Journal of Intelligent & Fuzzy Systems - Volume 44, issue 3

Authors: Khan, Indadul | Dutta, Prasanta | Maiti, Manas Kumar | Basuli, Krishnendu

Article Type: Research Article

Abstract: In this study, Bat algorithm (BA) is modified along with K -opt operation and one newly proposed perturbation approach to solve the well known covering salesman problem (CSP). Here, along with the restriction of the radial distances of the unvisited cities from the visited cities another restriction is imposed where a priority is given to some cities for the inclusion in the tour, i.e., some clusters to be created where the prioritised cities must be the visiting cities and the corresponding CSP is named as Prioritised CSP (PCSP). In the algorithm, 3-opt and 4-opt operations are used for two different …purposes. The 4-opt operation is applied for generating an initial solution set of CSP for the BA and the 3-opt operation generates some perturbed solutions of a solution. A new perturbation approach is proposed for generating neighbour solutions of a potential solution where the exchange of some cities in the tour is made and is named as K -bit exchange operation. The proposed solution approach for the CSP and PCSP is named as the modified BA embedded with K -bit exchange and K -opt operation (MBAKEKO). It is a two-stage algorithm where in the first stage of the algorithm the clustering of the cities is done with respect to a fixed visiting city of each cluster in such a manner that the distances of the other cities of the cluster must lie with in the fixed covering distance of the problem and in the second stage the BA is applied to find the minimum cost Hamiltonian circuit by passing through the visiting cities of the clusters. MBAKEKO is tested with a set of benchmark test problems with significantly large sizes from the TSPLIB. To measure the performance of MBAKEKO, its results are compared with the results of different well-known approaches for CSPs available in the literature. It is observed from the comparison studies that MBAKEKO searches the minimum cost tour for any of the considered instances compared to all other well-known algorithms in the literature. It can be concluded from the numerical studies that the performance of MBAKEKO is better with respect to the state-of-the-art algorithms available in the literature. Show more

Keywords: Combinatorial optimization, covering salesmen problem, priority based covering salesman problem, K-bit exchange operation, K-opt, Bat Algorithm

DOI: 10.3233/JIFS-220396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3825-3849, 2023

Authors: Kanimozhi Selvi, C.S. | Jayaprakash, D. | Poonguzhali, S.

Article Type: Research Article

Abstract: Autism spectrum disorder is a neuro-developmental disorder that affects communication and social skills in individuals. Screening and diagnosis of autism using conventional methods, such as interviews with parents or caregivers and observational assessments takes a long time. The accurate diagnosis of autism by physicians and healthcare professionals seems to be challenging. By analyzing data on autistic children, medical professionals can learn about autism screening assessment decision making. The present study aims to develop a parental autism screening tool termed the Indian Autism Grading Tool (IAGT) for early screening of autism. Data are collected using the Indian Autism Parental Questionnaire and …assigned with grades. This dataset is employed to test five supervised machine learning models, which compare classification performance based on accuracy, precision and recall. The most effective model should be used to implement the autism screening application. MLR is known to be more robust and to support fewer data sets, so it can be employed for the implementation of ML-powered mobile applications. MLR achieves the overall accuracy of 97.85%, which equates to 0.72%, 2.37%, 0.84% and 1.54% better than SVM, DT, KNN and GNB respectively. The proposed tool is developed in both Tamil and English. The pilot study is conducted with 30 children and the predictability of the tool is compared with the clinician. Therefore, the tool consistently achieves the same level of accuracy as clinicians. Show more

Keywords: Artificial intelligence, machine learning techniques, autism spectrum disorder, Indian autism parental questionnaire, mobile application

DOI: 10.3233/JIFS-221087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3851-3865, 2023

Authors: Amanullah, M. | Thanga Ramya, S. | Sudha, M. | Gladis Pushparathi, V.P. | Haldorai, Anandakumar | Pant, Bhaskar

Article Type: Research Article

Abstract: On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address …the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist. Show more

Keywords: Index Terms: Software defect prediction, improved random-SMOTE oversampling technique, linear pearson correlation, heuristic learning vector quantization (LVQ), training and test datasets

DOI: 10.3233/JIFS-220480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3867-3876, 2023

Authors: Suganthi, M. | Arun Prakash, R.

Article Type: Research Article

Abstract: Everything becomes smart in the modern era, for everything we need a better plan or arrangements. In the olden days, essential information was noted as a document with the help of paper and pen or printed texts. But the intelligent world needs a paperless environment by converting handwritten or printed text documents into software copies. This can be achieved by the electronic data conversion concept called Optical Character Recognition (OCR). OCR of some documents is complex because of different writing styles and quality of scanned image issues, which can be solved by adopting a deep learning technique for better accuracy. …We employed Long Short Term Memory (LSTM) for English Optical Character Recognition for paperless and effortless data storage and fast access in this work. Still, the records may contain the entities like names, contact details, drug details, diseases, educational qualifications, dates, etc. These entities cannot be separated by employing OCR alone; we need an entity recognition framework for deeper and faster data analysis. For efficient Named Entity Recognition, we utilize the Adaptive Fuzzy Inference System (ANFIS) powered by the algorithms CRF and BERT to automatically label each entity by training the vast amount of unlabeled text data. The ANFIS model is equipped with both linguistic and numerical knowledge. It is more accurate than the ANN when it comes to identifying patterns and classification data. Also, it is more transparent to the user. Our proposed framework aims to improve the performance of the character recognition system by using a feed-forward network. One of the main issues that have been identified in the development of this system is noise. Through this network, we can provide a single input and one output layer. The main components of the system are the training and recognition sections. These two sections are mainly focused on image acquisition and feature extraction. Besides these, they also include training and simulation of the classifier. The first step in the process of image recognition is to extract the features from the normalized image matrix. We then train the network using a proposed training algorithm. Experimentation on medical records attains a higher accuracy value of 0.9637, recall value of 0.9627, and f1 score of 0.9627, respectively. Show more

