Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Li, Zhaowen | Wei, Shengxue | Liu, Suping

Article Type: Research Article

Abstract: Outlier detection is critically important in the field of data mining. Real-world data have the impreciseness and ambiguity which can be handled by means of rough set theory. Information entropy is an effective way to measure the uncertainty in an information system. Most outlier detection methods may be called unsupervised outlier detection because they are only dealt with unlabeled data. When sufficient labeled data are available, these methods are used in a decision information system, which means that the decision attribute is discarded. Thus, these methods maybe not right for outlier detection in a a decision information system. This paper …proposes supervised outlier detection using conditional information entropy and rough set theory. Firstly, conditional information entropy in a decision information system based on rough set theory is calculated, which provides a more comprehensive measure of uncertainty. Then, the relative entropy and relative cardinality are put forward. Next, the degree of outlierness and weight function are presented to find outlier factors. Finally, a conditional information entropy-based outlier detection algorithm is given. The performance of the given algorithm is evaluated and compared with the existing outlier detection algorithms such as LOF, KNN, Forest, SVM, IE, and ECOD. Twelve data sets have been taken from UCI to prove its efficiency and performance. For example, the AUC value of CIE algorithm in the Hayes data set is 0.949, and the AUC values of LOF, KNN, SVM, Forest, IE and ECOD algorithms in the Hayes data set are 0.647, 0.572, 0.680, 0.676, 0.928 and 0.667, respectively. The advantage of the proposed outlier detection method is that it fully utilizes the decision information. Show more

Keywords: Rough set theory, outlier detection, outlier factor, conditional information entropy

DOI: 10.3233/JIFS-236009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1899-1918, 2024

Authors: Kahraman, Cengiz

Article Type: Research Article

Abstract: The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. The problem is that the expert cannot assign the same values to the degrees of membership, non-membership and hesitancy in decimal numbers for the same proposition in every attempt. Rather than the former, the assignment of proportional relationships between membership and non-membership degrees is more appropriate. We propose proportion-based models for intuitionistic fuzzy sets that include arithmetic and aggregation operators. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. These models will …make it easier to define intuitionistic fuzzy sets with more accurate data that better represents expert judgments. We transform AHP method, one of the traditional multi-criteria decision making methods, to PIF AHP using PIF sets. We compare the proposed PIF AHP method by interval-valued intuitionistic fuzzy AHP method existing in the literature. A wind turbine selection problem is handled to show the validity of the proposed PIF AHP method. Show more

Keywords: Proportional intuitionistic fuzzy sets, aggregation operators, multi-criteria decision making, AHP

DOI: 10.3233/JIFS-236035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1919-1933, 2024

Authors: Lai, Yibo | Fan, Libo | Sun, Zhiqing | Fang, Xiang | Shen, Bin | Tu, Yongwei

Article Type: Research Article

Abstract: Aiming at the problems of low convective heat transfer coefficient and high energy consumption in the air-cooled data center of immersed liquid cooling, an improved deep learning algorithm is proposed for the data center system of immersed liquid cooling equipment room. By improving the design of the immersed liquid cooling system, heat exchange is carried out between the immersed liquid cooling system and heating components such as the central processing unit of the server. The insulation coolant and cooling water achieve server heat dissipation through energy exchange, achieving data management of the immersed liquid cooling room. The proposed algorithm improves …data management efficiency while ensuring computational accuracy by conducting in-depth training and learning on the obtained immersed liquid cooling data, thus achieving the management of data in the immersed liquid cooling room. Through experiments, it has been proven that the immersed liquid cooling system in this study has high data management efficiency and low error, and can maintain server memory heat below 37 ° C, with a research accuracy of up to 92%. Show more

Keywords: Immersive liquid cooling, liquid cooling heat exchanger, deep learning, non relaxation hash algorithm, data management system

DOI: 10.3233/JIFS-233140

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1935-1944, 2024

Authors: Wu, Jian-Zhang | Zhang, Xue | Beliakov, Gleb

Article Type: Research Article

Abstract: Both the nonadditivity index and nonmodularity index have emerged as valuable indicators for characterizing the interaction phenomenon within the realm of fuzzy measures. The axiomatic representation plays a crucial role in distinguishing and elucidating the relationship and distinctions between these two interaction indices. In this paper, we employ a set of fundamental and intuitive properties related to interactions, such as equality, additivity, maximality, and minimality, to establish a comprehensive axiom system that facilitates a clear comprehension of the interaction indices. To clarify the impact of new elements’ participation on the type and density of interactions within an initial coalition, we …investigate and confirm the existence of proportional and linear effects in relation to null and dummy partnerships, specifically concerning the nonadditivity and nonmodularity indices. Furthermore, we propose the concept of the t -interaction index to depict a finer granularity for the interaction situations within a coalition, which involves subsets at different levels and takes the nonadditivity index and nonmodularity index as special cases. Finally, we establish and discuss the axiomatic theorems and empirical examples of this refined interaction index. In summary, the contributions of this work shed light on the axiomatic characteristics of the t -interaction indices, making it a useful reference for comprehending and selecting appropriate indices within this category of interactions. Show more

Keywords: Fuzzy measure, capacity, nonadditivity index, nonmodularity index, t-interaction index

DOI: 10.3233/JIFS-233196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1945-1956, 2024

Authors: Gobinath, C. | Gopinath, M.P.

Article Type: Research Article

Abstract: PURPOSE: Many researchers have found that the improvement in computerised medical imaging has pushed them to their limits in terms of developing automated algorithms for the identification of illness without the need for human participation. The diagnosis of glaucoma, among other eye illnesses, has continued to be one of the most difficult tasks in the area of medicine. Because there are not enough skilled specialists and there are a lot of patients seeking treatment from ophthalmologists, we have been encouraged to build efficient computer-based diagnostic methods that can assist medical professionals in early diagnosis and help reduce the amount of …time and effort they spend working on healthy situations. The Optic Disc position is determined with the help of the LoG operator, and a Disc Image map is projected with the help of a U-net architecture by utilising the location and intensity profile of the optic disc. After this, a Generative adversarial network is suggested as a possible solution for segmenting the disc border. In order to verify the performance of the model, a well-defined investigation is carried out on many retinal datasets. The usage of a multi-encoder U-net framework for optic cup segmentation is the second key addition made by this proposed work. This framework greatly outperforms the state-of-the-art in this area. The suggested algorithms have been tested on public standard datasets such as Drishti-GS, Origa, and Refugee, as well as a private community camp-based difficult dataset obtained from the All-India Institute of Medical Sciences (AIIMS), Delhi. All of these datasets have been verified. In conclusion, we have shown some positive outcomes for the detection of diseases. The unique strategy for glaucoma treatment is called ensemble learning, and it combines clinically meaningful characteristics with a deep Convolutional Neural Network. Show more

Keywords: Glaucoma, Cup-To-Disc Ratio (CDR), neuro-retinal rim (NRR) Loss, peripapillary atrophy (PPA), retinal nerve fiber layer (RNFL), deep convolutional neural network

DOI: 10.3233/JIFS-234363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1957-1971, 2024

Authors: Li, Junwei | Liu, Huanyu | Jin, Yong | Zhao, Aoxiang

Article Type: Research Article

Abstract: Research on conflict evidence fusion is an important topic of evidence theory. When fusing conflicting evidence, Dempster-Shafer evidence theory sometimes produces counter-intuitive results. Thus, this work proposes a conflict evidence fusion method based on improved conflict coefficient and belief entropy. Firstly, the proposed method uses an improved conflict coefficient to measure the degree of conflict, and the conflict matrix is constructed to get the support degree of evidence. Secondly, in order to measure the uncertainty of evidence, an improved belief entropy is proposed, and the information volume of evidence is obtained by the improve entropy. Next, connecting with the support …degree and information volume, We get the weight coefficient, and use it to modify the evidence. Finally, using the combination rule of Dempster for fusion. Simulation experiments have demonstrated the effectiveness and superiority of the proposed method in this paper. Show more

Keywords: Evidence theory, conflict evidence, conflict coefficient, beleief entropy, combination rule

DOI: 10.3233/JIFS-221507

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1973-1984, 2024

Authors: Yapali, Reha | Korkmaz, Erdal | Çinar, Muhammed | Çoskun, Hüsamettin

Article Type: Research Article

Abstract: The idea of lacunary statistical convergence sequences, which is a development of statistical convergence, is examined and expanded in this study on L - fuzzy normed spaces, which is a generalization of fuzzy spaces. On L - fuzzy normed spaces, the definitions of lacunary statistical Cauchy and completeness, as well as associated theorems, are provided. The link between lacunary statistical Cauchyness and lacunary statistical boundedness with regard to L - fuzzy norm is also shown.

