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

Authors: Sasirekha, N. | Poonguzhali, I. | Shekhar, Himanshu | Vimalnath, S.

Article Type: Research Article

Abstract: The image of liver which is the area of interest in this work is obtained from abdominal CT scan. It also contains details of other abdominal organs such as pancreas, spleen, stomach, gall bladder, intestine etc. Since all these organs are of soft tissues, the pixel intensity values differ marginally in the CT scan output and the organs overlap each other at their boundaries. Hence it is very difficult to trace out the exact contour of liver and liver tumor. The overlapping and obscure boundaries are to be avoided for proper diagnosis. Image segmentation process helps to meet this requirement. …The normal perception of the CT image can be improved by suitable segmentation techniques. This will help the physician to extract more information from the image and give an accurate diagnosis and better treatment. The projected images are processed using the Partial Differential Technique (PDT) to isolate the liver from the other organs. The Level Set Methodology (LSM) is then used to separate the cancerous tissue from the healthy tissue around it. The classification of stages may be done with the assistance of an Enhanced Convolutional Classifier. The classification of LSM is evaluated by producing many metrics of accuracy, sensitivity, and specificity using an Improved Convolutional classifier. Compared to the two current algorithms, the proposed technique has a sensitivity and specificity of 96% and 93%, respectively, with 95% confidence intervals of [0.7513 1.0000] and [0.7126 1.0000] for sensitivity, and specificity respectively. Show more

Keywords: Liver cancer, improved convolutional classifier, level set methodology, partial differential technique, accuracy

DOI: 10.3233/JIFS-232218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7939-7955, 2023

Authors: Megala, A. | Veeramani, C.

Article Type: Research Article

Abstract: Researchers in science and engineering face various obstacles due to a lack of specific and full data. Many different approaches have been devised to deal with these restrictive requirements, but two notable schools of thought are the fuzzy set (FS) theory and the rough set (RS) theory, both of which have spawned many extensions and hybridizations. Although RS theory originated from an indiscernibility relation (also known as an equivalence relation in mathematics), emphasis rapidly shifted to similarity or coverings (and their fuzzy analogues). Many other hybrid schemes were suggested with this goal in mind. The gap between those concepts shrank …because to this thorough analysis. Fuzzy set theory is a legitimate way to convey the ambiguity of assessment data, yet it is still inadequate for dealing with certain intricate problems in the actual world. In reality, decision makers will undoubtedly provide different kinds of ambiguous and nuanced assessments. Atanassov’s intuitionistic fuzzy set theory broadened the application of fuzzy set theory by imbuing it with an element of uncertainty. Sometimes in real life, you have to deal with a neutral element on top of the indeterminate one. Picture fuzzy sets were developed specifically for this purpose. Membership roles may be positive, neutral, or negative/refusal. In contrast, hesitant fuzzy sets and its hybrid models are useful when decision makers are on the fence about which option to choose. As a binary relation on a set, a graph is symmetric. It is a staple in mathematical modelling and is used in almost every scientific and technological discipline. Graph theory has been essential in the mathematical modelling and subsequent resolution of several real-world situations. Information about connections between things is often best represented using graph theory, which uses vertices to stand in for the items and edges for the relationships between them. The suggested dynamic algorithm is better to the static approach in dealing with the multidimensional dynamic changes of the hybrid incomplete decision system, according to a series of experiments carried out on nine UCI datasets. Show more

Keywords: Intuitionistic fuzzy set theory, graph theory, rough set theory, varying object sets and values

DOI: 10.3233/JIFS-232314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7957-7974, 2023

Authors: Huang, Zhen | Gao, Feng | Li, Xuesong | Jiang, Min

Article Type: Research Article

Abstract: The static risk assessment method has difficulty tracking variations of the risk level, which is not conducive to the dynamic control of construction. Tunnel collapse during the construction of mountain tunnels has a dynamic evolution law and contains great risk of harm, and the corresponding dynamic risk assessment is extremely important. This study proposes a static and dynamic fuzzy uncertainty assessment method for the collapse risk of mountain tunnels. First, 150 tunnel collapse accidents were investigated and analysed, and the static and dynamic risk assessment index system of mountain tunnel construction collapse was established. Second, the DEMATEL method is processed …by applying fuzzy logic, the subjective weight of each index is calculated, and the interaction between the indexes is analysed. Finally, the traditional VIKOR method is improved upon, and the weight of each assessment index is coupled and analysed. A static and dynamic uncertainty assessment model of the construction collapse risk of multiple construction sections is constructed. This method has been successfully applied to the risk assessment of tunnel collapse, and the assessment results are consistent with the actual construction situation. This study provides a new method for the static and dynamic assessment of mountain tunnel collapse risk. Show more

Keywords: Mountain tunnel, collapse, risk assessment, VIKOR method, DEMATEL method, Uncertainty analysis

DOI: 10.3233/JIFS-233149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7975-7999, 2023

Authors: Narayanan, M. Badri | Ramesh, Arun Kumar | Gayathri, K.S. | Shahina, A.

Article Type: Research Article

Abstract: Fake news production, accessibility, and consumption have all increased with the rise of internet-connected gadgets and social media platforms. A good fake news detection system is essential because the news readers receive can affect their opinions. Several works on fake news detection have been done using machine learning and deep learning approaches. Recently, the deep learning approach has been preferred over machine learning because of its ability to comprehend the intricacies of textual data. The introduction of transformer architecture changed the NLP paradigm and distinguished itself from recurrent models by enabling the processing of sentences as a whole rather than …word by word. The attention mechanisms introduced in Transformers allowed them to understand the relationship between far-apart tokens in a sentence. Numerous deep learning works on fake news detection have been published by focusing on different features to determine the authenticity of a news source. We performed an extensive analysis of the comprehensive NELA-GT 2020 dataset, which revealed that the title and content of a news source contain discernible information critical for determining its integrity. To this objective, we introduce ‘FakeNews Transformer’ — a specialized Transformer-based architecture that considers the news story’s title and content to assess its veracity. Our proposed work achieved an accuracy of 74.0% on a subset of the NELA-GT 2020 dataset. To our knowledge, FakeNews Transformer is the first published work that considers both title and content for evaluating a news article; thus, we compare the performance of our work against two BERT and two LSTM models working independently on title and content. Our work outperformed the BERT and LSTM models working independently on title by 7.6% and 9.6% , while performing better than the BERT and LSTM models working independently on content by 8.9% and 10.5% , respectively. Show more

Keywords: Fake news detection, FakeNews transformer, transformer encoder, NELA-GT 2020

