Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Purchase individual online access for 1 year to this journal.
Price: EUR 315.00Impact Factor 2023: 2
The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Fan, Jianping | Chai, Mingxuan | Wu, Meiqin
Article Type: Research Article
Abstract: In this manuscript, we construct a Multi-Criteria Decision-Making (MCDM) model to study the new energy vehicle (NEV) battery supplier selection problem. Firstly, we select criteria to build an evaluation index system. Secondly, SAWARA and MEREC methods are used to calculate subjective and objective weights in the ranking process, respectively, and PTIHFS (Probabilistic Triangular Intuitionistic Hesitant Fuzzy Set) is employed to describe the decision maker’s accurate preferences in performing the calculation of subjective weights. Then, the game theory is used to find the satisfactory weights. We use TFNs to describe the original information in the MARCOS method to obtain the optimal …alternative. Finally, a correlation calculation using Spearman coefficients is carried out to compare with existing methods and prove the model’s validity. Show more
Keywords: PTIHFS, SWARA, MEREC, MARCOS, game theory
DOI: 10.3233/JIFS-231975
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Devi, Salam Jayachitra | Doley, Juwar | Gupta, Vivek Kumar
Article Type: Research Article
Abstract: Object detection has made significant strides in recent years, but it remains a challenging task to accurately and quickly identify and detect objects. While humans can easily recognize objects in images or videos regardless of their appearance, computers face difficulties in this task. Object detection plays a crucial role in computer vision and finds applications in various domains such as healthcare, security, agriculture, home automation and more. To address the challenges of object detection, several techniques have been developed including RCNN, Faster RCNN, YOLO and Single Shot Detector (SSD). In this paper, we propose a modified YOLOv5s architecture that aims …to improve detection performance. Our modified architecture incorporates the C3Ghost module along with the SPP and SPPF modules in the YOLOv5s backbone network. We also utilize the Adam and Stochastic Gradient Descent (SGD) optimizers. The paper also provides an overview of three major versions of the YOLO object detection model: YOLOv3, YOLOv4 and YOLOv5. We discussed their respective performance analyses. For our evaluation, we collected a database of pig images from the ICAR-National Research Centre on Pig farm. We assessed the performance using four metrics such as Precision (P), Recall (R), F1-score and mAP @ 0.50. The computational results demonstrate that our method YOLOv5s architecture achieves a 0.0414 higher mAP while utilizing less memory space compared to the original YOLOv5s architecture. This research contributes to the advancement of object detection techniques and showcases the potential of our modified YOLOv5s architecture for improved performance in real world applications. Show more
Keywords: Object detection, YOLO, convolutional neural networks, pig, and computer vision
DOI: 10.3233/JIFS-231032
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Duan, Wenbiao | Yang, Mingjin | Sun, Weiliang | Xia, Mingmin | Zhu, Hui | Gu, Chijiang | Zhang, Haiqiang
Article Type: Research Article
Abstract: OBJECTIVE: A comprehensive evaluation of studies using DNA microarray datasets for screening and identifying key genes in gastric cancer is the goal of this systematic review and meta-analysis. To better understand the molecular environment associated with stomach cancer, this study aims to provide a quantitative synthesis of findings. PURPOSE: Using DNA microarray databases in a systematic manner, this study aims to analyze gastric cancer (GC) screening and gene identification efforts. Through a literature review spanning 2002–2022, this research aims to identify key genes associated with GC and develop strategies for screening and prognosis based on these …findings. METHODS: The following databases were searched extensively: Science Direct, NCKI, Web of Science, Springer, and PubMed. Fifteen studies met the inclusion and exclusion criteria; 10,134 tissues served as controls and 11,724 as GCs. The levels of critical genes, including COL1A1, COL1A2, THBS2, SPP1, SPARC, COL6A3, and COL3A1, were compared in normal and GC tissues. Rev Man 5.3 was used to do the meta-analysis. While applying models with fixed or random effects, 95% confidence intervals and weighted mean differences were computed. RESULTS According to the meta-analysis, GC tissues exhibited substantially elevated levels of important genes when contrasted with the control group. In particular, there were statistically significant increases in COL1A1 (MD = 2.43, 95% CI: 1.84–3.02), COL1A2 (MD = 2.75, 95% CI: 1.09–4.41), THBS2 (MD = 2.54, 95% CI: 1.66–3.41), SPP1 (MD = 3.64, 95% CI: 3.40–3.88), SPARC (MD = 1.57, 95% CI: 0.37–2.77), COL6A3 (MD = 2.31, 95% CI: 2.02–2.60), and COL3A1 (MD = 2.21, 95% CI: 1.59–2.82). CONCLUSIONS: The COL1A1, THBS2, SPP1, COL6A3, and COL3A1 genes were shown to have potential use in germ cell cancer screening and prognosis, according to this research. Clinical assessment and prognosis of heart failure patients may be theoretically supported by the results of this study. Show more
Keywords: DNA microarray database, gastric cancer, key genes, meta-analysis
DOI: 10.3233/JIFS-236416
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Li, Tao | Zhang, Zhongyu | Tao, Zhigang | Jia, Xinyu | Wang, Xiaolong | Wang, Jian
Article Type: Research Article
