Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Zhu, Yiping | Huang, Jiajia | Zhu, Yi | Guo, Yang

Article Type: Research Article

Abstract: Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index …system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms. Show more

Keywords: Knowledge transfer, online teaching platform, rough set theory, BP neural network

DOI: 10.3233/JIFS-231381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11769-11789, 2023

Authors: Wen, Shuting | Wen, Fangcheng

Article Type: Research Article

Abstract: Culture and tourism development through public services rely on accumulated big data and overall country/ province development. Accumulated data relies on various cultures, people, places, etc. attributes for which a heterogeneous and multi-faced analysis is required. This article introduces a Development-focused Data Handling Process (D-DHP) for providing insights through culture and tourism accumulated information. The proposed process relies on heterogeneous data attributes for identifying economic and society-based development stagnancies. The data analysis is performed for identifying missing sequences and invariable information that shows development stagnancies. The stagnancy rates between successive quarters (per annum) are accounted for identifying development drops. If …such drops are identified, the accumulated data outputs are analyzed through classification learning. In this classification, the development and drop-associated data are split for an independent analysis. This analysis helps to replace the mode of development focusing on tourism or culture or both based on dependability. The classification process is updated based on the replaced information for further improvements across various accumulated data inputs. Therefore, the proposed process is viable in identifying development-focused information from the accumulated data. Show more

Keywords: Big data, classification learning, culture and tourism, public services

DOI: 10.3233/JIFS-232318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11791-11806, 2023

Authors: Chen, Xin | Wang, Yan | Li, Fuzhen

Article Type: Research Article

Abstract: A singular system, assumed to possess both regularity and freedom from impulses, is categorized as a causal system. Noncausal systems (NSs) are a class of singular systems anticipated to exhibit regularity. This study focuses on investigating zero-sum games (ZSGs) in the context of NSs. We introduce recurrence equations grounded in Bellman’s optimality principle. The saddle-point solution for multistage two-player ZSGs can be obtained by solving these recurrence equations. This methodology has demonstrated its effectiveness in addressing two-player ZSGs involving NSs. Analytical expressions that characterize saddle-point solutions for two types of two-player ZSGs featuring NSs, encompassing both linear and quadratic control …scenarios, are derived in this paper. To enhance clarity, we provide an illustrative example that effectively highlights the utility of our results. Finally, we apply our methodology to analyze a ZSG in the realm of environmental management, showcasing the versatility of our findings. Show more

Keywords: Zero-sum game, noncausal system, saddle-point solution, recurrence equations

DOI: 10.3233/JIFS-232401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11807-11833, 2023

Authors: Sun, Xiujing

Article Type: Research Article

Abstract: With the rapid development and application of internet technology, cross-border e-commerce (CBEC) has begun to popularize globally and play an important role in China’s foreign trade. The Chinese government has successively introduced multiple policies and regulations to strongly support its rapid development. Compared to the booming trend of CBEC, the development of its supply chain is slightly lacking in momentum, which has formed a certain obstacle to the overall development of CBEC. The supply chain is the foundation of successful CBEC transactions, and the foundation of the supply chain is logistics. The primary task to improve the backwardness of supply …chain development is to solve logistics problems. Therefore, while enjoying the dividends brought by the rapid development of CBEC, international logistics enterprises should continuously improve their logistics service capabilities, effectively evaluate their service quality, and then identify problems based on the evaluation results, analyze and improve them. The service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is a classical multiple attribute group decision making (MAGDM). The Spherical fuzzy sets (SFSs) provide more free space for DMs to portray uncertain information during the service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain. Therefore, this paper expands the partitioned Maclaurin symmetric mean (PPMSM) operator and IOWA operator to SFSs based on the power average (PA) technique and construct induced spherical fuzzy weighted power partitioned MSM (I-SFWPPMSM) technique. Subsequently, a novel MAGDM method is constructed based on I-SFWPPMSM technique and SFNWG technique under SFSs. Finally, a numerical example for service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is employed to verify the constructed method, and comparative analysis with some existing techniques to testy the validity and superiority of the I-SFWPPMSM technique. Show more

Keywords: MAGDM, Spherical fuzzy sets (SFSs), I-SFWPPMSM operator, Service quality evaluation

DOI: 10.3233/JIFS-233384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11835-11851, 2023

Authors: Liu, Wenxiu | Xu, Lijun | Zhou, Yijia | Yu, Bo

Article Type: Research Article

Abstract: In this paper, we propose two novel Alternating Direction Method of Multipliers (ADMM) algorithms for the sparse portfolio problem via sorted ℓ1 -norm penalization (SLOPE). The first algorithm (FADMM) is presented by adding a prediction-correction step to the classic ADMM framework. Since the problem is not strongly convex, the second fast ADMM (FADMMR) is proposed by utilizing both prediction-correction step and restarting rules. Numerical experiments show that the FADMMR algorithm converges faster than the FADMM algorithm and ADMM algorithm when tuning parameters are relatively small. On the other hand, when tuning parameters are relative large, the FADMM algorithm performs better …than the FADMMR algorithm and ADMM algorithm. The FADMM algorithm and FADMMR algorithm converge faster than the ADMM algorithm in terms of convergence time for different sizes of tuning parameters. For large-scale portfolio problem, the proposed algorithms have highly performance as well. Finally, empirical analysis on five datasets of stocks index show that the proposed algorithms are efficient and superior for solving sparse portfolio problems via SLOPE. Show more

Keywords: Fast ADMM, fast ADMM with restart, sparse portfolio, sort ℓ1-norm penalty

DOI: 10.3233/JIFS-234381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11853-11872, 2023

Authors: Jin, Huilong | Du, Ruiyan | Wen, Tian | Zhao, Jia | Shi, Lei | Zhang, Shuang

Article Type: Research Article

Abstract: Compared with other facial expression recognition, classroom facial expression recognition should pay more attention to the feature extraction of a specific region to reflect the attention of students. However, most features are extracted with complete facial images by deep neural networks. In this paper, we proposed a new expression recognition based on attention mechanism, where more attention would be paid in the channel information which have much relationship with the expression classification instead of depending on all channel information. A new classroom expression classification has also been concluded with considering the concentration. Moreover, activation function is modified to reduce the …number of parameters and computations, at the same time, dropout regularization is added after the pool layer to prevent overfitting of the model. The experiments show that the accuracy of our method named Ixception has an maximize improvement of 5.25% than other algorithms. It can well meet the requirements of the analysis of classroom concentration. Show more

Keywords: Deep learning, classroom facial expression recognition, attention mechanism, activation function, dropout regularization

DOI: 10.3233/JIFS-235541

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11873-11882, 2023

Authors: Luo, Dang | Ambreen, Muffarah | Latif, Assad | Wang, Xiaolei | Samreen, Mubbarra | Muhammad, Aown