Keywords: ℒ-fuzzy normed space, lacunary double sequences, lacunary statistically convergence, lacunary statistical Cauchy, lacunary statistical boundedness

DOI: 10.3233/JIFS-222695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1985-1993, 2024

Authors: Prabaharan, P.

Article Type: Research Article

Abstract: Recent developments in wireless sensor networks (WSNs) have generated interest in the area of sensor tracking events. The proposed work aims to decrease energy usage by identifying functional relay nodes utilizing the enhanced energy proficient clustering (EEPC) method. To minimize long-distance interaction between CH and BS, a power-efficient relay chosen technique is proposed using improved Grasshopper Optimization algorithm (IGOA). The network is constructed using both mobile and fixed nodes. Mobile nodes first choose cluster head (CH) among fixed nodes after broadcasting information. Depending on the related positioning and power density, mobile nodes choose their CH. CH receives information from mobile …sensor nodes (SNs). Based on the nodes’ velocity and position, the EEPC method computes particle fitness value and chooses the relay nodes. Performance metrics include Throughput, End-to-End Delay, Packet Delivery Ratio (PDR), Quantity of Received Packets, Total Residual Energy, and Total Energy Consumption, network lifetime. The suggested technique enhances network lifetime and reduces energy consumption when compared to other existing protocols. After 200 simulation rounds, the suggested EEPC displays 98.87% PDR. However, during 200 simulation cycles, ANFISRS, ORNS and DTC-ORS show 97.82%, 96.03%, and 89.585% PDR, respectively. Show more

DOI: 10.3233/JIFS-231729

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1995-2008, 2024

Authors: Sandhu, Muhammad Abdullah | Amin, Asjad | Tariq, Sana | Mehmood, Shafaq

Article Type: Research Article

Abstract: Dengue mosquitoes are the only reason for dengue fever. To effectively combat this disease, it is important to eliminate dengue mosquitoes and their larvae. However, there are currently very few computer-aided models available in scientific literature to prevent the spread of dengue fever. Detecting the larvae stage of the dengue mosquito is particularly important in controlling its population. To address this issue, we propose an automated method that utilizes deep learning for semantic segmentation to detect and track dengue larvae. Our approach incorporates a contrast enhancement approach into the semantic neural network to make the detection more accurate. As there …was no dengue larvae dataset available, we develop our own dataset having 50 short videos with different backgrounds and textures. The results show that the proposed model achieves up to 79% F-measure score. In comparison, the DeepLabV3, Resnet achieves up to 77%, and Segnet achieves up to 76% F-measure score on the tested frames. The results show that the proposed model performs well for small object detection and segmentation. The average F-measure score of all the frames also indicates that the proposed model achieves a 76.72% F-measure score while DeepLabV3 achieves a 75.37%, Resnet 75.41%, and Segnet 74.87% F-measure score. Show more

Keywords: Dengue larvae, detection, tracking, semantic segmentation, image enhancement

DOI: 10.3233/JIFS-233292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2009-2021, 2024

Authors: Keikha, Abazar | Sabeghi, Narjes

Article Type: Research Article

Abstract: As the rapidly progressing applications of uncertainty theories, the need for modifications to some of their existing mathematical tools or creating new tools to deal correctly with them in various environments is also exposed. Hesitant fuzzy numbers (HFNs), as a particular case of fuzzy numbers, are not an exception to this rule. Considering the necessity of determining the distance between given HFNs in many of their practical applications, this article shows that the existing methods either do not provide correct results or are not able to meet the needs of users. This paper aims to present new methods for distance …measures of hesitant fuzzy numbers. To do them, three prevalent distance measures, i.e., the generalized distance measure, the Hamming distance measure, and the Euclidean distance measure, will be optimized into three distinct trinal categories. With the approach of reducing error propagation via reducing some unnecessary mathematical computations, new distance measures on HFNs will be introduced, first. The middle is the modification of the first category, which is more suitable when the given HFNs are equal-distance by the previous formula. Also, as the third category, the weighted form of these distance measures has been proposed, to be used where the real and membership parts of HFNs are not of equal importance. As an application of these, a TOPSIS-based technique for solving multi-attribute group decision-making problems with HFNs has been proposed. A numerical example will be implemented to describe the presented method. Finally, along with the validation of the proposed method, its numerical comparison with some other existing methods will be discussed in detail. Show more

Keywords: Hesitant fuzzy numbers, MAGDM, Hamming distance, Euclidean distance, TOPSIS

DOI: 10.3233/JIFS-234619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2023-2035, 2024

Authors: Yin, Rui | Lu, Wei | Yang, Jianhua

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-236087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2037-2052, 2024

Authors: Bhukya, Raghuram | Vodithala, Swathy

Article Type: Research Article

Abstract: Social media is becoming a crucial part of our everyday lives, whether it’s for product advertising, developing brand value, or reaching out to users. At the same time, sentiment analysis (SA) is a method for determining the emotions associated with online information. The main obstacle to SA’s success is the presence of sarcasm in the text. Previous studies on the identification of sarcasm use lexical and pragmatic signs such as interjection, punctuation, and sentimental change, amongst others. Deep learning (DL) models can be used to learn the lexical and contextual aspects of informal language because handcrafted features cannot be generalised. …In addition, word embedding can be used to train the DL models and provide effective results on big datasets at the same time. Optimal Deep Learning based Sarcasm detection and classification using an ODL-SDC method is presented in this study. ODL-SDC analyses social media data to look for and classify any sarcasm that may have been used there. In addition, the Glove embedding approach is used to transform feature vectors. A approach known as the chaotic crow search optimization on deep belief network (CCSO-DBN) is also used to classify and detect satire. Many benchmark datasets were used to evaluate the ODL-SDC method, and the results show it to be more effective than existing approaches in a number of performance metrics. Show more

Keywords: Sarcasm detection, deep learning, social media, word embedding, feature vectors, classification

DOI: 10.3233/JIFS-222633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2053-2066, 2024

Authors: Tasbozan, Hatice

Article Type: Research Article

Abstract: Hypersoft set theory represents an advanced version to soft set theory, offering enhanced capabilities for addressing uncertainty. By combining hypersoft set theory with nearness approximation spaces, a novel mathematical model known as near hypersoft set emerges. This hybrid model enables improved decision-making accuracy. In this study, our focus is on selecting an object from a product containing a function parameter set described by a distinct Cartesian feature with multiple arguments. Furthermore, we define fundamental features and topology on this set.

