Journal of Intelligent & Fuzzy Systems - Volume 45, issue 2

Authors: Zhang, Zhaojun | Lu, Jiawei | Xu, Zhaoxiong | Xu, Tao

Article Type: Research Article

Abstract: To solve the problems of the ant colony optimization (ACO), such as slow convergence speed, easy to fall into local extremum and deadlock in path planning, this paper proposed an improved ACO, which was hybridized by PSO based on logistic chaotic mapping, called hybrid ant colony optimization (HACO). According to the number of obstacles around the next feasible node, HACO distributes the initial pheromones unevenly to avoid the ant getting stuck in deadlock. According to the orientation of the next node selected by the ant, the heuristic information is adaptively adjusted to guide the ant to the direction of the …target position. When updating the pheromone, the local and global search mechanism of the particle swarm optimization is used to improve the pheromone update rule and accelerate convergence speed. Finally, the grid method is used to construct the environment map, and simulation experiments are conducted in different environments. The experimental results verify the effectiveness and feasibility of the improved algorithm. Show more

Keywords: ant colony optimization, path planning, grid method, pheromone update

DOI: 10.3233/JIFS-231280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2611-2623, 2023

Authors: Sundarakumar, M.R. | Salangai Nayagi, D. | Vinodhini, V. | VinayagaPriya, S. | Marimuthu, M. | Basheer, Shajahan | Santhakumar , D. | Johny Renoald, A.

Article Type: Research Article

Abstract: Improving data processing in big data is a delicate procedure in our current digital era due to the massive amounts of data created by humans and machines in daily life. Handling this data, creating a repository for storage, and retrieving photos from internet platforms is a difficult issue for businesses and industries. Currently, clusters have been constructed for many types of data, such as text, documents, audio, and video files, but the extraction time and accuracy during data processing remain stressful. Hadoop Distributed File System (HDFS) is a system that provides a large storage area in big data for managing …large datasets, although the accuracy level is not as high as desired. Furthermore, query optimization was used to produce low latency and high throughput outcomes. To address these concerns, this study proposes a novel technique for query optimization termed the Enhanced Salp Swarm Algorithm (ESSA) in conjunction with the Modified K-Means Algorithm (MKM) for cluster construction. The process is separated into two stages: data collection and organization, followed by data extraction from the repository. Finally, numerous experiments with assessments were carried out, and the outcomes were compared. This strategy provides a more efficient method for enhancing data processing speed in a big data environment while maintaining an accuracy level of 98% while processing large amounts of data. Show more

Keywords: Hadoop distributed file system, latency, throughput, query optimization, hash algorithms clustering

DOI: 10.3233/JIFS-231389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2625-2640, 2023

Authors: Chandana Mani, R.K. | Kamalakannan, J.

Article Type: Research Article

Abstract: Breast cancer (BC) is categorized as the most widespread cancer among women throughout the world. The earlier analysis of BC assists to increase the survival rate of the disease. BC diagnosis on histopathology images (HIS) is a tedious process that includes recognizing cancerous regions within the microscopic image of breast tissue. There are various methods to discovering BC on HSI, namely deep learning (DL) based methods, classical image processing techniques, and machine learning (ML) based methods. The major problems in BC diagnosis on HSI are the larger size of images and the high degree of variability in the appearance of …tumorous regions. With this motivation, this study develops a computer-aided diagnosis using a white shark optimizer with attention-based deep learning for the breast cancer classification (WSO-ABDLBCC) model. The presented WSO-ABDLBCC technique performs accurate classification the breast cancer using DL techniques. In the WSO-ABDLBCC technique, the Guided filtering (GF) based noise removal is applied to improve the image quality. Next, the Faster SqueezeNet model with WSO-based hyperparameter tuning performs the feature vector generation process. Finally, the classification of histopathological images takes place using attention-based bidirectional long short-term memory (ABiLSTM). A detailed experimental validation of the WSO-ABDLBCC occurs utilizing the benchmark Breakhis database. The proposed model achieved an accuracy of 95.2%. The experimental outcomes portrayed that the WSO-ABDLBCC technique accomplishes improved performance compared to other existing models. Show more

Keywords: Breast cancer, computer-aided diagnosis, histopathological images, deep learning, white shark optimizer

DOI: 10.3233/JIFS-231776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2641-2655, 2023

Authors: Prajitha, C. | Sridhar, K.P. | Baskar, S.