Article Type: Research Article

Abstract: Almost all cities of Pakistan are economically affected by the electricity shortage due to the continuously increasing demand for electricity. To correctly forecast the seasonal fluctuations of the electricity consumption of Lahore city in Pakistan, we proposed the SDGPM(1,1,N) model, which is a seasonal discrete grey polynomial model combined with seasonal adjustment. We conducted an empirical analysis using the proposed model based on the seasonal electricity consumption data of Lahore city in Pakistan from 2014 to 2021. The findings from the SDGPM (1,1,N) model are compared with those collected through the original grey model DGPM(1,1,N) and other eight models. The …comparison’s findings demonstrated that the SDGPM(1,1,N) model is indeed capable of correctly identifying seasonal fluctuations of electricity consumption in Lahore city and its prediction accuracy is significantly higher than the original DGPM(1,1,N) model and the other seven models. The SDGPM(1,1,N) model’s forecast findings for Lahore from 2022 to 2025 indicate that the city’s energy consumption is expected to rise marginally, although there will still be significant seasonal fluctuations. It is predicted that the annual electricity consumption from 2022 to 2025 will be 26249, 26749, 27928, and 28136 with an annual growth rate of 7.18%. This forecast can provide policymakers ahead start in planning to ensure that supply and demand are balanced. Show more

Keywords: Seasonal factor, Lahore electricity forecasting, seasonal discrete grey polynomial model, seasonal DGPM(1, 1,N)

DOI: 10.3233/JIFS-231106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11883-11894, 2023

Authors: Sangeetha, J. | Priyanka, M. | Jayakumar, C.

Article Type: Research Article

Abstract: Audio Event Detection (AED) and classification of acoustic events has become a notable task for machines to interpret the auditory information around us. Nevertheless, it has been a difficult and cumbersome task to extract the most basic characteristics of acoustic events that encapsulate the fundamental elements of the audio events. Previous works on audio event classification utilized supervised pre-training as well as meta-learning approaches that happened to depend on labeled data therefore facing instability. Deep Learning is progressing in an increasingly mature direction, and the application of deep learning methods to detect acoustic event has become more and more sought …after. The proposed hybrid method called Greedy Regression-based Convolutional Neural Network and Differential Convex Bidirectional Gated Recurrent Unit (GRCNN-DCBGRU) is introduced to learn a vector representation of an audio sequence for Audio Event Classification (AEC). Differential Convex Bidirectional Gated Recurrent Unit is analogous to long short-term memory and involves time-cyclic long-term dependencies with a lesser processing complexity. The model first extracts acoustic features from the sound event dataset through a Differential Convex Bidirectional Gated Recurrent Unit employing Gabor Filter bank features and then extracts the local static acoustic features through the Greedy Regression-based Convolutional Neural Network by utilizing Mel Frequency Cepstral Coefficients (MFCC). Finally, the Differential Convex Meta-Learning classifier is used for the final acoustic event classification. Extensive evaluation on large-size publicly available acoustic event database like Findsounds2016 will be performed in Python programming language to demonstrate the efficiency of the proposed method for the AEC task. To demonstrate the visualizations of individual modules and their influence on overall representation learning for AEC tasks, several parameters like audio detection time, audio detection accuracy, precision, and recall are measured. Show more

Keywords: Audio event detection, audio event classification, deep learning, greedy regression, convolutional neural network, differential convex, bidirectional gated recurrent unit

DOI: 10.3233/JIFS-232561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11895-11908, 2023

Authors: Tan, Mindong | Qu, Liangdong

Article Type: Research Article

Abstract: Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, …and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance. Show more

Keywords: BP neural network, crow search algorithm, feature selection, oral English teaching, quality evaluation

DOI: 10.3233/JIFS-222455

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11909-11924, 2023

Authors: Srivatsun, G. | Thivaharan, S.

Article Type: Research Article

Abstract: Writing is a crucial component of the language requirement and is an effective method for correctly reflecting language proficiency. Manually evaluating Tamil language exams becomes time-consuming and costly for standardized language administrators as they grow in popularity. Numerous studies on computerized English assessment systems have been conducted in recent years. Due to Tamil text’s complicated grammatical structures, less research has been done on computerized evaluation methods. In this research, we present a Tamil review comment analysis system using a novel multivariate naïve Bayes classifier (mv  - NB ) where the comments are acquired from an online social network and performed training …using the database for further analysis. Experiments show that the graded Kappa of 0.4239, error rate of 2.55 and precision of 85% was achieved on the online dataset by our contents grading system, which is superior in grading compared to the other widely used machine learning algorithms training on big datasets. Our findings are promising. Additionally, our contents analysis may provide beneficial criticism on Tamil writing on YouTube posts including comments, spelling errors and morphological issues that help to analyze thelanguage correlation. Show more

Keywords: Writing, Tamil content, grading system, reviews, morphological issues

DOI: 10.3233/JIFS-222504

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11925-11936, 2023

Authors: Cheng, Shumin | Zhou, Yan | Bao, Yanling

Article Type: Research Article

Abstract: With the increasing diversification and complexity of information, it is vital to mine effective knowledge from information systems. In order to extract information rapidly, we investigate attribute reduction within the framework of dynamic incomplete decision systems. Firstly, we introduce positive knowledge granularity concept which is a novel measurement on information granularity in information systems, and further give the calculation method of core attributes based on positive knowledge granularity. Then, two incremental attribute reduction algorithms are presented for incomplete decision systems with multiple objects added and deleted on the basis of positive knowledge granularity. Furthermore, we adopt some numerical examples to …illustrate the effectiveness and rationality of the proposed algorithms. In addition, time complexity of the two algorithms are conducted to demonstrate their advantages. Finally, we extract five datasets from UCI database and successfully run the algorithms to obtain corresponding reduction results. Show more

Keywords: Incomplete decision system, positive knowledge granularity, incremental attribute reduction

DOI: 10.3233/JIFS-230349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11937-11947, 2023

Authors: Wei, Qiuyue | Yang, Dong | Zhang, Mingjie

Article Type: Research Article

Abstract: Aspect-based sentiment analysis is a fine-grained task in the field of sentiment analysis. Various GCN approaches have recently emerged to work on this, but many approaches ignored the critical role of aspectual word information and the effect of noise. In view of this situation, we propose an aspect-based word embedding graph convolutional network (AWEGCN) model. In order to make good use of the aspect information and distinguish the contextual information that is more important for a particular aspect, the aspect information is embedded in the output of the hidden layer. To reduce the noise effect when multiple aspect words appear …in a sentence, after going through the bidirectional graph convolutional network, the aspect information is embedded. A specific contextual representation is computed through an attention mechanism, which is used as the final classification feature. Experiments show that our model achieves impressive performance on five public datasets, and we also apply BERT and XLNet pre-trained models to this task and obtain advanced results that validate the effectiveness of our model. Show more

Keywords: Aspect-level sentiment classification, aspect word embeddings, graph convolutional networks, attention mechanisms