Keywords: Soft sets, near sets, near soft sets, hypersoft set, near hypersoft set

DOI: 10.3233/JIFS-224526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2067-2076, 2024

Authors: Gong, Zengtai | Jiang, Taiqiang

Article Type: Research Article

Abstract: In the existing conflict analysis models, they used a triangular fuzzy number on [0, 1] to describe the range of an agent’s attitude towards an issue, but there are still some shortcomings in describing the specific attitude and degree of conflict represented by the triangular fuzzy number. In this paper, the conflict analysis model is extended, improved and perfected. Firstly, the expectation of triangular fuzzy number is used in the [-1, 1] triangular fuzzy information system to reasonably express the specific attitudes represented by a triangular fuzzy number. Secondly, the weights of each issue are obtained by using the Sugeno …measure, which determines the total attitude of the agent towards all issues. Thirdly, the relationship between agents is obtained with the help of the weighted distance of triangular fuzzy numbers. Finally, the thresholds α and β are calculated by means of triangular fuzzy decision theory rough sets. Show more

Keywords: Conflict analysis, three-way decisions, triangular fuzzy number

DOI: 10.3233/JIFS-231296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2077-2090, 2024

Authors: Huang, Juan | Gou, Fangfang | Wu, Jia

Article Type: Research Article

Abstract: With the development of Internet of Things technology, 5G communication has gradually entered people’s daily lives. The number of network users has also increased dramatically, and it has become the norm for the same user to enjoy the services provided by multiple network service providers and to complete the exchange and sharing of a large amount of information at the same time. However, the existing opportunistic social network routing is not sufficiently scalable in the face of large-scale network data. Moreover, only the transaction information of network users is used as the evaluation evidence, ignoring other information, which may lead …to the wrong trust assessment of nodes. Based on this, this study proposes an algorithm called Trust and Evaluation Mechanism for Users Based on Opportunistic Social Network Community Classification Computation (TEMCC). Firstly, communication communities are established based on community classification computation to solve the problem of the explosive growth of network data. Then a trust mechanism based on the Bayesian model is established to identify and judge the trustworthiness of the recommended information between nodes. This approach ensures that more reliable nodes can be selected for interaction and complete data exchange. Through simulation experiments, the delivery rate of this scheme can reach 0.8, and the average end-to-end delay is only 190 ms. Show more

Keywords: Trust mechanism, evaluation mechanism, community, opportunistic social networks

DOI: 10.3233/JIFS-232264

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2091-2108, 2024

Authors: Chen, Rong | Lan, Furong | Wang, Jianhua

Article Type: Research Article

Abstract: In order to effectively control the pressure and energy consumption of multiple air compressors within an acceptable range, an intelligent pressure switching control method for air compressor group control based on multi-agent RL is studied. This method uses sensors in the air compressor field control cabinet to collect data such as header pressure, air storage tank pressure, and air storage tank temperature and sends them to the edge data collector for integration. After integration, the main control cabinet sends them to the upper computer. Combined with the on-site collected data, a multi-agent-based air compressor group control model is designed to …convert multiple air compressors in the air compressor group control problem into a multi-agent mode, facilitating unified switching control of the air compressor group. Then, using the intelligent pressure switching control method based on deep Q-learning, driven by a neural network controller, the frequency of the frequency converter is adjusted to control the pressure at the outlet of the air compressor terminal header within the set value range, completing the pressure intelligent switching control. After testing, this method has good application results in pressure control, energy saving, and other aspects after being used for intelligent pressure switching control of air compressor group control. Show more

Keywords: Multi-agent, intensive learning, air compressor group control, pressure intelligence, neural network controller

DOI: 10.3233/JIFS-233217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2109-2122, 2024

Authors: Xu, Huifen | Fang, Cheng | Zhang, Shuai

Article Type: Research Article

Abstract: Remanufacturing, with its environmental and economic implications, is gaining significant traction in the contemporary industry. Owing to the complementarity between remanufacturing process planning and scheduling in actual remanufacturing systems, the integrated remanufacturing process planning and scheduling (IRPPS) model provides researchers and practitioners with a favorable direction to improve the performance of remanufacturing systems. However, a comprehensive exploration of the IRPPS model under uncertainties has remained scant, largely attributable to the high complexity stemming from the intrinsic uncertainties of the remanufacturing environment. To address the above challenge, this study proposes a new IRPPS model that operates under such uncertainties. Specifically, the …proposed model utilizes interval numbers to represent the uncertainty of processing time and develops a process planning approach that integrates various failure modes to effectively address the uncertain quality of defective parts during the remanufacturing process. To facilitate the resolution of the proposed model, this study proposes an extended non-dominated sorting genetic algorithm-II with a new multi-dimensional representation scheme, in which, a new self-adaptive strategy, multiple genetic operators, and a new local search strategy are integrated to improve the algorithmic performance. The simulation experiments results demonstrate the superiority of the proposed algorithm over three other baseline multi-objective evolutionary algorithms. Show more

Keywords: Integrated remanufacturing process planning and scheduling, remanufacturing systems, uncertainty environment, interval processing time, non-dominated sorting genetic algorithm-II

DOI: 10.3233/JIFS-233408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2123-2145, 2024

Authors: Xu, Dongsheng

Article Type: Research Article

Abstract: Universities are important talent training bases in China and the main driving force for achieving the strategic layout of “revitalizing the country through science and education” and “strengthening the country through talent". Oil painting is a global art with rich humanistic and artistic value. Most art colleges in China have set up oil painting courses. Analyze the current situation and value of oil painting course teaching in local art (teacher training) majors, and leverage the educational role of oil painting courses by enriching course offerings, emphasizing the integration of humanistic innovation, improving teacher literacy, and striving to further improve the …quality and efficiency of oil painting course teaching. The quality evaluation of oil painting teaching in universities is viewed as multiple-attribute decision-making (MADM). The grey relational analysis (GRA) is a useful tool to cope with the MADM issue. The probabilistic simplified Neutrosophic set (PSNSs) is easy to characterize uncertain information during the quality evaluation of oil painting teaching in universities. In this paper, in order to obtain the weight information, an optimization model implemented to obtain a simple and exact formula which can be employed to derive the attribute weights values based on the Lagrange function and the probabilistic simplified neutrosophic number grey relational analysis (PSNN-GRA) technique is implemented for MADM to rank the alternatives. Finally, a numerical example for quality evaluation of oil painting teaching in universities is used to verify the practicability of the PSNN-GRA technique and compares it with other techniques. Show more

Keywords: Multiple attributes decision making (MADM), probabilistic simplified neutrosophic sets (PSNSs), GRA technique, teaching quality evaluation

DOI: 10.3233/JIFS-235975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2147-2159, 2024

Authors: Liu, Chen | Zhou, Kexin | Zhou, Lixin

Article Type: Research Article

Abstract: Stance detection for user reviews on social platforms aims to classify the stance of users’ reviews toward a specific topic. Existing studies focused on the internal semantic features of reviews’ texts, but ignored the external knowledge associated with the review. This paper retrieves external knowledge related to the key information of each review by mapping it to a knowledge graph. Thereafter, this paper infuses the external knowledge into deep learning model for stance detection. Considering that infusing external knowledge may bring noise to the model, this paper adopts the personalized PageRank method to filter the introduced irrelevant external knowledge. Infusing …external knowledge can improve the classification performance by providing background knowledge. In addition to considering the textual features of reviews when constructing the stance detection model, this paper employs a gated graph neural network (GGNN) approach to fuse the structural information between reviews to capture the interactions of reviews. The experiments show that the model improves 1.5% –6.9% in macro-average scores compared to six benchmark models in this paper. By combining the textual features and structural information of reviews and introducing external knowledge, the model effectively improves the stance detection performance. Show more

Keywords: Knowledge graph, structural information, gate graph neural network, stance detection

DOI: 10.3233/JIFS-224217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2161-2177, 2024

Authors: Jayalakshmi, N. | Shanmugapriya, M.M.

