Journal of Intelligent & Fuzzy Systems - Volume 45, issue 5

Authors: Xiao, Huimin | Yang, Peng | Gao, Xiaosong | Wei, Meng

Article Type: Research Article

Abstract: This study addresses the inadequacy of the current quantitative calculation method for decision-maker credibility in hesitant fuzzy multi-attribute decision-making, where credibility is considered. To overcome this limitation, a novel quantitative calculation method for decision-maker credibility is proposed based on the principles of basic uncertainty information theory under a hesitant fuzzy environment. Furthermore, a credible-based hesitant fuzzy multi-attribute decision model is developed. Initially, the paper introduces the concept of a basic uncertainty hesitant fuzzy set by combining basic uncertainty information theory with hesitant fuzzy set theory, thereby enhancing the understanding of basic uncertainty information theory within the realm of non-interval fuzzy …information. Building on this foundation, the method for determining the hesitant degree of each element in the basic uncertainty hesitant fuzzy set is provided, followed by the proposed quantitative calculation method for decision-maker’s credibility under the hesitant fuzzy environment, which addresses the lack of a quantitative approach for assessing expert credibility under such circumstances. Subsequently, an attribute weight assignment method is introduced, considering the decision-maker’s credibility, leading to the formulation of a basic uncertainty hesitant fuzzy multi-attribute decision model based on credibility. This model enhances existing hesitant fuzzy multi-attribute decision-making methods that take credibility into account. To validate the proposed approach, the study applies it to the selection of new energy vehicle battery suppliers. The results of the analysis using actual data and sensitivity analysis demonstrate that decision-maker credibility can be quantitatively determined using the proposed method. Additionally, the basic uncertainty hesitant fuzzy multi-attribute decision-making model based on credibility effectively aids in supplier selection. The feasibility and stability of this method are verified through the examination of risk appetite coefficient and hesitancy coefficient. Show more

Keywords: Hesitant fuzzy set, basic uncertain information, basic uncertain information hesitant fuzzy sets, credibility, hesitance degree

DOI: 10.3233/JIFS-232820

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8429-8440, 2023

Authors: Wang, Yuan

Article Type: Research Article

Abstract: Recent years, research on automatic music transcription has made significant progress as deep learning techniques have been validated to demonstrate strong performance in complex data applications. Although the existing work is exciting, they all rely on specific domain knowledge to enable the design of model architectures and training modes for different tasks. At the same time, the noise generated in the process of automatic music transcription data collection cannot be ignored, which makes the existing work unsatisfactory. To address the issues highlighted above, we propose an end-to-end framework based on Transformer. Through the encoder-decoder structure, we realize the direct conversion …of the spectrogram of the collected piano audio to MIDI output. Further, to remove the impression of environmental noise on transcription quality, we design a training mechanism mixed with white noise to improve the robustness of our proposed model. Our experiments on the classic piano transcription datasets show that the proposed method can greatly improve the quality of automatic music transcription. Show more

Keywords: Music automatic transcription, transformer, piano, deep learning

DOI: 10.3233/JIFS-233653

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8441-8448, 2023

Authors: Sultanuddin, S.J. | Sudhee, Devulapalli | Prakash Satve, Priyanka | Sumithra, M. | Sathyanarayana, K.B. | Kumari, R. Krishna | Narasimharao, Jonnadula | Reddy, R. Vijaya Kumar | Rajkumar, R.

Article Type: Research Article

Abstract: Following the Covid-19 pandemic, the rapid spread of online education and tests demanded the implementation of cheating detection tools to ensure academic integrity. While advances in technology such as face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and IP spoofing detection have shown promising results in detecting fraudulent behavior, their integration raises ethical concerns that must be carefully considered. This work presents a cognitive computing strategy for investigating the ethical implications of using cheating detection systems in online tests. This study attempts to examine the potential impact on students’ privacy, fairness, and trust …in the examination process by employing cognitive computing, which models human cognitive capacities. A thorough literature review is used in the process to uncover existing ethical norms and regulatory frameworks linked to online assessments and cheating detection. Soft computing approaches are also used to evaluate the effectiveness and dependability of the aforementioned cheating detection strategies. The study looks into how far facial recognition and expression analysis can go in terms of privacy, as well as the possibility of bias in head posture analysis and eye gaze tracking algorithms. Furthermore, it investigates the ethical implications of monitoring network data traffic and detecting IP spoofing, with a focus on data security and user permission. The cognitive computing model, based on the analysis, presents a comprehensive framework for ethical decision-making when installing cheating detection technologies. The findings of this study contribute to the continuing discussion about the ethical concerns of using modern technologies to identify cheating in online exams. It provides educational institutions and policymakers with practical ideas for striking a balance between academic integrity and protecting students’ rights and dignity. By emphasizing ethical issues, this study aims to ensure that the implementation of cheating detection systems adheres to values of fairness, transparency, and privacy protection, promoting a trusting and supportive online learning environment for all parties involved. Show more

DOI: 10.3233/JIFS-235066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8449-8463, 2023

Authors: Chellam, S. | Kuruseelan, S. | Pravin Rose, T. | Jasmine Gnana Malar, A.

