Journal of Intelligent & Fuzzy Systems - Volume 45, issue 5

Authors: Yadav, Vishakha | Ganesh, P. | Thippeswamy, G.

Article Type: Research Article

Abstract: The determination and categorization of red blood cells (RBCs) from microscopic pictures is a critical step in the diagnosis of sickle cell disease (SCD). Traditionally, such procedures are performed manually by pathologists using a light microscope. Furthermore, manual visual evaluation is a time-consuming operation that relies on subjective judgment, resulting in variations in RBC recognition and counts. Mature If there is a blood problem, RBCs suffer morphological alterations. There are both automated and manual systems available on the market for counting the number of RBCs. Manual counting entails collecting blood cells with a Hemocytometer. The traditional procedure of exposing the …smear below a microscope and physically measuring the cells yields inaccurate findings, putting clinical laboratory staff under stress. Automatic counters are incapable of detecting aberrant cell. The computer-aided method will assist in achieving accurate outcomes in minimum time. In this study presents an image processing method for separating red blood cells from several other blood products. Its goal is to analyze and interpret blood smear images to aid in the categorizing of red blood cells across 11 categories. The WBCs are extracted from the image using the K-Medoids technique, that is resistant to exterior disturbance. Granulometric assessment has been used to distinguish between red and WBCs. Feature extraction is used to obtain important features that aid in categorization. The categorization outcomes aid in a rapid diagnosis of disorders such as Normochromic, Iron Deficiency, Hypochromic, Sickle Cell, and Megaloblastic. Show more

Keywords: Red blood cells (RBCs), determination, categorization, computer-aided framework, diagnosing disorder, Sickle cell

DOI: 10.3233/JIFS-234129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7647-7659, 2023

Authors: Liu, Ning | Zhao, Jianhua

Article Type: Research Article

Abstract: With the explosive increase of information, recommendation system is applied in a variety of areas. However, the performance of recommendation system is limited due to issues such as data sparsity, cold starts and poor semantic understanding. In order to make full use of external information to assist recommendation, deeply mine the semantic information of review text and further improve the performance of recommendation system, a deep recommendation system based on knowledge graph and review text (Drs-kgrt) is proposed in this paper. In Drs-kgrt, knowledge graph, review text and the social records between users are used as auxiliary information to improve …recommendation performance. Firstly, the review text is divided into user review text and item review text. BERT (Bidirectional Encoder Representation from Transformers) is used to accurately understand semantic information in user review text and the social records between users. The trust relationship between users and user preferences are fully mined to form user feature vectors. Secondly, BERT and knowledge graph entity recognition link technology are combined to extract item attribute feature entities and their associated entities. The fine-grained features of the items are analyzed to form item feature vectors. Thirdly, based on the scoring matrix, latent vectors of users and items are obtained by matrix decomposition. The deep features of users and items are generated based on user feature vectors, item feature vectors, latent vectors of users and items, the deep recommendation system is established to predict user scores for items. Finally, experiments are conducted on the Douban dataset and Amazon Movie Review dataset, the results show that the proposed algorithm can achieve better performance compared with other benchmark recommendation algorithms. Show more

Keywords: Knowledge graph, personalized recommendation, user review, item review, social relationships

DOI: 10.3233/JIFS-230584

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7661-7673, 2023

Authors: Aramuthakannan, S. | Ramya Devi, M. | Lokesh, S. | Kumar, R.

Article Type: Research Article

Abstract: The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network …(TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets. Show more

Keywords: Movie recommender system, deep learning, user experience, taymon, accuracy, movie rating

DOI: 10.3233/JIFS-231041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7675-7690, 2023

Authors: Niu, Lili

Article Type: Research Article

Abstract: As a convenient learning tool in the We Media era, mobile apps have been paid more and more attention by college students because of their accompanying timeliness and practicality. With the increasing number of English learning apps, many such apps provide college students with new ways to obtain learning resources and diversified learning modes. The related research in the field of mobile-assisted language learning at home and abroad has developed over nearly 20 years, basically following the route from theory to application in practice, but there have been few process studies on learners’ individual language skill learning behaviors based on …mobile platform data. In this study, the time series clustering method was adopted, and the learning behavior of college students in an English vocabulary learning app in China was selected for data mining. Firstly, taking the “single-day memorization amount” as the measurement index, the memorization records of college students in the whole use cycle were extracted and processed into trajectory data, and the KmL algorithm was used to cluster the trajectory of the memorization amount in the time series. According to the intra-class average trajectory, the characteristics of learning behavior changes among the different college students are summarized, and two learning modes are depicted. Secondly, through the experimental analysis, it was found that adopting the English learning model three weeks before an exam can effectively stimulate college students and improve their willingness to learn and continue using the app. Show more

Keywords: Time series clustering, English app, data mining, learning mode

DOI: 10.3233/JIFS-231476

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7691-7700, 2023

Authors: Mahaboob Basha, S.K. | Kalaiselvan, S.A.