DOI: 10.3233/JIFS-223980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8001-8013, 2023

Authors: Zhenlin, Wei | Chuantao, Wang | Xuexin, Yang | Wei, Zhao

Article Type: Research Article

Abstract: The purpose of sentiment classification is to accomplish automatic judssssgment of the sentiment tendency of text. In the sentiment classification task of online reviews, traditional models focus on the optimization of algorithm performance, but ignore the imbalanced distribution of the number of sentiment classifications of online reviews, which causes serious degradation in the classification performance of the model in practical applications. The experiment was divided into two stages in the overall context. The first stage trains SimBERT using online review data so that SimBERT can fully learn the semantic features of online reviews. The second stage uses the trained SimBERT …model to generate fake minority samples and mix them with the original samples to obtain a distributed balanced dataset. Then the mixed data set is input into the deep learning model to complete the sentiment classification task. Experimental results show that this method has excellent classification performance in the sentiment classification task of hotel online reviews compared with traditional deep learning models and models based on other imbalanced processing methods. Show more

Keywords: Sentiment classification, imbalance classification, deep learning, BERT, SimBERT

DOI: 10.3233/JIFS-230278

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8015-8025, 2023

Authors: Xia, Yan | Yu, Shun | Jiang, Liu | Wang, Liming | Lv, Haihua | Shen, Qingze

Article Type: Research Article

Abstract: Power system load forecasting is a method that uses historical load data to predict electricity load data for a future time period. Aiming at the problems of general prediction accuracy and slow prediction speed in using typical machine learning methods, an improved fuzzy support vector regression machine method is proposed for power load forecasting. In this method, the boundary vector extraction technique is employed in the design of the membership function for fuzzy support vectors to differentiate the importance of different samples in the regression process. This method utilizes a membership function based on boundary vectors to assign differential weights …to different sample points that used to differentiate the importance of different types of samples in the regression analysis process in order to improve the accuracy of electricity load prediction. The key parameters of the fuzzy support vector regression model are optimized, further enhancing the precision of the forecasting results. Simulation experiments are conducted using real power load data sets, and the experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and speed in predicting power load data compared to other prediction models. This method can be widely applied in real power production and scheduling processes. Show more

Keywords: Machine learning, fuzzy support vector regressive machine, power load prediction, membership function, boundary vector

DOI: 10.3233/JIFS-230589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8027-8048, 2023

Authors: Zhang, Ruihua | Han, Meng | He, Feifei | Meng, Fanxing | Li, Chunpeng

Article Type: Research Article

Abstract: In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the “combinatorial explosion” problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, …chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given. Show more

Keywords: Survey, high utility sequential patterns, incremental data, data streams, hidden patterns

DOI: 10.3233/JIFS-232107

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8049-8077, 2023

Authors: Wu, Xiaopeng

Article Type: Research Article

Abstract: In wireless-sensing networks (WSNs), the energy economy has lately emerged as the main problem. Since sensor networks run on batteries, they eventually run out of power. To increase the packet transmission ratio for sensing devices, it becomes more difficult to enhance data loss in an energy-efficient manner. In WSNs, the mobile drain causes high network energy usage and data delay. This paper suggests an Improved Ant Colony Clustering-Based Data Transmission Algorithm (EACODT) that first develops the network nodes’ energy density function before allocating sensing nodes with higher residual energy as cluster leaders using the energy density function. The EACODT is …thoroughly modeled for different WSN situations with variable numbers of sensing nodes and CHs, and the findings are contrasted with some recently developed meta-heuristic algorithms. As a consequence, it is discovered that EACODT gets 34% of energy usage, 98.8% of network lifespan, 95% of packet delivery ratio, 854 kbps of transmission, and a 98% convergence rate. Show more

Keywords: Wireless sensor networks, optimization, energy efficiency, packet delivery, data transmission

DOI: 10.3233/JIFS-232295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8079-8089, 2023

Authors: Zhao, Xiao-Rui | Wang, Jie-Sheng | Bao, Yin-Yin | Hou, Jia-Ning | Ma, Xin-Ru | Li, Yi-Xuan

Article Type: Research Article

Abstract: Wild Horse Optimizer (WHO) is a population-based metaheuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature to find the optimal. The initialization of the population by imitating the behavior of wild horses is prone to uneven distribution of population positions, and its position updating method is prone to local optimal problems while improving the efficiency of the search. In order to enhance the population diversity and to break out of the local optimum, an adaptive weighted wild horse optimizer based on backward learning and …small-hole imaging strategy is proposed. The backward learning strategy is used to enhance the population diversity and improve the uneven distribution of individuals; The adaptive weight and small-hole imaging strategy are added to the local search strategy to improve the global search ability and jump out of the local optimum. To verify the effectiveness of the proposed algorithm, simulation experiments were conducted by using 23 benchmark test functions to test the search ability and Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO) and Multi-Verse Optimizer (MVO) algorithms are compared in terms of their search performance, and finally four real engineering design problems are solved. The simulation results indicate that the proposed FHPWHO has excellent merit-seeking capability. Show more

Keywords: Wild horse optimizer, inverse learning, adaptive weights, small-hole imaging strategy, function optimization, engineering optimization

DOI: 10.3233/JIFS-232342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8091-8117, 2023

Authors: Cao, Jiangzhong | Liao, Siyi

Article Type: Research Article

Abstract: 3D shape recognition is a critical research topic in the field of computer vision, attracting substantial attention. Existing approaches mainly focus on extracting distinctive 3D shape features; however, they often neglect the model’s robustness and lack refinement in deep features. To address these limitations, we propose the point-view fusion attention network that aims to extract a concise, informative, and robust 3D shape descriptor. Initially, our approach combines multi-view features with point cloud features to obtain accurate and distinguishable fusion features. To effectively handle these fusion features, we design a dual-attention convolutional network which consists of a channel attention module and …a spatial attention module. This dual-attention mechanism greatly enhances the generalization ability and robustness of 3D recognition models. Notably, we introduce a strip-pooling layer in the channel attention module to refine the features, resulting in improved fusion features that are more compact. Finally, a classification process is performed on the refined features to assign appropriate 3D shape labels. Our extensive experiments on the ModelNet10 and ModelNet40 datasets for 3D shape recognition and retrieval demonstrate the remarkable accuracy and robustness of the proposed method. Show more

Keywords: 3D Shape recognition, multimodal feature fusion, feature refinement, attention mechanism

