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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: Ouyang, Zhiyuan | Wan, Yanling | Zhang, Tao | Wu, Wen-Ze
Article Type: Research Article
Abstract: The introduction of fractional order accumulation has played a crucial role in the development of grey forecasting methods. However, accurately identifying a single fractional order accumulation for modeling diverse sequences is challenging due to the dependence of different fractional order accumulations on data structure over time. To address this issue, we propose a novel fractional grey model abbreviated as FGMMA, incorporating a model averaging method. The new model combines existing fractional grey models by using four judgment criteria, including Akaike information criteria, Bayesian information criteria, Mallows criteria, and Jackknife criteria. Meanwhile, the cutting-edge algorithm named breed particle swarm optimization is …employed to search the optimal fractional order for each candidate model to enhance the effectiveness of the designed model. Subsequently, we conduct a Monte Carlo simulation for verification and validation purposes. Finally, empirical analysis based on energy consumption in three countries is conducted to verify the applicability of the proposed model. Compared with other benchmark models, we can conclude that the proposed model outperforms the other competitive models. Show more
Keywords: Grey forecasting model, fractional order accumulation, model averaging, breed particle swarm optimization
DOI: 10.3233/JIFS-237479
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6479-6490, 2024
Authors: Zhang, Yihao | Wang, Yuhao | Lan, Pengxiang | Xiang, Haoran | Zhu, Junlin | Yuan, Meng
Article Type: Research Article
Abstract: Conversational recommender systems use natural language conversations to elicit user preferences and recommend items proactively. Existing methods based on graph neural networks have been proven to be effective in exploiting knowledge graphs. However, node positions are often treated as constants, which leads to the neglect of graph connectivity due to fuzzy processing. In addition, although the transformer has significant advantages in understanding the text, its secondary computational complexity may be incapable when dealing with long texts. In order to solve these problems, we propose an additive positional conversational recommender model called APCR. This model converts the pair product of transformer …into a linear operation, and uses the Laplacian eigenvector to build a location graph. The extended graph neural network captures the topology structure of the location knowledge graph. Specifically, we design an encoder based on additive attention to break through the bottleneck of long text. Furthermore, we develop a recommendation model based on a positional graph neural network to match items with dialogue context, thereby capturing the graph topology. Extensive experiments on the REDIAL dataset show significant improvements in our proposed model over the state-of-the-art methods in recommendation and dialogue generation evaluations. Show more
Keywords: Interactive recommender systems, graph neural networks, knowledge graphs, additive attention
DOI: 10.3233/JIFS-230905
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6491-6503, 2024
Authors: Qian, Jin | Wang, Taotao | Lu, Yuehua | Yu, Ying
Article Type: Research Article
Abstract: Multi-granularity hesitant fuzzy linguistic terms set is an effective expression of linguistic information, which can utilize some fuzzy linguistic terms to evaluate various common qualitative information and plays an important role when experts provide linguistic information to express hesitancy. Since the alternative description in the decision-making information system is characterized by multi-granularity, uncertainty, and vagueness, this paper proposes a multi-granularity hesitant fuzzy linguistic decision-making VIKOR method based on entropy weight and information transformation. Specifically, this paper firstly adopts fuzzy information entropy to obtain the weights of different attributes and introduces a multi-granularity hesitant fuzzy linguistic term set conversion method to …realize the semantic information conversion between different granularities. Then for the converted affiliation linguistic decision matrix, the entropy weighting method is used to obtain the weights of different affiliation granularity layers, and a weight optimization VIKOR method based on the affiliation linguistic decision matrix is further proposed to rank the alternatives. Finally, the feasibility of the proposed method verified by arithmetic examples, experimental analysis is carried out in terms of parameter sensitivity analysis and comparison with other methods. The experimental results prove the rationality and effectiveness of the proposed method. Show more
Keywords: Multi-granularity hesitant fuzzy term set, affiliation degree, information transformation, VIKOR method
DOI: 10.3233/JIFS-237951
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6505-6516, 2024
Authors: Prasath, J.S. | Shyja, V. Irine | Chandrakanth, P. | Kumar, Boddepalli Kiran | Raja Basha, Adam
Article Type: Research Article
Abstract: Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the …IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively. Show more
Keywords: Defense mechanism, DDoS intrusion, intrusion detection system, feature selection, IoT
DOI: 10.3233/JIFS-235529
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6517-6534, 2024
Authors: Ye, Qing | Song, Zihan | Zhao, Yuqi | Zhang, Yongmei
Article Type: Research Article
Abstract: Video anomaly detection refers to the automatic identification of abnormal behaviors, objects, or events in videos. However, current methods for anomaly detection based on original frames lack a comprehensive understanding of the importance of foreground information, making it challenging to efficiently address video anomaly detection in the presence of complex background interference. In this paper, we propose a video anomaly detection algorithm based on Background Separation Network (BSN) to address this issue. Firstly, we utilize a video stabilization algorithm to reduce video jitter and enhance the quality of input video frames. Secondly, BSN shifts the focus from the entire frame …to the foreground region with higher anomaly detection value. BSN utilizes the motion pixel distribution of the video as the basis for foreground extraction, enabling pixel-level background separation to obtain more accurate and complete foreground targets. Lastly, a certain proportion of foreground targets in the foreground image are masked as background, reducing the interference caused by redundant targets on the detection results. The proposed method achieves an accuracy of 96.2% on the UCSD ped2 dataset, demonstrating its effectiveness. This method contributes to accurately detecting abnormal behaviors in real-world surveillance videos to protect the safety of public lives and assets. Show more
Keywords: Video anomaly detection, auto encoder, background separation network, video jitter elimination
DOI: 10.3233/JIFS-235717
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6535-6551, 2024
Authors: Sheikh, Ansar Isak | Sadish Sendil, M. | Sridhar, P. | Thariq Hussan, M.I. | Abidin, Shafiqul | Kumar, Ravi | Irshad, Reyazur Rashid | Muniyandy, Elangovan | Phani Kumar, Solleti
Article Type: Research Article
Abstract: Effective data management has arisen as a major concern in today’s era of ubiquitous data generation from a plethora of intelligent gadgets. While data proliferation promises unparalleled benefits, it imposes significant storage and computing constraints, particularly on end-users with limited capabilities. To solve these difficulties, this article investigates the confluence of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing. Because of their low-cost data storage and processing capabilities, cloud computing services have grown in popularity, leading customers to embrace data outsourcing to reduce local administrative overhead. This study digs into a novel paradigm for ensuring the integrity …and security of data stored in cloud environments using blockchain technology. Integrating public auditing systems enables visible and verifiable data audits, ensuring consumers of data trustworthiness. A reputation system is also included to build trust among cloud service providers and users, improving the overall trustworthiness of the ecosystem. The suggested system also includes dynamic auditing, which allows for real-time changes and data verification, reacting to the changing nature of cloud-stored information. This study provides a thorough examination of the architectural components, techniques, and protocols used in this novel approach. We illustrate the feasibility and usefulness of our approach in ensuring data integrity, security, and reliability in cloud storage systems through empirical analysis and case studies. The findings show the potential benefits of this integrated strategy to solving the issues posed by the modern digital landscape’s tremendous proliferation of data. Through the synergistic integration of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing, this research provides a holistic solution for managing data in the cloud while ensuring data integrity, security, and trust. This comprehensive strategy lays the way for a more robust and dependable cloud data management ecosystem, increasing user trust in cloud-based services. Show more
Keywords: Low-cost data storage, blockchain technology, data integrity, security, cloud storage
DOI: 10.3233/JIFS-237474
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6553-6564, 2024
Authors: Runkler, Thomas A.