Abstract: Rock crack is one of the main factors responsible for rock failure. Uniaxial compression creep tests are performed using acoustic emission techniques, a high-sensitivity, non-radiative, non-destructive testing method to understand the influence of crack number on the precursor characteristics of short-term creep damage in the fractured rock mass. Based on the Grassberger-Procaccia (G-P) algorithm, the calculation step size for the correlation dimension value (D 2 ) of the acoustic emission ringing count rate is consistent with that for the acoustic emission b -value. The influence of the number of pre-cracks on the Acoustic emission precursor characteristics of red sandstone …creep is analyzed. The results show that near the destabilization of the specimen, the Acoustic emission accumulative ringing count surges in a stepwise manner, the Acoustic emission b -value decreases, the D 2 -value increases, the Acoustic emission amplitude shows high intensity and high frequency, and the ringing count increases sharply, all with the characteristics of failure precursors. During the accelerated creep stage of the specimens, with the increase of pre-cracks number, the precursory time points of acoustic emission b -value and D 2 -value advance, and their acoustic emission ringing counts increase sharply. Show more
Keywords: Acoustic emission, b-value, correlation dimension value (D2), precursor information, pre-cracks
DOI: 10.3233/JIFS-238964
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Hou, Xiaoyu | Luo, Chao | Gao, Baozhong
Article Type: Research Article
Abstract: Candlesticks are widely used as an effective technical analysis tool in financial markets. Traditionally, different combinations of candlesticks have formed specific bullish/bearish patterns providing investors with increased opportunities for profitable trades. However, most patterns derived from subjective expertise without quantitative analysis. In this article, combining bullish/bearish patterns with ensemble learning, we present an intelligent system for making stock trading decisions. The Ensemble Classifier through Multimodal Perturbation (ECMP) is designed to generate a diverse set of precise base classifiers to further determine the candlestick patterns. It achieves this by: first, introducing perturbations to the sample space through bootstrap sampling; second, employing …an attribute reduction algorithm based on neighborhood rough set theory to select relevant features; third, perturbing the feature space through random subspace selection. Ultimately, the trading decisions are guided by the classification outcomes of this procedure. To evaluate the proposed model, we apply it to empirical investigations within the context of the Chinese stock market. The results obtained from our experiments clearly demonstrate the effectiveness of the approach. Show more
Keywords: Trading system, ensemble learning, multimodal perturbation method, neighborhood rough set theory
DOI: 10.3233/JIFS-237087
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Zhao, Bin | Cao, Wei | Zhang, Jiqun | Gao, Yilong | Li, Bin | Chen, Fengmei
Article Type: Research Article
Abstract: Aiming at the issue that the current click-through rate prediction methods ignore the varying impacts of different input features on prediction accuracy and exhibit low accuracy when dealing with large-scale data, a click-through rate prediction method (GBIFM) which combines Gradient Boosting Decision Tree (GBDT) and Input-aware Factorization Machine (IFM) is proposed in this paper. The proposed GBIFM method employs GBDT for data processing, which can flexibly handle various types of data without the need for one-hot encoding of discrete features. An Input-aware strategy is introduced to refine the weight vector and embedding vector of each feature for different instances, adaptively …learning the impact of each input vector on feature representation. Furthermore, a fully connected network is incorporated to capture high-order features in a non-linear manner, enhancing the method’s ability to express and generalize complex structured data. A comprehensive experiment is conducted on the Criteo and Avazu datasets, the results show that compared to typical methods such as DeepFM, AFM, and IFM, the proposed method GBIFM can increase the AUC value by 10% –12% and decrease the Logloss value by 6% –20%, effectively improving the accuracy of click-through rate prediction. Show more
Keywords: Click-through rate estimation, GBIFM, GBDT, IFM
DOI: 10.3233/JIFS-234713
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wang, Shuo | Yang, Jing | Yang, Yue
Article Type: Research Article
Abstract: Personalized recommendation systems fundamentally assess user preferences as a reflection of their emotional responses to items. Traditional recommendation algorithms, focusing primarily on numerical processing, often overlook emotional factors, leading to reduced accuracy and limited application scenarios. This paper introduces a collaborative filtering recommendation method that integrates features of facial information, derived from emotions extracted from such data. Upon user authorization for camera usage, the system captures facial information features. Owing to the diversity in facial information, deep learning methods classify these features, employing the classification results as emotional labels. This approach calculates the similarity between emotional and item labels, reducing …the ambiguity inherent in facial information features. The fusion process of facial information takes into account the user’s emotional state prior to item interaction, which might influence the emotions generated during the interaction. Variance is utilized to measure emotional fluctuations, thereby circumventing misjudgments caused by sustained non-interactive emotions. In selecting the nearest neighboring users, the method considers not only the similarity in user ratings but also in their emotional responses. Tests conducted using the Movielens dataset reveal that the proposed method, modeling facial features, more effectively aligns recommendations with user preferences and significantly enhances the algorithm’s performance. Show more