Article Type: Research Article

Abstract: This study provides the generalization of fuzzy real numbers by imposing the elevator’s condition upon it’s legs. Our aim is to construct three types of Lift Fuzzy Real Numbers, an extension of h-generalized fuzzy real numbers, to indicate medical signals, stock market values, and commercial establishment profits over time. It explores concepts like ɛ-cut, strong ɛ-cut, β-level set, and convexity, and presents a graphical representation based on profit earned by three industries. Appropriate numerical examples are provided to support the new ideas. It’s interesting to note that Lift Fuzzy Real Numbers are also used to represent real numbers. Additionally, the …connections between the Lift Fuzzy real numbers have been established. The new fuzzy real numbers offer an advantage in representing data sets not represented by existing fuzzy numbers. Show more

Keywords: Fuzzy set, fuzzy number, α-cut, strong α-cut

DOI: 10.3233/JIFS-224320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2179-2192, 2024

Authors: Hu, Fang

Article Type: Research Article

Abstract: There is a lack of domestic and foreign research on the evaluation and improvement strategies of business performance of performing arts enterprises, especially in the context of the “restructuring” of cultural groups in China. Most of the existing studies are distributed in bulk, not only lacking in theoretical depth, but also lacking in systematization to a certain extent, which shows that the existing studies have not fully formed a mature and valuable theoretical system. The business performance evaluation of performing arts enterprises is a multiple attributes group decision making (MAGDM). This paper constructs a novel probabilistic hesitant fuzzy Multi-Objective Optimization …Simple Ratio Analysis (PHF-MOOSRA) model based on the integrated determination of objective criteria weights (IDOCRIW) under the probabilistic hesitant fuzzy sets (PHFSs) for this issue. The PHFSs provides an evaluation circumstance containing more information which make the final decision-making results more accurately. Additionally, the IDOCRIW method separately and the MOOSRA method based on the MOORA method is proposed in PHFSs circumstance in this model. In the end, this model is then applied in a numerical case study for business performance evaluation of performing arts enterprises and compare this model with other existing methods. Show more

Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets (PHFSs), MOOSRA method, IDOCRIW method, business performance evaluation

DOI: 10.3233/JIFS-224342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2193-2205, 2024

Authors: Bhandari, Samir Kumar | De la Sen, Manuel | Chandok, Sumit

Article Type: Research Article

Abstract: In this article, the probabilistic metric distance between two disjoint sets is utilised to define the essential criteria for the existence and uniqueness of the best proximity point, which takes into account the global optimization problem. In order to solve this problem, we pretend that we are trying to obtain the optimal approximation to the solution of a fixed point equation. Here, we introduce two types of probabilistic proximal contraction mappings and use a geometric property called Ω -property in the context of probabilistic metric spaces. We also obtain some consequences for self-mappings, which give the fixed point results. Some …examples are provided to validate the findings. As an application, we obtain the solution to a second-order boundary value problem using a minimum t -norm in the context of probabilistic metric spaces. Show more

Keywords: Probabilistic metric spaces, best proximity point, Ω-property, fixed point

DOI: 10.3233/JIFS-231315

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2207-2218, 2024

Authors: Cui, Tong | Sun, Peixi | Liu, Xiao

Article Type: Research Article

Abstract: Corporate culture has its own development laws and may play a significant role in a short period of time, but its development and improvement are a relatively long-term task. The construction of corporate culture is a systematic project that varies depending on the enterprise. For enterprises, the construction of corporate culture has very important practical significance for the re integration of the enterprise team and the enhancement of competitive strength. Culture is a productive force, and the role of corporate culture is more direct. Therefore, enterprises should focus on their own development goals, create their own unique corporate culture based …on learning and reference, and meet market challenges with a new look and strong strength. The effectiveness evaluation of corporate culture construction is a classical multiple attribute decision making (MADM). Recently, the TODIM and VIKOR method has been used to cope with MADM issues. The neutrosophic cubic sets (NCSs) are used as a tool for characterizing uncertain information during the effectiveness evaluation of corporate culture construction. In this manuscript, the neutrosophic cubic number TODIM-VIKOR (NCN-TODIM-VIKOR) method is built to solve the MADM under NCSs. In the end, a numerical case study for effectiveness evaluation of corporate culture construction is given to validate the proposed method. The research aim of the paper is summarized: (1) the NCN-TODIM-VIKOR is proposed for MADM problem with NCSs; (2) The attributes weight information is obtained through information entropy; (3) the NCN-TODIM-VIKOR method is designed for effectiveness evaluation of corporate culture construction and were compared with some existing methods; (4) Through the comparison, it is found that NCN-TODIM-VIKOR method for effectiveness evaluation of corporate culture construction proposed are effective. Show more

Keywords: Multiple attribute decision making (MADM), Neutrosophic cubic sets (NCSs), TODIM, VIKOR, effectiveness evaluation of corporate culture construction

DOI: 10.3233/JIFS-231841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2219-2231, 2024

Authors: Hosseini Monfared, Seyede Nasrin | Hosseinzadeh Lotfi, Farhad | Mozaffari, Mohammad Reza | Rostamy malkhalifeh, Mohsen

Article Type: Research Article

Abstract: In conventional DEA models it has been assumed that each measure status is considered input or output. However, a performance measure in some cases can have input role for some DMUs and output role for others and is known as flexible measure. In this paper new slacks-based FNSBM models are proposed in general two-stage network DEA to determine the relative efficiency of units and the role of flexible measures. Then new radial FNDEA-R models and new slacks-based FNSBM-DEA-R models are developed in the presence of flexible measures based on the ratio of input components to output components or vice versa …in the input and output orientation under constant returns to scale in general two-stage network. In our proposed models, flexible measures are determined as input or output to improve performance to maximize the relative efficiency of the DMU under evaluation. The FNDEA-R and FNSBM-DEA-R models versus FNSBM models prevent efficiency underestimation and pseudo inefficiency issues. The status of one flexible measure in the input-oriented and output-oriented FNDEA-R and FNSBM-DEA-R models may have different conclusions. The radial FNDEA and FNDEA-R models have unitsinvariant and the objective function of the FNSBM and FNSBM-DEA-R models are invariant with respect to the units of data. A numerical example is used to illustrate the procedures. Show more

Keywords: Data envelopment analysis, flexible measures, SBM model, ratio analysis, general two-stage network

DOI: 10.3233/JIFS-231925

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2233-2259, 2024

Authors: Kumar, Ashish | Maan, Vijay Singh | Choudhary, Ravi | Saini, Monika

Article Type: Research Article

Abstract: The main objective of present investigation is to evaluate and optimize the operational availability of the solar photovoltaic systems. As the solar energy is a prominent source of renewal energy and contribute a lot in global development having less environmental impacts but the safety and reliability issues of these systems also observed during the operational phase. Availability is an effective tool that is used to discourse the safety and performance issues of renewal energy sources especially solar photovoltaic systems. Here, a stochastic model is developed for solar photovoltaic system having solar photovoltaic plates, solar charger, solar battery, and inverter. The …Markov birth-death process is applied for development of the mathematical model of the proposed system. The chapman-Kolmogorov differential difference equations of the proposed solar photovoltaic system used to predict the steady state availability of system. On the basis of literature, the failure and repair rates of all components of solar photovoltaic system are considered as exponentially distributed. In addition, an effort is also made to predict the optimum availability of solar photovoltaic system using well-known optimization technique cuckoo search algorithm. It is revealed that, the predicted availability of the solar photovoltaic system is 0.9988799 at population size 60 after 700 iterations. The estimated parametric values of the failure and repair rates also derived. To highlight the importance of the study the numerical and graphical results are presented and shared with the system designers and maintenance engineers. Show more

Keywords: Renewal energy sources, solar photovoltaic systems, markov models, cuckoo search algorithm, availability

DOI: 10.3233/JIFS-231940

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2261-2272, 2024

Authors: Elrawy, A. | Smarandache, Florentin | Temraz, Ayat A.

