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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: Midula, P. | Shine, Linu | George, Neetha
Article Type: Research Article
Abstract: Fabrication of semiconductor wafers is a complex process and chances of defect wafers are high. Because of defective wafers the circuit patterns will not be created correctly and it is necessary to identify them. Manual identification of defects are time consuming and expensive. Deep learning methods are widely used for defect detection. In this paper we propose a simple Convolutional Neural Network (CNN) model for classification of nine defects in wafers. A custom CNN consisting of 9 layers is used for the classification of defects as Center, Donut, Edge-Loc, Edge-Ring, Loc, Random, Scratch, Near-full, and None. Performance of the model …is evaluated using WM-811K dataset. Results shows that the model classifies the defects with high confidence score and an accuracy of 99.1% is achieved using this method. Further, the convolution operation in the CNN is realized using Coordinate Rotation Digital Computer (CORDIC) algorithm. The model is implemented in Field Programmable Gate Arrays (FPGA) and proved less complex method and consume less computational power than conventional methods. Show more
Keywords: CNN, CORDIC, FPGA, wafer maps
DOI: 10.3233/JIFS-219430
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Kaur, Amandeep | Rama Krishna, C. | Patil, Nilesh Vishwasrao
Article Type: Research Article
Abstract: Software-Defined Networking (SDN) is a modern networking architecture that segregates control logic from data plane and supports a loosely coupled architecture. It provides flexibility in this advanced networking paradigm for any changes. Further, it controls the complete network in a centralized using controller(s). However, it comes with several security issues: Exhausting bandwidth and flow tables, Distributed Denial of Service (DDoS) attacks, etc. DDoS is a powerful attack for Internet-based applications and services, traditional and SDN paradigms. In the case of the SDN environment, attackers frequently target the central controller(s). This paper proposes a Kafka Streams-based real-time DDoS attacks classification approach …for the SDN environment, named KS-SDN-DDoS. The KS-SDN-DDoS has been designed using highly scalable H2O ML techniques on the two-node Apache Hadoop Cluster (AHC). It consists of two modules: (i) Network Traffic Capture (NTCapture) and (ii) Attack Detection and Traffic Classification (ADTClassification). The NTCapture is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-1). It captures incoming network traffic, extracts and formulates attributes, and publishes significant network traffic attributes on the Kafka topic. The ADTClassification is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-2). It consumes network flows from the Kafka topic, classifies it based on the ten attributes, and publishes it to the decision Kafka topic. Further, it saves attributes with outcome to the Hadoop Distributed File System (HDFS). The KS-SDN-DDoS approach is designed and validated using the recent “DDoS Attack SDN dataset”. The result shows that the proposed system gives better classification accuracy (100%). Show more
Keywords: Control plane, real-time, dynamic network, Apache Hadoop, data plane, Kafka streams
DOI: 10.3233/JIFS-219405
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Xu, Ying | Ji, Xinrong | Zhu, Zhengyang
Article Type: Research Article
Abstract: With the increasing penetration of distributed energy resources (DER) in microgrids, DER power inverters have become a critical asset for providing power support to these microgrids. Meanwhile, the grid-forming (GFM) inverters, among these DER inverters, have gained significant attention in microgrid applications for their capability to enable the DERs to operate in different microgrid conditions and various operation modes. Moreover, with the implementation of these GFM inverters, smooth operation mode transition, GFM functions as well as black start functions can be obtained to improve the operation of the microgrid systems. In this article, a generalized control method for a single-phase …GFM inverter is developed for community microgrid applications, facilitating smooth operation behavior in both operation modes with grid support functions and stable transition for different microgrid conditions. The control design procedure and function analysis of the proposed control method are explained in detail based on the community microgrid system. The effectiveness of the method in this paper is demonstrated on a 10 kW single-phase GFM inverter prototype with comparison to a model predictive method in recent literature. Show more
Keywords: Grid-forming inverter, microgrid, grid-support function, stable transition
DOI: 10.3233/JIFS-236902
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Tian, Jing | Zhao, Ziqi | Lin, Zheng | Zhang, Fengling | Chen, Renzhen
Article Type: Research Article