Article Type: Research Article
Abstract: Pairwise fuzzy preference matrices can be constructed using expert ratings. The number of pairwise preference values to be specified by the experts increases quadratically with the number of options. Consistency (transitivity) allows to reduce this quadratic complexity to linear complexity which makes this approach feasible also for large scale applications. Preference values are usually expected to be on a fixed finite interval. Additive preference is defined on such a finite interval. However, completing preference matrices using additive consistency may yield preferences outside this finite interval. Multiplicative preference is defined on an infinite interval and is therefore not suitable here. …To overcome this problem we extend the concept of consistency beyond additive and multiplicative to arbitrary commutative, associative, and invertible operators. Infinitely many of such operators induce infinitely many types of consistency. As one example, we examine Einstein consistency, which is induced by the Einstein sum operator. Completing preference matrices using Einstein consistency always yields preferences inside the finite interval, which yields the first method that allows to construct large scale finite preference matrices using expert ratings. A case study with the real–world car preference data set indicates that Einstein consistency also yields more accurate preference estimates than additive or multiplicative consistency. Show more
Keywords: Fuzzy preference relations, consistent preference, additive preference, multiplicative preference, Einstein sum
DOI: 10.3233/JIFS-224179
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6565-6576, 2024
Authors: Bu, Yanbin | Chen, Ting | Duan, Hongxiu | Liu, Mei | Xue, Yandan
Article Type: Research Article
Abstract: In the modern world, structured and semi-structured knowledge bases hold a considerable amount of data. There-fore, people who are familiar with formal query languages should not be the only ones who can efficiently and clearly query them. Semantic Parsing (SP) is converting natural language utterances into formal meaning representations. The paper suggests a model for SP that uses a novel method of utilizing the Semi-Supervised Generative Adversarial Network (SS-GAN) to enhance the classifier performance. The proposed SS-GAN extends the fine-tuning of word embedding architectures using unlabeled examples in a generative adversarial environment. We provide a regularization strategy for addressing the …mode missing problem and unstable training in SS-GAN. The main viewpoint is to use the extracted feature vectors from the discriminator. Hence, the generator produces outputs by aiding the discriminator’s learned features. A reconstruction loss is added to the loss function of the SS-GAN to drive the genera-tor to reconstruct outputs from the discriminator’s features, hence steering the generator toward actual data configurations. The proposed reconstruction loss improves the performance of SS-GAN, produces high-quality outputs, and may be combined with other regularization loss functions to improve the performance of diverse GANs. We employ BERT word embedding for our model, which can be included in a downstream task and fine-tuned as a model, while the pre-trained BERT model can capture various linguistic properties. We examine the suggested model using the WikiSQL and SparC datasets, and the analysis findings reveal our model outperforms its rivals. The findings from our experiments indicate that the need for labeled samples can be minimized, down to as few as 100 instances, while still achieving commendable classification outcomes. Show more
Keywords: Semantic parsing, generative adversarial network, semi-supervised learning, BERT
DOI: 10.3233/JIFS-233212
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6577-6588, 2024
Authors: Li, Feng | Zhu, Mozhong | Lin, Ling
Article Type: Research Article
Abstract: Once industrial control systems are targeted by cyber-attacks, the consequences can be severe, including asset loss, environmental pollution, and public security risks. Risk assessment is an important way to ensure that industrial control systems operate efficiently, steadily and safely. The purpose of this paper is to develop a risk assessment model for industrial control systems based on asymmetric connection cloud and Choquet integral, which fully takes into account the fact that values of risk indicators are often fuzzy, random, asymmetrically distributed in finite intervals, and there are interactions among different indicators. To do so, we first establish a risk assessment …index system to ensure the full reflection of availability, integrity, and confidentiality in the results of risk assessment for industrial control systems. Then we establish classification standards for each evaluation indicator based on the importance of assets, vulnerabilities, and threats in evaluating the risk of industrial control systems. Next we develop a risk assessment model based on asymmetric connection cloud and Choquet integral to determine the risk level of industrial control systems. In the following, an example is provided to demonstrate the feasibility and reliability of this proposed model. The experimental results have demonstrated a high level of credibility in assessing cyber-attacks by the proposed model, indicating its potential for analyzing the current security and risk posture of industrial control systems. Show more
Keywords: Industrial control systems, risk assessment, asymmetric connection cloud, choquet integral
DOI: 10.3233/JIFS-234686
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6589-6605, 2024
Authors: Ma, Fanglan | Zhu, Changsheng | Liu, Dukui
Article Type: Research Article
Abstract: Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student’s knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and …learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students’ performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior. Show more
Keywords: Knowledge tracing, cognitive law, learning behavior, ResNet, deep learning
DOI: 10.3233/JIFS-235723
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6607-6617, 2024
Authors: Yang, Shuyi | Li, Lusu | Feng, Libo
Article Type: Research Article
Abstract: Currently, scientific big data management is generally faced with the problems of scattered data resources, inconsistent data standards, and the inability to share and circulate data safely. Research personnel attaches great importance to whether sharing the first-hand property is secure under clear ownership and whether it can contribute to the large society. The isolation of the data management system is the obvious obstacle to collecting and managing across-disciplinary data. To a large extent, sharing and trading scientific big data is the primary purpose to realize the clarity of property rights, secure data sharing, and the value of the data assets …step by step. We propose to construct a public platform for scientific big data management. The system is managed to unify and authorize the on-chain data, on which data sharing and trading is tracked throughout the process. Smart contracts are executed with vital functions and guarantee price matching in data transactions. We design the incentive mechanism which measures the incentive yield of data cost quality based on Evolutionary Game Theory and Data Quality Control Theory (EGQCY), considering how the cost of data quality performs in controlling and impacting the rational release of the incentive yields in the sharing and trading process. The experiments found that the design of incentive yield and incentive coefficients only significantly affected the transition from low-quality data to medium-quality data. Both parameters converged to fixed values as the cost of data quality increased. Show more
Keywords: Scientific big data, blockchain, smart contract, data sharing and transaction, data incentive mechanism, the cost of data quality control
DOI: 10.3233/JIFS-236521
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6619-6635, 2024
Authors: Lin, Shanlang | Lin, Zeyu
Article Type: Research Article
Abstract: The relationship between transportation infrastructure and entrepreneurship has been widely discussed by scholars. However, as an important transportation infrastructure, the impact of subway construction on entrepreneurship has been less studied. Based on the Synthetic Control Method, this paper takes the urban data of China from 2003 to 2017 as the research sample and uses the synthetic control method to study the influence of eight cities with subway service on entrepreneurship. The results show that: (1) The impact of subway openings on entrepreneurship varies across different cities. Specifically, it has a positive effect on entrepreneurship in Hangzhou, Zhengzhou, and Changsha, while …it has a negative impact on entrepreneurship in Harbin and Ningbo. In the cases of Suzhou, Wuxi, and Kunming, the influence on entrepreneurship levels could not be conclusively established. (2) For cities where entrepreneurship activity increased following the opening of subways, further investigation revealed that subway openings did not directly stimulate entrepreneurship within transport-related industries. Instead, they indirectly boosted the entrepreneurial landscape in Hangzhou, Zhengzhou, and Changsha by accelerating the flow of resources and enhancing spillover effects within their respective advantageous industries. This study’s contributions are twofold. Firstly, it introduces innovative perspectives and methodologies for assessing the impact of subway systems on entrepreneurship, highlighting the differentiated effects observed across various cities and industries. Secondly, it emphasizes the importance of considering local advantageous industries in subway construction planning for government authorities, as this can maximize the subway’s potential to drive entrepreneurship in urban areas. Show more
Keywords: Subway system, synthetic control analysis, entrepreneurship, analysis of urban differences
DOI: 10.3233/JIFS-233366
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6637-6655, 2024
Authors: Li, Dongmei | Yang, Lehua | Liu, Shaojun | Tan, Ruipu
Article Type: Research Article