DOI: 10.3233/JIFS-232800

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8119-8133, 2023

Authors: Huang, Hangxing | Ma, Lindong

Article Type: Research Article

Abstract: In late 2019, coronavirus disease (COVID-19) began to spread globally and is highly contagious. Due to its exceptionally rapid spread and high mortality rate, it is not yet possible to be eradicated. In order to halt the spread of COVID-19, there is a pressing need for effective screening of infected patients and immediate medical intervention. The absence of rapid and accurate methods to identify infected patients has led to a need for a model for early diagnosis of patients with and suspected of having COVID-19 to reduce the probability of missed diagnosis and misdiagnosis. Modern automatic image recognition techniques are …an important diagnostic method for COVID-19. The aim of this thesis is to propose a novel deep learning technique for the automatic diagnosis and recognition of coronavirus disease (COVID-19) on X-ray images using a transfer learning approach. A new dataset containing COVID-19 information was created by merging two publicly available datasets. This dataset includes 912 COVID-19 images, 4273 pneumonia images, and 1583 normal chest X-ray images. We used this dataset to train and test the deep learning algorithm. With this new dataset, two pre-trained models (Xception and ResNetRS50) were trained and validated using transfer learning techniques. 3-class images were identified (Pneumonia vs. COVID-19 vs. Normal), and the two models generated validation accuracies of 90% and 97.21%, respectively, in the experiments. This demonstrates that our proposed algorithm can be well applied in diagnosing patients with lung diseases. In this study, we found the ResNetRS50 model to be superior. Show more

Keywords: ResNetRS50, deep learning, X-ray images, transfer learning, COVID-19

DOI: 10.3233/JIFS-232866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8135-8144, 2023

Authors: Yao, Jingkun | Guo, Beibei | Pang, Zheng

Article Type: Research Article

Abstract: In order to improve the coordinated control effect of hierarchical power balance of new energy microgrid, this paper applies fuzzy control method to this system, and proposes a hierarchical control strategy based on event-triggered communication. Each DG is regarded as a proxy, and the continuous actual value of output is replaced by the state prediction value. Moreover, two different event trigger condition functions for frequency and voltage are designed based on Lyapunov method respectively. At the same time, each DG only communicates with its neighbor DG aperiodic at the event trigger time, and finally all DG are restored to the …reference value provided by the virtual leader. Finally, this paper constructs a coordinated fuzzy control simulation system for hierarchical power balance of new energy microgrid. Combined with the simulation results, the method proposed in this paper is feasible. Show more

Keywords: New energy, microgrid, hierarchical power, balance, fuzzy control

DOI: 10.3233/JIFS-232963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8145-8158, 2023

Authors: Natarajan, Kirthika | Chelliah, Jeyalakshmi | Mariyarose, Jemin Vijayaselvan | Andi, Senthilkumar | Venkatachalam, Bharathi | Alagarsamy, Manjunathan

Article Type: Research Article

Abstract: This is contrary for Voice impaired people since their speech is tough for others to recognize even by their parents and teachers. Provided if their parents are illiterate. So our TTS system can be used for converting their written text to speech for their illiterate parents and friends around them. Though many methods have been adopted for the concatenation of the basic sound units, the HMM-based approach in modeling the sound is utilized by many researchers in many languages. In this paper, we have tried to implement, text to speech systems of synthesis for a Tamil text uses a phonemic …concatenation approach in MATLAB. Instead of utilizing Tamil letters as it is, due to its difficulty in production, Tamil text is transliterated into English then it is converted into intelligible speech. The performance of the output is verified for various examples by changing its parameters, in which the quality of the sound is comparable to that of English text. So the proposed system is utilized for all languages other than Tamil also if it is properly transliterated for limited vocabulary. Show more

Keywords: Phoneme, text normalization, voice impaired, subharmonic ratio, pitch, transliteration

DOI: 10.3233/JIFS-231680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8159-8169, 2023

Authors: Syed Anwar Hussainy, F. | Thillaigovindan, Senthil Kumar | Sabhanayagam, T.

Article Type: Research Article

Abstract: The present growth in Internet of Medical Things (IoMT) and Artificial Intelligence (AI) paved a way for advanced healthcare systems from conventional methods. The integration of AI and IoMT provides varied chances in medical domain. With that concern, the proposed model derives a novel model for Heart Disease Prediction (HDP), incorporates IoMT and AI. The proposed model comprises of different phases of functions, as, data collection, data preparation, feature optimization and selection, classification. IoMT devices include medical or wearable sensors are used for continuous collection of medical statistics while machine learning model process the data for disease prediction. Here, a …new feature selection model called Enhanced Binary Particle Swarm Optimization (EBPSO) for reducing joint feature selection problems. With the extracted features, classification is performed with Cascaded Long Short Term Memory (CLSTM) model for attaining better accuracy of medical data classification. During evaluation, the proposed HDP model achieved the maximal accuracy in disease prediction. Hence, the model can be effectively used for diagnosing heart disease in Smart Healthcare Models. Show more

Keywords: Internet of medical things, Artificial Intelligence, Enhanced Binary Particle Swarm Optimization, machine learning, Heart Disease

DOI: 10.3233/JIFS-232517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8171-8180, 2023

Authors: Wang, Lu

Article Type: Research Article

Abstract: In recent years, due to the further development of the market economy, the internal competition in the large-cargo transportation industry has become increasingly fierce, and the profit space has been greatly compressed. Therefore, large-cargo logistics enterprises are paying more and more attention to the research of highway transportation route plan. The highway transportation scheme selection is looked as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers (TFNN) grey relational analysis (TFNN-GRA) method is established based on the classical grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs) with completely unknown weight information. In order to …obtain the weight values, the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs. Then, combining the traditional fuzzy GRA model with TFNSs information, the TFNN-GRA method is set up and the computing steps for MADM are established. Finally, a numerical example for highway transportation scheme selection was established and some comparisons are established to study the advantages of TFNN-GRA. The main contributions of this paper are established as follows: (1) the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs; (2) the TFNN-GRA method is established with completely unknown weight information. (2) the TFNN-GRA method is established and the computing steps for MADM are established. (3) Finally, a numerical example for highway transportation scheme selection was established and some comparisons is employed to study advantages of TFNN-GRA method. Show more

Keywords: Multiple attribute decision making (MAGDM) problems, triangular fuzzy neutrosophic sets (TFNSs), GRA method; highway transportation scheme selection