Article Type: Research Article

Abstract: Electrocardiogram (ECG) signal analyses can enhance human life in various ways, from detecting and treating heart illness to controlling the lives of cardiac-diseased people. ECG analysis has become crucial in medical studies for accurately detecting cardiovascular diseases (CVDs). Cardiac Arrhythmia is one of the major life-threatening diseases. Analyzing ECG signals is the easiest way to detect Arrhythmia. Different noises often corrupt the ECG signals, like power line interference, electromyographic (EMG) noise, and electrode motion artifact noise. Such noises make it difficult to identify the various peaks in the ECG signal for arrhythmia classification. To overcome such problems, Noise Removal-based Thresholding …(NRT) framework has been introduced to remove noises from ECG signals and accurately classify Arrhythmia. Discrete Wavelet transform reduces noise from ECG signals in the pre-processing stage. The noise-removed signal is segmented by K-means clustering for R-peak detection by finding all local maximum points from the signal. The signal features are extracted by Burg’s method to obtain good frequency resolution and quick integration for short-time signals in the form of a cumulative distribution function. All features collected from R-peak are fed to the Iterative Convolutional Neural Network (ICNN) and classified the arrhythmia types based on the alignment of a few variables to work well with the Euclidean distance metric. The NRT framework is evaluated based on the data obtained from the MIT-BIH Arrhythmia dataset and achieves the Accuracy of 99.45 %, Positive Prediction of 98.92%, F1-Score of 98.95%, SNR of 35 dB, MSE of 0.001, RMSE of 0.002 Show more

Keywords: K-means clustering, Iterative Convolutional Neural Network, arrhythmia classification, R-peak

DOI: 10.3233/JIFS-223719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2657-2668, 2023

Authors: Huang, Jr-Jen | Yang, Cheng-Ying | Lin, Yi-Nan | Shen, Victor R.L. | Lin, Chia-Tsai | Shen, Frank H.C.

Article Type: Research Article

Abstract: Human faces have been naturally viewed as a central part in each image. One interesting task is to classify each face into different categories based on the emotion shown in the facial expression. In addition, an awareness of emotion during work on a project and how affective states are presented in the communication style might help system developers work more effectively, thus improving the performance of a collaborative team. Currently, the feasibility and portability of emotion recognition in the platform with Raspberry PI are insufficient. Hereby, a novel emotion recognition system in real time using the edge computing platform with …deep learning has been implemented successfully. The feature values of objects are calculated by a high computing processor on the embedded platform. When an object with the matching features is detected, it is drawn as a rectangular bounding box and the results are displayed on the screen. In the proposed system, it first annotates the image datasets and saves them in the corresponding input data format for model training. Thus, the You Only Look Once (YOLOv5) model has been employed for training because it is a state-of-the-art object detection system. In other words, a fast and accurate emotion recognition is the main benefits of choosing YOLOv5 model. Then, the correctly trained YOLOv5 model file is loaded into an edge computing platform; and the feature values of objects are analyzed by a high computing processor. Finally, the experimental results show that the promising mean Average Precision (mAP), 92.6%, and recognition speed in Frames Per Second (FPS), 40, are obtained, which outperforms other existing systems. Show more

Keywords: Deep learning, emotion recognition, high computing platform, face recognition, image recognition, object detection

DOI: 10.3233/JIFS-223801

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2669-2683, 2023

Authors: Arivalagan, Divya | Bhoopathy Began, K. | Ewins Pon Pushpa, S. | Rajendran, Kiruthiga

Article Type: Research Article

Abstract: Fingerprints are widely used as effective personal authentication systems, because they constitute unique, robust, and risk-free evidence. Fingerprinting techniques refer to biometric procedures used for identifying individuals based on their physical characteristics. A fingerprint image contains ridges and valleys forming a directionally-oriented pattern. The robustness of the fingerprint authentication technique determines the quality of the fingerprint image. This study proposed an intelligent 12-layered Convolutional Neural Network (CNN) model using Deep learning (DL) for gender determination based on fingerprints. Further, the study compared the performance of this model to existing state-of-the-art methods. The primary goal of this study was to reduce …the number of comparisons within a large database obtained from automatic fingerprint recognition systems. The classification process was found to be swifter and more accurate when analysis of the DL algorithm was performed. With reference to the criteria of precision, recall, and accuracy evaluation during classification, this proposed 12-layered CNN model outperformed the Residual Neural Network with 50 Layers (ResNet-50) and Dense Convolutional Network with 201 Layers (DenseNet-201) models. The accuracies obtained were 97.0%, 95.8%, 98.0%, and 96.8% for female-left, female-right, male-left, and male-right classes respectively, while achieving an overall accuracy of 94.0%. Show more

Keywords: Fingerprint image, intelligent system, authentication, convolutional neural network, deep learning algorithm, precision, recall, accuracy, DenseNet201, ResNet-50

DOI: 10.3233/JIFS-224284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2685-2706, 2023

