Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Babypriya, B. | Renoald, A. Johny | Shyamalagowri, M. | Kannan, R.

Article Type: Research Article

Abstract: In the context of this paper a three phase grid connected Photo-Voltaic (PV) system that is used to design with MPPT and developed Grey Wolf optimization (GWO) algorithm for analyzing the power quality issues in the grid system. The proposed Grey Wolf optimization (GWO) algorithm is incorporated in the prototype model and compared with other related optimization algorithms namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The various loading conditions as well as solar irradiances are modeled by using MATLAB simulation and experimentally validated by a DSPIC (DS 1104) based prototype model. A three phase PV grid connected non-linear …load is observed in different operating environmental conditions. The optimization control algorithms was developed and implemented in Super-Lift Inverter (SLI) grid connected system. The findings of this work are, grid reactive power demand is compensated using DSTATCOM, and also from the real power of renewable energy system. But, majority of the active power is provided or absorbed by DSTATCOM component. The objective of this proposed work is that the three optimization control algorithms are examined, and the PV integrated grid tied system maintains a compensation power at Unity Power Factor (UPF). The proposed optimization methods produce load output power factor values such as 0.89 (GWO), 0.88, (PSO) and 0.86 (GA). Show more

Keywords: PV system, particle swarm optimization, genetic algorithm, Grey Wolf optimization, Grid

DOI: 10.3233/JIFS-213259

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2022

Authors: Muruganandham, R. | Abdullah, A. Sheik | 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. Pre-press, no. Pre-press, pp. 1-13, 2022

Authors: Zhao, Shuping | Wang, Dong | Lei, Ting | Wang, Yifan

Article Type: Research Article

Abstract: The selection of a waste-to-energy (WTE) plant site is the core issue that determines whether the WTE project can effectively treat municipal solid waste, reduce environmental pollution, and promote the development of a circular economy, and is often determined through group decision-making. The complexity of this group decision problem makes the opinions of decision makers often with uncertainty. The single-valued neutrosophic set (SVNS) can reduce the loss of information that contains uncertainty by quantitatively describing the information through three functions. In addition, existing studies on group decision-making for WTE plant siting suffer from the problem that decision maker weights do …not change in concert with those decision makers’ decision information. Therefore, this study proposes a group decision-making method based on SVNSs. First, a group consensus strategy is proposed to improve group consensus by removing the evaluation value of the corresponding solution for decision makers who do not reach consensus and are unwilling to modify their preferences. Second, a decision maker weight determination and adjustment method is proposed to readjust the decision maker weights from the solution level according to their respective consensus degree when the decision makers’ preference information changes. This method enables the decision makers’ preferences and weights to be changed jointly. An illustrative example and a comparative analysis of WTE plant siting decisions demonstrate the feasibility and superiority of the method. The experimental results show that the method is effective in helping decision makers to select the optimal WTE plant site more accurately. Show more

Keywords: Waste-to-energy, site selection, single-valued neutrosophic sets, group consensus

DOI: 10.3233/JIFS-220124

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-1, 2022

Authors: Jiang, Yirong | Qiu, Jianwei | Meng, Fangxiu

Article Type: Research Article

Abstract: In this article, we explore the question of existence and finite time stability for fuzzy Hilfer-Katugampola fractional delay differential equations. By using the generalized Gronwall inequality and Schauder’s fixed point theorem, we establish existence of the solution, and the finite time stability for the presented problems. Finally, the effectiveness of the theoretical result is shown through verification and simulations for an example.

Keywords: Finite time stability, fuzzy Hilfer-Katugampola fractional differential equations, delay

DOI: 10.3233/JIFS-220588

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2022

Authors: Rajnish, Kumar | Bhattacharjee, Vandana

Article Type: Research Article

Abstract: Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional software defect prediction methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers to predict defects in the code. To achieve the desired classifier performance, appropriate design decisions are required for deep neural network (DNN) and convolutional neural network (CNN) models. This is especially important when predicting software module fault proneness. When correctly identified, this could help to reduce testing costs by concentrating efforts on the modules that have been identified …as fault prone. This paper proposes a CONVSDP and DNNSDP (cognitive and neural network) approach for predicting software defects. Python Programming Language with Keras and TensorFlow was used as the framework. From three NASA system datasets (CM1, KC3, and PC1) selected from PROMISE repository, a comparative analysis with machine learning algorithms (such as Random Forest (RF), Decision Trees (DT), Nave Bayes (NF), and Support Vector Machine (SVM) in terms of F-Measure (known as F1-score), Recall, Precision, Accuracy, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) has been presented. We extract four dataset attributes from the original datasets and use them to estimate the development effort, development time, and number of errors. The number of operands, operators, branch count, and executable LOCs are among these attributes. Furthermore, a new parameter called cognitive weight (Wc) of Basic Control Structure (BCS) is proposed to make the proposed cognitive technique more effective, and a cognitive data set of 8 features for NASA system datasets (CM1, KC3, and PC1) selected from the PROMISE repository to predict software defects is created. The experimental results showed that the CONVSDP and DNNSDP models was comparable to existing classifiers in both original datasets and cognitive data sets, and that it outperformed them in most of the experiments. Show more