DOI: 10.3233/JIFS-233620

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8181-8195, 2023

Authors: Dawlet, Omirzhan | Bao, Yan-Ling

Article Type: Research Article

Abstract:  As dual hesitant fuzzy sets can express the uncertainty of data efficiently, the aggregation of dual hesitant fuzzy information plays an important role in both theory and application. However, some existing dual hesitant fuzzy aggregation operators are not rigorous enough actually. In this note, we show that some theorems in an earlier paper by Ju et al. [1 ] (Journal of Intelligent & Fuzzy Systems 27 (2014) 2481–2495) are not correct, i.e., the dual hesitant fuzzy Hamacher weighted averaging operator (DHFHWA) and some other aggregation operators proposed by Ju et al. don’t satisfy idempotency and boundedness. Therefore, the purpose of …this paper is to make researchers aware of that some aggregation operators in literature [1 ] are flawed and limited for many applications. Show more

Keywords: Dual hesitant fuzzy set, Aggregation operator, Idempotency

DOI: 10.3233/JIFS-230764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8197-8201, 2023

Authors: Chandra Murty, Patnala S.R. | Anuradha, Chinta | Appala Naidu, P. | Balaswamy, C. | Nagalingam, Rajeswaran | Jagatheesaperumal, Senthil Kumar | Ponnusamy, Muruganantham

Article Type: Research Article

Abstract: This study quantifies individual stress levels through real-time analysis of wearable sensor data. An embedded setup utilizes artificial neural networks to analyze R-R intervals and Heart Rate Variability (HRV). Emotion recognition of happiness, sadness, surprise, fear, and anger is explored using seven normalized HRV features. Statistical analysis and classification with a neural network model are performed on approximately 20,700 segments,with participants within the age ranged from 23 to 40, mixed gender, and normal health status, along with other pertinent demographics included. Findings show stress observation’s potential for mental well-being and early detection of stress-related disorders. Three classification algorithms (LVQ, BPN, …CART) are evaluated, comparing ECG signal correlation features with traditional ones. BPN achieves the highest emotional recognition accuracy, surpassing LVQ by 5.9% – 8.5% and CART by 2% – 6.5%. Maximum accuracy is 82.35% for LVQ and 97.77% for BPN, but does not exceed 95%. Using only 72 feature sets yields the highest accuracy, surpassing S1 by 17.9% – 20.5% and combined S1/S2 by 11% – 12.7%. ECG signal correlation features outperform traditional features, potentially increasing emotion recognition accuracy by 25%. This study contributes to stress quantification and emotion recognition, promoting mental well-being and early stress disorder detection. The proposed embedded setup and analysis framework offer real-time monitoring and assessment of stress levels, enhancing health and wellness. Show more

Keywords: Psychological behavior, stress monitoring, artificial neural networks, wearable embedded sensors, heart rate variability, ECG

DOI: 10.3233/JIFS-233791

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8203-8216, 2023

Authors: Dutta, Kusumika Krori | Manohar, Premila | Indira, K.

Article Type: Research Article

Abstract: Although epilepsy is one of the most prevalent and ancient neurological disorder, but, still difficult to identify the specific type of seizure, due to artefacts, noise, and other disturbances, because of acquisition of Scalp EEG. It necessitating the use of skilled medical professionals as incorrect diagnosis lead to wrong Anti Seizure Drug (ASDs) and face it’s side effects. On the other hand machine learning plays a crucial role in seizure detection by analyzing and identifying patterns in brain activity data that are indicative of seizures. It can be used to develop predictive models that can detect the onset of seizures …in real-time, allowing for early intervention and improved patient outcomes. Most of the research work focuses on seizure detection using various machine learning techniques pre-processed by different mathematical models. But, very less attention is paid towards seizure type detection. In this study, multiple Machine and Deep Learning algorithms were used in conjunction with time-domain and frequency-domain pre-processing to classify epileptic seizures into multiple types. The ictal period of various seizure types were extracted from Temple University Hospital EEG (TUHEEG) and the pre-processed data was tried out with multiple classifiers, including support vector classifiers (SVC), K- Nearest Neighbor (KNN), and Long short term memory (LSTM), among others. By using SVM, KNN, and LSTM, multiclass classification of seven types of epileptic seizures with 19 channels were considered for each EEG data and a 75–25 train–test ratio was accomplished with 90.41%, 94.46%, and 86.2% accuracy respectively. Epileptic seizure’s ictal phase EEG signals are categorized using a variety of machine learning(ML) and deep learning(DL) methods after being pre-processed using time domain and frequency domain approaches. The KNN yields the best results of all. Show more

Keywords: Seizure classification, TUHEEG, ABSZ, CPSZ, FNSZ, GNSZ, SPSZ, TNSZ, TCSZ, SVM, KNN, LSTM, EEG

DOI: 10.3233/JIFS-224570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8217-8226, 2023

Authors: Mahalingam, Priyadarshini | Kalpana, D. | Thyagarajan, T.

Article Type: Research Article

Abstract: This paper disseminates an extra dimension of substantial analysis demonstrating the trade-offs between the performance of Parametric (P) and Non-Parametric (NP) classification algorithms when applied to classify faults occurring in pneumatic actuators. Owing to the criticality of the actuator failures, classifying faults accurately may lead to robust fault tolerant models. In most cases, when applying machine learning, the choice of existing classifier algorithms for an application is random. This work, addresses the issue and quantitatively supports the selection of appropriate algorithm for non-parametric datasets. For the case study, popular parametric classification algorithms namely: Naïve Bayes (NB), Logistic Regression (LR), Linear …Discriminant Analysis (LDA), Perceptron (PER) and non-parametric algorithms namely: Multi-Layer Perceptron (MLP), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) are implemented over a non-parametric, imbalanced synthetic dataset of a benchmark actuator process. Upon using parametric classifiers, severe adultery in results is witnessed which misleads the interpretation towards the accuracy of the model. Experimentally, about 20% improvement in accuracy is obtained on using non-parametric classifiers over the parametric ones. The robustness of the models is evaluated by inducing label noise varying between 5% to 20%. Triptych analysis is applied to discuss the interpretability of each machine learning model. The trade-offs in choice and performance of algorithms and the evaluating metrics for each estimator are analyzed both quantitatively and qualitatively. For a more cogent reasoning through validation, the results obtained for the synthetic dataset are compared against the industrial dataset of the pneumatic actuator of the sugar refinery, Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS). The efficiency of non-parametric classifiers for the pneumatic actuator dataset is well proved. Show more

Keywords: Parametric classifiers, non-parametric classifiers, trade-offs, pneumatic actuator, DAMADICS, accuracy, interpretability

DOI: 10.3233/JIFS-231026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8227-8247, 2023

