S.

Article Type: Research Article

Abstract: Pancreatic tumor is the deadliest disease which needs earlier identification to reduce the mortality rate. With this motivation, this study introduces a Multi-Objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis (MOM-IDL) model. The proposed MOM-IDL technique encompasses an adaptive Weiner filter based pre-processing technique to enhance the image quality and get rid of the noise. In addition, multi-level thresholding based segmentation using Kapur’s entropy is employed where the threshold values are optimally chosen by the barnacles mating optimizer (BMO). Besides, densely connected network (DenseNet-169) is employed as a feature extractor and fuzzy support vector machine (FSVM) is …utilized as a classifier. For improving the classification performance, the BMO technique was implemented for fine-tuning the parameters of the FSVM model. The design of MOBMO algorithm for threshold selection and parameter optimization processes shows the novelty of the work. A wide range of simulations take place on the benchmark dataset and the experimental results highlighted the enhanced performance of the MOM-IDL technique over the recent state of art techniques. Show more

Keywords: Pancreatic tumor, computer aided diagnosis, deep learning, image classification, parameter optimization

DOI: 10.3233/JIFS-221171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6793-6804, 2022

Authors: Qiu, Chenye | Fang, Huixing | Liu, Ning

Article Type: Research Article

Abstract: Microgrid (MG) systems are growing at a rapid pace since they can accommodate the high amount of renewable energy. Since the MG consists of small distributed generators (DG) with volatile characteristics, an efficient energy management system is the main requisite in MG. In this paper, a chaotic sine cosine algorithm with crossover operator (CSCAC) is proposed for the day-ahead MG optimal energy scheduling problem. CSCAC includes a novel non-linear transition parameter based on the chaos system which can help the algorithm escape from local optima. A chaotic search operator is proposed to enhance the local search ability. Furthermore, a crossover …operator is devised to combine the advantages of different search strategies and achieve a comparatively better balance of exploration and exploitation. First, the effectiveness of CSCAC is validated on several benchmark functions. Then, it is applied to the day-ahead energy scheduling in a MG with three wind power plants, two photovoltaic power plants and a combined heat and power plant (CHP). Furthermore, it is implemented in two more cases considering the uncertainty and stochastic nature of the renewable power sources. Experimental results demonstrate the superiority of CSCAC over other comparative algorithms in the optimal MG energy management problem. Show more

Keywords: Sine cosine algorithm, microgrid, chaotic system, energy scheduling, uncertainty

DOI: 10.3233/JIFS-221178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6805-6819, 2022

Authors: Zhang, Taoyun | Zhang, Yugang | Zhang, Guangdong | Xue, Ling | Wang, Jin

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-221185

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6821-6830, 2022

Authors: Li, Xiang | Yu, Junqi | Wang, Qian | Dong, Fangnan | Cheng, Renyin | Feng, Chunyong

Article Type: Research Article

Abstract: Short-term energy consumption prediction of buildings is crucial for developing model-based predictive control, fault detection, and diagnosis methods. This study takes a university library in Xi’an as the research object. First, a time-by-time energy consumption prediction model is established under the supervised learning approach, which uses a long short-term memory (LSTM) network and a Multi-Input Multi-Output (MIMO) strategy. The experimental results validate the model’s validity, which is close enough to physical reality for engineering purposes. Second, the potential of the people flows factor in energy consumption prediction models is explored. The results show that people flow has great potential in …predicting building energy consumption and can effectively improve the prediction model performance. Third, a diagnostic method, which can recognize abnormal energy consumption data is used to diagnose the unreasonable use of the building during each hour of operation. The method is based on differences between actual and predicted energy consumption data derived from a short-term energy consumption prediction model. Based on actual building operation data, this work is enlightening and can serve as a reference for building energy efficiency management and operation. Show more

Keywords: Deep learning, energy consumption prediction, energy consumption diagnosis, people flows

DOI: 10.3233/JIFS-221188

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6831-6848, 2022

Authors: Senthil, P. | Selvakumar, S.