Abstract: Emergency rescue decisions in case of a typhoon disaster can be considered multi-attribute decision-making problems. Considering the need for the timeliness and authenticity of decision-making information sources after such a disaster, this study proposed using learning methods to process real-time online data and interval-valued neutrosophic numbers (NNs) to express the classification results. Using Typhoon Hagupit as an example, a trained text classification model was used to classify real-time data (online comments), following which the classification results were used as weights to convert these data into interval-valued NNs. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) …method was adopted to rank the extent of damage caused by the typhoon in each region; the sorting results were consistent with the official statistical data, proving the effectiveness of the proposed method. A detailed sensitivity analysis was conducted to determine the optimal parameter settings of the classification model. Furthermore, the proposed method was compared with existing methods in terms of data conversion and deep learning efficiency; the results confirmed the superior capabilities of the proposed method. Notably, the proposed method can provide support to disaster management professionals in their post-disaster emergency relief work. Show more
Keywords: Deep learning, interval-valued neutrosophic numbers, multi-attribute decision making, typhoon disaster
DOI: 10.3233/JIFS-235315
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6657-6677, 2024
Authors: Liu, Huilin | Wang, Yansi | Yang, Gaoming | Xu, Huan | Wang, Tao
Article Type: Research Article
Abstract: Photorealistic image style transfer aims to transfer style information while preserving the realistic details of the content image. However, an existing limitation is the inability to effectively balance the relationship between image realism and stylization intensity, resulting in poor image transfer performance. To address this issue, we propose an photorealistic style transfer method that fusing Frequency Separation Channel Attention Mechanism (FSCAM) and Mirror Fluid Pyramid Integration (MFPI). This method achieves superior stylization intensity while improves image realism. Firstly, we propose an improved channel attention mechanism called FSCAM. This mechanism utilizes Discrete Cosine Transform (DCT) to decompose features into different frequency …components and screens out high-valued texture and color features, thereby enhancing the stylization intensity of the generated images. In addition, we designed a MFPI module. The module is able to integrate information from different scales, enhance the preservation of low-level detail features in high-level features, and thus improve the realism of the images. Experimental results demonstrate that our method not only enhances the stylization intensity but also improves the image realism. It achieves satisfactory performance in terms of subjective visual performance and objective evaluation metrics. Show more
Keywords: Generate image, style transfer, discrete cosine transform, channel attention mechanism, feature fusion
DOI: 10.3233/JIFS-235903
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6679-6696, 2024
Authors: Zhan, Linjie | Tang, Zhenpeng
Article Type: Research Article
Abstract: Effective energy futures price prediction is an important work in the energy market. However, the existing research on the application of “decomposition-prediction” framework still has shortcomings in noise processing and signal reconstruction. In view of this, this paper first uses PSO to optimize VMD to improve the effectiveness of single decomposition, and further uses SGMD to capture the remaining key information after extracting low-frequency modal components by using PSO-VMD technology. Further, combined with LSTM to predict each component, a new PSO-VMD-SGMD-LSTM hybrid model is innovatively constructed. The empirical research results based on the real energy market transaction price show that …compared with the benchmark model, the hybrid model proposed in this paper has obvious forecasting advantages in different forecasting scenarios. Show more
Keywords: Energy futures price forecast, secondary decomposition technique, long short term memory
DOI: 10.3233/JIFS-236019
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6697-6713, 2024
Authors: Koam, Ali N.A. | Ahmad, Ali | Azeem, Muhammad | Qahiti, Raed
Article Type: Research Article
Abstract: Let G be a graph and R = {r 1 , r 2 , …, r k } be an ordered subset of vertices of G , if every two vertices of G have different representation r (v |R ) = (d (v , r 1 ) , d (v , r 2 ) , …, d (v , r k )) with respect to R , then R is said to be a metric-based resolving parameter or resolving set of G and its minimum cardinality is called the metric dimension of graph G . Metric dimension …is considered as an important applied concept of graph theory especially in the localization of a network and also in the chemical graph theoretical study of molecular compounds. Therefore, it is hot topic to study for different families of graphs as well. Convex polytopes play an important role both in various branches of mathematics and in applied areas, most notably in linear programming. In this paper, we determine the metric-based resolving parameter of line graph of a convex polytope S n , and conclude that it has constant metric dimension but vary with the parity of n . This article presents a measurement of the line graph of a convex polytope, denoted as ( S n ) . The subsequent section provides the metric dimension of the resulting graph. There are two scenarios pertaining to the metric dimension of a selected graph with respect to the metric dimension. The metric dimension of even cycle-based convex polytopes is three, whereas for other values, the metric dimension is four. Show more
Keywords: Convex polytope, metric dimension, resolving set, constant metric dimension, line graph of convex polytope
DOI: 10.3233/JIFS-236517
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6715-6727, 2024
Authors: Arul, A. | Kathirvelu, M.
Article Type: Research Article
Abstract: In this paper, we present a novel DILTS algorithm that uses a new approach inspired by the energy efficiency of dragonflies. The algorithm optimizes the energy-harvesting mechanisms in IoT devices, inspired by the way dragonflies use wind energy to fly. A sophisticated algorithm optimizes power consumption during task execution, saving energy and speeding up tasks while maintaining the application throughput. The algorithm leverages lazy task scheduling (LTS) to enhance task execution performance. The proposed algorithm evaluates the energy levels of each task and implements an LTS method. This LTS approach improves performance and task management by streamlining scheduling data and …reducing overhead. The LTS model reliably optimizes the energy across microbenchmarks and real-time IoT devices. To assess the efficiency and practicality of our algorithm, we compared it to four alternatives. Our novel algorithm outperformed the others with a chip area of 856 μm2 , performance speed of 7.11 ns, scheduling accuracy of 94%, and response time of 2.61 ns. Our simulations showed that our proposed method reduced energy consumption by up to 10.02% compared to existing methods. We evaluated the performance of the algorithms on a Zynq 7000 FPGA using the Xilinx Vivado platform via simulations. Our novel algorithm can improve the energy efficiency of green data centers. Show more
Keywords: Dragonfly algorithm, lazy task scheduling, VHDL, internet of things, energy-efficiency, Xilinx Vivado
DOI: 10.3233/JIFS-237475
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6729-6746, 2024
Authors: Sun, Shaoye
Article Type: Research Article
Abstract: In recent years, the lack of coordination in cross-border logistics has been one of the challenges and challenges faced by cross-border e-commerce. As the primary link in cross-border logistics, the selection of logistics service providers is an important foundation for promoting the development of cross-border e-commerce, and also a key link in improving the competitiveness of cross-border e-commerce enterprises. How to choose suitable and effective cross-border e-commerce logistics service providers has important theoretical significance and practical application value. The cross-border e-commerce logistics service providers evaluation is a multiple-attributed decision-making (MADM) problem. In this paper, the Type-2 neutrosophic number cross-entropy (T2NN-CE) …technique is designed with help of cross-entropy and Type-2 neutrosophic number (T2NN). Furthermore, Then, T2NN-CE technique is built to solve the MADM. Finally, a numerical example for cross-border e-commerce logistics service providers evaluation is given and some comparisons are conducted to illustrate advantages of the designed T2NN-CE technique. The research contribution of the paper is outlined: (1) The T2NN-CE is managed under T2NNs; (2) the T2NN-CE method is implemented for MADM under T2NNs; (3) the T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is constructed and were compared with some existing techniques; (4) Through the comparison, it is known that T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is effective. Show more
Keywords: Multiple-attributed decision-making (MADM), Type-2 neutrosophic number (T2NN), cross entropy, cross-border e-commerce logistics service providers evaluation
DOI: 10.3233/JIFS-238592
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6747-6762, 2024
Authors: Rao, Fengshuo | Chung, Sung-Pil | Xing, Kailin
Article Type: Research Article
Abstract: With the continuous improvement of modern basketball technology, higher requirements have been put forward for the personal abilities of basketball players. As the core of an organization, the offensive ability of a defender largely determines the team’s performance. Therefore, it is necessary to objectively evaluate the attacking ability of defenders. Traditional techniques cannot objectively reflect the true level of players due to their strong subjectivity. Therefore, establishing a scientific evaluation technique is particularly important. The fuzzy comprehensive evaluation of attack ability of basketball defenders is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy …(TFNN-CE) technique is designed with help of cross-entropy and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE technique is addressed to solve the MADM. Finally, a numerical example for fuzzy comprehensive evaluation of attack ability of basketball defenders is given and some comparisons are conducted to r illustrate advantages of the designed technique. The main contribution of this paper is addressed: (1) The TFNN-CE technique is addressed under TFNSs; (2) the TFNN-CE technique is addressed for MADM under TFNSs; (2) the TFNN-CE technique for fuzzy comprehensive evaluation of attack ability of basketball defenders is addressed; (3) Through the several efficient comparisons, it is addressed that TFNN-CE technique is effective for fuzzy comprehensive evaluation of attack ability of basketball defenders. Show more
Keywords: Multiple attribute decision making (MADM), triangular fuzzy neutrosophic sets (TFNSs), cross-entropy technique, TFNN-CE technique, attack ability of basketball defenders
DOI: 10.3233/JIFS-238836
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6763-6780, 2024
Authors: Radhakrishnan, P. | Senthilkumar, G.