Abstract: Inter-shaft bearings are an essential component of aircraft engines, and their operational status determines the safety of aircraft engine operation. Therefore, to improve the accuracy of fault type prediction and enrich the feature information in vibration signals of aircraft engine inter-shaft bearings, this paper proposes an STFT-CNN model based on the AlexNet architecture, extending its application to the research of aircraft engine inter-shaft bearing fault diagnosis. This approach addresses the common reliance on personnel experience for fault type diagnosis in traditional aircraft engine inter-shaft bearing fault diagnosis. Firstly, real vibration fault signals from inter-shaft bearings are collected through experiments to …enrich feature information in non-stationary signals using STFT time-frequency methods. Secondly, utilizing the high interpretability of the STFT-CNN model, fault feature data from inter-shaft bearings under various operating conditions are extracted to refine our understanding of fault feature information. Finally, leveraging the robustness of the STFT-CNN model, fault types are classified and predicted. The training process involves comparative analysis using different pooling algorithms, time-frequency analysis methods, and various deep learning network models. The results demonstrate that the STFT-CNN model, employing the maximum pooling algorithm, outperforms other models in predicting inter-shaft bearing faults, achieving an average fault prediction accuracy of 98.8% . Show more
Keywords: Inter-shaft bearings, STFT-CNN model, pooling algorithms, feature extraction, classification prediction
DOI: 10.3233/JIFS-240044
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Li, Yibing | Jiang, Shijin | Wang, Lei
Article Type: Research Article
Abstract: With explosive growth of industrial big data, workshop scheduling faces problems such as high complexity, multi-dimensionality and low stability. Recent years, the wide application of deep learning provides new idea for scheduling problem. In this paper, a hybrid deep convolution network and differential evolution algorithm is proposed to solve the non-permutation flow shop scheduling problem with the goal of minimizing total completion time. Mining relationship between job attributes and process priority by deep convolutional network is core idea of this method. In this paper, differential evolution algorithm is used to obtain the data set for deep learning, and neighborhood search …algorithm is used to optimize scheduling solution. Additionally, a method combining k-means algorithm and data statistics is proposed, which provides a reasonable way for priority division. The experimental results show that this method can greatly improve scheduling efficiency. Show more
Keywords: Differential evolution algorithm, convolutional neural network, K-means algorithm; priority, flow shop scheduling
DOI: 10.3233/JIFS-236874
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Duvvuri, Kavya | Kanisettypalli, Harshitha | Masabattula, Teja Nikhil | Amudha, J. | Krishnan, Sajitha
Article Type: Research Article
Abstract: Glaucoma is an eye disease that requires early detection and proper diagnosis for timely intervention and treatment which can help slow down further progression and to manage intraocular pressure. This paper aims to address the problem by proposing a novel approach that combines a model-based Reinforcement Learning (RL) approach, called DynaGlaucoDetect, with ocular gaze data. By leveraging the RL algorithms to simulate and predict the dynamics of glaucoma, a model-based approach can improve the accuracy and efficiency of glaucoma detection by enabling better preservation of visual health. The RL agent is trained using real experiences and synthetic experiences which are …generated using the model-based algorithm Dyna-Q. Two different Q-table generation methods have been discussed: the Direct Synthesis Method (DSM) and the Indirect Synthesis Method (IdSM). The presence of glaucoma has been detected by comparing the reward score a patient obtains with the threshold values obtained through the performed experimentation. The scores obtained using DSM and IdSM have been compared to understand the learning of the agent in both cases. Finally, hyperparameter tuning has been performed to identify the best set of hyperparameters. Show more
Keywords: Glaucoma detection, model-based RL, Dyna-Q algorithm, reward system
DOI: 10.3233/JIFS-219400
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wang, Jing | Gao, Tingting | Du, Hongxu | Tu, Chuang
Article Type: Research Article