Authors: Yan, Zhenggang

Article Type: Research Article

Abstract: With the continuous deepening of the construction of urban-rural economic integration in China, rural construction activities supported by rural revitalization strategies have changed the development thinking of rural economy. While implementing the goal of rural ecological economy, optimizing the rural living environment has become one of the important contents of rural revitalization, including the planning and design of rural landscapes. Rural landscape planning and design need to comprehensively consider the adaptability of landscape and rural ecological environment, emphasize the impact of rural spatial structure differences on landscape planning and design, and achieve scientific and humanized landscape planning and design, thereby …creating a more warm, natural, and comfortable rural living space. The quality evaluation of tourism rural landscape planning and design is a multiple attribute group decision making (MAGDM) problems. Recently, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method has been inaugurated to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are inaugurated as a effective tool for characterizing uncertain information during the quality evaluation of tourism rural landscape planning and design. In this paper, the 2-tuple linguistic neutrosophic TODIM-VIKOR (2TLN-TODIM-VIKOR) method is inaugurated to solve the MAGDM under 2TLNSs. In the end, a numerical case study for quality evaluation of tourism rural landscape planning and design is inaugurated to confirm the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic neutrosophic sets (2TLNSs), TODIM, VIKOR, tourism rural landscape planning and design

DOI: 10.3233/JIFS-231400

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8249-8261, 2023

Authors: Malavath, Pallavi | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: A crucial component of human-computer interaction is 3D hand posture assessment. The most recent advancements in computer vision have made estimating 3D hand positions simpler by using deep sensors. The main challenge still stems from unrealistic 3D hand poses because the existing models only use the training dataset to learn the kinematic rules, which is ambiguous, and it is a difficult task to estimate realistic 3D hand poses from datasets because they are not free from anatomical errors. The suggested model in this study is trained using a closed-form expression that encodes the biomechanical rules, thus it does not entirely …reliant on the pictures from the annotated dataset. This work also used a Single Shot Detection and Correction convolutional neural network (SSDC-CNN) to handle the issues in imposing anatomically correctness from the architecture level. The ResNetPlus is implemented to improve representation capability with enhanced the efficiency of error back-propagation of the network. The datasets of the Yoga Mudras, like HANDS2017, and MSRA have been used to train and test the future model. As observed from the ground truth the previous hand models have many anatomical errors but, the proposed hand model is anatomically error free hand model compared to previous hand models. By considering the ground truth hand pose, the recommended hand model has shown good accuracy when compared to the state-of-art hand models. Show more

Keywords: Biomechanical constraints, Anatomical correction, single-shot detection and correction CNN, 3-Dimensional hand pose estimation

DOI: 10.3233/JIFS-231779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8263-8277, 2023

Authors: Chen, Dongning | Liu, Jitao | Yao, Chengyu | Ma, Lei | Wang, Kuantong | Zhou, Ziyu | Wu, Xuefei | Chen, Yanan

Article Type: Research Article

Abstract: The lack of effective failure correlation analysis is one main reason for the gap between the reliability models and the actual complex systems with mixed static and dynamic characteristics. Takagi and Sugeno (T-S) dynamic fault tree is one powerful tool to analyze the static and dynamic failure logic relationship but it assumes the failure probability of the event is independent. Therefore, this paper proposes a multi-dimensional T-S dynamic fault tree analysis method involving failure correlation. The method integrates the failure probability distribution function of basic events with multi-factors and the multi-dimensional copula function, and the important measure of this method …is also deduced. The reliability model expression for systems with failure correlations, both in series and in parallel, is discussed and verified. Compare the proposed method with the assumption that the probability of a failure event is independent. This method solves the problem of a large error when ignoring the failure correlation between parts and the degree of the correlation between variables can be characterized. The reliability analysis can be conducted on complex systems affected both by multi-factors and failure correlations. The proposed method is applied to the reliability analysis of a hydraulic height adjustment system and the correctness and superiority of the method are verified. Show more

Keywords: Multi-dimensional T-S dynamic fault tree, copula function, failure correlation, importance measure, reliability analysis

DOI: 10.3233/JIFS-231939

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8279-8296, 2023

Authors: Min, Qu | Zhaoxian, Ren | Jiang, Wu

Article Type: Research Article

Abstract: To inherit and promote the excellent design characteristics of Chinese-style furniture, this study focuses on Chinese-style stools and proposes an integrated design and evaluation approach with combination of shape grammar, KANO model, and entropy-weighted VIekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. Firstly, based on the initial forms of five Chinese-style stools, a shape feature library is constructed by extracting shape features using regional cultural symbols. Secondly, combining shape grammar and inference rules, innovative design alternatives are generated for Chinese-style stools, incorporating regional cultural symbol features. Thirdly, an in-depth investigation of Chinese-style furniture market is conducted, and user requirements are analyzed using KANO …model questionnaire, categorizing the requirements into three attributes: appearance, technological, and economic. Based on KANO model’s classification of user requirements, a set of 14 evaluation criteria for Chinese-style stools is established. Finally, to avoid subjective factors in weighting the criteria, the entropy-weighted method is applied, and VIKOR method is utilized to obtain the optimal ranking of the design alternatives for Chinese-style stools, ultimately selecting the optimal alternative. The results show that based on VIKOR method, the optimal solution is the same with comparison to the results obtained from Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), preference ranking organization methods for enrichment evaluations (PROMETHEE) and elimination and et choice translating reality (ELECTRE) methods. In addition, to verify its ergonomic characteristics, feasibility and rationality, the optimal alternative is simulated by JACK software. By integrating shape grammar, KANO model, and the entropy-weighted VIKOR method, this study provides some insights for incorporating regional cultural symbols into the design of Chinese-style furniture and exhibits certain advantages in terms of comprehensive evaluation, user orientation, decision objectivity, and consideration of diversity. Show more

Keywords: Shape grammar, KANO model, entropy weight-VIekriterijumsko KOmpromisno Rangiranje method (VIKOR) method, Chinese-style Stools(CSS)