Article Type: Research Article

Abstract: Congestion of the power system is the most common challenge an Independent System Operator (ISO) faces in restructured electricity markets. It affects the efficiency of the market when transmission lines are congested causing transmission costs to rise. To prevent transmission line congestion, ISO needs to take the necessary steps. To solve these issues, this paper introduces a new method namely the Adaptive Red Fox Optimization algorithm (ARFOA) to compute the congestion cost considering the power losses in the transmission line system. Initially, all the generators in the system are selected to reschedule real power outputs. Second, by establishing a proposed …optimization issue, ARFOA is employed to control transmission line congestion. The implementation of the proposed method is evaluated on the IEEE 30 bus system. The algorithm’s adaptability is tested using several case studies involving the base case and line outages, also compared with the other existing techniques such as PSO, ASO, and GSO approaches. The simulation outcomes indicate that the proposed strategy outperforms existing techniques in terms of congestion cost, power loss, generation rescheduled power, and computational time. Show more

Keywords: Restructured power systems, congestion management, generator rescheduling, Adaptive Red Fox Optimization algorithm, optimal power flow

DOI: 10.3233/JIFS-224559

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8465-8477, 2023

Authors: Xu, Tiefeng | Wang, Tao | Jiang, Xianwei | Liu, Gensheng

Article Type: Research Article

Abstract: In the initial construction process of smart grid dispatching control system in power grid dispatching control center, because different subsystems are in decentralized development, independent operation and independent management, it is easy to reduce data interconnection, which leads to difficulties in data sharing and restricts the information level of the system. The data is multi-source, and the data format is inconsistent, resulting in the application problems that the data can not be shared, accessed, managed, analyzed and mined in real time among different subsystems. In order to solve the problems of data sharing and mining, this paper constructs a knowledge …map entity extraction model to study the power grid fault events. Based on the knowledge map theory, the structured and unstructured data related to power grid dispatching are processed to improve the application efficiency of data. Cleaning the preprocessed data to obtain the corresponding entity value and attribute value. The knowledge extraction model of power grid fault event reasoning knowledge mapping is constructed, and the power grid fault event reasoning knowledge edge mapping system is designed to extract the relationship between events and complete data storage. The experimental results show that the text prediction degree of the proposed model is high, which can reach more than 95; The accuracy is 96.71%, the recall rate is 94.88%, and the F1 value is 9.27%. This proves the feasibility of this study, in order to provide data and theoretical support for intelligent management and real-time dispatching of power grid. Show more

Keywords: Power grid fault, event reasoning, knowledge map, data extraction, data mining

DOI: 10.3233/JIFS-232370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8479-8488, 2023

Authors: Anuradha, P. | Navitha, Ch. | Renuka, G. | Jithender Reddy, M. | Rajkumar, K.

Article Type: Research Article

Abstract: Nowadays, WSN-IoT may be used to remotely and in real-time monitor patients’ vital signs, enabling medical practitioners to follow their status and deliver prompt treatments. This equipment can evaluate the gathered data on-site thanks to the integration of edge computing, enabling quicker diagnostic and medical options with the need for massive data transmission to a centralized server. Making the most of the resources accessible without sacrificing monitoring efficiency is critical due to the constrained lifespan and resource availability that these intelligent devices still encounter. To make the most of the assets at hand and achieve excellent categorization performance, intelligence must …be applied through a learning model. Making the most of the resources that are available without sacrificing performance monitoring is essential given the restricted lifespan and resource availability that these intelligent devices still suffer. A learning model must incorporate intelligence in order to maximize the utilization of resources while maintaining excellent classification performance. In this study, a unique Harris Hawks Optimized Long Short-Term Memory (HHO-LSTM) that categorizes Electrocardiogram (ECG) data without compromising optimum utilization of resources is proposed for Edge enabled WSN devices. We will train the model to correctly categorize various kinds of ECG readings by employing cutting-edge techniques and neural networks. Significant testing is carried out on fifty individuals utilizing real-time test chips with integrated controllers coupled to ECG sensors and NVIDIA Jetson Nano Boards as edge computing devices. To show the benefits of the suggested model, performance comparisons with various deep-learning techniques for peripheral equipment are conducted. Experiments show that in terms of classification results (98% accuracy) and processing expenses, the suggested model, which is based on Edge-enabled WSN devices, beat existing state-of-the-art learning algorithms. The ability of this technology to help medical personnel diagnose a range of heart issues would eventually enhance customer management. Show more

Keywords: WSN, IoT, edge computing, Harris Hawks Optimization, gated recurrent neural networks, electrocardiograms