Article Type: Research Article

Abstract: Digital evidence is an integral part of any trial. Data is critical facts, encrypted information that requires explanation in order to gain meaning and knowledge. The current process of digital forensic research cannot effectively address the various aspects of a complex infrastructure. Therefore, digital forensics requires the optimal processing of a complex infrastructure that differs from the current process and structure. For a long time, digital forensic research has been utilized to discuss these issues. In this research, we offer a forensic investigation hybrid deep learning approach based on integrated multi-model data fusion (HDL-DFI). First, we concentrate on digital evidence …collection and management systems, which can be achieved by an integrated data fusion model with the help of an improved brain storm optimization (IBSO) algorithm. Here, we consider several multimedia data’s for evidence purposes, i.e. text, image, speech, physiological signals, and video. Then, we introduce a recurrent multiplicative neuron with a deep neural network (RM-DNN) for data de-duplication in evidence collection, which avoids repeated and redundant data. After that, we design a multistage dynamic neural network (MDNN) for sentimental analysis to decide what type of crime has transpired and classify the action on it. Finally, the accuracy, precision, recall, F1-score, G-mean, and area under the curve of our proposed HDL-DFI model implemented with the standard benchmark database and its fallouts are compared to current state-of-the-art replicas (AUC). The results of our experiments show that the computation time of the proposed model HDL-DFI is 20% and 25% lower than the previous model’s for uploading familiar and unfamiliar files, 22% and 29% lower for authentication generation, 23% and 31% lower for the index service test scenario, and 24.097% and 32.02% lower for familiarity checking . Show more

Keywords: Digital forensics, evidence collection, evidence protection, deep learning, multi model fusion

DOI: 10.3233/JIFS-221307

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6849-6862, 2022

Authors: Muruganandham, R. | Sheik Abdullah, A. | Selvakumar, S.

Article Type: Research Article

Abstract: The primary goal of this study is to optimize web content for a positive user experience and to develop a data-driven methodology to assess the success of visitor flow on a website for school children. Through Vision-Based Page Segmentation, the suggested study work intends to broaden the stated web approach’s reach and statistical inference. The improvisation has been made accordingly with the semantic structure observed from each node with the designated degree of coherence to indicate the content in spatial and block based on visual perception for each event. The click count (number of clicks) is calculated for all the …possibilities of Quest Software. The most frequently accessed event is displayed at the top to enhance usability and visibility with an accuracy of about 92.80%. From the experimental analysis, it has been observed that most of the students preferred events corresponding to drawing, rhymes, and rangoli with a willingness rate of above 80%, respectively. Statistical analysis has been made using chi-square analysis, and it has been found that the levels from A to D are significant for three years with a P -value < 0.001. Sentimental analysis of feedback collected from the participants about the events is also done, and the most preferred event is suggested for the upcoming years. Show more

Keywords: Data driven model, event analysis, optimization, page segmentation, web analytics

DOI: 10.3233/JIFS-221392

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6863-6875, 2022

Authors: Mohana Sundaram, K.D. | Shankar, T. | Sudhakar Reddy, N.

Article Type: Research Article

Abstract: Computer vision functions like object detection, image segmentation, and image classification were recently getting advance due to Convolutional Neural Networks (CNNs). In the food and agricultural industries, image classification plays a critical role in quality control. CNNs are made up of layers that alternate between convolutional, nonlinearity, and feature pooling. In this article, we proposed Fuzzy Pooling, a novel pooling approach that works based on fuzzy logic, that can increase the accuracy of the CNNs by replacing the conventional pooling layer. This proposed Fuzzy Pooling was put to the test with CIFAR-10 and SVHN data sets on single layer CNN, …and it outperformed previous pooling strategies by achieving 92% and 97% classification accuracy. This proposed Fuzzy Pooling layer was replaced the Max Pooling layer in the ThinNet architecture, and it was trained using the back propagation method. It was demonstrated experimentally on the Lemon fruit data set to classify the fruits into three categories such as Good, Medium, and Poor. In order to classify the lemon fruit into three categories, the 1000 Fully Connected layer in ThinNet architecture was replaced with three Fully Connected layers. The Modified ThinNet architecture called ThinNet_FP was trained with a learning rate of 0.001 and achieved 97% accuracy in classifying the images and outperformed previous CNN architectures when trained on the same data set. Show more

Keywords: Convolutional neural network (CNN), fuzzy logic, fuzzy pooling, back propagation, fruit classification

DOI: 10.3233/JIFS-221550

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6877-6891, 2022