Article Type: Research Article

Abstract: We use a neutrosophic set, instead of an intuitionistic fuzzy because the neutrosophic set is more general, and it allows for independent and partial independent components μ (χ) , γ (χ) , ζ  (χ), while in an intuitionistic fuzzy set, all components are totally dependent. In this article, we present and demonstrate the concept of neutrosophic invariant subgroups. We delve into the exploration of this notion to establish and study the neutrosophic quotient group. Further, we give the concept of a neutrosophic normal subgroup as a novel concept.

Keywords: Neutrosophic set, invariant sub-groups, normal sub-group

DOI: 10.3233/JIFS-232941

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2273-2280, 2024

Authors: Mu, Li

Article Type: Research Article

Abstract: The financial management capability of enterprises, as an important component of their soft power, has a decisive impact on the success or failure of their operations. In the increasingly fierce market competition, enterprises must continuously improve their financial management capabilities in order to ensure efficient operation and achieve better economic benefits. Insufficient financial management capabilities in enterprises can seriously affect the stability of production and operation, hinder the realization of profits, and hinder the long-term development of enterprises. In order to better improve the financial management level of enterprises and promote the standardization of financial management, it is necessary to …use scientific techniques to evaluate the financial management ability of enterprises, so as to accurately grasp the key links in the financial management process of enterprises and implement targeted effective measures. The enterprise financial management capability evaluation is a classical multiple attribute group decision making (MAGDM). In recent years, the MAGDM problem has become an important research field in modern decision science. This paper extends the EDAS technique to the 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). On the basis of the original EDAS technique, 2-tuple linguistic Pythagorean fuzzy number EDAS (2TLPFN-EDAS) technique based on cosine similarity measure (CSM) and Hamming distances is managed for MAGDM. Finally, a case study for enterprise financial management capability evaluation and some comparative analysis with the other techniques show that the new technique proposed in this paper is effective, reasonable and accurate. The main contribution of the paper is summarized: (1) the 2TLPFN-EDAS technique based on CSM and Hamming distances is managed for MAGDM under 2TLPFSs; (2) The entropy is employed to manage the attribute weight based on cosine similarity measure(CSM) and Hamming distances under 2TLPFSs; (3) the 2TLPFN-EDAS technique is employed for enterprise financial management capability evaluation and were compared with some existing techniques; (4) Through the comparison, it is found that 2TLPFN-EDAS technique for enterprise financial management capability evaluation proposed are effective. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs), EDAS technique, financial management capability

DOI: 10.3233/JIFS-233395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2281-2296, 2024

Authors: Alkhalifah, Eman S.

Article Type: Research Article

Abstract: A satisfactory graphic design and good-looking 3D models and environments are the backbones of a positive user experience, especially in Augmented Reality (AR) / Virtual Reality (VR) app development. Where these technologies is seen as the an excellent realm of human-computer interaction. The purpose is to fool the viewer by the seamless incorporation of simulated features. Every AR system relies on true interaction and three-dimensional registration to function properly. In this research, we present a strategy for real-world 3D image registration and tracking. The primary foci of this study are the first three stages: initial registrations and matrix acquisitions, road …scene feature extraction, and virtual information registration. At initial registration, a rough virtual plane is estimated onto which the objects will be projected. To this, we propose YoloV3 for transferring features from a virtual to a real-world setting. The projection process concludes with a guess at the camera’s posture matrix. This tech is used in the vehicle’s head-up display to augment reality. The average time required to register a virtual item is 43 seconds. The final step in making augmented reality content is to merge the computer-generated images of virtual objects with real-world photographs in full colour. Our results indicate that this method is effective and precise for 3D photo registration but has the potential to dramatically increase the verisimilitude of AR systems. Show more

Keywords: Graphic designs, human-computer interaction, computer vision, real-scene, AR/VR applications, 3D image registration, and tracking and mapping

DOI: 10.3233/JIFS-233878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2297-2309, 2024

Authors: Meera, S. | Valarmathi, K.

Article Type: Research Article

Abstract: Load balancing is an element that must exist for a cloud server to function properly. Without it, there would be substantial lag and the server won’t be able to operate as intended. In a Cloud computing (CC) establishing, load balancing is the process of dividing workloads and computer resources. The distribution of assets from a data centre involves many different factors, including load balancing of workloads in cloud environment. To make best use each virtual machine’s (VM) capabilities, load balancing needs to be done in a way that ensures that all VMs have balanced loads. Both overloading and underloading are …examples of load unbalance, and both of these types of load unbalance could be fixed by using techniques created especially for load balancing. The research that has been done on the subject have not attempted to take into account the factors that affect the problem of load unbalancing. Results indicate that the LSTM and DForest-based load balancing approach significantly improves cloud resource utilization, reduces response times, and minimizes the occurrence of overloading or underloading scenarios. To effectively design those programmes, it is essential to first understand the advantages and disadvantages of current methodologies before developing efficient AI-based load balancing programmes. Compared to existing method the proposed method is high accuracy 0.98, KNN accuracy is 0.97, SVM accuracy is 0.99, and DForest accuracy is 0.987. Show more

Keywords: Load balancing, artificial intelligence, machine learning, DForest, Long Short-Term Memory

DOI: 10.3233/JIFS-234054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2311-2330, 2024

Authors: Yalçın, Selin | Kaya, İhsan

Article Type: Research Article

Abstract: Process capability analysis (PCA) is an important stage to check variability of process by using process capability indices (PCIs) that are very effective statistics to summarize process’ performance. Traditional PCIs can produce some incorrect results and declare misinterpretation about process’ quality if the process includes uncertainties. Additionally, definitions of process’ parameters with exact values is not possible when there are uncertainty caused by measurement errors, sensitivities of measuring instruments or quality engineers’ hesitancies. Although the fuzzy set theory (FST) has been successfully used in PCA, it is the first time to use of Pythagorean fuzzy sets (PFSs) to model uncertainties …of process more than traditional fuzzy sets in PCA. Since the PFSs has two-dimensional configurations by defining membership and non-membership values, they also have a huge ability to model uncertainty that arises from the human’s thinking and hesitancies, and has brought flexibility, sensitivity and reality for PCA. In this paper, specification limits (SLs), mean (μp ), standard deviation (σ ) and target value (T ) main parameters of PCIs have been analyzed by using PFSs and Pythagorean fuzzy process capability indices (PFPCIs) for two well-known PCIs such as ( C ˜ pm ) and ( C ˜ pmk ) have been derived. The Pythagorean ( C ˜ pm ) and ( C ˜ pmk ) indices have also been applied and tested on some numerical examples based on real case applications from manufacturing industry. The obtained results show that PFPCIs provide wider knowledge about capability of process and to obtain more realistic results. As a result of considering all possibilities about the process, it has been concluded that the process is incapable. In light of this information, the results obtained using different fuzzy set extensions for (C pm ) and (C pmk ) indices can be compared. Show more

Keywords: Process capability analysis, process capability indices, flexible parameters Pythagorean fuzzy sets

DOI: 10.3233/JIFS-234683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2331-2355, 2024