DOI: 10.3233/JIFS-230537

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11949-11962, 2023

Authors: Cheng, Tao | Cheng, Hua | Fang, Yiquan | Liu, Yufei | Gao, Caiting

Article Type: Research Article

Abstract: As prototype-based Few-Shot Learning methods, Prototypical Network generates prototypes for each class in a low-resource state and classify by a metric module. Therefore, the quality of prototypes matters but they are inaccurate from the few support instances, and the domain-specific information of training data are harmful to the generalizability of prototypes. We propose a C onceptual P rototype (CP), which contains both rich instance and concept features. The numerous query data can inspire the few support instances. An interactive network is designed to leverage the interrelation between support set and query-detached set to acquire a rich Instance Prototype which is …typical on the whole data. Besides, class labels are introduced to prototype by prompt engineering, which makes it more conceptual. The label-only concept makes prototype immune to domain-specific information in training phase to improve its generalizability. Based on CP, C onceptual P rototypical C ontrastive L earning (CPCL) is proposed where PCL brings instances closer to its corresponding prototype and pushes away from other prototypes. “2-way 5-shot” experiments show that CPCL achieves 92.41% accuracy on ARSC dataset, 2.30% higher than other prototype-based models. Meanwhile, the 0-shot performance of CPCL is comparable to Induction Network in the 5-shot way, indicating that our model is adequate for 0-shot tasks. Show more

Keywords: Prototypical network, text classification, Few-Shot learning, prompt learning

DOI: 10.3233/JIFS-231570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11963-11975, 2023

Authors: AlAlaween, Wafa’ H. | AlAlawin, Abdallah H. | AbuHamour, Saif O. | Gharaibeh, Belal M.Y. | Mahfouf, Mahdi | Alsoussi, Ahmad | AbuKaraky, Ashraf E.

Article Type: Research Article

Abstract: Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first …time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient. Show more

Keywords: Fuzzy logic, particle swarm optimization, radial based integrated network, right-first-time production

DOI: 10.3233/JIFS-232135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11977-11991, 2023

Authors: Uganya, G. | Bommi, R.M. | Muthu Krishnammal, P. | Vijayaraj, N.

Article Type: Research Article

Abstract: Internet of things (IoT) is a recent developing technology in the field of smart healthcare. But it is difficult to transfer the patient’s health record as a centralized network. So, “blockchain technology” has excellent consideration due to its unique qualities such as decentralized network, openness, irreversible data, and cryptography functions. Blockchain technology depends on cryptography hash techniques for safe transmission. For increased security, it transforms the variable size inputs into a constant length hash result. Current cryptographic hash algorithms with digital signatures are only able to access keys up to a size of 256 bytes and have concerns with single …node accessibility. It just uses the bits that serve as the key to access the data. This paper proposes the “Revised Elliptic Curve Cryptography Multi-Signature Scheme” (RECC-MSS) for multinode availability to find the nearest path for secure communications with the medical image as keys. Here, the input image key can be converted into an array of data that can be extended up to 512 bytes of size. The performance of the proposed algorithm is analyzed with other cryptography hash functions like Secure Hashing Algorithms (SHAs) such as “SHA224”, “SHA256”, “SHA384”, “SHA512”, “SHA3-224”, “SHA3-256”, “SHA3-384”, “SHA3-512”, and “Message Digest5” (MD5) by “One-way ANOVA” test in terms of “accuracy”, “throughput” and “time complexity”. The proposed scheme with ECC achieved the throughput of 17.07 kilobytes per 200 nano seconds, 93.25% of accuracy, 1.5 nanoseconds latency of signature generation, 1.48 nanoseconds latency of signature verification, 1.5 nanoseconds of time complexity with 128 bytes of hash signature. The RECC-MSS achieved the significance of 0.001 for accuracy and 0.002 for time complexity which are less than 0.05. From the statistical analysis, the proposed algorithm has significantly high accuracy, high throughput and less time complexity than other cryptography hash algorithms. Show more

Keywords: Internet of Things, blockchain technology, multi-signature, Secure Hash Algorithm, Revised Elliptic Curve Cryptography, medical image

DOI: 10.3233/JIFS-232802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11993-12012, 2023

Authors: Zhang, Guowei | Tang, Yutong | Tang, Hulin | Li, Wuzhi | Wang, Li

Article Type: Research Article

Abstract: Unmanned sorting technology can significantly improve the transportation efficiency of the logistics industry, and package detection technology is an important component of unmanned sorting. This paper proposes a lightweight deep learning network called EPYOLO, in which a lightweight self-attention feature extraction backbone network named EPnet is also designed. It also reduces the Floating-Point Operations (FLOPs) and parameter count during the feature extraction process through an improved Contextual Transformer-slim (CoTs) self-attention module and GSNConv module. To balance network performance and obtain semantic information for express packages of different sizes and shapes, a multi-scale pyramid structure is adopted using the Feature Pyramid …Network (FPN) and the Path Aggregation Network (PAN). Finally, comparative experiments were conducted with the state-of-the-art (SOTA) model by using a self-built dataset of express packages by using a self-built dataset of express packages, results demonstrate that the mean Average Precision (mAP) of the EPYOLO network reaches 98.8%, with parameter quantity only 11.63% of YOLOv8 s and FLOPs only 9.16% of YOLOv8 s. Moreover, compared to the YOLOv8 s network, the EPYOLO network shows superior detection performance for small targets and overlapping express packages. Show more

Keywords: Object detection, express package detection, lightweight, deep learning

DOI: 10.3233/JIFS-232874

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12013-12025, 2023

Authors: Li, Yue | Mao, Liang

Article Type: Research Article

Abstract: Automatic detection of defects in mature litchi plays a vital role in the classification of fruit grades. The existing method mainly relies on manual, it is difficult to meet the needs of different varieties of litchi various types of commodity packaging, and there are problems such as low efficiency, high cost and poor quality of goods. To address the above problems, this paper proposes an improved You Only Look Once(YOLO)v7 algorithm for the automatic detection of post-harvest mature litchi epidermal defects. First, a dataset of litchi defects (black spot, fall off, crack) was constructed, in which the train and test …sets had 4133 and 516; Next, A Simple Parameter-Free Attention(SimAM) mechanism is introduced into the original YOLOv7 backbone network, while GSconv is used in the neck instead of convolution, and the shallow network is used instead of the deep network for lateral linking, finally, the Mish function is used as the activation function. Experimental results show the precious and mAP of the original YOLOv7 are 87.66% and 88.98%, and those of the improved YOLOv7 are 91.56% and 93.42%, improvements of 3.9% and 4.44%. A good foundation is laid for the automated classification of ripe litchi after harvesting. Show more

Keywords: YOLOv7, litchi epidermal defects, SimAM, GSconv, shallow networks

DOI: 10.3233/JIFS-233440

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12027-12036, 2023

Authors: He, Ping | Chen, Jingfang

Article Type: Research Article

Abstract: In this paper, a question answering method is proposed for educational knowledge bases (KBQA) using a question-aware graph convolutional network (GCN). KBQA provides instant tutoring for learners, improving their learning interest and efficiency. However, most open domain KBQA methods model question sentences and candidate answer entities independently, limiting their effectiveness. The proposed method extracts description information and query entity sets for a specific question, processes them with Transformer and pre-trained embeddings of the knowledge base, and extracts a subgraph of candidate answer sets from the knowledge base. The node information is updated by GCN with two attention mechanisms expressed by …the question description and query entity set, respectively. The query description information, query entity set, and candidate entity representation are fused to calculate the score and predict the answer. Experiments on MOOC Q&A dataset show that the proposed method outperforms benchmark models. Show more