Article Type: Research Article
Abstract: Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text …is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively. Show more
Keywords: Text summarization, automatic text summarization, Nesterov-accelerated Adaptive Moment Estimation, Long Short-Term Memory
DOI: 10.3233/JIFS-224299
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6781-6793, 2024
Authors: Liu, Qi
Article Type: Research Article
Abstract: In the era of advanced technology, integrating and distributing data are crucial in smart grid-connected systems. However, as energy loads continue to increase, practical implementation of these systems faces challenges in resource allocation and lacks efficient data collaboration. In this study, the ant colony optimization algorithm is further investigated for stochastic crossover systems and cluster nodes in intelligent path planning management. To improve the pheromone setting method in smart grid-connected systems, we propose an adaptive intelligent ant colony optimization algorithm called the Group Allocation Optimization Algorithm (GAOA). This algorithm expands the pheromone transmission rate of network nodes, establishes a multi-constrained …adaptive model with data mining as the pheromone target, and analyzes the accuracy of resource allocation to import the optimal scheme for smart grid-connected systems. Through experimental results, we demonstrate that the optimized adaptive ant colony algorithm leads to effective improvements in grid-connected systems, pheromone evaluation, data throughput, convergence speed, and data load distribution. These findings provide evidence that the optimized ant colony algorithm is both feasible and effective for resource allocation in smart grid-connected systems. Show more
Keywords: Smart Grid-connected system, data-driven allocation, ant colony algorithm, group allocation optimization algorithm
DOI: 10.3233/JIFS-235091
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6795-6805, 2024
Authors: Pandey, Raksha | Kushwaha, Alok Kumar Singh | Kumar, Vinay
Article Type: Research Article
Abstract: Video forgery, a prevalent concern in today’s digital age, involves the deliberate manipulation of video content, often carried out using sophisticated video editing software. In response to this challenge, the need for an automated approach to detect forged video footage has become increasingly pressing. Our proposed methodology addresses this need by employing a multi-faceted strategy. It begins with the classification of video frames as either originating from genuine sources or having undergone manipulation. To assess the authenticity, the Δ r ¯ s metric is applied to evaluate the coherence of frame sequences. …Additionally, we’ve harnessed the power of machine learning, training a model on a diverse dataset, namely the VIFFD dataset. This robust machine learning approach, particularly the suggested Support Vector Machine (SVM) method, consistently achieves an impressive average accuracy of 94.4%, showcasing its potential as a dependable and effective solution for video forgery detection. In an era where the trustworthiness of video content is of paramount importance, our method emerges as a pivotal safeguard, contributing significantly to the preservation of the integrity and credibility of visual media. Show more
Keywords: Correlation coefficient, forgery detection, interframe video forgery, machine learning, video forensic
DOI: 10.3233/JIFS-235818
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6807-6820, 2024
Authors: Ma, Mengyuan | Huang, Huiling | Han, Jun | Feng, Yanbing | Yang, Yi
Article Type: Research Article
Abstract: Semantic segmentation is a pivotal task in the field of computer vision, encompassing diverse applications and undergoing continuous development. Despite the growing dominance of deep learning methods in this field, many existing network models suffer from trade-offs between accuracy and computational cost, or between speed and accuracy. In essence, semantic segmentation aims to extract semantic information from deep features and optimize them before upsampling output. However, shallow features tend to contain more detailed information but also more noise, while deep features have strong semantic information but lose some spatial information. To address this issue, we propose a novel mutual optimization …strategy based on shallow spatial information and deep semantic information, and construct a details and semantic mutual optimization network (DSMONet). This effectively reduces the noise in the shallow features and guides the deep features to reconstruct the lost spatial information, avoiding cumbersome side auxiliary or complex decoders. The Mutual Optimization Module (MOM) includes Semantic Adjustment Details Module (SADM) and Detail Guided Semantic Module (DGSM), which enables mutual optimization of shallow spatial information and deep semantic information. Comparative evaluations against other methods demonstrate that DSMONet achieves a favorable balance between accuracy and speed. On the Cityscapes dataset, DSMONet achieves performances of 79.3% mean of class-wise intersection-over-union (mIoU)/44.6 frames per second (FPS) and 78.0% mIoU/102 FPS. The code is available at https://github.com/m828/DSMONet . Show more
Keywords: Semantic segmentation, real time, deep learning, mutual optimization, accuracy
DOI: 10.3233/JIFS-235929
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6821-6834, 2024
Authors: Zhang, Zhifei | Wang, Shenmin
Article Type: Research Article
Abstract: The focus of attention has shifted to land use and land cover changes as a result of the world’s fast urbanisation, and logical planning of urban land resources depends greatly on the forecast and analysis of these changes. In order to more precisely forecast and assess patterns of land use change, the study suggests a grey Markov land pattern analysis and prediction model that incorporates social aspects. The study builds a land pattern analysis and prediction model using a major city as the research object. The outcomes demonstrated the high accuracy and reliability of the grey Markov land pattern analysis …and prediction model incorporating social factors, which can more accurately reflect and predict the land use pattern of the study area, with an average relative error of less than 0.01, an accuracy of more than 98%, and an overall fit that has increased by more than 3%. The overall pattern of change is very consistent with the reality. The model predicts that the main trend of future land use in the study area is the continued expansion of urban land such as industrial land, land for transport facilities and land for settlements, while non-construction land such as agricultural land and forest land will continue to decrease. The optimized land pattern analysis and prediction model of the study has a good application environment. Show more
Keywords: Grey system theory, land use change, prediction model, socio-economic factors
DOI: 10.3233/JIFS-235965
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6835-6850, 2024
Authors: Arivanandhan, Rajesh | Ramanathan, Kalaivani | Chellamuthu, Senthilkumar
Article Type: Research Article
Abstract: Users possess the option to rent instances of various sorts, in a variety of regions, and a variety of availability zones, thanks to cloud service carriers like AWS, GCP, and Azure. In the cloud business right now, fixed price models are king when it comes to pricing. However, as the diversity of cloud providers and users grows, this approach is unable to accurately reflect the market’s current needs for cost savings. As a consequence, a dynamic pricing strategy has become a desirable tactic to better handle the erratic cloud demand. In this study, a deep learning model was used to …propose a dynamic pricing structure that ensures service providers are treated fairly in a multi-cloud context. The computational optimization of DL approaches can be severely hampered by the requirement for human hyperparameter selection. Traditional automated solutions to this issue have inadequate durability or fail in specific circumstances. To choose the hyper-parameters in the Dueling Deep Q-Network (DDQN), the hybrid DL approach in this study uses the concept-based wild horse optimization (WHO) method. A community of untamed horses is evolved, and the fitness of the population is evaluated concurrently to estimate the optimum hyper-parameters. The plan changes the price appropriately to promote the use of underutilized resources and discourage the use of overutilized resources. The evaluation’s findings demonstrated that the suggested strategy can lower end-user costs while conducting compute- and data-intensive activities in a multi-cloud environment. The research was concluded by comparing current models after the results were analyzed using various performance indicators. Show more
Keywords: Cloud providers, dynamic pricing scheme, Deep Learning, hyper-parameter selection, Oppositional-Based Learning, Wild Horse Optimization and Dueling Deep Q-Network
DOI: 10.3233/JIFS-236043
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6851-6865, 2024
Authors: Essaki Muthu, A. | Saravanan, K.
