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

Authors: Paul, Milner | Adhikari, Shuma | Singh, Loitongbam Surajkumar | Parekkattil, Adarsh V. | Athappilly, George

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Abstract: This article has been retracted.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-236583

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1735-1752, 2024

Authors: Chen, Tianwen | Zhou, Ronghu | Chen, Haoliang | Liu, Changqing

Article Type: Research Article

Abstract: The main purpose of this paper is to study the coordination, price and sales effort decisions of a dual channel supply chain under live streaming commerce mode. In nowadays’ e-commerce age, more and more people have interest in live streaming especially after the outbreak of COVID-19, but the research on live streaming supply chain is lacking. To fill this gap, a supply chain composed of a manufacturer and an internet celebrity is established, in which the demand is affected by the internet celebrity’s sales effort and personal influence. Considering different power structures of the supply chain, price and sales effort …decisions are studied in four models: Nash, manufacturer dominant (MD), internet celebrity dominant (KD) and cooperative game models. Subsequently, the feasible region of bargaining game is discussed in terms to share the extra profits and coordinate the supply chain. The manufacturer and the internet celebrity can be coordinated through bargaining problem in the cooperation model, and the extra profits sharing ratio is depend on each other’s bargaining power. Numerical analysis is further provided to test the propositions and show the impacts of market share rate, internet celebrity’s commission rate and personal influence on supply chain’s performance. Show more

Keywords: Supply chain, live E-commerce, internet celebrity, sales effort, personal influence

DOI: 10.3233/JIFS-231500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1753-1769, 2024

Authors: He, Yu | Pan, Yigong | Hu, Xinying | Sun, Guangzhong

Article Type: Research Article

Abstract:  Concept prerequisite relation refers to the learning order of concepts, which is useful in education. Concept prerequisite learning refers to using machine learning methods to infer prerequisite relation of a concept pair. The process of concept prerequisite learning requires large amounts of labeled data to train classifier. Usually, the labels of prerequisite relation are assigned by specialists. The specialist labelling method is costly. Thus, it is necessary to reduce labeling expense. An effective strategy is using active learning methods. In this paper, we propose a pool-based active learning framework for concept prerequisite learning named PACOL. It is a …fact that concept u and concept v cannot be prerequisite of each other simultaneously. The idea of PACOL is to select the concept pair with the greatest deviation between the classifier’s prediction and the fact. Besides, PACOL can be used in two situations: when specialists assign three kinds of labels or two kinds of labels. In experiments, we constructed data sets for three subjects. Experimental results on both our constructed data sets and public data sets demonstrate that PACOL outperforms than existing active learning methods in all situations. Show more

Keywords: Educational data mining, prerequisite relation, active learning, Wikipedia

DOI: 10.3233/JIFS-231878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1771-1787, 2024

Authors: Naveen, Palanichamy | NithyaSai, S. | Udayamoorthy, Venkateshkumar | Ashok kumar, S.R.

Article Type: Research Article

Abstract: In the current industry, quality inspection in semiconductor manufacturing is of immense significance. Significant achievements have been made in fault diagnosis in fabricated semiconductor wafer manufacturing due to the development of machine learning. Since real-time intermediate signals are non-linear and time-varying, the signals undergo various distortions due to changes in equipment, material, and process. This leads to a drastic change in information in intermediate signals. This paper presents a fault diagnosis model for semiconductor manufacturing processes using a generative adversarial network (GAN). The study aims to address the challenges associated with efficient and accurate fault identification in these complex processes. …Our approach involves the extraction of relevant components, development of a paired generator model, and implementation of a deep convolutional neural network. Experimental evaluations were conducted using a comprehensive dataset and compared against six existing models. The results demonstrate the superiority of our proposed model, showcasing higher accuracy, specificity, and sensitivity across various shift tasks. This research contributes to the field by introducing a novel approach for fault diagnosis, paving the way for improved process control and product quality in semiconductor manufacturing. Future work will focus on further optimizing the model and extending its applicability to other manufacturing domains. Show more

Keywords: Semiconductor manufacturing, GAN, fault diagnosis, quality inspection, wafer fabrication, deep CNN

DOI: 10.3233/JIFS-231948

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1789-1800, 2024

Authors: Pitchandi@Sankaralingam, R. | Arunachalaperumal, C. | Mary Anita, E.A.

Article Type: Research Article

Abstract: Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS …method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models. Show more

Keywords: Wireless sensor networks, metaheuristics, source location privacy preserving, fitness function, routing, false data forwarding

DOI: 10.3233/JIFS-233541

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1801-1812, 2024

Authors: Zhou, Bolong | Yu, Menghong | Guo, Jie

Article Type: Research Article

Abstract: Trailing suction hopper dredger is a kind of hydraulic dredger, it has the characteristics of self-propelled, selfloading, self-dredging, self-unloading, it is the main force in dredging and blowing works, it is widely used in the world, it can be said that where there is a big dredging project where there is a trailing suction hopper dredger’s figure. The loading optimization process of trailing suction hopper dredger contains a lot of dredging parameters related to soil type, and the soil type under different working conditions is not very clear. In this study, we present a hybrid optimization technique based on simulated …annealing and multi-population genetic algorithm to enhance the loading efficiency of a trailing suction hopper dredger and to examine the variation of dredged soil parameters. The soil parameters of the spoil hopper deposition model were estimated using this hybrid optimization algorithm. The experimental results show that the soil parameters are successfully estimated and verified by our measured construction data of a trailing suction hopper dredger. In addition, our proposed method has the highest accuracy of soil parameter estimation, the fastest algorithm convergence, and excellent robustness compared to the other three intelligent optimization methods. In addition, our method successfully avoids the phenomenon of premature convergence that usually occurs in traditional genetic algorithms, and the parameters show strong adaptability to different vessels under the same dredging area. Show more