Keywords: Paperless environment, Optical Character Recognition, Named Entity Recognition, faster data analysis, accuracy

DOI: 10.3233/JIFS-221486

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3877-3890, 2023

Authors: Hovorushchenko, Tetiana | Medzatyi, Dmytro | Voichur, Yurii | Lebiga, Mykyta

Article Type: Research Article

Abstract: The paper develops the method for forecasting the level of software quality based on quality attributes. This method differs from the known ones in that it provides forecasting the quality level of future software based on the processing the software quality attributes’ values, which are available in the software requirements specification (SRS). So, the proposed method makes it possible to compare the SRSs, to immediately refuse the realization of a software based on unsuccessful SRS (saving money and time, reducing the probability of failed and challenged projects), and to make a reasonable choice of the specification for the further implementation …of a software with the highest quality (of course, if errors will not be introduced at subsequent stages of the software life cycle). During the experiments, 4 SRS were analyzed, which were fulfilled by different IT firms of Khmelnytskyi (Ukraine) for the solution of the same task. Taking into account the forecasted quality level of the future software, which will have developed according to each of the analyzed SRS, a comparison of the 4 analyzed SRS was made, and a reasoned choice of the specification was made for the further realization of the highest quality software. Show more

Keywords: Software quality, software quality attributes, software quality characteristics, software quality level, artificial neural network (ANN)

DOI: 10.3233/JIFS-222394

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3891-3905, 2023

Authors: Ma, Hongdong | Li, Gang | Liu, Rongyue | Shen, Mengdi | Liu, Xiaohui

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-212780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3907-3925, 2023

Authors: Ferrari, Allan Christian Krainski | Leandro, Gideon Villar | Coelho, Leandro dos Santos | Delgado, Myriam Regattieri De Biase Silva

Article Type: Research Article

Abstract: The rat swarm optimizer is one of the most recent metaheuristics focused on global optimization. This work proposes a fuzzy mechanism that aims to improve the convergence of this algorithm, adjusting the amplitude of the parameter that directly affects the chasing mechanism of the behavior of rats. The proposed fuzzy model uses the normalized fitness of each individual and the population diversity as input information. For evaluation criteria, the fuzzy mechanism proposed, was implemented in the optimization of third-three single objective problems. For comparison criteria, the proposed fuzzy variant is compared with other algorithms, such as GWO (Grey Wolf Optimizer), …SSA (Salp Swarm Algorithm), WOA (Whale Optimization Algorithm), and also with two proposed alternative fuzzy variants. One of the simpler fuzzy variants uses only population diversity as input information, while the other uses only the normalized fitness value of each rat. The results show that the proposed fuzzy system improves the convergence of the conventional version of the rat algorithm and is also competitive with other metaheuristics. The Friedman test shows statistically the results obtained. Show more

Keywords: Rat swarm optimizer, metaheuristics, fuzzy system, optimization, friedman test

DOI: 10.3233/JIFS-222522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3927-3942, 2023

Authors: Ranjeeth Kumar, C. | Kalaiarasu, M.

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-222795

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3943-3958, 2023

Authors: Paramasivam Thuraipandi, Sivagurunathan | Nagarajan, Sathish Kumar

Article Type: Research Article

Abstract: The spectrum scarcity problem in today’s wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels …(R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models. Show more

Keywords: Energy detector, primary user, cognitive radio network, secondary user, cooperative spectrum sensing

DOI: 10.3233/JIFS-222983

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3959-3971, 2023

Authors: Wang, Jing

Article Type: Research Article

Abstract: Before neural networks, image style transfer procedures had a common idea: analyze images with a certain style, build a mathematical or statistical model for the style, and then change the image to be transferred, so that it better fits the established model. In this paper, k-means and semantic closed natural matting algorithm is combined for image segmentation, the style and content in the image are extracted based on neural network, and the resulting image is synthesized by image reconstruction technology to realize the migration of national costume styles. Due to the serious artifacts of the output image, an improved image …style transfer algorithm is adopted to constrain the transformation from the input image to the output image in the local affine transformation of the color space, and this constraint is expressed as a completely differentiable parameter term, image distortion is suppressed effectively. In the process of real photo style transfer, there is also space inconsistency. Smoothing is done to ensure that the space style is consistent after style processing, it greatly speeds up the operation speed. On NVIDIA GTX1080TI graphics card, algorithm is tested with 256×256 resolution images. It includes three indicators of average running time, memory usage and the number of styles generated by a single model, which are 0.06 s, 136.06 MB and 1 respectively. These indicators can reflect the efficiency and flexibility of the algorithm. Show more

Keywords: Garment feature extraction, deep learning, closed form natural image matting algorithm, K-means, image segmentation, image style transfer

DOI: 10.3233/JIFS-220761

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3973-3986, 2023