Keywords: Educational knowledge base, data-driven intelligent education, question answering method, Graph convolutional network (GCN), prediction accuracy

DOI: 10.3233/JIFS-233915

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12037-12048, 2023

Authors: Wang, Yashao

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-234605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12049-12063, 2023

Authors: Zhao, Jie | Wang, Shuo | Wu, Haotian

Article Type: Research Article

Abstract: To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2 ) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In …addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOC UC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOC B was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified. Show more

Keywords: DC microgrid, model predictive control, mixed energy storage, objective function, secondary planning

DOI: 10.3233/JIFS-234849

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12065-12077, 2023

Authors: Maguluri, Lakshmana Phaneendra | Vinya, Viyyapu Lokeshwari | Goutham, V. | Uma Maheswari, B. | Kumar, Boddepalli Kiran | Musthafa, Syed | Manikandan, S. | Srivastava, Suraj | Munjal, Neha

Article Type: Research Article

Abstract: Depression is a prevalent mental health disorder that affects people of all ages and origins; therefore, early detection is essential for timely intervention and support. This investigation proposes a novel method for detecting melancholy in young, healthy individuals by analysing their gait and balance patterns. In order to accomplish this, a comprehensive system is designed that incorporates cutting-edge technologies such as a Barometric Pressure Sensor, Beck Depression Inventory (BDI), and t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. The system intends to capitalize on the subtle motor and physiological changes associated with melancholy, which may manifest in a person’s gait and balance. …The Barometric Pressure Sensor is used to estimate variations in altitude and vertical velocity, thereby adding context to the evaluation. The mood states of participants are evaluated using the BDI, a well-established psychological assessment instrument that provides insight into their emotional health. Integrated and pre-processed data from the Barometric Pressure Sensor, BDI responses, and gait and balance measurements. The t-SNE algorithm is then used to map the high-dimensional data into a lower-dimensional space while maintaining the local structure and identifying underlying patterns within the dataset. The t-SNE algorithm improves visualization and pattern recognition by reducing the dimensionality of the data, allowing for a more nuanced analysis of depression-related markers. As the proposed system combines objective physiological measurements with subjective psychological assessments, it has the potential to advance the early detection and prediction of depression in young, healthy individuals. The results of this exploratory study have implications for the development of non-intrusive and easily accessible instruments that can assist healthcare professionals in identifying individuals at risk and implementing targeted interventions. Show more

Keywords: Depression, barometric pressure sensor, beck depression inventory, t-SNE, mental health

DOI: 10.3233/JIFS-235058

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12079-12093, 2023

Authors: Vidhya, R. | Banavath, Dhanalaxmi | Kayalvili, S. | Naidu, Swarna Mahesh | Charles Prabu, V. | Sugumar, D. | Hemalatha, R. | Vimal, S. | Vidhya, R.G.

Article Type: Research Article

Abstract: Early Alzheimer’s disease detection is essential for facilitating prompt intervention and enhancing the quality of care provided to patients. This research presents a novel strategy for the diagnosis of Alzheimer’s disease that makes use of sophisticated sampling methods in conjunction with a hybrid model of deep learning. We use stratified sampling, ADASYN (Adaptive Synthetic Sampling), and Cluster- Centroids approaches to ensure a balanced representation of Alzheimer’s and non-Alzheimer’s cases during model training in order to meet the issues posed by imbalanced data distributions in clinical datasets. This allows us to solve the challenges posed by imbalanced data distributions in clinical …datasets. A strong hybrid architecture is constructed by combining a Residual Neural Network (ResNet) with Residual Neural Network (ResNet) units. This architecture makes the most of both the feature extraction capabilities of ResNet and the capacity of LSTM to capture temporal dependencies. The findings demonstrate that the model is superior to traditional approaches to machine learning and single-model architectures in terms of accuracy, sensitivity, and specificity. The hybrid deep learning model demonstrates exceptional capabilities in identifying early indicators of Alzheimer’s disease with a high degree of accuracy, which paves the way for early diagnosis and treatment. In addition, an interpretability study is carried out in order to provide light on the decision-making process underlying the model. This helps to contribute to a better understanding of the characteristics and biomarkers that play a role in the identification of Alzheimer’s disease. In general, the strategy that was provided provides a promising foundation for accurate and reliable Alzheimer’s disease identification. It does this by harnessing the capabilities of hybrid deep learning models and sophisticated sampling approaches to improve clinical decision support and, as a result, eventually improve patient outcomes. Show more

Keywords: Alzheimer’s disease, Residual Neural Network (ResNet), Residual Neural Network (ResNet), Cluster Centroids, stratified sampling, ADASYN (Adaptive Synthetic Sampling)

DOI: 10.3233/JIFS-235059

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12095-12109, 2023

Authors: Nandhini, Ramesh Sneka | Lakshmanan, Ramanathan

Article Type: Research Article

Abstract: Cyber-physical systems (CPS) play a pivotal role in various critical applications, ranging from industrial automation to healthcare monitoring. Ensuring the reliability and accuracy of sensor data within these systems is of paramount importance. This research paper presents a novel approach for enhancing fault detection in sensor data within a cyber-physical system through the integration of machine learning algorithms. Specifically, a hybrid ensemble methodology is proposed, combining the strengths of AdaBoost and Random Forest with Rocchio’s algorithm, to achieve robust and accurate fault detection. The proposed approach operates in two phases. In the first phase, AdaBoost and Random Forest classifiers are …trained on a diverse dataset containing normal and faulty sensor data to develop individual base models. AdaBoost emphasizes misclassified instances, while Random Forest focuses on capturing complex interactions within the data. In the second phase, the outputs of these base models are fused using Rocchio’s algorithm, which exploits the similarities between faulty instances to improve fault detection accuracy. Comparative analyses are conducted against individual classifiers and other ensemble methods to validate the effectiveness of the hybrid approach. The results demonstrate that the proposed approach achieves superior fault detection rates. Additionally, the integration of Rocchio’s algorithm significantly contributes to the refinement of the fault detection process, effectively leveraging the strengths of AdaBoost and Random Forest. In conclusion, this research offers a comprehensive solution to enhance fault detection capabilities in cyber-physical systems by introducing a novel ensemble framework. By synergistically combining AdaBoost, Random Forest, and Rocchio’s algorithm, the proposed methodology provides a robust mechanism for accurately identifying sensor data anomalies, thus bolstering the reliability and performance of cyber-physical systems across a multitude of critical applications. Show more

Keywords: Cyber-physical systems, fault detection, sensor data, ensemble learning, random forest

DOI: 10.3233/JIFS-235809

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12111-12122, 2023

Authors: Ng, Grace Yee Lin | Ang, Kim Loon | Tan, Shing Chiang | Ong, Chia Sui | Ngeow, Yun Fong