Keywords: Trailing suction hopper dredger, spoil hopper deposition model, simulated annealing and multi-population genetic algorithm, soil parameters estimation

DOI: 10.3233/JIFS-233959

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1813-1831, 2024

Authors: Li, Xingge | Zhang, Shufeng | Chen, Xun | Wang, Yashun | Fan, Zhengwei

Article Type: Research Article

Abstract: The proliferation of artificial intelligence (AI) devices has generated an increasing demand for reliability in their utilization. Nevertheless, the significant concern persists regarding the absence of suitable assessment and testing techniques to evaluate the performance of these intelligent systems in real-world conditions. In response to these issues, this paper conducts research on the reliability testing and assessment of AI visual perception systems under vibration stress. The paper introduces the working mechanism of the visual perception system and the various testing methods for AI devices. Based on this, a reliability assessment method for intelligent devices is proposed, which uses the Fréchet …distance as the measurement function and environmental adaptability as the reliability metric. Additionally, a vibration test platform for the visual perception system is established, which offers a cost-effective and reliable solution to the high cost issue of field testing for AI devices. Finally, the reliability level of the visual perception system under various vibration conditions is tested through vibration testing. The research findings indicate that the reliability of AI models decreases as the degradation caused by vibration increases, following a normal distribution. Show more

Keywords: Reliability, fréchet distance (FD), visual perception system (VPS), environmental adaptability, vibration test

DOI: 10.3233/JIFS-234179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1833-1852, 2024

Authors: Wang, Feng

Article Type: Research Article

Abstract: A real-time sharing model of energy big data based on end cloud collaboration technology is built to safely and efficiently share energy big data in all fields. Through the collaboration between the client layer and the cloud platform layer in the end cloud collaboration module, combined with the vertical federation learning algorithm and the homomorphic encryption algorithm, the energy big data knowledge in various fields is extracted and encrypted, and the encrypted knowledge is stored in the cloud platform as shared data. After the blockchain module combines the smart contract identification coding and parsing of such shared ciphertext, the ciphertext …key is provided to the data user, and the shared energy big data plaintext is obtained after decryption, so as to realize the real-time security sharing of energy big data. According to the result analysis, the model performs well in data knowledge extraction and encryption, and has a good effect in ensuring the security and reliability of energy big data sharing. At the same time, the identification coding and analysis time of shared data knowledge is relatively short, making energy big data can be shared in real time. These results demonstrate the potential and feasibility of the model in facilitating big data sharing in the energy sector. Show more

Keywords: End cloud collaboration, energy big data, real-time sharing, client, cloud platform, blockchain

DOI: 10.3233/JIFS-234892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1853-1865, 2024

Authors: Shanthi, A.S. | Ignisha Rajathi, G. | Velumani, R. | Srihari, K.

Article Type: Research Article

Abstract: In older people, mild cognitive impairment (MCI) is a precursor to more severe forms of dementia like AD (AD). In diagnosing patients with primary AD and amnestic MCI, modern neuroimaging techniques, especially MRI, play a key role. To efficiently categorize MRI images as normal or abnormal, the research presents a machine learning-based automatic labelling system, with a focus on boosting performance via texture feature analysis. To this end, the research implements a preprocessing phase employing Log Gabor filters, which are particularly well-suited for spatial frequency analysis. In addition, the research uses Gray Wolf Optimization (GWO) to acquire useful information from …the images. For classification tasks using the MRI images, the research also make use of DenseNets, a form of deep neural network. The proposed method leverages Log Gabor filters for preprocessing, Gray Wolf Optimization (GWO) for feature extraction, and DenseNets for classification, resulting in a robust approach for categorizing MRI images as normal or abnormal. When compared to earlier trials performed without optimization, the proposed systematic technique shows a significant increase in classification accuracy of 15%. For neuroimaging applications, our research emphasizes the use of Log Gabor filters for preprocessing, GWO for feature extraction, and DenseNets for classification, which can help with the early detection and diagnosis of MCI and AD. Show more

Keywords: Dementia, mild cognitive impairment, MRI, AD, Gray Wolf Optimization, DenseNets, log gabor filter

DOI: 10.3233/JIFS-235118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1867-1879, 2024

Authors: Wang, Kejun | Zhang, Hebing

Article Type: Research Article

Abstract: With the ongoing evolution of the novel coronavirus pathogen and continuous improvements in our social environment, the mortality rate of COVID-19 is on a decline. In response to this, we introduce an adaptive control strategy known as intentional control, which offers cost-efficiency and superior control effectiveness. The classical SEIR model faces limitations in accurately representing close contacts and sub-close contacts and fails to distinguish their varying levels of infectivity. To address this, our study modifies the classical model by incorporating close contact (E) and a sub-close contact (E2) while reworking the infectious mechanism. Once the model is formulated, we employ …various statistical methods to identify crucial parameters, including R 2 , adjusted R 2 , and standard deviation. For disease control, we implement an intentional control program with four distinct grades. We develop and apply a scheme in MATLAB for our proposed model, generating diverse simulation results based on realistic parameter values for discussion. Additionally, we explore a range of strategy combinations to differentiate their effectiveness under various social conditions, aiming to identify an optimal approach. Comparing the intentional control strategy to random control, our findings consistently demonstrate the superiority of intentional control across all scenarios. Furthermore, the results indicate that our approach better aligns with the characteristics of the novel coronavirus, characterized by an “extremely low fatality rate and strong infectivity,” while offering detailed insights into the transmission dynamics among different compartments. Show more

Keywords: COVID-19, SEIR model, intentional control

DOI: 10.3233/JIFS-235149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1881-1898, 2024