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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Pandey, Vibha | Choubey, Siddhartha | Patra, Jyotiprakash | Mall, Shachi | Choubey, Abha
Article Type: Research Article
Abstract: Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic …License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. Show more
Keywords: Data security, secure image analysis, automatic license plate recognition, segmentation, image classification, convolution neural network, character recognition
DOI: 10.3233/JIFS-235400
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Wu, Chengding | Xu, Zhaoping | Liu, Liang | Yang, Tao
Article Type: Research Article
Abstract: There are limitations of personalization in Advanced Driver Assistance Systems (ADAS) that have a serious impact on driver acceptance and satisfaction. This study investigates driving style recognition method to achieve personalization of longitudinal driving behavior. Currently, driving style recognition algorithms for Personalized Adaptive Cruise Control (PACC) rely on integrated recognition. However, disturbances in the driving cycle may lead to changes in a driver’s integrated driving style. Therefore, the integrated driving style cannot accurately and comprehensively reflect the driver’s driving style. To solve this problem, a new driving style recognition method for PACC is proposed, which considers integrated driving style and …driving cycle. Firstly, the method calculates the constructed feature parameters of driving cycle and style, and then reduces the dimensionality of the feature parameter matrix by principal component analysis (PCA). Secondly, a two-stage clustering algorithm with self-organizing mapping networks and K-means clustering (SOM-K-means) is used to obtain the type labels. Then, a transient recognition model based on random forest (RF) is established and the hyperparameters of this model are optimized by sparrow search algorithm (SSA). Based on this, a comprehensive driving style recognition model is established using analytic hierarchy process (AHP). Finally, the validity of the proposed method is verified by a natural dataset. The method incorporates the driving cycle into driving style recognition and provides guidance for improving the personalization of adaptive cruise control system. Show more
Keywords: Personalized adaptive cruise control, SOM-K-means two-stage clustering, random forest (RF), sparrow search algorithm (SSA), driving style recognition
DOI: 10.3233/JIFS-235045
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Atef, Shimaa | El-Seidy, Essam | Reda, Naglaa M.
Article Type: Research Article
Abstract: Decisions in many dilemmas are based on a combination of factors, including as incentive, punishment, reputation, and memory. The impact of memory information on cooperative evolution in multi-round games is a decision-making process in group evolution. The iterated prisoner’s dilemma is an excellent model for the development of cooperation amongst the payoff-maximizing individuals. Since tit-for-tat proved successful in Axelrod’s repeated prisoner’s dilemma tournaments, there has been a great deal of interest in creating new strategies. Every iterative prisoner’s dilemma method bases its decision-making on a specific duration of past contacts with the opponent, which is referred to as the memory’s …size. This study examines the impact of strategy memory size on the evolutionary stability of n-person iterated prisoner’s dilemma strategies. In this paper, we address the role that memory plays in decision-making. We interested in the model of the Iterated Prisoner’s Dilemma game for three players with memory two, and we will look at strategies with similar behavior, such as Tit-For-Tat (TFT) strategies as well as Win Stay-Lose Shift (WSLS) strategies. As a result of this paper, we have shown that the effect of memory length is almost non-existent in the competitions of strategies that we studied. Show more
Keywords: Memory-Two, Tit-For-Tat strategies (TFT), three-players iterated prisoner’s dilemma game (3P-IPD), transition matrix, Win Stay-Lose Shift strategies (WSLS)
DOI: 10.3233/JIFS-233690
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Chen, Zhipeng | Liu, Xiao | Qin, Jianhua
Article Type: Research Article
Abstract: To solve the problem that the walking jitter of quadruped robots leads to the degradation of clarity of visual imaging, a quadruped robot visual imaging jitter compensation algorithm based on the theory of walking jitter is proposed. The D-H coordinate transformation method is used to establish the coordinate system of each joint of the leg. The kinetic equations of the leg are derived from the relationship between the rotational velocity and the moment of the leg joint, and the kinetic equilibrium equations of the quadruped robot body are established based on the spatial moment equilibrium theorem; the spring-mass model of …the leg of the quadruped robot is used to construct the kinetic equations of the leg jittering, and the kinetic equations of the body jittering are derived using the moment equilibrium condition of the body center of gravity position and under the effect of the leg and body jitter to obtain the visual imaging device jitter quantity; finally, the tremor quantity is combined with the jitter quantity and rotation matrix to derive the walking jitter mathematical model of the quadruped robot visual imager, and the jitter compensation algorithm of quadruped robot visual imager is verified. The experimental results show that compared with the traditional Wiener filter algorithm for jitter compensation and the BP neural network jitter compensation algorithm, this algorithm improves the visual imaging by 10.8% and 3.3% in the two evaluation indexes of peak signal-to-noise ratio and structural similarity, respectively, and the de-jittering effect is better. Show more
Keywords: Quadruped robot, visual imaging, walking jitter, compensation algorithm
DOI: 10.3233/JIFS-235345