DOI: 10.3233/JIFS-232580

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8297-8316, 2023

Authors: Gu, Xinxin

Article Type: Research Article

Abstract: In modern social APP interface design, how to effectively improve the corporate image and create the connotation of corporate culture is a significant key problem. With the emergence of APP, a growing number of people use them, increasing communication energy usage and slowing network operation. To improve app compatibility and speed, it is necessary to combine it with the most advanced and dependable technology, such as ZigBee, which is regarded as the best solution for wireless sensor networks. The ZigBee protocol is primarily used to incorporate working and data transmission in wireless sensor networks that are based on ZigBee technology. …As a result, incorporating ZigBee technology into APP interface design in the Internet of Things (IoT) domain can significantly improve brand APP interface design’s network operation efficiency. This paper presents a novel approach to enhance the performance and corporate image of brand mobile applications (APPs) by integrating ZigBee technology. The primary objective is to improve the operating efficiency and user experience of the brand APPs. The study involves a comparison between 10 brand APPs that have not integrated ZigBee technology and 10 brand APPs that have adopted ZigBee technology. The experimental results indicate that the operating efficiency of the brand APPs incorporating ZigBee technology is 97%, while the efficiency of the brand APPs without ZigBee technology is 85%, resulting in a notable difference of 12%. To assess the effectiveness of ZigBee technology integration, the study conducted experiments with 100 users, randomly assigned to interact with both types of brand APPs. The user feedback and observations revealed that brand APPs integrated with ZigBee technology exhibit significantly higher operating efficiency, contributing to a 12% improvement over their counterparts lacking ZigBee integration. Moreover, 90 out of 100 users reported a preference for the brand APPs integrated with ZigBee technology due to their superior user experience. The integration of ZigBee technology in brand APPs not only enhances the user experience but also contributes to the improvement of the company’s corporate image. Adopting ZigBee technology in brand APPs is a valuable strategy that can facilitate the long-term development and success of the company. Show more

Keywords: APP interface, ZigBee technology, Internet of Things, Clustering Algorithm, LEACH algorithm, Internet

DOI: 10.3233/JIFS-233343

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8317-8333, 2023

Authors: Senthil Kumar, K. | Anandamurugan, S.

Article Type: Research Article

Abstract: Cloud computing has become a crucial paradigm for large-scale data-intensive applications, but it also brings challenges like energy consumption, execution time, heat, and operational costs. Improving workflow scheduling in cloud environments can address these issues and optimize resource utilization, leading to significant ecological and financial benefits. As data centres and networks continue to expand globally, efficient scheduling becomes even more critical for achieving better performance and sustainability in cloud computing. Schedulers mindful of energy and deadlines will assign resources to jobs in a way that consumes the least energy while upholding the task’s quality standards. Because this scheduling involves a …Non-deterministic Polynomial (NP)-hard problem, the schedulers are able to minimize complexity by utilizing metaheuristic techniques. This work has developed methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing the scheduler. Local search and exploration are respectably supported by heuristic algorithms. The algorithm’s exploration and exploitation features must also be balanced. The primary objective is to optimize computation-intensive workflows in a way that minimizes both energy consumption and execution time while maximizing throughput. This optimization should be achieved without compromising the Quality of Service (QoS) guarantee provided to users. The focus is on striking a balance between energy efficiency and performance to enhance the overall efficiency and cost-effectiveness of cloud computing environments. According to the simulation findings, the suggested ABC has a higher guarantee ratio for 5000 jobs when compared to the GA, PSO, GA with the longest processing time, and GA with the lowest processing time, by 7.14 percent, 4.7 percent, 3.5 percent, and 2.3 percent, respectively. It is observed that the proposed ABC possesses qualities like high flexibility, great robustness, and quick convergence leading to good performance. Show more

Keywords: Cloud computing, virtualization, scheduler, Virtual Machines (VMs), resource management

DOI: 10.3233/JIFS-234776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8335-8348, 2023

Authors: Wang, Chuncha

Article Type: Research Article

Abstract: The hardness properties of constructional materials should be investigated as important factors in assessing the performance over the operation period. Two tests are performed to determine the stiffness characteristic, including slump and compressive strength (CS). They must be considered to examine efficiency, durability, and resistance to pressure. Due to the structure’s susceptibility and usage in dams, bridges, etc., high-performance concrete must have an appropriate set of these tests. There are two soft-based and laboratory methods for performing these tests. The laboratory method is not economical in terms of cost and time, and artificial intelligence (AI) is used to reduce the …aforementioned factors. Models and optimizers use software-based methods to help reduce errors and increase model accuracy. So, The main purpose of this research has been introducing novel ways of coupling an ensemble model with optimizers by adjusting some internal parameters. In this article, two models, the Radial Basis Function Neural network and Support Vector Regression were combined and coupled with General Normal Distribution Optimization (GNDO) and Archimedes optimization algorithm (AOA) into the two frameworks of SVRRBF-AOA and SVRRBF-GNDO. As a result, the hybrid model of SVRRBF-AOA could perform well by obtaining R2 and RMSE of 0.9915 and 2.71 for the slump and 0.9845 and 3.34 for CS, respectively. Show more

Keywords: High-performance concrete, slump, compressive strength, support vector regression, Radial basis function, generalized normal distribution optimization, archimedes optimization algorithm

DOI: 10.3233/JIFS-232114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8349-8364, 2023

Authors: Tao, Nana | Hua, Yang | Ding, Chunxiao

Article Type: Research Article

Abstract: It is generally considered that attractivity is a concept that describes the overall characteristics of a system. This paper aims to study Pth moment attractivity for one order uncertain differential systems. According to the theory of uncertain differential systems, the concept of Pth moment attractivity is given. Moreover, the Pth moment attractivity of a class of nonlinear uncertain differential systems is studied and the judgment conditions of linear uncertain differential systems are derived.

Keywords: Pth moment, attractivity, uncertain differential systems, concept, judgment conditions

DOI: 10.3233/JIFS-232233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8365-8370, 2023

Authors: Yu, Shujuan | Wu, Mengjie | Zhang, Yun | Xie, Na | Huang, Liya

Article Type: Research Article

Abstract: Reading Comprehension models have achieved superhuman performance on mainstream public datasets. However, many studies have shown that the models are likely to take advantage of biases in the datasets, which makes it difficult to efficiently reasoning when generalizing to out-of-distribution datasets with non-directional bias, resulting in serious accuracy loss. Therefore, this paper proposes a pre-trained language model based de-biasing framework with positional generalization and hierarchical combination. In this work, generalized positional embedding is proposed to replace the original word embedding to initially weaken the over-dependence of the model on answer distribution information. Secondly, in order to make up for the …influence of regularization randomness on training stability, KL divergence term is introduced into the loss function to constrain the distribution difference between the two sub models. Finally, a hierarchical combination method is used to obtain classification outputs that fuse text features from different encoding layers, so as to comprehensively consider the semantic features at the multidimensional level. Experimental results show that PLM-PGHC helps learn a more robust QA model and effectively restores the F1 value on the biased distribution from 37.51% to 81.78%. Show more

Keywords: Natural language processing, machine reading comprehension, pre-trained language model, de-biasing framework

DOI: 10.3233/JIFS-233029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8371-8382, 2023