Abstract: To address the issue of final delivery route planning in the community group purchase model, this study takes into full consideration logistics vehicles of different energy types. With the goal of minimizing the sum of vehicle operating costs, delivery timeliness costs, goods loss costs, and carbon emissions costs, a multi-objective optimization model for community group purchase final delivery route planning is constructed. An improved genetic algorithm with a hill-climbing algorithm is utilized to enhance adaptive genetic operators, preventing the algorithm from getting stuck in local optima and improving the solution efficiency. Finally, a case study simulation is conducted to validate …the feasibility of the model and algorithm. Experimental results indicate that currently, among the three types of vehicles, fuel logistics vehicles still have an advantage in terms of vehicle usage cost. Electric logistics vehicles exhibit the poorest performance with the highest cost per hundred kilometers, but their sole advantage lies in their high energy release efficiency, enabling optimal low-carbon vehicle performance. Battery-swapping logistics vehicles perform the best in terms of carbon emissions, combining the advantages of both fuel-based and electric logistics vehicles. Therefore, battery-swapping logistics vehicles are a favorable choice for replacing fuel-based logistics vehicles in the future, offering promising prospects for future development. Show more
Keywords: Community group-buying, the route problem of end-distribution, improved genetic algorithm, carbon emission cost
DOI: 10.3233/JIFS-234773
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Gao, Dongling | Ma, Suhong | Kong, Xiangchuan
Article Type: Research Article
Abstract: In today’s Higher Education System (HES), Smart Learning (SL), also known as Intelligent Learning (IL) or Adaptive Learning (AL), plays an increasingly vital role. No longer is the traditional, one-size-fits-all method of education suitable for filling the several demands of students. Using SL technologies powered by Artificial Intelligence (AI) and Machine Learning (ML) algorithms can potentially revolutionize the HES. An emerging area of study, edge-based SL helps use Edge Computing (EC) to provide learners with instantaneous, specialized, and context-aware learning. Internet of Things (IoT) devices are becoming increasingly well-liked, and data is proliferating. Using video data as a primary source …of learning content and delivering it via EC infrastructure is what is referred to as “Video Streaming (VS)” in Edge-Based Learning (EBL). By examining the importance of providing mobile video clients with a high-quality visual experience—especially considering that video streaming (VS) traffic makes up a significant amount of mobile network traffic—the research gap is filled. The proposed Content Delivery Scheme (CDS), which is based on long short-term memory, is intended to improve security and privacy protocols, accelerate network service response times, and increase application intelligence. The project intends to close the current gap in edge-based Smart Learning (SL) technologies, namely in the distribution of video material for adaptive learning in higher education, by concentrating on these elements. Given that VS traffic forms a considerable portion of mobile network traffic, this paper aims to investigate the significance of delivering a performing visual experience to mobile video clients. Fast network service response, enhanced application intelligence, and enhanced security and privacy are all made possible by the proposed LSTM-based Content Delivery Scheme (CDS). The proposed approach attains minimal stall time of 2347 ms, which outperforms the existing techniques. Show more
Keywords: Higher education system, IoT, machine learning, e-Learning, edge computing, content delivery scheme, security
DOI: 10.3233/JIFS-237485
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ayub, Mohammed | El-Alfy, El-Sayed M.
Article Type: Research Article
Abstract: Energy is a critical resource for daily activities and lifestyles with direct impacts on the economy, health and environment. Therefore, monitoring its efficient use is essential to reduce energy waste and lessen related concerns such as global warming and climate change. One of the prominent and evolving solutions is Non-Intrusive Load Monitoring (NILM) smart meters, which enables consumers to track their per-appliance energy consumption more effectively. Some recent approaches have proposed deep learning as a powerful tool for energy disaggregation. However, it is difficult to employ these models in resource-constrained end devices for effective energy monitoring. In this paper, we …explore and evaluate a lightweight improved model for multi-target non-intrusive load monitoring based on MobileNet architectures. With extensive experiments using the ENERTALK dataset, the results show that MobileNetV3-large is the most appealing for energy disaggregation as it requires about 55% less storage for trained model and about 6% less training time than MobileNetV2 with almost the same performance. On average, version 3 large has a 17.63% reduction in SAE and requires 54.21% and 8.93% less space and less training time than version 2, respectively. Moreover, the average performance is boosted using an ensemble multi-target MobileNet model across all houses, leading to significant reduction of MAE, SAE, and RMSE errors of about 6%, 48%, and 4%, respectively. In comparison to other work, the proposed MMNet-NILM shows superior performance for the majority of appliances in terms of all considered evaluation metrics. Show more
Keywords: Multi-target MobileNet, ENERTALK, Lightweight NILM, energy disaggregation, ensemble MobileNet
DOI: 10.3233/JIFS-219426
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Yang, Yeling
Article Type: Research Article