Article Type: Research Article
Abstract: Cataract, a common eye disease, causes lens opacification, which can lead to blindness. Early cataract detection in a privacy-preserving approach has led us to investigate the concept of Federated Learning (FL) and its prominent technique, known as Federated Averaging (FedAVG). Federated learning has the potential to solve the privacy issues by allowing data servers to train their models natively and distribute them without invading patient confidentiality. This research introduces an interactive federated learning framework that permits multiple medical institutions to screen cataract from split lamp images utilising convolutional neural network (CNN) without sharing patient data, as well as grade normal, …mild, moderate, and severe cataracts. The CNN is developed based on Modified-ResNet-50 and FedAVG technique could achieve relatively high accuracy. The experimental results demonstrate that the proposed modification reduces the processing time to a greater extent. Show more
Keywords: Federated learning, confidentiality, accuracy, CNN
DOI: 10.3233/JIFS-223465
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6867-6880, 2024
Authors: Wang, Haohao | Li, Wei | Yang, Bin
Article Type: Research Article
Abstract: Rosenfeld defined a fuzzy subgroup of a given group as a fuzzy subset with two special conditions and Mustafa Demirci proposed the idea of fuzzifying the operations on a group through a fuzzy equality and a fuzzy equivalence relation. This paper mainly focuses on fuzzy subsets and vague sets of monoids with several extended algebraic properties. Firstly, we generalize some algebraic properties of t -norms to fuzzy t -norms, this allows for a broader analysis and classification of fuzzy t -norms, enabling their wider application. Furthermore, we explore specific research on the properties of vague t -norms. Finally, selected conclusions …about fuzzy t -norms are extended to bounded lattices. Show more
Keywords: t-norm, t-conorm, uninorm, nullnorm, aggregation function, fuzzy monoid, vague monoid
DOI: 10.3233/JIFS-231401
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6881-6891, 2024
Authors: Du, Wen Sheng
Article Type: Research Article
Abstract: The geometric-arithmetic mean inequality is undoubtedly the most important one in the area of information aggregation. Recently, some q -rung orthopair fuzzy aggregation operators were proposed based on the Hamacher operations. In this paper, we give a detailed theoretical and practical analysis of the developed Hamacher arithmetic and geometric operators for q -rung orthopair fuzzy values. First, we investigate the monotonicity of these Hamacher aggregation operators on q -rung orthopair fuzzy values with respect to the parameter within Hamacher operations. Then, we discuss the limiting cases of these q -rung orthopair fuzzy Hamacher aggregation operators as the parameter therein approaches …to zero or infinity and give a new characterization of the boundedness of these aggregation operators. Subsequently, we establish the geometric-arithmetic mean inequality for q -rung orthopair fuzzy information based on Hamacher operations. Finally, we present a decision making method by use of these aggregation operators and apply it to the problem of enterprise resource planning system selection. Show more
Keywords: Aggregation operator, enterprise resource planning system, geometric-arithmetic mean inequality, Hamacher operation, q-rung orthopair fuzzy value
DOI: 10.3233/JIFS-231452
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6893-6910, 2024
Authors: Xu, Xiaohui
Article Type: Research Article
Abstract: In the new normal period, the trend changes and adjustments of the environment such as international trade, production capacity, labor supply and resource constraints have put forward new requirements for China’s industrial development, which have brought new challenges and given new opportunities. In the new normal stage where economic growth continues to decline, industrial growth is still an important support for economic growth. The advancement of industrial technology is the main driving force for improving the total factor productivity of the industrial industry. Therefore, the most important thing to promote industrial growth is to upgrade the level of industrial technology. …In response to the above-mentioned problems, this paper analyzed the relationship between industrial technology and industrial output in the new normal environment by using the BP neural network (BPNN) algorithm. The connection between the two has been found, which provided a clear direction for the functional adjustment of economic law. Experimental studies have shown that there is a positive relationship between industrial technological progress and industrial output. When other conditions are the same, and when the non-new normal is selected, industrial output increases by about 0.36% for every 1% increase in industrial technological progress. When choosing to be in the new normal, industrial technological progress has a higher impact on industrial output. For every 1% increase in technological progress, industrial output increases by about 0.39%. Show more
Keywords: Sustainable development, new industrial normal, economic law, functional adjustment, artificial neural network
DOI: 10.3233/JIFS-233251
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6911-6924, 2024
Authors: Wang, Fei
Article Type: Research Article
Abstract: Recently, there has been a lot of interest in using the wearable sensors for tracking the exercise progress because of the unbiased accuracy and precision they are provided throughout the continual monitoring. For those with physical impairments, the system’s non-intrusive, lightweight ways of the monitoring activity may ease their load and enhance the quality of their decision-making. As a different measuring unit measures the exercise activity levels recorded by the each wearable sensor, it is challenging to assess the monitoring system. Hence, this paper proposes a Hybridized Fuzzy Multi-Attribute for Exercise Monitoring System (HFMA-EMS) to address the uncertainty issues of …the wearable sensors. The Triangular Fuzzy membership function is proposed to begin classifying the observed values. Pair-wise attribute comparison and evaluator weighting in a T-spherical uncertain linguistic set setting utilizing the Techniques for Ordering of Preferences by Similarities to Ideal Solutions (TOPSIS). In the suggested method, a utility function is used to assess the merits of a model in which attribute the weights are calculated, followed by an exercise in which the attributes are ordered employing the Measurements of the Alternative and Ranking Compromise Solutions model (MARCOS). The performance is performed to analyze the proposed method’s accuracy, precision, recall, f1-score, and correct and incorrect exercise assessment by an accelerometer, gyroscope, and magnetic field sensor unit. The application scenario of the HFMA-EMS can be used in the clinical applications, healthcare management, and sports injury detection. Show more
Keywords: Exercise monitoring system, wearable sensors, disabled individuals, TOPSIS, MARCOS, fuzzy multi-attribute model
DOI: 10.3233/JIFS-235112
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6925-6938, 2024
Authors: Zhou, Yuzhong | Lin, Zhengping | Wu, Zhengrong | Zhang, Zifeng
Article Type: Research Article
Abstract: Due to the complexity of the calculation process of the existing methods, the efficiency of data fusion of the power grid model is low. In order to improve the knowledge fusion effect of power grid model, this paper studied the knowledge fusion method of power grid model based on Seq2seq half pointer and half label method. The Text Rank algorithm is used to calculate the weight of semantic nodes of each grid model, and combined with the topological potential method, the semantic information of the grid model is extracted according to the final weight value, and the Seq2Seq semi-pointer semi-label …model framework is constructed. The data of the scheduling automation system OMS and the production management system PMS are used as input. The extracted candidate mesh model semantics and the original mesh model semantics are encoded by Seq2Seq half-pointer half-label model. The semantic data of the power grid model is fused and sent to the Seq2Seq encoder. After the training is completed, the effective information is extracted from the power grid model through the Seq2Seq model to complete the knowledge fusion of the power grid model. Experimental results show that this method eliminates the redundant part of the basic attributes of each data source in the substation grid model after knowledge fusion, and the description of each basic attribute is more standardized, unified and perfect. Under different mesh model data dimensions, the support of the proposed method is all above 98%. The model trained by the proposed method tends to be stable after 120 iterations, and the precision, recall and F1 of the test set are 0.98, 0.93 and 0.91, respectively. At the same time, this method has high efficiency in the knowledge fusion processing of the power grid model, and its data processing speed is less than 160 s. The average integrity of the private data of the power grid model is 98.86%, indicating that the proposed method can better ensure the integrity of the data. Finally, compared with the application of other methods under different data amounts, the mean square error obtained by the proposed method is the smallest, indicating that the proposed method effectively improves the fusion accuracy. Show more
Keywords: Grid model, knowledge fusion method, half label method, LSTM neural network, Seq2seq half pointer, TPC TextRank algorithm
DOI: 10.3233/JIFS-236465
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6939-6950, 2024
Authors: Xiao, Yuteng | Liu, Zhaoyang | Yin, Hongsheng | Wang, Xingang | Zhang, Yudong
Article Type: Research Article
Abstract: Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not effectively tackle the challenges posed by noise and unordered data. Drawing inspiration from advancing the Transformer model, we introduce a transformer-based method called STFormer to address this predicament. The STFormer utilizes a two-stage Transformer to capture spatio-temporal relationships and tackle the issue of noise. Furthermore, the MTS incorporates adaptive spatio-temporal graph structures to tackle the issue of unordered data specifically. The Transformer incorporates graph embedding to combine spatial position information with long-term …temporal connections. Experimental results based on typical finance and environment datasets demonstrate that STFormer surpasses alternative baseline forecasting models and achieves state-of-the-art results for single-step horizon and multistep horizon forecasting. Show more
Keywords: Multivariate time series forecasting, Spatio-temporal structure, transformer, graph embedding, recurrent neural network
DOI: 10.3233/JIFS-237250
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6951-6967, 2024
Authors: Behera, Santi Kumari | Rao, Mannava Srinivasa | Amat, Rajat | Sethy, Prabira Kumar
Article Type: Research Article
Abstract: Mineral classification is a crucial task for geologists. Minerals are identified by their characteristics. In the field, geologists can identify minerals by examining lustre, color, streak, hardness, crystal habit, cleavage, fracture, and specific features. Geologists sometimes use a magnifying hand lens to identify minerals in the field. Surface color can assist in identifying minerals. However, it varies widely, even within a single mineral family. Some minerals predominantly show a single color. So, identifying minerals is possible considering surface color and texture. But, again, a limited database of minerals is available with large-scale images. So, the challenges arise to identify the …minerals using their images with limited images. With the advancement of machine learning, the deep learning approach with bi-layer feature fusion enhances the dimension of the feature vector with the possibility of high accuracy. Here, an experimental analysis is reported with three possibilities of bi-layer feature fusion of three CNN models like Alexnet, VGG16 & VGG19, and a framework is suggested. Alexnet delivers the highest performance with the bi-layer fusion of fc6 and fc7. The achieved accuracy is 84.23%, sensitivity 84.23%, specificity 97.37%, precision 84.7%, FPR 2.63%, F1 Score 84.17%, MCC 81.75%, and Kappa 53.59%. Show more
Keywords: Mineral identification, deep learning, bi-layer feature fusion, deep feature
DOI: 10.3233/JIFS-221987
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6969-6976, 2024
Authors: Danielraj, A. | Venugopal, P. | Padmapriya, N.