Authors: Dong, Hao | Ali, Zeeshan | Mahmood, Tahir | Liu, Peide

Article Type: Research Article

Abstract: Algebraic and Einstein are two different types of norms which are the special cases of the Hamacher norm. These norms are used for evaluating or constructing three different types of aggregation operators, such as averaging/geometric, Einstein averaging/geometric, and Hamacher averaging/geometric aggregation operators. Moreover, complex Atanassov intuitionistic fuzzy (CA-IF) information is a very famous and dominant technique or tool which is used for depicting unreliable and awkward information. In this manuscript, we present the Hamacher operational laws for CA-IF values. Furthermore, we derive the power aggregation operators (PAOs) for CA-IF values, called CA-IF power Hamacher averaging (CA-IFPHA), CA-IF power Hamacher ordered …averaging (CA-IFPHOA), CA-IF power Hamacher geometric (CA-IFPHG), and CA-IF power Hamacher ordered geometric (CA-IFPHOG) operators. Some dominant and valuable properties are also stated. Moreover, the multi-attribute decision-making (MADM) methods are developed based on the invented operators for CA-IF information and the detailed decision steps are given. Many prevailing operators are selected as special cases of the invented theory. Finally, the derived technique will offer many choices to the expert to evaluate the best alternatives during comparative analysis. Show more

Keywords: Complex intuitionistic fuzzy sets, power aggregation operators, decision-making problems, hamacher t-norm and t-conorm

DOI: 10.3233/JIFS-230323

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8383-8403, 2023

Authors: Wang, Yubiao | Wen, Junhao | Zhou, Wei | Tao, Bamei | Wu, Quanwang | Fu, Chunlei | Li, Heng

Article Type: Research Article

Abstract: With the development of the Internet and the informatization construction of universities, the massive data accumulated by “campus big data” presents problems such as discreteness and sparseness. Students with abnormal behaviors have become an urgent problem to be solved in student behavior analysis. This paper proposes an early warning method for abnormal behaviour of college students based on multimodal fusion and an improved decision tree (EWMABCS-MFIDT). First, given the insufficient representation of student behavioral portraits and the problems of timeliness and dynamics in behavioral labels, a student behavioral portrait based on the multimodal fusion method is proposed. Second, aiming at …the timeliness and backwardness of abnormal behavior prediction, based on student behavior classification prediction, this paper proposes an improved decision tree-based early warning method for abnormal student behavior. Finally, we design a student behavior analysis and early warning framework under the campus big data environment. Taking the abnormal early warning of students’ academic performance as an example, compared with other early warning algorithms, the EWMABCS-MFIDT method can improve the accuracy of early warning and make students’ educational work more targeted, personalized, and predictive. Show more

Keywords: Education big data, student behavior portrait, multimodal fusion, abnormal behavior early warning, improved decision tree

DOI: 10.3233/JIFS-231509

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8405-8427, 2023

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

Authors: Hashemi, Hebatollah | Ezzati, Reza | Mikaeilvand, Naser | Nazari, Mojtaba

Article Type: Research Article

Abstract: This research paper presents an innovative approach for modeling and analyzing complex systems with uncertain data. Our strategy leverages fuzzy calculus and time-fractional differential equations to achieve this goal. Specifically, we propose the utilization of the fuzzy Atangana-Baleanu time-fractional derivative, which incorporates non-singular kernels for fuzzy functions. This derivative type is particularly suitable for qualitative analysis of fractional differential equations in fuzzy space. We establish the existence and uniqueness of solutions for fuzzy linear time-fractional problems based on this differentiability concept. Additionally, we introduce a numerical solution method, namely the fuzzy homotopy perturbation transform method (FHPTM), to solve these problems. …To demonstrate the effectiveness and practical applicability of our approach, we provide concrete examples such as the fuzzy time-fractional Advection-Dispersion equation, the fuzzy time-fractional Diffusion equation, and the fuzzy time-fractional Black-Scholes European option pricing problem. These examples not only illustrate the solution steps involved but also showcase the potential of our method in addressing real-world problems. The outcomes of our research underscore the significance of considering fuzzy calculus and time-fractional differential equations when modeling and analyzing intricate systems with uncertain data. Show more

Keywords: Fuzzy atangana-baleanu time-fractional derivative, fuzzy homotopy perturbation transform method, fuzzy time-fractional black-scholes european option pricing problem, fuzzy time-fractional advection-dispersion equation, fuzzy time-fractional diffusion equation

DOI: 10.3233/JIFS-232094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8567-8582, 2023

Authors: Guo, Liang | Zhang, Junzhao | Dong, Peiyi | Wan, Yuanzheng | Li, Wenhui

Article Type: Research Article

Abstract: To solve the problem of inaccurate user phase identification, the paper proposes a new algorithm based on improved cloud model and adaptive segmented voltage algorithm. Firstly, the new algorithm uses improved cloud model to calculate the digital features of station area and users’ voltage sequences quickly. Secondly, the paper uses the adaptive segmentation voltage algorithm to divide the full voltage sequences into three parts automatically to add local features into phase identification. Finally, the paper calculates cosine similarity between each segmented voltage cloud model to identify users’ voltage phase. The analysis based on station data and field verification shows that …the new algorithm has not only improved the calculation efficiency by 41% compared with traditional user phase identification algorithm, but also increased the difference in identification results between different phases by 1000 times. In the final result, the accuracy of the new algorithm is 95%. The new algorithm has more obvious differentiation and higher accuracy. The analysis results based on the actual engineering data also prove the feasibility and effectiveness of the new user phase identification algorithm. Show more

Keywords: Phase identification, adaptive segmentation voltage, improved cloud model, cosine similarity

DOI: 10.3233/JIFS-232415

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8583-8594, 2023

Authors: Wang, Haochen | Zhang, Changlun | Chen, Shuang | Wang, Hengyou | He, Qiang | Mu, Haibing