Article Type: Research Article

Abstract: Multilocus variable number tandem repeat analysis (MLVA) utilizes short DNA repeat polymorphism in genomes, which is termed variable number tandem repeat (VNTR), to differentiate closely related organisms. One research challenge is to find an optimal set of VNTR to distinguish different members accurately. An intuitive method is to use an exhaustive search method. However, this method is not an efficient way to find optimal solutions from a dataset comprising many attributes (loci) due to the curse of dimensionality. In this study, metaheuristic methods are proposed to find an optimal set of loci combination. Basic genetic algorithm (BGA) and modified genetic …algorithm (MGA) were proposed in our previous work for this purpose. However, they require prior knowledge from an experienced user to specify the minimum number of loci for achieving good results. To impose no such expertise requirement for parameter setting, a GA with Duplicates (GAD), which allows the inclusion of duplicated loci in a chromosome (potential solution) during the search process, is developed. The study also investigates the search performance of a hybrid metaheuristic method, namely quantum-inspired differential evolution (QDE). Hunter-Gaston Discriminatory Index (HGDI) is used to indicate the discriminatory power of a loci combination. Two Mycobacterium tuberculosis MLVA datasets obtained from a public portal and a local laboratory respectively, are used. The results obtained by using exhaustive search and metaheuristic methods are first compared, followed by a performance comparison among BGA, MGA, GAD, and QDE by a statistical approach. The best-performing GA method (i.e., GAD) and QDE are selected for a performance comparison with several recent metaheuristic methods using both MLVA datasets by a statistical approach. The statistical results show that both GAD and QDE could achieve higher HGDI than the recent methods using a small but informative set of loci combination. Show more

Keywords: Variable number tandem repeat (VNTR), multiple locus VNTR analysis (MLVA), genotyping, metaheuristic algorithms, genetic algorithm

DOI: 10.3233/JIFS-231367

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12123-12142, 2023

Authors: Yaqoot, Iqra | Riaz, Muhammad | Al-Quran, Ashraf | Tehreem,

Article Type: Research Article

Abstract: This research work proposes a novel approach for multi stage decision analysis (MSDA) using innovative concepts of cubic intuitionistic fuzzy set (CIFS) theory. The paper introduces CIF-technique for order preference by similarity to ideal solution (TOPSIS) as a robust method for MSDA problems, particularly for the diagnosis of epilepsy disorders. To achieve this goal, new similarity measures (SMs) are developed for CIFS, including the Cosine angle between two vectors, a new distance measure, and the Cosine function, presented as three different types of Cosine similarity measures. The proposed CIF-TOPSIS approach is found to be suitable for precise value performance ratings …and is expected to be a viable approach for case studies in the diagnosis of epilepsy disorders. The efficiency and reliability of the proposed MSDA methods is efficiently carried through numerical examples and comparative analysis. Show more

Keywords: CIF information, CIF-TOPSIS, similarity, measures, epilepsy disorders, multi stage decision analysis

DOI: 10.3233/JIFS-232085

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12143-12166, 2023

Authors: Zhou, Zilong | Yu, Yue | Song, Chaoyang | Liu, Zhen | Shi, Manman | Zhang, Jingxiang

Article Type: Research Article

Abstract: Reducing noise in CT images and extracting key features are crucial for improving the accuracy of medical diagnoses, but it remains a challenging problem due to the complex characteristics of CT images and the limitations of existing methods. It is worth noting that multiple views can provide a richer representation of information compared to a single view, and the unique advantages of the wavelet transform in feature analysis. In this study, a novel Multi-View Weighted Feature Fusion algorithm called MVWF is proposed to address the challenge of enhancing CT image recognition utilizing wavelet transform and convolutional neural networks. In the …proposed approach, the wavelet transform is employed to extract both detailed and primary features of CT images from two views, including high frequency and low frequency. To mitigate information loss, the source domain is also considered as a view within the multi-view structure. Furthermore, AlexNet is deployed to extract deeper features from the multi-view structure. Additionally, the MVWF algorithm introduces a balance factor to account for both specific information and global information in CT images. To accentuate significant multi-view features and reduce feature dimensionality, random forest is used to assess feature importance followed by weighted fusion. Finally, CT image recognition is accomplished using the SVM classifier. The performance of the MVWF algorithm has been compared with classical multi-view algorithms and common single-view methods on COVID-CT and SARS-COV-2 datasets. The experimental results indicate that an average improvement of 6.8% in CT image recognition accuracy can be achieved by utilizing the proposed algorithm. Particularly, the MVF algorithm and MVWF algorithm have attained AUC values of 0.9972 and 0.9982, respectively, under the SARS-COV-2 dataset, demonstrating outstanding recognition performance. The proposed algorithms can capture more robust and comprehensive high-quality feature representation by considering feature correlations across views and feature importance based on Multi-view. Show more

Keywords: Multi-view, CT image recognition, feature fusion, wavelet transform, random forest

DOI: 10.3233/JIFS-233373

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12167-12183, 2023

Authors: Xu, Le | Wang, Jinghua | Kuang, Ciwei | Xu, Yong

Article Type: Research Article

Abstract: The 0-1 grid method is commonly used to divide a fire building into fully passable and fully impassable areas. Firefighters are only able to perform rescue tasks in the fully passable areas. However, in an actual building fire environment, there are three types of areas: fully impassable areas (areas blocked by obstacles or with heavy smoke and fire), fully passable areas, and partially passable areas (areas without obstacles or fire, but with some smoke risk). Due to the urgency of rescue, firefighters can consider conducting rescue tasks in both fully passable and partially passable areas to save valuable rescue time. …To address this issue, we propose a three-value grid method, which classifies the fire environment into fully impassable areas, fully passable areas, and partially passable areas, represented by 1, 0, and 0.5, respectively. Considering that the ACO algorithm is prone to local optimum, we propose an enhanced ant colony algorithm (EACO) to solve the fire rescue path planning problem. The EACO introduces an adaptive heuristic function, a new pheromone increment strategy, and a pheromone segmentation rule to predict the shortest rescue path in the fire environment. Moreover, the EACO takes into account both the path length and the risk to balance rescue effectiveness and safety. Experiments show that the EACO obtains the shortest rescue path, which demonstrates its strong path planning capability. The three-value grid method and the path planning algorithm take reasonable application requirements into account. Show more

Keywords: Fire rescue, path planning, 0-1 grid method, three-value grid method, EACO

DOI: 10.3233/JIFS-233862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12185-12200, 2023

Authors: Deng, Qiao

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-234396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12201-12212, 2023

Authors: Xing, Yu-Xuan | Wang, Jie-Sheng | Zhang, Shi-Hui | Bao, Yin-Yin | Zheng, Yue | Zhang, Yun-Hao