Authors: Xu, Xuezhu

Article Type: Research Article

Abstract: Sports events, as large-scale events that provide products and services, have received widespread attention for their economic benefits and influence. Event organizers expect to achieve high efficiency by providing high-quality products and services. The quality of competition products and services is mainly evaluated through the subjective feelings of the audience, and usually the audience’s evaluation of service quality is vague. Therefore, this article intends to establish an evaluation index system for the quality of spectator service in sports events, in order to provide a reasonable evaluation of the service products provided by sports event organizers. The audience service quality evaluation …for large-scale sports-events is a MAGDM problems. Recently, the EDAS and CRITIC technique has been employed to cope with MAGDM issues. The interval neutrosophic sets (INSs) are employed as a tool for characterizing uncertain information during the audience service quality evaluation for large-scale sports-events. In this paper, the interval neutrosophic number EDAS (INN-EDAS) technique based on the Hamming distance and Euclid distance is founded to manage the MAGDM under INSs. The CRITIC technique is employed to obtain the weight information based on the Hamming distance and Euclid distance under INSs. Finally, a numerical case study for audience service quality evaluation for large-scale sports-events is employed to validate the proposed technique. The main contributions of this paper are proposed: (1) The INN-EDAS technique based on the Hamming distance and Euclid distance is founded to manage the MAGDM under INSs; (2) The CRITIC technique is employed to obtain the weight information based on the Hamming distance and Euclid distance under INSs; (3) a numerical case study for audience service quality evaluation for large-scale sports-events is employed to validate the proposed technique. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval neutrosophic sets (INSs), EDAS technique, CRITIC technique, audience service quality evaluation

DOI: 10.3233/JIFS-236124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2357-2370, 2024

Authors: Liu, Mingtang | Zhang, Mengxiao | Zhang, Peng | Wang, Guanghui | Chen, Xiaokang | Zhang, Hao

Article Type: Research Article

Abstract: Aiming at the shortcomings of traditional water level prediction methods such as insufficient information mining ability and unclear mechanism of heuristic algorithms, this paper proposes for the first time a water level prediction method based on blockchain technology fused with long short-term memory (LSTM) network. The method utilizes blockchain and LSTM neural network to build a combined model, and directly uploads monitoring data such as import and export water flow and water level to predict the water level, which avoids the secondary error brought by the indirect calculation of flow. In this paper, the flow compensation strategy is proposed for …the first time, and the monitoring data with large deviations are compensated accordingly to reduce the prediction error from the source. The results show that the combined Blockchain-LSTM model has the smallest prediction error after adopting the compensation strategy, with the MAE of 0.290 and the RMSE of 0.490, which are smaller than those of other models, and has high prediction accuracy and practicability, which provides technical support for real-time scheduling of the South-to-North Water Diversion Reservoir. Show more

Keywords: LSTM, Blockchain-LSTM, water level prediction, compensation strategy

DOI: 10.3233/JIFS-231411

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2371-2380, 2024

Authors: Ameksa, Mohammed | Elamrani Abou Elassad, Zouhair | Elamrani Abou Elassad, Dauha | Mousannif, Hajar

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-232078

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2381-2397, 2024

Authors: Arulalan, V. | Premanand, V. | Kumar, Dhananjay

Article Type: Research Article

Abstract: An efficient model to detect and track the objects in adverse weather is proposed using Tanh Softmax (TSM) EfficientDet and Jaccard Similarity based Kuhn-Munkres (JS-KM) with Pearson-Retinex in this paper. The noises were initially removed using Differential Log Energy Entropy adapted Wiener Filter (DLE-WF). The Log Energy Entropy value was calculated between the pixels instead of calculating the local mean of a pixel in the normal Wiener filter. Also, the segmentation technique was carried out using Fringe Binarization adapted K-Means Algorithm (FBKMA). The movement of segmented objects was detected using the optical flow technique, in which the optical flow was …computed using the Horn-Schunck algorithm. After motion estimation, the final step in the proposed system is object tracking. The motion-estimated objects were treated as the target that is initially in the first frame. The target was tracked by JS-KM algorithm in the subsequent frame. At last, the experiential evaluation is conducted to confirm the proposed model’s efficacy. The outcomes of Detection in Adverse Weather Nature (DAWN) dataset proved that in comparison to the prevailing models, a better performance was achieved by the proposed methodology. Show more

Keywords: Object detection, adverse weather, weiner filter, object tracking, Retinex

DOI: 10.3233/JIFS-233623

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2399-2413, 2024

Authors: Wang, Yao | Yu, Tao | Luo, Tianmin | Ye, Haojie | Pan, Yiru

Article Type: Research Article

Abstract: Fault detection and diagnosis in electrical machines are periodical for preventing operational interruptions and unexpected shutdowns. However, a Wavelet Feature-dependent Clustering Technique (WFCT) is introduced to address the cyclic fault detection between successive operation intervals. This technique identifies override features from the time-frequency operational wavelets throughout the machine running time. This grouping binds time and operational frequency for identifying override exceeding shutdown/ failure instances. Based on their revamping time, the identified instances are further grouped to prevent overrides in successive operational hours. The fuzzy clustering prevents variation features based on conventional to high-fuzzified extractions.

Keywords: Electrical machines, fault diagnosis, feature extraction, fuzzy clustering, time-frequency wavelet

DOI: 10.3233/JIFS-234256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2415-2431, 2024

Authors: Muniz, Rafael Ninno | de Sá, José Alberto Silva | da Rocha, Brigida Ramati Pereira | Buratto, William Gouvêa | Nied, Ademir | da Costa Jr., Carlos Tavares

Article Type: Research Article

Abstract: Energy sustainability indicators are essential for evaluating and measuring energy systems’ environmental, social, and economic impact. These indicators can be used to assess the sustainability of different energy sources, such as renewable or fossil fuels, as well as the performance of energy systems in various regions or countries. The goal of this paper is to propose a new energy sustainability index based on fuzzy logic for the Amazon region. The fuzzy inference system enabled the operationalization of subjective sustainability concepts, resulting in a final index that can evaluate the performance of the states in the Legal Amazon and compare them …to each other. The results indicated that Mato Grosso had the highest ranking, followed by Tocantins, Amapá, Roraima, Rondônia, Pará, Acre, Maranhão, and Amazonas in the last position. These findings demonstrate that the selected indicators and the final index are effective tools for evaluating the energy sustainability of the Amazon region and can aid public managers in making decisions and proposing sustainable regional development policies for the region. Show more

Keywords: Amazon, energy planning, fuzzy logic, indicators, sustainability

DOI: 10.3233/JIFS-235750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2433-2446, 2024

Authors: Sweatha, S. | Sindu Devi, S.

Article Type: Research Article

Abstract: During the period of 2019–20, forecasting was of utmost priority for health care planning and to combat COVID-19 pandemic. Almost everyone’s life has been greatly impacted by COVID-19. Understanding how the disease spreads is crucial to know how the disease behaves dynamically. The aim of the research is to construct an SEI Q 1 Q 2 R model for COVID-19 with fuzzy parameters. The fuzzy parameters are the transmission rate, the infection rate, the recovery rate and the death rate. We compute the basic reproduction number, using next-generation matrix method, which will be used further to study the model’s …prediction. The COVID-free and endemic equilibrium points attain local and global stability when R0  < 1. A sensitivity analysis of the reproduction number against its internal parameter has been done. The results of this model showed that intervention measures. The numerical simulation along with graphical representations at COVID-free and endemic points are shown. The SEIQ 1 Q 2 R model is a successful model to analyse the spreading and controlling the epidemics like COVID-19. Show more

Keywords: Stability, fuzzy basic reproduction number, sensitivity analysis

DOI: 10.3233/JIFS-231945

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2447-2460, 2024

Authors: Saranya, D. | Bharathi, A.