Article Type: Research Article
Abstract: Graph Neural Networks (GNNs) have gained popularity across various research fields in recent years. GNNs utilize graphs to construct an embedding that includes details about the nodes and edges in a graph’s neighborhood. In this work, a set of Region Adjacency Graphs (RAG) derives the attribute values from Static Signature (SS) images. These attribute values are used to label the nodes of the complete graph, which is formed by considering each signature as a node taken from the sample of signatures of a specific signer. The complete graph is trained by using GraphSAGE, an inductive representation learning method. This trained …model helps to determine any newly introduced node (static signature to be tested) as genuine or fake. Standard static signature datasets, notably GPDSsynthetic and MCYT-75 are used to test the prevailing model. Experimental results on genuine and counterfeit signature networks demonstrate that our computed model enables a high rate of accuracy (GPDSsynthetic 99.91% and MCYT-75 99.56%) and minimum range of loss (GPDSsynthetic 0.0061 and MCYT-75 0.0070) on node classification. Show more
Keywords: Signature verification, GNN, region adjacency graph, complete graph, GraphSAGE Node classifications
DOI: 10.3233/JIFS-231369
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6977-6994, 2024
Authors: Li, Jing | Hu, Yifan | Fan, Jiulun | Yu, Haiyan | Jia, Bin | Liu, Rui | Zhao, Feng
Article Type: Research Article
Abstract: The Fuzzy C-means (FCM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition. As the intensity of observation noise increases, FCM tends to produce large center deviations and even overlap clustering problems. The relative entropy fuzzy C-means algorithm (REFCM) adds relative entropy as a regularization function to the fuzzy C-means algorithm, which has a good ability for noise detection and membership assignment to observed values. However, REFCM still tends to generate overlapping clusters as the size of the cluster increases and becomes imbalanced. Moreover, the convergence speed of this algorithm is slow. To solve this problem, …modified suppressed relative entropy fuzzy c-means clustering (MSREFCM) is proposed. Specifically, the MSREFCM algorithm improves the convergence speed of the algorithm while maintaining the accuracy and anti-noise capability of the REFCM algorithm by adding a suppression strategy based on the intra-class average distance measurement. In addition, to further improve the clustering performance of MSREFCM for multidimensional imbalanced data, the center overlapping problem and the center offset problem of elliptical data are solved by replacing the Euclidean distance in REFCM with the Mahalanobis distance. Experiments on several synthetic and UCI datasets indicate that MSREFCM can improve the convergence speed and classification performance of the REFCM for spherical and ellipsoidal datasets with imbalanced sizes. In particular, for the Statlog dataset, the running time of MSREFCM is nearly one second less than that of REFCM, and the accuracy of MSREFCM is 0.034 higher than that of REFCM. Show more
Keywords: Fuzzy c-means clustering, relative entropy fuzzy c-means clustering, modified suppressed relative entropy fuzzy c-means, Mahalanobis distance
DOI: 10.3233/JIFS-231515
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6995-7019, 2024
Authors: Liu, Ziwei | Xu, Ziyu | Zheng, Xiyu | Zhao, Yongxing | Wang, Jinghua
Article Type: Research Article
Abstract: Ground mobile robots can replace human beings to perform special tasks in threatened areas. Path planning technology provides mobile robots with the ability to reach the target position autonomously. When there are threats in the environment, the ground mobile robot needs to be able to reach the target position quickly and safely. Because threats are often difficult to calculate in such environments, and planned paths are difficult to use for path tracing. Therefore, path planning should comprehensively consider the distance, continuity and possible threats when moving. Aiming at the problem that the threat in the environment cannot be accurately calibrated …usually, this paper proposes a method to mark the threat degree on the global elevation map by using the fuzzy logic system. In order to verify the feasibility of the algorithm, the improved algorithm with the classical algorithm in different environments and the current similar algorithm are compared with the current simulation experiment. The simulation results show that the algorithm has achieved good results, which proves the superiority of the algorithm. The path planning results of the algorithm in the threatened 3D environment not only have less threat, but also have better adaptability to the natural environment, and the planning path quality is better than that of the same type of algorithm. Show more
Keywords: Mobile robot, fuzzy logic system, threat assessment, Hybrid-A*, path planning
DOI: 10.3233/JIFS-232076
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7021-7034, 2024
Authors: Behera, Santi Kumari | Anitha, Komma | Amat, Rajat | Sethy, Prabira Kumar
Article Type: Research Article
Abstract: Recognizing and classifying citrus fruits is a challenging yet crucial task for agriculture, food processing, and quality control. Classifying citrus fruits is challenging because of their wide variety, often with a similar flesh appearance, shape, and size. Therefore, efficient and effective approaches are required for accurate identification. Our study focused on efficiently identifying citrus fruit types by utilizing a hybrid ResNet101-SVM model. ResNet101-SVM is the combination of the feature extraction capabilities of the ResNet101 with the classification power of SVM. This hybrid approach leverages the strengths of both deep learning (feature extraction) and traditional machine learning (SVM classification) to improve …the accuracy and robustness of citrus fruit classification. The model outperformed the standard ResNet101 model across various performance metrics, achieving impressive accuracy, sensitivity, specificity, precision, F1 Score, MCC, and Kappa values of 99.81%, 99.81%, 99.8%, 99.82%, 0.18%, 99.81%, 99.80%, and 98.77%, respectively. This study holds significant promise for various applications, particularly in the domains of food processing and quality control. Show more
Keywords: Citrus fruits, classification, support vector machine, convolutional neural network, feature extraction
DOI: 10.3233/JIFS-233910
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7035-7045, 2024
Authors: Suganthi, J. Roselin | Rajeswari, K.