Article Type: Research Article

Abstract: The p-Hub allocation problem is a classic problem in location assignment, which aims to optimize the network by placing Hub devices and allocating each demand node to the corresponding Hub. A mutation Transit search (TS) algorithm with the introduction of the black hole swallowing strategy was proposed to solve the p-Hub allocation problem. Firstly, the mathematical model for the p-Hub allocation problem is established. Six mutation operators specifically designed for p-Hub allocation problem are introduced to enhance the algorithm’s ability to escape local optima. Additionally, the black hole swallowing strategy was incorporated into TS algorithm so as to accelerate its …convergence rate while ensuring sufficient search in the solution space. The improved TS algorithm was applied to optimize three p-Hub location allocation problems, and the simulation results are compared with those of the basic TS algorithm. Furthermore, the improved TS algorithm is compared with the Honey Badger Algorithm (HBA), Sparrow Search Algorithm (SSA), Harmony Search Algorithm (HS), and Particle Swarm Optimization (PSO) to solve three of p-Hub allocation problems. Finally, the impact of the number of Hubs on the cost of three models was studied, and the simulation results validate the effectiveness of the improved TS algorithm. Show more

Keywords: p-Hub allocation problem, transit search algorithm, black hole strategy, mutation operator

DOI: 10.3233/JIFS-234695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12213-12232, 2023

Authors: Migdadi, Hatim Solayman | Al-Olaimat, Nesreen M.

Article Type: Research Article

Abstract: In this paper, a new extension of the standard Rayleigh distribution called the Power Rayleigh distribution (PRD) is investigated for the accelerated life test (ALT) using the geometric process (GP) under Type-I censored data. Point estimates of the formulated model parameters are obtained via the likelihood estimation approach. In addition, interval estimates are obtained based on the asymptotic normality of the derived estimators. To evaluate the performance of the obtained estimates, a simulation study of 4, 5 and 6 levels of stress is conducted for ALT in different combinations of sample sizes and censored times. Simulation results indicated that point …estimates are very close to their initial true values, have small relative errors, are robust and are efficient for estimating the model parameters. Similarly, the interval estimates have small lengths and their coverage probabilities are almost converging to their 95% nominated significance level. The estimation procedure is also improved by the approach of finding optimum values of the acceleration factor to have optimum values for the reliability function at the specified design stress level. This work confirms that PRD has the superiority to model the lifetimes in ALT using GP under any censoring scheme and can be effectively used in reliability and survival analysis. Show more

Keywords: Accelerated life test, geometric process, power ryleigh distribution, maximum likelihood estimation, optimum test plan

DOI: 10.3233/JIFS-232084

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12233-12242, 2023

Authors: Jiang, Shaojie | Wu, Jiang

Article Type: Research Article

Abstract: Point-of-Interest (POI) recommendation is one of the most important tasks in the field of social network analysis. Many efforts have been proposed to enhance the model performance for the POI recommendation task in recent years. Existing studies have revealed that the temporal factor and geographical factor are two crucial contextual factors which influence user decisions. However, they only learn representations of POIs and users from the single contextual factor and fuse the learned representations in the final stage, which ignores the interactions of different contextual factors, leading to learning suboptimal representations of POIs and users. To overcome this gap, we …propose a novel Temporal-Geographical Attention-based Transformer (TGAT) for the POI recommendation task. Specifically, TGAT develops a hybrid sequence sampling strategy that samples the sequence of POIs from the different contextual factor POI graphs generated by the users’ check-in records. In this way, the interactions of different contextual factors can be care-fully pre-served. Then TGAT conducts a Transformer-based neural network backbone to learn representations of POIs from the sampling sequences. In addition, a weighted aggregation strategy is proposed to fuse the representations learned from different context factors. The extensive experimental results on real-world datasets have demonstrated the effectiveness of TGAT. Show more

Keywords: Point-of-interest, social network, contextual factor, hybrid sequence sampling, transformer

DOI: 10.3233/JIFS-234824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12243-12253, 2023

Authors: Feng, Dongmei | Kang, Yifan

Article Type: Research Article

Abstract: With the continuous development of China’s economic system, the development of the construction industry is becoming more and more rapid, and the number and scale of construction projects are increasing. Due to the characteristics of large projects and long cycles, there are a large number of construction parties involved in construction projects. The increase in the number of participating partners makes it difficult for their projects to be integrated and managed by management departments such as owners, let alone for various parties to collaborate in the construction of projects. In order to effectively solve this problem, the engineering procurement construction …(EPC) general contracting model has emerged. The risk assessment of EPC project is classical multiple attributes group decision making (MAGDM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the risk assessment of EPC project. In this paper, the classical grey relational analysis (GRA) method is extended to PHFSs. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are introduced. Then, the definition of the score values is employed to obtain the attribute weights based on the information entropy. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MAGDM under PHFSs. Finally, a practical case study for risk assessment of EPC project is designed to validate the proposed method and some comparative studies are also designed to verify the applicability. Show more

Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets, grey relational analysis method (GRA), information entropy, risk assessment of EPC project

DOI: 10.3233/JIFS-231726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12255-12266, 2023

Authors: Jiang, Wenchao | Yang, Xiaolei | Zang, Yuqi | Yuan, Xumei | Liu, Rui

Article Type: Research Article

Abstract: In view of the technical defects of the existing grey relational projection method, a new grey compromise relational bidirectional projection method is proposed. By incorporating the information expression advantage of picture hesitant fuzzy number, the distance formula of picture hesitant fuzzy statistics is constructed based on the centralized trend measurement and discrete trend measurement in descriptive statistics. On this basis, a multi-attribute recommendation method of picture hesitant fuzzy grey compromise relational bidirectional projection is proposed by combining compromise idea and bidirectional projection technology. The validity and advantage of this method are verified by numerical analysis, which also suggested the rationality …of the picture hesitant fuzzy statistical distance and the grey compromise relational bidirectional projection method. Show more

Keywords: Picture hesitant fuzzy number, grey compromise relational, bidirectional projection, multi-attribute recommendation

DOI: 10.3233/JIFS-233016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12267-12278, 2023

Authors: Wang, Jia-Li | Jiang, Wen-Qi | Tao, Xi-Wen | Yang, Shan-Shan

Article Type: Research Article

Abstract: The processing method of fuzzy information is a critical element in multi-criteria group decision-making (MCGDM). The hesitant Pythagorean fuzzy set (HPFS) has a higher capacity in express the uncertainty of human inherent preference. A composite weighted mathematical programming model with prospect theory and best-worst method (BWM) is proposed to solve the uncertainty of criterion weight acquisition and decision-makers (DMs) psychological behavior under the HPF environment. The decision-making process is as follows: Firstly, a novel spatial distance measurement method is designed which considers the extension space of HPFSs space by five parameters under the HPF environment. Secondly, the optimal criteria weights …model minimizes the total distance between the alternatives and the HPF positive ideal solution (HPFPIS), as well as minimizes the consistency ratio of BWM. Thirdly, we propose the prospect decision matrix by the prospect theory and optimal weights, then use the ordered weighted average operator under the normal distribution to calculate the weight of DMs and rank the decision alternatives. Finally, an example is illustrated here, sensitivity and reliability, and comparative analysis are conducted to verify the effectiveness of the proposed method. Show more