Abstract: Vocal music training for college students impacts the social and emotional aspects of better learning. This impact must be classified progressively to improve the social and musical connectivity coinciding with real-time emotions. Therefore, an intermittent analysis of music learning is required for augmenting socio-emotional changes to the learning method. This article introduces Impact-centric Learning Analysis (ILA) using the Fuzzy Control Algorithm (FCA) for the purpose above. The control algorithm operates in two linear stages: in the first stage, the socio-emotional impact of the learning on the students is analyzed, pursued by the learning changes in the second stage. This first …stage inputs student activity scores based on real-time implications. The lowest scores are classified independently in the second stage, and learning changes are carried out. The learning change is targeted to meet the maximum (optimal) impact score from the first stage using fuzzy differentiations based on training sessions and student performance. Therefore, the proposed algorithm generates an optimal impact for the considered features (socio-emotional), preventing trivial vocal music sessions. Show more
Keywords: Fuzzy control, impact optimization, socio-emotional learning, vocal music
DOI: 10.3233/JIFS-233922
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Sindge, Renuka Sambhaji | Dutta, Maitreyee | Saini, Jagriti
Article Type: Research Article
Abstract: Video Super Resolution (VSR) applications extensively utilize deep learning-based methods. Several VSR methods primarily focus on improving the fine-patterns within reconstructed video frames. It frequently overlooks the crucial aspect of keeping conformation details, particularly sharpness. Therefore, reconstructed video frames often fail to meet expectations. In this paper, we propose a Conformation Detail-Preserving Network (CDPN) named as SuperVidConform. It focuses on restoring local region features and maintaining the sharper details of video frames. The primary focus of this work is to generate the high-resolution (HR) frame from its corresponding low-resolution (LR). It consists of two parts: (i) The proposed model decomposes …confirmation details from the ground-truth HR frames to provide additional information for the super-resolution process, and (ii) These video frames pass to the temporal modelling SR network to learn local region features by residual learning that connects the network intra-frame redundancies within video sequences. The proposed approach is designed and validated using VID4, SPMC, and UDM10 datasets. The experimental results show the proposed model presents an improvement of 0.43 dB (VID4), 0.78 dB (SPMC), and 0.84 dB (UDM10) in terms of PSNR. Further, the CDPN model set new standards for the performance of self-generated surveillance datasets. Show more
Keywords: Super-resolution, image super-resolution, video super-resolution, recurrent network, residual learning
DOI: 10.3233/JIFS-219393
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Ezeji, Ijeoma Noella | Adigun, Matthew | Oki, Olukayode
Article Type: Research Article
Abstract: The rise of decision processes in various sectors has led to the adoption of decision support systems (DSSs) to support human decision-makers but the lack of transparency and interpretability of these systems has led to concerns about their reliability, accountability and fairness. Explainable Decision Support Systems (XDSS) have emerged as a promising solution to address these issues by providing explanatory meaning and interpretation to users about their decisions. These XDSSs play an important role in increasing transparency and confidence in automated decision-making. However, the increasing complexity of data processing and decision models presents computational challenges that need to be investigated. …This review, therefore, focuses on exploring the computational complexity challenges associated with implementing explainable AI models in decision support systems. The motivations behind explainable AI were discussed, explanation methods and their computational complexities were analyzed, and trade-offs between complexity and interpretability were highlighted. This review provides insights into the current state-of-the-art computational complexity within explainable decision support systems and future research directions. Show more
Keywords: Explainable decision support systems, computational complexity, optimization, explainable artificial intelligence, review
DOI: 10.3233/JIFS-219407
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Liu, Fuchen | Zhou, Sijia | Zhang, Dezhou | Wang, Xiaocui
Article Type: Research Article