Authors: Zhang, Linzi | Shi, Yong

Article Type: Research Article

Abstract: Classical supply chain finance (SCF) primarily focuses on the financial service among all upstream and downstream supply chain participants. Due to the continuously deteriorating of the ecological environment, an environmental-friendly SCF system is urgently needed. In this paper, we propose a novel SCF design scheme with environmental concerns, i.e., green supply chain finance (GSCF), consider the financing channels both from banks and from consumers, and design a bi-objective optimization model that depicts the trade-off between the benefit and the emission. Further, an improved normalized normal constraint (INNC) Pareto method is developed to address the optimal financing strategy of the bi-objective …model. We then conduct a numerical case of a Taiwanese steel firm to verify the effectiveness and efficiency of our method. Results show that our model provides a portfolio of optimal solutions on Pareto frontier which can be applied as an effective decision support system when designing a GSCF. Furthermore, the sensitivity analysis also presents the impact of environmental investment cost, technological ratio of companies and the interest rate of trade credit on the optimal configuration of the GSCF. Show more

Keywords: Green supply chain finance, Multi-objective optimization, Network design, Pareto frontiers, Trade credit

DOI: 10.3233/JIFS-230270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2707-2721, 2023

Authors: Al-Jamaan, Rawabe | Ykhlef, Mourad | Alothaim, Abdulrahman

Article Type: Research Article

Abstract: Social networks like Twitter are extremely popular and widely used, which has increased interest in studying the information posted there. One such analytical application is extracting location information of users for real-time monitoring of the objects and events of interest, such as political and social events, disease surveillance, natural calamities, and crime prevention. Identifying geographic location is a nontrivial task, as user profiles contain outdated and inaccurate location information. Furthermore, extracting geographical information from Arabic tweets is challenging since they contain many nonstandard data (dialects), complex structures, abbreviations, grammatical and spelling mistakes, etc. This study focuses on the localization of …Saudi Arabian users who tweet in Arabic. This study proposes a convolutional neural network-based deep learning model to predict a Twitter user’s region-level location using user profiles, text texts, place attachments, and historical tweets. The model was evaluated empirically on a dataset of 95,739 tweets written in Arabic and produced by 4,331 users from Saudi Arabia cities. Regarding classification accuracy, the proposed CNN model outperformed machine learning classifiers such as NB, LR, and SVM with a 60% accuracy on the test set. This study is the first of its kind, aimed at localizing Saudi users based on their tweets. Show more

Keywords: Convolutional neural network, location estimation, machine learning, natural language processing, Twitter

DOI: 10.3233/JIFS-230518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2723-2734, 2023

Authors: Özer, N. Ceyda | Tuzkaya, U. Rıfat

Article Type: Research Article

Abstract: City logistics approaches and modeling struggles have a significant role in urban areas in increasing the efficiency of logistics operations and reducing traffic jams and their environmental effects. By developing an effective distribution network for cities, it is possible to compete with the changing world and satisfy flexible customer requirements. In this study, as a real-world case, a city logistics model for Istanbul metropolitan area is designed using multi-objective linear programming that considers the different objectives of the stakeholders in cities by integrating the fuzzy Choquet integral technique in a multi-level distribution network for the automotive spare part industry. This …paper makes decisions regarding the amount of product flowing among the echelons, the amount of stock to be kept in the warehouses, and the product delays allowed. While minimizing the transportation cost, holding cost and emission levels during these decisions, the study also aims to maximize the service quality in the warehouses. The model is applied to a logistics network of fifty demand points and thirty time periods which can be considered a middle or large-scale problem. In the model, it is also decided to transport the products with electric or fuel vehicles. In the transport sector, electric vehicles are the key to meet future needs for social, health and other human services. The results are discussed under different scenarios. This research allows the use of such a model in making strategic decisions for the distribution network design in big cities. Show more

Keywords: Fuzzy Choquet integral, electric vehicles, multi-criteria decision making, city logistics, mathematical modeling

DOI: 10.3233/JIFS-223282

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2735-2752, 2023

Authors: Yang, Jun | Qiao, Linke | Li, Changjiang | Wu, Xing

Article Type: Research Article

Abstract: Roof collapse is the most frequent production accident in the mine production process, which seriously threatens the efficient and safe production of the mine. Therefore, it is urgent to carry out practical research on the roof collapse tendency of the roadway. After searching and analyzing the relevant documents, the primary influencing factors of roof collapse risk based on AHP are determined, namely engineering geology, rock mass support, construction management and natural environment. After refining the main influencing factors, the evaluation factor set is obtained, the fuzzy comprehensive evaluation relationship matrix is established, and the fuzzy comprehensive evaluation model of roof …collapse risk is obtained. Finally, the quantitative evaluation of no collapse risk, weak collapse risk, medium collapse risk and high collapse risk is carried out. Taking a metal mine as an example, the risk of roof collapse of its C11 haulage roadway is selected for fuzzy evaluation. The evaluation result is high collapse risk, which is consistent with the evaluation result of the current specification, indicating that the model can be used for mine roof collapse risk evaluation. This method of estimating roof collapse has been applied on-site, which is consistent with the actual situation and has achieved good results. It has guiding significance for predicting the stability of tunnels and supporting operations. Show more

Keywords: Analytic hierarchy process, risk assessment, roof collapse, fuzzy theory

DOI: 10.3233/JIFS-224146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2753-2762, 2023