Article Type: Research Article

Abstract: A sudden increase in electrical activity in the brain is a defining feature of one of the severe neurological diseases known as epilepsy. This abnormality appears as a seizure, and identifying seizures is an important field of research. An essential technique for examining the features of neurological issues brain activities, and epileptic seizures is electroencephalography (EEG). In EEG data, analyzing epileptic irregularities visually requires a lot of time from neurologists. For accurate detection of epileptic seizures, numerous scientific techniques have been used with EEG data, and most of these techniques have produced promising results. For EEG signal classification with a …high classification accuracy rate, the present research proposes an enhanced machine learning-based epileptic seizure detection model. The present research provides a hybrid Improved Adaptive Neuro-Fuzzy Inference System (IANFIS)-Light Gradient Boosting Machine (LightGBM) technique for automatically detecting and diagnosing epilepsy from EEG data. The experimental findings were supported by EEG records made available by the German University of Bonn and scalp EEG data acquired at Children’s Hospital Boston. The suggested IANFIS-LightGBM, according to the results, offers the most significant classification accuracy ratings in both situations. Show more

Keywords: Electroencephalography (EEG), epileptic seizure detection, machine learning, LightGBM, and accuracy rate

DOI: 10.3233/JIFS-233430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2463-2482, 2024

Authors: Subbiah, Priyanga | Nagappan, Krishnaraj

Article Type: Research Article

Abstract: Since it satisfies all prerequisites for the growth of humanity, agriculture is currently regarded as being the most significant sector for civilization. One of the main forms of human energy production is thought to be plants, which also provide nutrients, cures, etc. Any damage or disease brought on by exposure to pathogens, viruses, bacteria, etc., while cultivating plants results in a decline in productivity, making it crucial to prevent such diseases and take the required precautions to avoid them. Accurately identifying such fatal diseases is a crucial first step for both the businesses and farmers. Six different Convolutional Neural Networks …(CNNs) that accept plant leaf images as input, along with the Enhanced Symbiotic Organism Search (ESOS) optimization algorithm, have been implemented in our research. We intend to extensively contrast the various models based on accuracy, precision, recall, and F1-score. In the area of image recognition and classification, convolutional neural networks (CNNs), in particular, and deep learning, in general, are developing. The literature contains a variety of CNN designs. The dataset size, the number of classes, the model’s weights, hypermeters, and optimizers are a few examples of the variables that have an impact on a CNN model’s performance. Because of its benefits, transfer learning and fine-tuning a pre-trained model are now very popular. This study examines the impact of six popular CNN models: DenseNet, MobileNet, EfficientNet, VGG19, ResNet and Inception. As a result, DenseNet demonstrates an optimal accuracy rate of 98% when compared to other models. Show more

Keywords: Plant disease detection, tomato plant leaf disease detection, deep learning, CNN, DenseNet, MobileNet, EfficientNet, VGG19, ResNet and inception

DOI: 10.3233/JIFS-232067

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2483-2494, 2024

Authors: Jenifer, L. | Radhika, S.

Article Type: Research Article

Abstract: Cardiovascular disease is the leading cause of death and more than half million people were died around the world. However, cardiovascular health monitoring is crucial for effective heart disease diagnosis and management. In this paper, a novel deep learning-based YOLO-ECG model is proposed to ECG arrhythmia classification method for portable monitoring. Initially, the ECG signals are gathered using 12-lead electrodes in the real time and these signals are denoised using two-dimensional stationary wavelet transform (2D-SWT). In SWT, zeros are inserted between filter taps rather than decimal points to eliminate repetitions and increase robustness. The denoised ECG signals are fed into …the deep learning-based YOLO network with Gaussian error linear unit (GELU) activation function for detecting the ECG abnormalities of arrythmia. ECG waveforms are analyzed for the local fractal dimension at each sample point before heartbeat waveforms are extracted within a set length window. A squeeze and excitation attention (SEAN) module is introduced in the YOLO network for selecting size of 1D convolution kernel, and the dimension is preserved during local cross-channel interactions, decrease network complexity and enhance model efficiency. The classification findings demonstrate that the proposed YOLO-ECG model performs better by ECG recordings from the MIT-BIH arrhythmia dataset. From the experimental analysis, the proposed YOLO-ECG model yields the overall accuracy of 99.16% for efficient classification of arrythmia ECG signals. Show more

Keywords: Arrythmia classification, ECG signal, deep learning, 2D stationary wavelet transform, YOLO network

DOI: 10.3233/JIFS-235858

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2495-2505, 2024

Authors: Jiang, Xianliang | Yang, Ze | Huang, Junkai | Jin, Guang | Yu, Guitao | Zhang, Xi | Qin, Zhen

Article Type: Research Article

Abstract: Rivers serve as vital water sources, maintain ecological equilibrium, and enhance landscapes. However, the looming issue of floating debris stemming from improper waste disposal and illegal discharge, poses an imminent threat to river ecosystems and their aesthetic appeal. Conventional human-led inspections prove labor-intensive, inefficient, and prone to errors. This study introduces an innovative approach for river debris detection, employing Unmanned Aerial Vehicles (UAVs) imagery in conjunction with a refined YOLOv5n model. This approach offers three key contributions. Primarily, the YOLOv5n model is bolstered by integrating the Efficient Channel Attention (ECA) module and reshaping the MobileNetV3 backbone to align with MobileNetV3S, …thereby significantly streamlining computational demands and model intricacy. Additionally, precision and speed are augmented by eliminating the detection head for larger targets, while decreasing computational requirements. Subsequently, to counter dataset scarcity, we curate a UAV-derived river debris dataset, encompassing five prevalent debris types, serving as an indispensable resource for method refinement and assessment. Lastly, the upgraded model’s evaluation on Jetson Nano yields an mAP of 87.2%, merely 0.7% lower than the original YOLOv5n model. Remarkably, the refined model achieves substantial reductions of 57.1% in parameters, 52.6% in volume, and 54.8% in GFLOPs. Additionally, inference time is abbreviated to 57.3ms per Jetson Nano image, 13.4ms faster than the original. These findings underscore edge computing’s potential in river restoration. In conclusion, the fusion of deep learning object detection and UAV imagery empowers adept river debris detection. Show more

Keywords: Rivers, floating debris, UAV Imagery, YOLOv5n model, edge computing

DOI: 10.3233/JIFS-234222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2507-2520, 2024

Authors: Sruthi, S. | Anuradha, B.

Article Type: Research Article

Abstract: Fire poses a significant threat to both lives and property, necessitating effective early detection measures. Despite challenges in identifying smoke and fire in their initial stages, we have devised a cost-efficient visual detection system. Early fire detection enhances its potential effectiveness. CCTV surveillance systems are now commonplace in developed countries, serving as tools for periodic monitoring of various locations. However, fluctuating ambient light conditions, camera angles, and seasonal variations can introduce data distortions, occlusions, and impact model accuracy. To address these issues, we’ve implemented a method combining deep learning networks and machine learning strategies for flame detection and direction classification. …Our innovative QuickDenseNet extracts dense features from segmented flame video frames. We introduce the Ensemble Score Voted SVM (ESV-SVM), employing SVM as the primary learner and score voting as the auxiliary learner. Our approach is rigorously evaluated through simulations, measuring accuracy and various Key Performance Indices (KPIs), including Precision, F1-score, Recall, Correlation, Error, FPR, and Correlation Coefficients. Remarkably, our proposed method achieves an impressive precision rate of approximately 99.5%. Show more

Keywords: Fire detection, ensemble learning, deep feature, CNN, video surveillance, color segmentation, dense network

DOI: 10.3233/JIFS-236387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2521-2535, 2024

Authors: Kaur, Ranjeet | Tripathi, Alka

Article Type: Research Article

Abstract: The present work is an effort to support the typographical errors of keywords that are not supported by existing compilers and integrated development environment(IDE) in ’C’ language. The fuzzy automata modelling approximate string matching is proposed for error handling during lexical analysis. By introducing fuzziness to lexemes the typographical errors can be rectified at the time of compilation and flexibility of lexical analyser can be greatly improved. The recognition of fuzzy tokens during lexical analysis is described in order to correct errors caused by sticking key, deletion, typing and swapping key in keywords during C programming. Algorithms and pseudo code …are being developed to measure the degree of membership of crisp and fuzzy lexemes. Accuracy is tested and examined once the fuzzy lexemes are trained using a neural network. The proposed method is an add on feature that can be incorporated in existing compilers and IDEs to increase their flexibility. Show more

Keywords: Fuzzy lexemes, fuzzy automata, error handling, approximate string matching, fuzzy lexical analysis

DOI: 10.3233/JIFS-223021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2537-2546, 2024

Authors: Konduru, Ashok Kumar | Mazher Iqbal, J.L.