Abstract: Deep learning has demonstrated remarkable advantages in the field of human pose estimation. However, traditional methods often rely on widening and deepening networks to enhance the performance of human pose estimation, consequently increasing the parameter count and complexity of the networks. To address this issue, this paper introduces Ghost Attentional Down network, a lightweight human pose estimation network based on HRNet. This network leverages the fusion of features from high-resolution and low-resolution branches to boost performance. Additionally, GADNet utilizes GaBlock and GdBlock, which incorporate lightweight convolutions and attention mechanisms, for feature extraction, thereby reducing the parameter count and computational complexity …of the network. The fusion of relationships between different channels ensures the optimal utilization of informative feature channels and resolves the issue of feature redundancy. Experimental results conducted on the COCO dataset, with consistent image resolution and environmental settings, demonstrate that employing GADNet leads to a reduction of 60.7% in parameter count and 61.2% in computational complexity compared to the HRNet network model, while achieving comparable accuracy levels. Moreover, when compared to commonly used human pose estimation networks such as Cascaded Pyramid Network (CPN), Stacked Hourglass Network, and HRNet, GADNet achieves high-precision detection of human keypoints even with fewer parameters and lower computational complexity, our network has higher accuracy compared to MobileNet and ShuffleNet. Show more
Keywords: Human pose estimation, high-resolution network, attention mechanism, feature redundancy
DOI: 10.3233/JIFS-233501
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Vusirikkayala, Gowthami | Madhu Viswanatham, V.
Article Type: Research Article
Abstract: Detecting communities within a network is a critical component of network analysis. The process involves identifying clusters of nodes that exhibit greater similarity to each other compared to other nodes in the network. In the context of Complex networks (CN), community detection becomes even more important as these clusters provide relevant information of interest. Traditional mathematical and clustering methods have limitations in terms of data visualization and high-dimensional information extraction. To address these challenges, graph neural network learning methods have gained popularity in community detection, as they are capable of handling complex structures and multi-dimensional data. Developing a framework for …community detection in complex networks using graph neural network learning is a challenging and ongoing research objective. Therefore, it is essential for researchers to conduct a thorough review of community detection techniques that utilize cutting-edge graph neural network learning methods [102 ], in order to analyze and construct effective detection models. This paper provides a brief overview of graph neural network learning methods based on community detection methods and summarizes datasets, evaluation metrics, applications, and challenges of community detection in complex networks. Show more
Keywords: Community detection (CD), complex networks (CN), graph neural network (GNN), deep learning (DL), communities, clusters
DOI: 10.3233/JIFS-235913
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2024
Authors: Abu-Sharkh, Osama M.F. | Surkhi, Ibrahim | Zabin, Hamzah | Alhasan, Maher
Article Type: Research Article
Abstract: As the entire world is becoming increasingly a global village, the need for reliable, smooth, and easy-to-use applications that facilitate the communication process between people speaking different languages worldwide becomes essential, especially in the tourism industry. While numerous online and mobile applications attempt to bridge the linguistic gap using text-to-text, text-to-voice, or voice-to-text-to-voice translators, they often fall short due to constraints such as the need for a single shared device, manual setup of speaker’s gender and preferred language, and an inability to communicate from a distance. These applications struggle to mimic the practical nature of real-time multilingual conversations where immediate …and clear communication is paramount. This paper introduces an intelligent peer-to-peer polyglot voice-to-voice mobile application to facilitate the communication of people speaking different languages worldwide transparently mimicking a live conversation whether the involved parties are close to each other or at a nearby distance. People can interact with others transparently using their preferred language, irrespective of others’ languages, while the application automatically recognizes the language, gender of the speaker, and spoken words with very high accuracy. Five languages were implemented in the developed application as a proof-of-concept, and it is designed to smoothly and simply adapt more in future updates. Show more
Keywords: Multilingual, intelligent, text-to-voice, translation, voice-to-text
DOI: 10.3233/JIFS-219388
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Tariq, Sana | Amin, Asjad
Article Type: Research Article
