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

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