Keywords: Collaborative filtering algorithm, facial information features, emotional factors, non-interactive emotion
DOI: 10.3233/JIFS-232718
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Zhai, Shanshan | Fan, Jianping | Liu, Lin
Article Type: Research Article
Abstract: Neutrosophic cubic set (NCS) can process complex information by choosing both interval value and single value membership and indeterminacy and falsehood components. The aggregation operators based on Aczel-Alsina t-norm and t-corm are quite effective for evaluating the interrelationship among attributes. The purpose of this paper is to diagnose the interrelationship among attributes with neutrosophic cubic information, and propose a multi-attribute decision-making(MADM) method for supplier selection problem with unknown weight under neutrosophic cubic environment. We defined neutrosophic cubic Aczel-Alsina (NC-AA) operator and neutrosophic cubic Aczel–Alsina weighted arithmetic average (NCAAWAA) operator, then we discussed various important results and some properties of the …proposed operators. Additionally, we proposed a MADM method under the presence of the NC-AAWAA operator. When the weights of attributes are unknown, we use the MEREC method to determine the weights. Later, the NC-AAWAA operator and MEREC method are applied to address the supplier selection problem. Finally, a sensitivity analysis and a comparative analysis are conducted to illustrate the stability and superiority of the proposed method. The results show the NC-AAWAA operator can handle the interrelationship among complex information more effectively, and MEREC method can weight the attributes based on the removal effect of a neutrosophic cubic attribute. Show more
Keywords: Multi-attribute decision-making (MADM), neutrosophic cubic set (NCS), Aczel-Alsina aggregation operators, MEREC method
DOI: 10.3233/JIFS-235274
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Hong, Yuntao
Article Type: Research Article
Abstract: Obsessive-compulsive disorder (OCD) is a chronic disease and psychosocial disorder that significantly reduces the quality of life of patients and affects their personal and social relationships. Therefore, early diagnosis of this disorder is of particular importance and has attracted the attention of researchers. In this research, new statistical differential features are used, which are suitable for EEG signals and have little computational load. Hilbert-Huang transform was applied to EEGs recorded from 26 OCD patients and 30 healthy subjects to extract instant amplitude and phase. Then, modified mean, variance, median, kurtosis and skewness were calculated from amplitude and phase data. Next, …the difference of these statistical features between various pairs of EEG channels was calculated. Finally, different scenarios of feature classification were examined using the sparse nonnegative least squares classifier. The results showed that the modified mean feature calculated from the amplitude and phase of the interhemispheric channel pairs produces a high accuracy of 95.37%. The frontal lobe of the brain also created the most distinction between the two groups among other brain lobes by producing 90.52% accuracy. In addition, the features extracted from the frontal-parietal network produced the best classification accuracy (93.42%) compared to the other brain networks examined. The method proposed in this paper dramatically improves the accuracy of EEG classification of OCD patients from healthy individuals and produces much better results compared to previous machine learning techniques. Show more
Keywords: Obsessive-compulsive disorder (OCD), Electroencephalogram (EEG), Hilbert-Huang transform, statistical features, classification
DOI: 10.3233/JIFS-237946
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhao, Xixi | Gu, Liang | Duan, Xiaorong | Wang, Liguo | Li, Zhenxi
Article Type: Research Article
Abstract: Cloud storage attribute libraries usually store a large amount of sensitive data such as personal information and trade secrets. Attackers adopt diverse and complex attack methods to target the cloud storage attribute database, which makes the defense work more challenging. In order to realize the secure storage of information, an attribute based cloud storage anti-attack algorithm based on dynamic authorization access is proposed. According to the characteristic variables of the sample, the data correlation matrix is calculated, and the principal component analysis method is adopted to reduce the dimension of the data, build the anti-attack code model, simulate the dynamic …authorization access rights, and calculate the packet loss rate according to the anti-attack flow. Design the initialization stage, cluster stage and cluster center update stage to realize the attack prevention of cloud storage attribute database. The experimental results show that the proposed algorithm can accurately classify the anti-attack code, has good packet processing ability, relatively short page request time, and anti-attack success rate is higher than 90%, which can effectively ensure the stability of the algorithm. Show more
Keywords: Dynamic authorization access, cloud storage attributes, basic anti-attack algorithm, anti-attack code model, access permissions
DOI: 10.3233/JIFS-237409
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]