Keywords: Multiple-criteria group decision-making, BWM, prospect theory, mathematical programming model, combination weights, spatial distance measure

DOI: 10.3233/JIFS-233339

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12279-12299, 2023

Authors: Wang, Lei

Article Type: Research Article

Abstract: The core of logistics is scheduling and monitoring. After the modern interprise logistics development concept change, the development prospect of enterprise logistics is more optimistic. Major enterprises have begun to use intelligent logistics scheduling platforms. In order to solve the problem that heterogeneous information fusion is complex in the temporal heterogeneous graphs, this paper proposes to dynamically store and update node representation through an augmented memory matrix in a memory network. At the same time, the model also designs a novel read-write module for the memory matrix, which can effectively capture the timing information in the long interaction sequence and …has high flexibility. The model has significantly improved in tasks such as node classification, timing recommendation and visualization. This paper studies the logistics supply chain of modern enterprises and establishes the mathematical model of vehicle scheduling. This paper takes the non-full load scheduling model as the critical research object. Based on the research of logistics supply chain, the vehicle scheduling model is established. The intelligent heuristic algorithm is applied to solve it, and the effective vehicle distribution scheme and driving route are formed. The simulation results show that the approximate Pareto optimal solution obtained by our designed model and algorithm has good robustness. NSGAIIROELSDR can get a better solution in small-scale scheduling. However, in large-scale numerical experiments, the final solution obtained by MOEA/DROELSDR is obviously better than that of NSGAIIROELSDR, and the running time of MOEA/DROELSDR is also shorter. Therefore, we conclude that MOEA/DROELSDR is more suitable for large-scale scheduling, and NSGAIIROELSDR is more suitable for more minor scheduling. Show more

Keywords: Logistics scheduling, heterogeneous graph neural network, edge feature coding, memory network

DOI: 10.3233/JIFS-234562

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12301-12312, 2023

Authors: Mannanuddin, Khaja | Vimal, V.R. | Srinivas, Angalkuditi | Uma Mageswari, S.D. | Mahendran, G. | Ramya, J. | Kumar, Ashok | Das, Pranjal | Vidhya, R.G.

Article Type: Research Article

Abstract: Diseases of the retina continue to be a leading cause of blindness and visual impairment around the world. In the field of medical image analysis, specifically retinal disease identification, deep learning techniques, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have showed remarkable potential. In this paper, we present a unique method for detecting retinal diseases by combining the advantages of the Inception-V3, ResNet-50, and Vision Transformer architectures into a single model called a Cascade CNN-ViT. The suggested Cascade CNN-ViT model extracts local features from retinal pictures by leveraging the spatial hierarchy learning capabilities of Inception-V3 and ResNet-50. …The Vision Transformer takes these regional characteristics and uses self-attention mechanisms to pick up global context information and long-range interdependence. The model successfully combines fine-grained local information with semantically significant global contextual cues by merging the output representations from the CNNs and Vision Transformer. undertaking comprehensive experiments on a large and varied dataset of multimodal retinal pictures to evaluate the performance of the proposed technique. Cascade CNN-ViT model outperforms standalone CNNs and Vision Transformers, as shown by the experimental findings. The model is also resilient across all classes of retinal diseases and is able to successfully deal with the complications introduced by using multiple picture types. Overall, the power of cascading Inception-V3, ResNet-50, and Vision Transformer topologies for improved retinal illness diagnosis has been demonstrated. Potentially improving the management of retinal illnesses and preserving visual health, the proposed approach could have important consequences for early detection and timely intervention. Show more

Keywords: Multimodal retinal images, deep learning, Inception-V3, vision transformer, cascade CNN-ViT

DOI: 10.3233/JIFS-235055

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12313-12328, 2023

Authors: Liu, Qingyang | Yahyapour, Ramin

Article Type: Research Article

Abstract: The considerable fluctuation of the stock market caused by COVID-19 tends to have a tremendous and long-lasting adverse impact on the economy. In this work, we propose a novel methodology to investigate this impact on the Chinese medical stock market. We examine changes in the stock network structure using the Triangulated Maximally Filtered Graph (TMFG ), which is computationally faster and more adaptable to enormous datasets. Additionally, we develop the LoGo model, which combines a local-global approach in its construction, to predict the stock prices of the Chinese medical stock market. In addition to traditional predictors, we incorporate …daily new infected numbers as an additional predictor to reflect the impact of COVID-19 . We select data from the 2019-2020 period and divide it into two datasets: one for the period during COVID-19 and another for the period before COVID-19 . Firstly, we compute the grey correlation coefficients between stocks instead of standard correlation coefficients. We use these coefficients to build the TMFG , enabling us to identify which stocks played the leading roles. Subsequently, we choose six stocks to build the price prediction models. Compared with the LSTM and SVR models, the LoGo models demonstrates higher accuracy, achieving an average accuracy of 71.67 percent. Furthermore, the execution time of the Logo models is 200 times faster than that of the SVR models and 50 times faster than that of the LSTM models. Show more

Keywords: Grey relation analysis (GRA), LoGo, TMFG, Information filtering networks, Stock price

DOI: 10.3233/JIFS-232479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12329-12339, 2023

Authors: Bisht, Garima | Pal, A.K.

Article Type: Research Article

Abstract: In today’s complex decision-making environment, accounting for attribute interdependencies and expert relationships is crucial. Traditional models often assume attribute independence and overlook the significant impact of expert relationships on decision outcomes. Also, amidst the dynamic and ever-changing decision-making landscape, the effect of news and real-time updates on alternative rankings is significant. In complex decision-making environments, information is constantly evolving, and staying up-to-date with the latest developments is paramount. To overcome these limitations, this study aims to develop a novel model that effectively captures attribute dependencies and incorporates the influence of social media on alternative ordering. To establish the model, the …Decision-making trial and evaluation laboratory (DEMATEL) method and regression analysis are integrated to capture attribute dependencies. Furthermore, social network analysis (SNA) is employed to develop a trust propagation model for determining experts’ weights. Additionally, we present a two-stage multi-skilled and high potential multi-criteria decision-making (MCDM) framework, where the base-criterion method (BCM) is adopted to evaluate attribute weights and the well-known traditional Vlekriterijumsko KOmpromisno Rangiranje (VIKOR) method is redefined using Heronian mean (HM) operator to capture the relationships between arguments. Despite uncertainties, the proposed fuzzy-BCM-VIKOR-Heronian (F-BCM-VIKOR-H) approach enhances flexibility by addressing inconsistent data in complex decision-making problems. Similarly, certain news or future updates about any alternative or attribute can significantly affect the ranking. Acknowledging the significance of timely information, the proposed approach actively considers the effect of such news through the formation of an updated matrix. By factoring in the latest developments, we ensure that the proposed decision-making model remains relevant and adaptable, capturing the most current insights into alternative performance. To demonstrate the model’s effectiveness, we apply the proposed approach to a numerical illustration in the electronics industry, specifically for ranking cars. Sensitivity analysis evaluates the model’s stability, and comparing the results with existing approaches showcases its advantage and superiority. Show more