Article Type: Research Article
Abstract: Communication is an essential component of human nature. It connects humans, allowing them to learn, grow, col-laborate, and resolve conflicts. Several aspects of human society, relationships, and growth would be significantly hampered in the absence of efficient communication. Hand gesture recognition is a way to interact with technology that can be particularly useful for individuals with disabilities. This hand gesture recognition is mainly employed in sign language translation, healthcare, rehabilitation, prosthesis, and Human-Computer Interaction (HCI). The high degree of dexterity is a main challenge for prosthetic limbs. In order to meet this challenge, hand gesture recognition is employed for the …prosthetic limb, which can be used for rehabilitation. The objective of this article is to show the methodology for the recognition of hand gestures using Electromyography (EMG) signals. This article uses the pro-posed time domain feature extraction method called Absolute Fluctuation Analysis (AFA) along with the Root Mean Square (RMS) for the feature extraction method. Along with these feature extraction methods, repeated stratified K-fold cross validation is used for the validation of the classifiers such as the XGB classifier, the K-Nearest Neighbour (KNN) classifier, the Decision Tree classifier, the Random Forest classifier, and the SVM classifier, whose mean recognition accuracy is given by 93.26%, 87.42%, 85.26%, 92.23%, and 91.78%, respectively. The recognition accuracy of machine learning classifiers is being compared with state-of-the-art networks such as artificial neural net-works (ANN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent units (GRU), and convolution-al neural networks (CNN), which provide recognition accuracy of 96.65%, 99.16%, 99.94%, and 99.99%, respectively. Show more
Keywords: Human computer interaction(HCI), Absolute fluctuation analysis, LSTM, GRU, CNN
DOI: 10.3233/JIFS-234196
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7047-7059, 2024
Authors: Lian, Jing | Chen, Shi | Li, Linhui | Sui, Duo | Ren, Weiwei
Article Type: Research Article
Abstract: Intelligent vehicles require accurate identification of traversable road areas and the ability to provide precise and real-time localization data in unstructured road environments. To address these issues, we propose a system for traversable map construction and robust localization in unstructured road environments based on a priori knowledge. The proposed method performs traversable area segmentation on the LiDAR point cloud and employs a submap strategy to jointly optimize multiple frames of data to obtain a reliable and accurate point cloud map of the traversable area, which is then rasterized and combined with the vehicle kinematic model for global path planning. Then, …it integrates priori map information and real-time sensor information to provide confidence and priori constraints to ensure the robustness of localization, and it fuses multi-sensor heterogeneous data to improve real-time localization. Experiments are conducted in a mining environment to evaluate the performance of the proposed method on an unstructured road. The experimental results demonstrate that the traversable map and localization results based on the proposed method can meet the requirements for autonomous vehicle driving on unstructured roads and provide reliable priori foundation and localization information for autonomous vehicle navigation. Show more
Keywords: Autonomous vehicles, traversability analysis, map construction, robust localization
DOI: 10.3233/JIFS-235063
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7061-7075, 2024
Authors: Srinivasa Rao Illapu, Sankara | Mula, Aswini | Malarowthu, Padmaja
Article Type: Research Article
Abstract: Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized …by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR. Show more
Keywords: Energy efficiency, life expectancy, multiobjective-energy centric honey badger optimization, time division multiple access, wireless body area network
DOI: 10.3233/JIFS-235387
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7077-7091, 2024
Authors: Qin, Zhi | Liu, Enyang | Zhang, Shibin | Chang, Yan | Yan, Lili
Article Type: Research Article
Abstract: Currently, word segmentation errors and polysemy problems are common in the field of Chinese relationship extraction. Although character-based model input can avoid word segmentation errors, in order to obtain the word information of a sentence, it is often necessary to introduce a dictionary or an external knowledge base to expand the word information, which requires a lot of manpower and time. In response to the above existing problems, this article uses characters as input, uses multiple embedding models to jointly form a character vector sequence, and obtains features containing character information through BiLSTM and attention layers; considering that convolutional neural …networks are good at extracting local features, obtain features containing word information through multi-kernel convolutional layers and multi-head self-attention layers, and finally use a gating mechanism to fuse the features. The model was tested on the public SanWen data set and our own cultural-travel data set, and obtained F1 values of 61.22% and 60.26% respectively. Experimental results show that our method can achieve better relationship extraction effects without using word segmentation tools and without building a dictionary or external knowledge base, and the effect is better than most commonly used models currently. Show more
Keywords: Chinese relation extraction, multiple embedded representations, muti-head self-attention, gating mechanism
DOI: 10.3233/JIFS-237391
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7093-7107, 2024
Authors: Sundar, R. | Choudhury, Ziaul Haque | Chiranjivi, M. | Parasa, Gayatri | Ravuri, Praseeda | Sivaram, M. | Subramanian, Balambigai | Muppavaram, Kireet | Lakshmi.Challa, Vijaya Madhavi
Article Type: Research Article
Abstract: Embracing Artificial Intelligence (AI) is becoming more common in a variety of areas, including healthcare, banking, and transportation, and it is based on substantial data analysis. However, utilizing data for AI raises a number of obstacles. This extensive article examines the challenges connected with using data for AI, including data quality, volume, privacy and security, bias and fairness, interpretability and ethical considerations, and the required technical knowledge. The investigation delves into each obstacle, providing insightful solutions for businesses and organizations to properly handle these complexities. Organizations may effectively harness AI’s capabilities to make educated decisions by understanding and proactively tackling …these difficulties, obtaining a competitive edge in the digital era. This review study, which provides a thorough examination of numerous solutions developed over the last decade to address data difficulties for AI, is expected to be a helpful resource for the scientific research community. It not only provides insights into current difficulties, but it also serves as a platform for creating novel ideas to alter our approaches to data strategies for AI. Show more
Keywords: Artificial intelligence, data quality, privacy, security, ethical consideration
DOI: 10.3233/JIFS-238830
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7109-7122, 2024
Authors: Xing, Haihua | Zhang, Min | Tong, Qixiang | Zeng, Xiya | Chen, Huannan
Article Type: Research Article
Abstract: Cluster analysis is an important method for data analysis, which is also widely used in remote sensing image classification. An effective clustering algorithm is proposed for the uncertainty caused by the overlap between categories in pattern data and remote sensing images. First, an improved PCM (Possibilistic C-means) algorithm based on distance of cluster center (dc-PCM) is proposed to solve the clustering consistency problem of PCM algorithm. Then, based on dc-PCM algorithm, an improved interval type-2 probabilistic c-means clustering algorithm (IT2-dcPCM) is proposed for interclass maximization. The computational complexity of the proposed algorithm is analyzed, and its performance is evaluated by …three data sets, two groups of images segmentation and two groups of remote sensing image classification experiments. The results show that IT2-dcPCM algorithm can assign proper membership degrees to clustering samples, and it has good performance in image segmentation and remote sensing image classification with serious spectral aliasing. Show more
Keywords: Possibility C-mean clustering, interval type-2 fuzzy sets, penalty factor, uncertainty
DOI: 10.3233/JIFS-231883
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7123-7141, 2024
Authors: Ding, Ling | Xiong, Xiaobing | Bao, Zhenyu | Hu, Luokai | Chen, Yu | Li, Bijun | Cheng, Yong
Article Type: Research Article
Abstract: The scene environment in the cab is complex and changeable, which is easy to be disturbed by the changes of weather, light and other conditions, which brings many difficulties to the driver’s fatigue driving detection. This paper mainly analyzes the specific characteristics of the driving indoor environment, uses some technologies in the field of computer vision and image processing to improve the image quality, uses the Adaboost face detection algorithm based on Haar-like features, to detect and locate the driver’s face in the cockpit, and reduces the face detection area to determine whether the driver looks left and right or …bowed. Then, on the basis of detecting the face, the method of gray scale integral projection is adopted to locate the eyes and the mouth, and the Bezier curve is used to fit the contour of the eyes and the mouth, and to judge the driver’s eye opening and the mouth opening by comprehensively analyzing the vertical and horizontal ratio. Later, ocular fatigue analysis was performed by using the PERCLOS(Percentage of Eyelid Closure over the Pupil over Time) criteria and by eyeblink frequency, and mouth fatigue analysis was also performed by the frequency of yawning. Show more
Keywords: Fatigue driving detection, face detection, the haar feature, the Bezier curve, adaboost
DOI: 10.3233/JIFS-234248
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7143-7153, 2024
Authors: Wei, Xiaohong
Article Type: Research Article