Article Type: Research Article

Abstract: Quality of Experience (QoE) is a critical aspect of multimedia applications, which directly impacts user satisfaction and adoption. QoE predictions are used to optimize various parameters such as video quality, bitrate, and network bandwidth to enhance the user experience. However, accurate QoE prediction is a challenging task, as it involves various factors such as network conditions, video content, and user preferences. Therefore, there is a need for enhancing QoE predictions with advanced techniques to improve user satisfaction and adoption. This paper proposes incorporating more complex neural network architectures and using more diverse datasets to improve the accuracy and generalization of …Quality of Experience (QoE) predictions. The paper suggests experimenting with more advanced architectures such as convolutional neural networks and recurrent neural networks, which have been shown to be effective in various applications. Additionally, the paper highlights the limitation of using a single dataset and proposes using more diverse datasets that capture different types of video content and network conditions. Enhancing QoE predictions with complex neural networks and diverse datasets include improved accuracy, better generalization, more sophisticated models, enhanced user satisfaction and increased adoption. These enhancements are expected to lead to more accurate and reliable QoE predictions, which are crucial for improving user experience in multimedia applications. Show more

Keywords: Quality of Experience (QoE), Neural networks, multimedia applications

DOI: 10.3233/JIFS-233777

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7701-7711, 2023

Authors: Bera, Sanchari | Muhiuddin, Ghulam | Pal, Madhumangal

Article Type: Research Article

Abstract: Graph theory plays a crucial role in the era of computer science, medical science and information technology. The fundamental motivation behind this paper is to present some availability ideas in the m polar interval-valued fuzzy graph (m -PIVFG), which are utilized to portray the interval of the uncertainty of items. What’s more, the m -PIVFG graphs are utilized to portray the underlying connection between ideas in which the vertices and edges are of multi-poles and in the form of interval values to feature the uncertainty conditions. The dominating set involves a basic situation in graph analysis. This paper essentially …adds to expanding the idea of double domination in the fuzzy graph to the m -PIVFG and getting the related extended ideas of m -PIVFG. In the interim, the ways to get the particular double dominating sets are introduced. At long last, a numeral model on ambulance service on some villages information in India is introduced to clarify the necessity of double domination in m -PIVFG in the particular application. Show more

Keywords: m-PIVFG, double domination in m-PIVFG, acurate dominating set on m-PIVFG, accurate double dominating set on m-PIVFG, facility location problem

DOI: 10.3233/JIFS-223054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7713-7726, 2023

Authors: Al-shami, Tareq M. | Hosny, Rodyna A. | Mhemdi, Abdelwaheb | Abu-Gdairi, Radwan | Saleh, Salem

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-230436

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7727-7738, 2023

Authors: Dang, Trong Hop | Do, Viet Duc | Mai, Dinh Sinh | Ngo, Long Thanh | Trinh, Le Hung

Article Type: Research Article

Abstract:  In image processing, segmentation is a fundamental problem but an important step for advanced image processing problems. When dealing with hyperspectral image data, the task becomes much more challenging due to the large number of features (dimension), higher nonlinearity, and greater capacity of the data. This paper proposes a solution of features reduction collaborative fuzzy c-means clustering (FR-CFCM) for hyperspectral remote sensing image analysis using random projection. The dimensional reduction technique is based on the Johnson Lindenstrauss lemma algorithm, preserving the relative distance between data samples. This can make clustering easier without affecting the clustering results. Moreover, by reducing dimensionality …and sharing information among sub-data in collaborative clustering, it is possible to improve the performance and accuracy of hyperspectral remote sensing image analysis results. The experiments conducted on two hyperspectral image data sets with five validity indexes show that the proposed methods perform better compared with the other methods. Show more

Keywords: hyperspectral image, fuzzy clustering, collaborative clustering, feature reduction

DOI: 10.3233/JIFS-230511

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7739-7752, 2023

Authors: Lv, Qian

Article Type: Research Article

Abstract: English teaching at college levels is more sophisticated and advanced compared to high schools and professionals. The teaching must have high-quality meetings, real-world interactions, and professional applications. Therefore teaching quality evaluation periodically is performed internally and externally through skill validation and joint training. This article introduces a Regressive Fuzzy Evaluation Model (RFEM) for analyzing the quality of college classroom English teaching quality. This evaluation model operates over the teaching quality metrics such as performance, student understandability, and application. The understandability and English application to the real world is modeled by referring to the performance as the regressive factor. The regressive …factor is analyzed for two fuzzification outputs: high and low, by analyzing the individual factors over cumulative teaching grades. The regression for low fuzzy outputs is analyzed using mean understandability and application score from the previous assessment instance. This is required for training the fuzzification from the mean score rather than the low level. Therefore the quality improvements from the lagging features are addressed by providing a new teaching method. Further fuzzy regression is initiated from the mean to the high level reducing the computation time and errors. Show more

Keywords: English teaching, fuzzy logic, quality evaluation, regressive analysis

DOI: 10.3233/JIFS-231321

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7753-7767, 2023

Authors: Ju, Hongmei | Yi, Huan

Article Type: Research Article

Abstract: The classification problem is a key area of research in machine learning. The Least Squares Support Vector Machine (LSSVM) is an important classifier that is commonly used to solve classification problems. Its widespread use stems from its replacement of the inequality constraint in the Support Vector Machine (SVM) with the equality constraint, which transforms the convex quadratic programming (QP) problem of SVM into the solution of linear equations. However, when dealing with multi-class classification problems, LSSVM faces the challenges of lack of sparsity and sample noises, which can negatively impact its performance. Based on the modeling characteristics and data distribution …of the multi-class LSSVM model, this paper proposes two improvements and establishes an improved fuzzy sparse multi-class least squares support vector machine (IF-S-M-LSSVM). The first improvement adopts a non-iterative sparse algorithm, which can delete training sample points to different degrees by adjusting the sparse ratio. The second improvement addresses the impact of sample noise on determining the optimal hyperplane by adding a fuzzy membership degree based on sample density. The advantages of the new model, in terms of training speed and classification accuracy, are verified through UCI machine learning standard data set experiments. Finally, the statistical significance of the IF-S-M-LSSVM model is tested using the Friedman and Bonferroni-Dunn tests. Show more

Keywords: Least squares support vector machine, multi-class classification problem, fuzzy membership, sparse

DOI: 10.3233/JIFS-231738

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7769-7783, 2023