Keywords: Group decision making, VIKOR, SNA, attribute dependencies, news influence

DOI: 10.3233/JIFS-232608

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12341-12363, 2023

Authors: Khan, Majid | Batool, Syeda Iram | Munir, Noor | Alshammari, Fahad Sameer

Article Type: Research Article

Abstract: The design and development of secure nonlinear cryptographic Boolean function plays an unavoidable measure for modern information confidentiality schemes. This ensure the importance and applicability of nonlinear cryptographic Boolean functions. The current communication is about to suggest an innovative and energy efficient lightweight nonlinear multivalued cryptographic Boolean function of modern block ciphers. The proposed nonlinear confusion element is used in image encryption of secret images and information hiding techniques. We have suggested a robust LSB steganography structure for the secret hiding in the cover image. The suggested approach provides an effective and efficient storage security mechanism for digital image protection. …The technique is evaluated against various cryptographic analyses which authenticated our proposed mechanism. Show more

Keywords: Nonlinear multivalued cryptographic Boolean function, lightweight, encryption, information hiding

DOI: 10.3233/JIFS-233823

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12365-12379, 2023

Authors: Guo, Hongyue | Deng, Qiqi | Jia, Wenjuan | Wang, Lidong | Sui, Cong

Article Type: Research Article

Abstract: The implied volatility plays a pivotal role in the options market, and a collection of implied volatilities across strike and maturity is known as the implied volatility surface (IVS). To capture the dynamics of IVS, this study examines the latent states of IVS and their relationship based on the regime-switching framework of the hidden Markov model (HMM). The cross-sectional models are first built for daily implied volatilities, and the obtained regression factors are regarded as the proxies of the IVS. Then, having these latent factors, the HMM is employed to model the dynamics of IVS. Take the advantages of HMM, …the hidden state for each daily data is identified to achieve the corresponding time distribution, the characteristics, and the transition between the hidden states. The empirical study is conducted on the Shanghai 50ETF options, and the analysis results indicate that the HMM can capture the latent factors of IVS. The achieved states reflect different financial characteristics, and some of their typical features and transfer are associated with certain events. In addition, the HMM exploited to predict the regression factors of the cross-sectional models enables the further forecasting of implied volatilities. The autoregressive integrated moving average model, the vector auto-regression model, and the support vector regression model are regarded as benchmarks for comparison. The results show that the HMM performs better in the implied volatility prediction compared with other models. Show more

Keywords: Hidden Markov model, regime-switching frameworks, implied volatility surface, prediction

DOI: 10.3233/JIFS-232139

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12381-12394, 2023

Authors: Kong, Decai | Tang, Yi | Zhang, Hao | Bi, Aorui

Article Type: Research Article

Abstract: Technology trading matching facilitates quicker solution-finding for technology demanders and expedites the transformation of scientific and technological achievements. Yet, unstable matchings often lead traders to renounce existing contracts, sidestep trading intermediaries, and resort to private transactions. This results in inefficient trading mechanisms and market disarray. To ensure a stable and mutually satisfactory match for both suppliers and demanders, we propose a stable two-sided matching decision-making method that incorporates intuitionistic fuzzy multi-attribute information. Initially, we introduce an intuitionistic fuzzy TOPSIS approach to compute the comprehensive satisfaction of both suppliers and demanders by aggregating intuitionistic fuzzy information across various attributes. Subsequently, we …design a multi-objective optimization model that weighs both stability and satisfaction to determine the ideal technology trading pairs. We conclude with a real-world example that demonstrates the proposed method’s application, and its effectiveness is corroborated through sensitivity and comparative analyses. Show more

Keywords: Technology trading, two-sided matching, stable matching, intuitionistic fuzzy sets

DOI: 10.3233/JIFS-232275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12395-12409, 2023

Authors: Wang, Hao | Xu, Yanyan | Han, Yue | Zhang, Kejia

Article Type: Research Article

Abstract: With the rapid growth of the global population and the increasing urbanization, the urban landscape in China is gradually enriched, and the scale of the landscape that plays a healing role is expanding. However, curing the problem of landscape ecological security is an important part of Homeland security, economic and social sustainable development. We must deal with the relationship between high-quality social development and ecological environment protection on the basis of scientific evaluation. To address this issue, research has provided better data support for feature extraction through image preprocessing. Then the Convolutional neural network in deep learning is trained through …a large number of collected measured data. Finally, the pressure state response model is used to evaluate the ecological security of the healing landscape. The results show that the average error of the ground class in 2010 was 13.65%, and the fitting accuracy reached 86.35%, indicating that this method has high accuracy and can be effectively applied in evaluation. Meanwhile, in 2010 and 2019, the average landscape ecological security levels of City A were 7.27 and 6.65, both at a “safe” level, but the overall security level showed a downward trend. It is recommended to optimize the land use pattern in future urban planning and construction, improve the urban landscape ecological security index value, and maintain consistency with the actual situation of the city. This can provide reference for the evaluation model of urban landscape ecological security, and further provide scientific basis and guidance for the ecological civilization construction of urban agglomerations. In subsequent research, the evolution trend of urban landscape ecological security can be taken as the research goal, and finally, guidance on optimizing urban landscape ecological security can be provided. Show more

Keywords: Deep learning, PSR model, ecological security assessment

DOI: 10.3233/JIFS-233040

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12411-12424, 2023

Authors: Huang, Shuaina | Zhang, Zhiyong | Song, Bin | Mao, Yueheng

Article Type: Research Article

Abstract: Social network attackers leverage images and text to disseminate sensitive information associated with pornography, politics, and terrorism,causing adverse effects on society.The current sensitive information classification model does not focus on feature fusion between images and text, greatly reducing recognition accuracy.To address this problem, we propose an attentive cross-modal fusion model (ACMF), which utilizes mixed attention mechanism and the Contrastive Language-Image Pre-training model.Specifically, we employ a deep neural network with a mixed attention mechanism as a visual feature extractor. This allows us to progressively extract features at different levels. We combine these visual features with those obtained from a text feature …extractor and incorporate image-text frequency domain information at various levels to enable fine-grained modeling. Additionally, we introduce a cyclic attention mechanism and integrate the Contrastive Language-Image Pre-training model to establish stronger connections between modalities, thereby enhancing classification performance.Experimental evaluations conducted on sensitive information datasets collected demonstrate the superiority of our method over other baseline models. The model achieves an accuracy rate of 91.4% and an F1-score of 0.9145. These results validate the effectiveness of the mixed attention mechanism in enhancing the utilization of important features. Furthermore, the effective fusion of text and image features significantly improves the classification ability of the deep neural network. Show more

Keywords: Multi-modal, sensitive information, spatial attention mechanism, channel attention mechanism, deep learning

DOI: 10.3233/JIFS-233508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12425-12437, 2023

Authors: Nalini Joseph, L. | Anand, R.

Article Type: Retraction

DOI: 10.3233/JIFS-219330

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12439-12439, 2023