Article Type: Research Article

Abstract: Emotion recognition from speech signals serves a crucial role in human-computer interaction and behavioral studies. The task, however, presents significant challenges due to the high dimensionality and noisy nature of speech data. This article presents a comprehensive study and analysis of a novel approach, “Digital Features Optimization by Diversity Measure Fusion (DFOFDM)”, aimed at addressing these challenges. The paper begins by elucidating the necessity for improved emotion recognition methods, followed by a detailed introduction to DFOFDM. This approach employs acoustic and spectral features from speech signals, coupled with an optimized feature selection process using a fusion of diversity measures. The …study’s central method involves a Cuckoo Search-based classification strategy, which is tailored for this multi-label problem. The performance of the proposed DFOFDM approach is evaluated extensively. Emotion labels such as ‘Angry’, ‘Happy’, and ‘Neutral’ showed a precision rate over 92%, while other emotions fell within the range of 87% to 90%. Similar performance was observed in terms of recall, with most emotions falling within the 90% to 95% range. The F-Score, another crucial metric, also reflected comparable statistics for each label. Notably, the DFOFDM model showed resilience to label imbalances and noise in speech data, crucial for real-world applications. When compared with a contemporary model, “Transfer Subspace Learning by Least Square Loss (TSLSL)”, DFOFDM displayed superior results across various evaluation metrics, indicating a promising improvement in the field of speech emotion recognition. In terms of computational complexity, DFOFDM demonstrated effective scalability, providing a feasible solution for large-scale applications. Despite its effectiveness, the study acknowledges the potential limitations of the DFOFDM, which might influence its performance on certain types of real-world data. The findings underline the potential of DFOFDM in advancing emotion recognition techniques, indicating the necessity for further research. Show more

Keywords: Hidden markov model, emotion detection, speech signal, artificial intelligence, cuckoo search, distributed diversity measures

DOI: 10.3233/JIFS-231263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2547-2572, 2024

Authors: Gao, Lijun | Zhu, Jialong | Zhang, Xuedong | Wu, Jiehong | Yin, Hang

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-231653

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2573-2584, 2024

Authors: Liu, Cong | She, Wenhao

Article Type: Research Article

Abstract: Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing …feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing. Show more

Keywords: Defect detection, image segmentation, feature fusion, deep learning, mobile camera

DOI: 10.3233/JIFS-232664

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2585-2594, 2024

Authors: Jiang, Li | Yang, Lu | Zang, Xiaoning | Dong, Junfeng | Lu, Wenxing

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-233045

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2595-2614, 2024

Authors: Zheng, Lingfei | Hu, Zhubing | Yao, Meiling | Xu, Pengwei | Ma, Jing

Article Type: Research Article

Abstract: Hand gesture recognition is important in human-computer interaction with wide applications in many fields. Different from common hand gesture recognition based on 2D images acquired from RGB camera, the utilization of 3D images provides additional spatial information about the target and attracts more and more attention in hand gesture recognition. However, most 3D images for hand gesture recognition are based on depth maps, which only take the distance information as a channel of 2D images, without taking full use of the 3D information. Besides, greater data volume of 3D images brings challenges to the arithmetic facility of hand gesture recognition. …Here, we proposed a point cloud based method for hand gesture recognition. To fully use the 3D information, plane points for template matching were extracted based on their normal distributions, which leads to the average recognition rate over 97%. Pre-classification was implemented to ensure a high-efficient recognition without additional requirements for the computer. The proposed method may provide approach for accurate and efficient hand gesture recognition based on 3D images. Show more

Keywords: Hand gesture recognition, point cloud, 3D images, template matching

DOI: 10.3233/JIFS-233120

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2615-2627, 2024

Authors: Hameed, Saira | Ahmad, Uzma | Ullah, Samee | Shah, Abdul Ghafar

Article Type: Research Article

Abstract: Fuzzy graphs are of great significance in the modeling and analysis of complex systems characterized by uncertain and imprecise information. Among various types of fuzzy graphs, cubic fuzzy graphs stand out due to their ability to represent the membership degree of both vertices and edges using intervals and fuzzy numbers, respectively. The study of connectivity in fuzzy graphs depends on understanding key concepts such as fuzzy bridges, cutnodes and trees, which are essential for analyzing and interpreting intricate networks. Mastery of these concepts enhances decision-making, optimization and analysis in diverse fields including transportation, social networks and communication systems. This paper …introduces the concepts of partial cubic fuzzy bridges and partial cubic fuzzy cutnodes and presents their relevant findings. The necessary and sufficient conditions for an edge to be a partial cubic fuzzy bridge and cubic fuzzy bridge are derived. Furthermore, it introduces the notion of cubic fuzzy trees, provides illustrative examples and discusses results relevant to cubic fuzzy trees. The upper bonds for the number of partial cubic fuzzy bridges in a complete CFG is calculated. As an application, the concept of partial cubic fuzzy bridges is used to identify cities most severely affected by traffic congestion resulting from accidents. Show more

Keywords: Fuzzy graph, connectivity, bridges, trees, cubic fuzzy graph

DOI: 10.3233/JIFS-233142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2629-2647, 2024

Authors: Mohamed Nusaf, A. | Kumaravel, R.

Article Type: Research Article

Abstract: Air pollution exerts a profound impact on both public health and the natural environment. In India, festivals like Diwali also contaminate the air by releasing pollutants into the atmosphere. It is essential to identify the most polluted region by estimating these pollutants. Since air quality assessment involves multiple air pollutants, there may be inherent uncertainty associated with data. This study employs a fuzzy Multi Attribute Decision Making (MADM) framework fuzzy Analytical Hierarchy Process-Entropy-fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FAHP-Entropy-FVIKOR) to model the impact of air pollution as a decision-making problem to address the uncertainty and assess the air quality during …the Diwali festival from 2019 to 2021 in Tamil Nadu, India. An integrated weighting approach is utilised to determine the weights of the air pollutants using a fuzzy Analytical Hierarchy Process and Entropy methods. Mainly, the fuzzy VIKOR approach is employed to rank the polluted regions. The validation of the proposed model is established through a comparative analysis using Spearman’s rank correlation with two other existing fuzzy MADM methods. Furthermore, a sensitivity analysis is conducted to evaluate the influence of priority weights and the interdependence of pollutants in determining regional rankings. The results conclude that a strong positive correlation is attained between the proposed and existing methods and the highest levels of air pollution during the festival period are observed in Gandhi Nagar (2019), Rayapuram (2020), T. Nagar, Sowcarpet and Triplicane (2021) in their respective years. These findings substantiate the consistency and effectiveness of the proposed approach. Show more

Keywords: Air pollution, entropy, fuzzy MADM, fuzzy VIKOR, fuzzy AHP

DOI: 10.3233/JIFS-233593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2649-2663, 2024