Abstract: The emergence of machine learning in the recent decade has excelled in determining new potential features and nonlinear relationships existing between the data derived from the DNA sequences of genetic diseases. Machine learning also enhances the process of handling data with maximum predicted variables compared to observations during the data mining process of prediction. In this context, our study presents a deep learning model for predicting Transcription Factor Binding Sites (TFBS) in DNA sequences, with a focus on features within genetic data associated with diseases. Transcription Factors (TFs) play a crucial role in modulating gene expression by binding to TFBS. …The accurate prediction of TFBS is essential for understanding genome function and evolution. Thus, we develop an efficient deep learning model that considers TFBS prediction as a nucleotide-level binary classification task. In our proposed model, first we create an input matrix using the original DNA sequences. Next, we encode these DNA sequences using one-hot encoding, representing them as a sequence of numerical values. We then employ three convolutional layers, allowing our model to capture intricate patterns and motif features over a larger spatial range. To capture important features within the DNA sequence and to focus on them, we incorporate an attention layer. Finally, a dense layer, consisting of two fully connected layers and a dropout layer, calculates the probability of TF binding site occurrence based on the features learned by the proposed model. Our experimental results, using in-vivo datasets obtained from Chip-seq, demonstrate the superior performance of our proposed deep learning model in TFBS prediction compared to other existing state-of-the-art methods. The improvement in accuracy is due to additional layers of CNN and then an attention layer in the model. Thus, this result in a better performance of our approach in predicting the transcription factor binding sites and enhancing our understanding of gene regulation and genome function. Show more
Keywords: Transcription factor binding sites, one-hot encoding, convolutional layer, attention layer
DOI: 10.3233/JIFS-238159
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Vu, Hoai Nam | Nguyen, Quang Dung | Nguyen, Thuy Linh | Tran-Anh, Dat
Article Type: Research Article
Abstract: In the real world, the appearance of similar rice varieties depends on various factors such as resolution, angle, lighting conditions, and perspective. Additionally, complex environmental factors and characteristics of each rice type, such as enhanced light intensity, cross-polarization, and shading, rice background color, and image similarity, play a role. This indicates that the data augmentation process may enhance the accuracy of crop identification, particularly in the context of self-supervised machine learning. The aim of this research is to develop a precise rice segmentation method based on the improved Mask R-CNN (Region-based Convolutional Neural Network) with multitask data augmentation. The Mask …R-CNN model is enhanced by incorporating multitask input to improve feature extraction for rice. Experimental results demonstrate that the improved Mask R-CNN model can accurately segment various rice types under different conditions, such as different background colors and varying sizes of rice grains. The achieved precision, recall, F1 score, and segmentation mean Average Precision (mAP) are 95.5%, 96.3%, 95.9%, and 0.924, respectively. The average runtime on the test set is 0.35 seconds per image. Our method outperforms two comparative approaches, showcasing its ability to accurately segment rice in the market deployment phase with near real-time performance. This study establishes the foundation for the accurate detection of valuable agricultural products. Show more
Keywords: Multi-augmentation, deep learning, Mask RCNN, rice recognition, fusion metric
DOI: 10.3233/JIFS-241133
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Wang, Lin | Ye, Hongling | Wang, Pengfei | Xu, Chi | Qian, Aiwen
Article Type: Research Article
Abstract: To enhance the control performance of semi-active suspension systems, this research proposes a particle swarm optimization algorithm (PSO) with adaptive nonlinear correction of inertia weights, which is then integrated with a proportional integral differential (PID) algorithm. To this end, this research establishes quarter semi-active and passive suspension models of automobiles by utilizing the Matlab/Simulink simulation platform. In this foundation, this research further compares the advantages and disadvantages regarding performance indexes of semi-active suspension controlled by the adaptive inertia weighted particle swarm optimization (APSO) algorithm and the PID algorithm, as well as the PID-controlled semi-active suspension and passive suspension through simulation. …Simulation results indicate that performance indicator values for different suspension types increase with higher pavement grades. Compared with passive suspension, the semi-active suspension controlled by APSO and PID algorithms presents significantly improved performance indexes, with reductions of at least 31.61% in root mean square (RMS) concerning body vertical acceleration, 1.78% in suspension dynamic deflection, and 22.13% in tire dynamic loads. Moreover, analysis of suspension system frequency response characteristics demonstrates a significant decrease in droop acceleration transmission rate for the semi-active suspension with APSO and PID algorithms across the whole frequency range compared with that of the PID-controlled suspension and passive suspension. On the same note, despite the higher values of suspension dynamic deflection and tire dynamic load transfer rate in certain frequency bands, they are generally within acceptable suspension limits. Simply put, the findings confirm the feasibility of applying the APSO algorithm in PID-controlled semi-active suspension systems, which effectively improves both vehicle ride comfort and handling stability. Show more