DOI: 10.3233/JIFS-233442

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8489-8501, 2023

Authors: Lakshmi, H. | Queen, M.P. Flower

Article Type: Research Article

Abstract: Demand side management (DSM) is a smart grid technology that enables consumers to make decisions about their energy use, lowers energy suppliers’ peak hour demand, and changes the load profile. Demand Side Management (DSM) is regarded as the most significant method used in a Smart Grid (SG), as it helps consumers produce accurate information about their electrical energy usage and assists the utility in reducing peak load demand and reshaping the demand curve. By effectively utilising storage with Renewable Energy Systems (RES), DSM seeks to reduce peak demand, electricity costs, and emission rates. In this paper, we have proposed a …load-shifting method for the DSM with a large number of controllable devices. The load-shifting issue has been handled hourly, throughout the course of a 24-hour day, in order to reduce the peak demand, lower the power cost, and minimise the Peak to Average load Ratio (PAR). The Archimedes Optimization (AO) method has been utilised in residential loads in SG to achieve the goal of load shifting by minimising of the problem to the DSM. The simulation findings demonstrate that the suggested demand side management technique generates significant cost savings while lowering the smart grid’s peak load demand. Show more

Keywords: Demand side management (DSM), peak to average load ratio (PAR), archimedes optimization (AO) algorithm, smart grid (SG)

DOI: 10.3233/JIFS-222828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8503-8517, 2023

Authors: Shan, Renliang | Nie, Mingyue | Zheng, Peng | Dong, Ruiyu | Bai, Yao | Ma, Tiancheng | Wang, Yuxin | Dou, Haoyu

Article Type: Research Article

Abstract: To study the effects of the anisotropic matrix and structural planes on the splitting strength and failure mode of rocks, Brazilian splitting tests were carried out with seven different loading angles on specimens of rock-like materials with rough structural planes. The surface strains of the samples during the failure process were monitored and analysed with the help of a high-speed camera and digital image correlation (DIC) technology. The test results showed that the Brazilian splitting strength (BSS) decreased gradually with an increased loading angle. According to the crack morphology, the samples showed three failure modes, and the structural plane and …the loading angle (θ) had an important effect on the failure mode. When θ < 75°, the sample failure was mainly affected by the matrix, and when θ > 75°, the sample failure was mainly controlled by the structural plane. The numerical simulation of the sample with a structural plane was carried out by the PFC2D particle flow program, the micro parameters were calibrated using a back propagation (BP) neural network model. The internal cracks of the sample under a splitting load were mainly matrix tensile microcracks and structural plane shear microcracks, and the tensile microcracks in the side with the weak matrix appeared significantly earlier than those in the side with the strong matrix. With increasing loading angle, the proportion of tensile microcracks in the matrix increased, while the proportion of shear microcracks in the matrix decreased, especially in the weak matrix. The microcracks at the structural plane mainly changed from tensile microcracks to shear microcracks, and the development degree of microcracks along the structural plane was more significant than that on the weak matrix with increasing loading angle. The results of the study can provide a reference for rock stability evaluation and utilization. Show more

Keywords: Structural plane, Brazilian test, failure mode, particle flow code, BP neural network

DOI: 10.3233/JIFS-232386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8519-8539, 2023

Authors: Ibrahim, Nechervan B. | Khalaf, Alias B.

Article Type: Research Article

Abstract: In this paper we create a new topological structure induced by connected simple undirected graphs called maximal block topological space and study some properties of this new type of topology. Also, define some concepts in maximal block topological space like (derived subgraph, closure subgraph and interior subgraph). Some results and properties of vertices and subgraphs in G due to maximal block topological space are proved and discussed. Moreover, showed that a maximal block topological space is T 0 -space and T 1/2 -space if and only if G is acyclic graph. Finally, irreducibility and topologically independent of maximal block …topological space are introduced. Show more

Keywords: Topological space, Maximal block topological space, T0-space, T1/2-space.

DOI: 10.3233/JIFS-223749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8541-8551, 2023

Authors: Sonugür, Güray | Çayli, Abdullah

Article Type: Research Article

Abstract: This work aimed to develop a data glove for the real-time translation of Turkish sign language. In addition, a novel Fuzzy Logic Assisted ELM method (FLA-ELM) for hand gesture classification is proposed. In order to acquire motion information from the gloves, 12 flexibility sensors, two inertial sensors, and 10 Hall sensors were employed. The NVIDIA Jetson Nano, a small pocketable minicomputer, was used to run the recognition software. A total of 34 signal information was gathered from the sensors, and feature matrices were generated in the form of time series for each word. In addition, an algorithm based on Euclidean …distance has been developed to detect end-points between adjacent words in a sentence. In addition to the proposed method, CNN and classical ANN methods, whose model was created by us, were used in sign language recognition experiments, and the results were compared. For each classified word, samples were collected from 25 different signers, and 3000 sample data were obtained for 120 words. Furthermore, the dataset’s size was reduced using PCA, and the results of the newly created datasets were compared to the reference results. In the performance tests, single words and three-word sentences were translated with an accuracy of up to 96.8% and a minimum 2.4 ms processing time. Show more

Keywords: Extreme learning machines (ELM), fuzzy logic, sign language recognition, data glove, CNN, ANN

DOI: 10.3233/JIFS-231601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8553-8565, 2023