Keywords: Machine learning, software defect prediction, CNN model, cognitive weight, basic control structures, neural network

DOI: 10.3233/JIFS-220497

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-28, 2022

Authors: Srinivas, Kachibhotla | Kumar, Raghavendra Phani

Article Type: Research Article

Abstract: The segmentation of images is a technique used to extract information from a digital picture. One of the main applications in image segmentation is especially in medical image processing detection of an abnormal aspect to diagnose diseases. Ovarian cysts are formed in women who are unbalanced in estrogen and progesterone hormones. The polycystic ovarian syndrome is known as this condition. Women have a fluid collection in their ovaries called follicles. The image captures the follicles by ultrasound scanning. The detection of follicles from the echo sound image requires an optimized segmentation algorithm. Quantification of the ovary and follicle volumes and …follicle counts for diagnosis and management in assisted replication is performed in clinical practice. Now for a few days, most women face infertility problems in the age group between 22 and 35. To analyze and classify the problems, the decision can start with the use of advanced technology to structurally compare the normal ovary to the affected ovary. Ovarian imagery is an effective instrument for the treatment of infertility. In human reproduction, follicle monitoring is particularly important. The primary method of doctors’ assessment is a periodic measurement of the size and form of follicles over several days. The field of medical imaging is one of the most popular applications of image processing techniques. The widespread popularity of image analysis technology in the field of diagnostic devices is due to the advancement of advanced imaging instruments combined with developments in algorithms unique to medical image processing, both for diagnostic tests and therapeutic preparation. Ultrasound imaging is a technique that uses high-frequency sound waves to capture images from within the human body. The echoes of reflected sound waves are captured and shown in real-time. It’s a good way to look at the nucleus, liver, kidneys, gall bladder, and ovaries, among other internal organs. The main contribution lies in identifying dominant follicles, that is growing and capable of producing an egg after the follicular phase, which is our primary goal, and this is where our suggested study comes in. Follicular ovulation doesn’t occur in all women, and not all of the dominant follicular levels are strong enough just to result in a pregnancy. Today, the follicles monitor human interaction using non-automatic means. Our proposed approach for the detection of follicle polycystic ovarian using AKF is not only helpful for generating highly efficient results but also proves to be best when compared with the state of art results from the existing methods. Show more

Keywords: Advanced Kalman Filter (AKF), Adaptive Particle Swarm Optimization (APSO), Dice similarity coefficient (DSC), Kalman Filter (KF), Pigeon Inspired Optimization (PIO), Machine Learning (ML), True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN)

DOI: 10.3233/JIFS-212857

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2022

Authors: Hussain, Azmat | Mahmood, Tahir | Ali, Muhammad Irfan | Iampan, Aiyared

Article Type: Research Article

Abstract: Recently, some improvement has been made in the dominant notion of fuzzy set that is Yager investigated the generalized concept of fuzzy set, Intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS) and called it q-rung orthopair fuzzy (q-ROF) set (q-ROFS). The aim of this manuscript is to present the concept of q-ROF soft (q-ROFS t ) set (q-ROFS t S) based on the Dombi operations. Since Dombi operational parameter possess natural flexibility with the resilience of variability. Some new operational laws are defined based on hybrid study of soft sets and q-ROFS. The advantage of Dombi operational …parameter is very important to express the experts’ attitude in decision making. In this paper, we present q-ROFS t Dombi average (q-ROFS t DA) aggregation operators including q-ROFS t Dombi weighted average (q-ROFS t DWA), q-ROFS t Dombi ordered weighted average (q-ROFS t DOWA) and q-ROFS t Dombi hybrid average (q-ROFS t DHA) operators. Moreover, we investigate q-ROFS t Dombi geometric (q-ROFS t DG) aggregation operators including q-ROFS t Dombi weighted geometric (q-ROFS t DWG), q-ROFS t Dombi ordered weighted geometric (q-ROFS t DOWG), and q-ROFS t Dombi hybrid geometric (q-ROFS t DHG) operators. The basic properties of these operators are presented with detail such us Idempotency, Boundedness, Monotonicity, Shift invariance, and Homogeneity. Thus from the analysis and advantages of proposed model, it is clear that the investigated q-ROFS t DWA operator is the generalized form of IF S t DWA, PFS t DWA and q-ROFDWA operators. Similarly, the investigated q-ROFS t DWG operator is the generalized form of IF S t DWG, PFS t DWG and q-ROFDWG operators. By applying the develop approach, this manuscript contains the technique and algorithm for multicriteria decision making (MCDM). Further a numerical example is developed to illustrate the flexibility and applicability of the developed operators. Show more