Keywords: Semi-active suspension, PID control, improved particle swarm optimization algorithm
DOI: 10.3233/JIFS-234812
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Wang, Xiao | Wang, Dan | Zhou, Jincheng
Article Type: Research Article
Abstract: The correspondence between the decision space and the objective space is often many-to-one in multi-objective optimization problems. Therefore, a class of problems with such mapping relationships is defined as a MMOPs. For these problems, how to ensure the final solution converges to each Pareto solution set and guarantees the diversity of the algorithm is an urgent problem. The method of the paper with opposition-based strategy, a multimodal multi-objective optimization algorithm, is proposed. The algorithm proposed is called MMODE_OP, which is framed by a differential evolutionary algorithm, and opposition-based learning is applied to the initialization phase and generation-hopping phase to filter …out the more promising individuals in the population for iteration to enhance the global search capability and the diversity of population. In addition, different Gaussian perturbation strategies are adopted with iteration to achieve the search of the neighborhood, which can further not only improve the quality of the Pareto solution set but also enable the convergence of the Pareto solution set quickly. This method improves the algorithm’s local and global search ability, and enables multiple the Pareto solution set and improving the convergence. In the meantime, adaptive scaling factors and crossover factors are designed in this paper to enhance the improved search capability. Finally, the experiment results of MMODE_OP and other excellent algorithms on 13 test problems corroborate the proposed methods have superior performance. Show more
Keywords: Multimodal, multi-objective, differential evolutionary algorithm, opposition-based learning
DOI: 10.3233/JIFS-233826
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M. | Berretti, Stefano
Article Type: Editorial
DOI: 10.3233/JIFS-219381
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: David Raj, G. | Mukherjee, Saswathi | Jasmine, R.L.
Article Type: Research Article
Abstract: To enhance the reliability of the document retrieval system, the most efficient techniques such as Query Expansion (QE) are utilized. It has offered more adequate queries for the user when assimilated over original or initial queries by adding up one or more expansion keywords. Moreover, these techniques are more effective to enhance the performance of document retrieval and return the unnecessary information. In recent times, searching the suitable documents in the huge datasets is tiresome work. Generally, the automatic QE is used to address the refining query. A typical technique for QE has included the extracted close expression and the …related documents clustering by utilizing the clusters. However, classical clustering poses some issues to QE. Hence, a novel optimized bi-clustering mechanism is proposed in this paper for patent retrieval by QE. The ultimate aim of this implemented model is to retrieve the patent information by expanding the request query. Initially, the patent-related data is collected from standard data sources in terms of abstract and text. It is then given to the text pre-processing stage. Consequently, the pre-processed text or word is converted into vector formation by using the Multi-cascade Transformer Network (MTN). Finally, the retrieval process is done by proposing the Optimal Bi-Clustering (OptBi-C) process, in which the parameters are optimally determined by a hybrid algorithm of Reptile Search Algorithm (RSA) and Lion Algorithm (LA) termed as Iteration-based Reptile Search and Lion Algorithm (IRSLA). Thus, the performance of the model is examined with certain metrics and compared with traditional techniques. The precision of the implemented patent retrieval system using the QE model is maximized by 8.82% of DHOA-OptBi-C, 7.35% of HHO-OptBi-C, 10.29% of RSA-OptBi-C, and 7.35% of LA-OptBi-C respectively when the number of retrieved data is 10. Moreover, the recall of the designed patent retrieval system using the QE model is enhanced by 21.83% of KNN, 24.13% of CNN, 19.54% of FUZZY, and 11.49% of Bi-clustering respectively when the number of retrieved data is 6. Thus, the findings demonstrate that the system improves the retrieval performance. Show more
Keywords: Patent retrieval system, query expansion, multi cascaded transformer network, iteration-based reptile search and lion algorithm, optimal bi-clustering
DOI: 10.3233/JIFS-241138
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
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