Article Type: Research Article

Abstract: Point cloud upsampling can improve the resolutions of point clouds and maintain the forms of point clouds, which has attracted more and more attention in recent years. However, upsampling networks sometimes generate point clouds with unclear contours and deficient topological structures, i.e., the problem of insufficient form fidelity of upsampled point clouds. This paper focuses on the above problem. Firstly, we manage to find the points located at contours or sparse positions of point clouds, i.e., the form describers, and make them multiply correctly. To this end, 3 statistics of points, i.e., local coordinate difference, local normal difference and describing …index, are designed to estimate the form describers of the point clouds and rectify the feature aggregation of them with reliable neighboring features. Secondly, we divide points into disjoint levels according to the above statistics and apply K nearest neighbors algorithm to the points of different levels respectively to build an accurate graph. Finally, cascaded networks and graph information are fused and added to the feature aggregation so that the network can learn the topology of objects deeply, enhancing the perception of model toward graph information. Our upsampling model PU-FPG is obtained by combining these 3 parts with upsampling networks. We conduct abundant experiments on PU1K dataset and Semantic3D dataset, comparing the upsampling effects of PU-FPG and previous works in multiple metrics. Compared with the baseline model, the Chamfer distance, the Hausdorff distance and the point-to-surface distance of PU-FPG are reduced by 0.159 × 10-3 , 2.892 × 10-3 and 0.852 × 10-3 , respectively. This shows that PU-FPG can improve the form fidelity and raise the quality of upsampled point clouds effectively. Our code is publicly available at https://github.com/SATURN2021/PU-FPG . Show more

Keywords: Point cloud, upsampling, convolutional networks, completion

DOI: 10.3233/JIFS-232490

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8595-8612, 2023

Authors: Călin, Mariana Floricica | Flaut, Cristina | Piciu, Dana

Article Type: Research Article

Abstract: Algebras of Logic deal with some algebraic structures, often bounded lattices, considered as models of certain logics, including logic as a domain of order theory. There are well known their importance and applications in social life to advance useful concepts, as for example computer algebra. Starting from results obtained by Di Nolla and Lettieri in [1 ], in which they analyzed the structure of finite BL-algebras, in this paper we find properties and give examples of commutative unitary rings R with its set of ideals Id  (R ) to be a BL-algebra of a given type. Moreover, we …present properties of finite rings or rings with a finite number of ideals in their connections with BL-rings. Show more

Keywords: Algebras of Logic, BL-algebras, BL-rings

DOI: 10.3233/JIFS-232815

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8613-8622, 2023

Authors: Li, Yuejie | Liu, Chang’an | Li, Shijun

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-233700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8623-8636, 2023

Authors: Gokila, R.G. | Kannan, S.

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-234311

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8637-8649, 2023

Authors: Chen, Junfen | Han, Jie | Xie, Bojun | Li, Nana

Article Type: Research Article

Abstract: Contrastive learning is a powerful technique for learning feature representations without manual annotation. The K-nearest neighbor (KNN) method is commonly used to construct positive sample pairs to calculate the contrastive loss. However, it is challenging to distinguish positive sample pairs, reducing clustering performance. We propose a novel D eep C ontrastive C lustering method based on a G rapH convolutional network called GHDCC. It uses an instance-level contrastive loss with mean square error (MSE) regularization and a cluster-level contrastive loss to incorporate semantic features and perform cluster assignments. The method utilizes a graph convolutional network (GCN) to improve the …semantic consistency of features and linear interpolation data augmentation to improve the representation ability of the model. To minimize the occurrence of false positive sample pairs, we select only samples whose similarity exceeds a predefined threshold to construct the adjacency matrix. The experimental results on six public datasets demonstrate that the GHDCC significantly outperforms contrastive clustering (CC, 500) by a large margin except on CIFAR-10. The GHDCC performs well compared to other deep contrastive clustering methods and achieves the highest clustering accuracy of 0.913 on ImageNet-10. Show more

Keywords: Self-supervised clustering, graph convolutional network, linear interpolation data augmentation, contrastive learning

DOI: 10.3233/JIFS-230208

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8651-8661, 2023

Authors: Cao, Mengmeng | Hu, Jian | Wang, Zeming | Yao, Jianyong

Article Type: Research Article

Abstract: In this paper, the high accuracy motion output feedback control of a kind of launching platforms driven by motors is focused. The launching platform is used to launch kinetic load to hit the target so it is susceptible to external disturbance. In addition, significant issues arise due to limitations on the plant inputs, such as actuator energy limits and velocity state is usually unavailable due to the limitation of system cost and volume. A new adaptive fuzzy output feedback controller based on dual observers is proposed for solving these problems. A smooth and continuous model is established for input saturation …to compensate it. A sliding mode observer and a fuzzy observer with proper membership function are combined to estimate the unmeasured system states more accurately. An adaptive robust controller and the fuzzy observer are combined to realize a motion control with disturbance rejection, which allows correct adaptation while the plant input is saturated. Lyapunov theorem proves the bounded stability of the proposed controller when there exists observation error. Extensive comparative simulation and experiment results verify the effectiveness and practicability of the proposed controller and show that the control accuracy can be improved by an order of magnitude compared with the traditional PID controller and better than some other nonlinear controllers. Show more

Keywords: Launching platform, fuzzy observer, output feedback control, adaptive robust control, input saturation

DOI: 10.3233/JIFS-230688

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8663-8678, 2023

Authors: Chen, Dewang | Zhou, Jiali | Tong, Wenlin | Kong, Lingkun | Chen, Yuandong

Article Type: Research Article

Abstract: As a model for reasoning and decision-making based on fuzzy rules, fuzzy systems have high interpretability. However, when the data dimension increases, the fuzzy system will face the problem of “rule explosion”, making it difficult to learn and predict effectively. In this paper, the fuzzy system trained by the FLOWFS (Fast-Learning with Optimal Weights for Fuzzy Systems) algorithm is used as sub-module in the deep fuzzy system, and the deep fuzzy system DFLOWFS (Deep FLOWFS) is constructed from the bottom-up hierarchical structure as the following three steps. 1) The FLOWFS algorithm assigns weight attributions to each fuzzy rule, and the …rule weights are trained by the least square method with regularization terms to shorten training time and improve accuracy. 2) Three strategies of dividing high-dimensional inputs into multiple low-dimensional inputs are proposed as sequential division, random division and correlation division. Then, it is verified by experiments that the correlation division has the best performance. 3) The sub-module discarding method is proposed to discard the sub-modules with poor performance to have a maximum improvement of 13.8% compared to the DFLOWFS without using the sub-module discarding method. Then, the optimized DFLOWFS is verified and compared with the other three classic regression models on the three UCI datasets. Experiments show that with the increase of the data dimension, DFLOWFS not only have good interpretability but also have good accuracy. Furthermore, DFLOWFS performs best among all models in comprehensive scores, with good learning ability and generalization ability. Therefore, the proposed strategies with hierarchical structure for optimal shallow fuzzy systems are effective, which give a new insight for fuzzy system research. Show more

Keywords: Correlation division, fuzzy system, interpretability, rule weights, submodule discarding method

DOI: 10.3233/JIFS-231050

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8679-8690, 2023

Authors: Qiu, Guangying | Tao, Dan | Su, Housheng

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-232846

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8691-8701, 2023