Keywords: PFS, q-ROFS, Soft Sets, q-ROFS tS, Dombi Operators, q-ROFS t DWA, q-ROFS t DOWA, q-ROFS t DHA, q-ROFS t DWG, q-ROFS t DOWG and q-ROFS t DHG Operator, MCDM

DOI: 10.3233/JIFS-212921

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2022

Authors: Liu, Jing | Tian, Shengwei | Yu, Long | Long, Jun | zhou, Tiejun | Wang, Bo

Article Type: Research Article

Abstract: Sarcasm is a way to express the thoughts of a person. The intended meaning of the ideas expressed through sarcasm is often the opposite of the apparent meaning. Previous work on sarcasm detection mainly focused on the text. But nowadays most information is multi-modal, including text and images. Therefore, the task of targeting multi-modal sarcasm detection is becoming an increasingly hot research topic. In order to better detect the accurate meaning of multi-modal sarcasm information, this paper proposed a multi-modal fusion sarcasm detection model based on the attention mechanism, which introduced Vision Transformer (ViT) to extract image features and designed …a Double-Layer Bi-Directional Gated Recurrent Unit (D-BiGRU) to extract text features. The features of the two modalities are fused into one feature vector and predicted after attention enhancement. The model presented in this paper gained significant experimental results on the baseline datasets, which are 0.71% and 0.38% higher than that of the best baseline model proposed on F1-score and accuracy respectively. Show more

Keywords: Multi-modal, sarcasm detection, Attention, ViT, D-BiGRU

DOI: 10.3233/JIFS-213501

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2022

Authors: Sood, Mansi | Gera, Jaya | Kaur, Harmeet

Article Type: Research Article

Abstract: This work creates, evaluates, and optimizes a domain-based dictionary using labeled domain documents as the input. The dictionary is created using selected unigrams and bigrams from the labeled text documents. Dictionary is evaluated using the Naïve Bayes classification model. Classification Accuracy obtained is used as a metric to evaluate the effectiveness of the dictionary. The paper also studies the impact of applying the Stochastic Gradient Descent (SGD) technique, with Lasso and Ridge Regularization, on the effectiveness of a domain-based dictionary. Both, Lasso and Ridge regularization, with Ridge faring better than Lasso, help to optimize the dictionary size, without any significant …reduction in the accuracy. The created dictionaries are evaluated on the dataset used for their creation and subsequently on an unseen dataset as well. The applicability of a created dictionary to classify the documents belonging to a different dataset gives an idea about the generality of that dictionary. The paper establishes that the dictionaries created using the above methodology are generic enough to classify documents of other unseen datasets. Show more

Keywords: Domain-based dictionary, unigram, bigram, Naïve Bayes classification, Stochastic Gradient Descent

DOI: 10.3233/JIFS-220110

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2022

Authors: Zhao, Zhen-Yu | Ma, Xu

Article Type: Research Article

Abstract: The power industry has significantly contributed to the prosperity of the national economy, and accurate prediction can reflect the development trend of the power system and power market. The short-term electricity consumption of a country exhibits both annual growth certainty and random change uncertainty, which can be suitably considered with the grey forecasting model. Regarding the short-term trends of electricity consumption in China, this study established an optimized multivariate grey forecasting model with variable background values (OGM(1, N) model) to forecast the electricity consumption level in China. The established model could be converted into the GM(1, N) model and different …variant models by adjusting the model parameters. With Beijing, Tianjin and Shanghai as examples, the OGM(1, N) model is compared to the GM(1, N) model and its variant model. The excellent prediction results confirm the feasibility of the proposed model. Then, the proposed model is applied to study China’s electricity consumption. The research results indicated that the OGM(1, N) model attains an extraordinarily high precision in the prediction of electricity consumption and can provide a practical reference for accurate electricity consumption prediction. Show more

Keywords: Electricity consumption, multivariate grey forecasting model, variable background values, China

DOI: 10.3233/JIFS-213210

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2022