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Liu, Gan | Qi, Guirong | Wan, Sanyu
Article Type: Research Article
Abstract: Imbalanced data is a serious binary classification difficulty in forecasting the well-being of the elderly. This paper improves the Smote algorithm from the algorithm and sample dimensions to tackle the issue of imbalanced distribution of questionnaire data. The k-means Smote is combined with RBFNN as K-RBFNN Smote in the algorithm dimension and add FCM link to resample the minority set in the sample dimension as FCM K-RBFNN Smote. In order to improve the generalization of models, the RUS module is added to the algorithm. Experiments are carried out on four improved Smote technologies and two existing Smote technologies combined with …XGBoost, which is superior than the other five conventional classification models. The experimental results indicate that the performance order is RUS FCM K-RBFNN Smote > K-RBFNN Smote > FCM K-RBFNN Smote > RUS K-RBFNN Smote > K-Means Smote > FCM Smote. The RUS FCM K-RBFNN method has been identified as the optimal approach for enhancing performance, resulting in a 98.58% accuracy rate. In conclusion, Smote algorithm undergoes the implementation of K-RBFNN shows greater performance and the enhancement of FCM and RUS relies on the structure of sampling. Show more
Keywords: RUS FCM K-RBFNN Smote, XGBoost, imbalanced data, elderly well-being classification
DOI: 10.3233/JIFS-235213
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Allouche, Moez | Dahech, Karim | Gaubert, Jean-Paul
Article Type: Research Article
Abstract: This paper proposes a multi-objective H2 /H ∞ maximum power tracking control of a variable speed wind turbine to minimize the H2 tracking error and ensure the H ∞ model reference-tracking performance, simultaneously. The optimal condition is obtained via a boost converter use, which adapts the load impedance to the wind turbine generator. Thus, based on the fuzzy T-S model, a multi-objective Maximum Power Point Tracking (MPPT) controller is developed, ensuring maximum power transfer, despite wind speed variation and system uncertainty. To specify the optimal trajectory to follow, a TS reference model is proposed taking as input the optimal …rectified DC current. The conditions of stability and stabilization are expressed in terms of linear matrix inequality (LMI) for uncertain and disturbed T-S models leading to determining the controller gains. Finally, an example of MPP tracking applied to a Wind Energy Conversion System (WECS) illustrates the effectiveness of the proposed fuzzy control law. Show more
Keywords: Multi-objective fuzzy tracking control, maximum power point tracking (MPPT), linear matrix inequalities (LMIs), robust control, T-S fuzzy model
DOI: 10.3233/JIFS-222887
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Xiong, Haoyu | Yang, Leixin | Fang, Gang | Li, Junwei | Xiang, Yu | Zhang, Yaping
Article Type: Research Article
Abstract: Test-time augmentation (TTA) has become a widely adopted technique in the computer vision field, which can improve the prediction performance of models by aggregating the predictions of multiple augmented test samples without additional training or hyperparameter tuning. While previous research has demonstrated the effectiveness of TTA in visual tasks, its application in natural language processing (NLP) tasks remains challenging due to complexities such as varying text lengths, discretization of word elements, and missing word elements. These unfavorable factors make it difficult to preserve the label invariance of the standard TTA method for augmented text samples. Therefore, this paper proposes a …novel TTA technique called Defy, which combines nearest-neighbor anomaly detection algorithm and an adaptive weighting network architecture with a bidirectional KL divergence entropy regularization term between the original sample and the aggregated sample, to encourage the model to make more consistent and reliable predictions for various augmented samples. Additionally, by comparing with Defy, the paper further explores the problem that common TTA methods may impair the semantic meaning of the text during augmentation, leading to a shift in the model’s prediction results from correct to corrupt. Extensive experimental results demonstrate that Defy consistently outperforms existing TTA methods in various text classification tasks and brings consistent improvements across different mainstream models. Show more
Keywords: Test-time augmentation, test-time robustification, text classification, language model, anomaly detection
DOI: 10.3233/JIFS-236010
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Shengbin, Liang | Haoran, Sun | Fuqi, Sun | Hongjian, Wu | Wencai, Du
Article Type: Research Article
Abstract: Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer’s disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI …features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD. Show more
Keywords: Alzheimer’s disease classification, computer aided diagnosis, medical image processing, megnetic resonance imaging, deep learning
DOI: 10.3233/JIFS-236542
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Karthika, K. | Rangasamy, Devi Priya
Article Type: Research Article
Abstract: In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information …is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. Show more
Keywords: Hash based message authentication code (HMAC), cryptographic hash functions, Diffie Hellman, communications
DOI: 10.3233/JIFS-234641
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Vijaya Lakshmi, A. | Vaitheki, K. | Suresh Joseph, K.
Article Type: Research Article
Abstract: Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled …SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. Show more
Keywords: Slender loris optimization algorithm, exploitation and exploration, optimization problems, swarm intelligence algorithm, metaheuristic
DOI: 10.3233/JIFS-236737
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
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