Abstract: Higher vocational mathematics education is advanced and related to real-time applications providing vast knowledge. Teaching and training peculiar mathematical problems improve their educational and career-focused performance. Therefore optimal performance assessment methods are required for reducing the lack of knowledge in mathematics learning. This article hence introduces an Articulated Performance Assessment Model (APAM) for consenting mathematics assessment. In this model, fuzzy optimization is used for consenting different factors such as understandability, problem-solving, and replication. The understandability is identified using similar problem progression by the students, whereas replication is the application of problem-solving skills for articulated mathematical models. From perspectives, problem-solving and …solution extraction is the theme that has to be met by the student. The assessments hence generate a perplexed outcome due to which the fuzzy optimization for high and low-level understandability is evaluated. The optimization recommends the change in varying steps in problem explanation and iterated replication for leveraging the students’ performance. This process swings between irrelevant and crisp inputs during fuzzification. In this process, the crisp inputs are the maximum replications produced by the students for better understanding. Therefore, the proposed model is evaluated using efficiency, maximum replication, fuzzification rate, and analytical time. Show more
Keywords: Fuzzy optimization, mathematics education, performance evaluation, student assessment
DOI: 10.3233/JIFS-235564
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7155-7171, 2024
Authors: Zhao, RongLe | Tang, Xiao
Article Type: Research Article
Abstract: The theory of interval-valued formal contexts was originally derived from fuzzy formal contexts. While the fuzzy formal context can extract information from fuzzy formal contexts more precisely, it lacks theoretical analysis of formal contexts with interval-valued data types. This paper incorporates the three-way concept into interval-valued formal contexts, and partitions the interval value range of objects and attributes into three regions utilizing the notion of three-way decisions. On the basis of interval-valued information granules, the concepts of negative operators and interval-valued three-way concepts are proposed. They can conduct profounder knowledge discovery in interval-valued formal contexts, and a generation algorithm of …interval-valued three-way concepts is devised. Finally, the effectiveness of the algorithm is substantiated through experimentation Show more
Keywords: Three-way decision model, formal concept analysis, interval-valued, three-way concept, granular computing
DOI: 10.3233/JIFS-236146
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7173-7184, 2024
Authors: Feng, Rui | Weng, Lie’en
Article Type: Research Article
Abstract: The text information processing technology of public health service is one of the hot research topics at present. To improve the defects of public health service texts, such as inaccurate word segmentation, spelling errors and professional vocabulary understanding, this study designed a character-level deep neural network model on the characteristics of public health service texts. In this model, the bidirectional short and short time memory and the attention pooling operation layer are introduced to make the model better classify the text according to the context. In addition, counter perturbation is introduced in this study to improve the robustness and generalization …ability of the model, thus improving its classification effect. The performance verification results show that the proposed model has better classification performance on the public health service text data set. The anti-disturbance samples generated by the model are all in the range of 0–0.2 when WMD deviation degree is measured, while most of the other methods are in the range of 0.4–0.6. The experimental object of this study is ultrasonic examination data. The experimental results show that the automatic analysis model of public health service text based on character level convolutional neural network constructed in this study has excellent accuracy and convergence speed, and has excellent performance in the classification of public health service text in different subject areas. Show more
Keywords: Public health service text, character level convolutional neural network, automatic analysis, counter sample, text classification
DOI: 10.3233/JIFS-236470
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7185-7197, 2024
Authors: Alrasheedi, Adel Fahad | Mishra, Arunodaya Raj | Pamucar, Dragan | Devi, Sarita | Cavallaro, Fausto
Article Type: Research Article
Abstract: In the theory of interval-valued intuitionistic fuzzy set (IVIFS), the rating/grade of an element is the subset of the closed interval [0, 1], therefore the IVIFS doctrine is more useful for the decision expert to present their judgments in terms of intervals rather than the crisp values. The present work develops an integrated decision-making methodology for evaluating sustainable wastewater treatment technologies within the context of IVIFS. The proposed decision-making framework is divided into three stages. First, some Yager weighted aggregation operators and their axioms are developed to combine the interval-valued intuitionistic fuzzy information. These operators can offer us a flexible …way to solve the realistic multi-criteria decision-making problems under IVIFS context. Second, an extension of Symmetry Point of Criterion model is introduced to determine the criteria weights under IVIFS environment. Third, an integrated alternative ranking order model accounting for two-step normalization (AROMAN) approach is proposed from IVIF information perspective. Next, the practicability and efficacy of the developed model is proven by implementing it on a case study of sustainable wastewater treatment technologies evaluation problem with multiple criteria and decision experts. Finally, comparative analysis is discussed to illustrate the consistency and robustness of the obtained outcomes. Show more
Keywords: Interval-valued intuitionistic fuzzy set, sustainability, waste water treatment, Yager aggregation operators, score function, AROMAN
DOI: 10.3233/JIFS-236697
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7199-7222, 2024
Authors: Shanthini, J. | Poovizhi, P. | Kavitha, M.S. | Karthik, S.
Article Type: Research Article
Abstract: PURPOSE: Increasing technological advancements in processing and storage have made it easier to handle formerly difficult jobs like disease diagnosis or semantic segmentation. Eye cancer is a rare but deadly disorder that, if misdiagnosed, can cause blindness or even death. It is essential to find eye cancer early in order to successfully treat it and enhance patient outcomes. The usage of DL methods for medical image analysis, particularly the identification of eye cancer, has fascinated increasing consideration in current era. The demand for efficient tool to detect the eye cancer emphasize the need for reliable detection systems. Examining how explainable …deep learning techniques, in which the model’s decision-making process can be understood and visualized, can increase confidence in and adoption of the deep learning-based approach for detecting eye cancer. Expert input is necessary to train machine learning algorithms properly. As it necessitates knowledge of ophthalmology, radiography, and pathology, this can be difficult for eye cancer identification. The main purpose of the study is to detect the eye cancer with at most accuracy with the utilization of Deep learning-based approach. METHODS: There are four steps involved to achieve the efficient detection system. They are pre-processing, segmentation, augmentation, feature extraction with classification. The Circle Hough Transform is applied to detect the edges in the image. The dataset size is increased by shifting, rotating and flipping augmentation techniques. Deep learning-based approach is suggested for the automatic detection of eye cancer. The two methods named 3XConPool and 10XCon5XPool were investigated using Python learning environment. The two techniques 3XConPool and 10XCon5XPool imply on the Sine Cosine Fitness Grey Wolf Optimization (SCFGWO) algorithm for the adjustment of the hyperparameters. The 3XConPool and 10XCon5XPool methods with SCFGWO are compared with each other and also with the other existing methods. RESULTS: As a comparison to the earlier techniques, the suggested configured Convolution Neural Network with SCFGWP exceeds with regard to high accuracy, recall and precision. The suggested 10XCon5XPool with SCFGWO obtains 98.01 as accuracy compared to other method 3XConPool which results 97.23% accuracy. CONCLUSION: The Proposed Method 1 and Proposed Method 2 is presented here, where Proposed Method 2 with 5 times convolution layer with pooling layer yields high accuracy compared to proposed method 1. The main contribution by the SCFGWO algorithm resulted in the achievement of accuracy. This study will open the door for further investigation and the creation of deep learning-based techniques with optimization for ophthalmic processing. Show more
Keywords: Eye cancer, deep learning model, Sine Cosine Fitness, Grey Wolf Optimizer, fully connected layer
DOI: 10.3233/JIFS-237083
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7223-7239, 2024
Authors: Selvin Prem Kumar, S. | Agees Kumar, C. | Venugopal, Anita | Sharma, Aditi
Article Type: Research Article
Abstract: The central nervous system can develop complex and deadly neoplastic growths called brain tumors. Despite being relatively uncommon in comparison to other cancers, brain tumors pose particular challenges because of their delicate anatomical placement and interactions with critical brain regions. The data are taken from TCIA (The Cancer Image Archive) and Kaggle Datasets. Images are first pre-processed using amplified median filter techniques. The pre-processed images are then segmented using the Grabcut method. Feature extraction is extracted using the Shape, ABCD rule, and GLCM are the features were retrieved. The MRI images are then classified into several classes using the Bi-directional …Encoder Representations from Transformers-Bidirectional Long Short Term Memory (BERT-Bi-LSTM) model. Kaggle and TICA datasets are used to simulate the proposed approach, and the results are evaluated in terms of F1-score, recall, precision and accuracy. The proposed model shows improved brain tumour identification and classification. To evaluate the expected technique’s efficacy, a thorough comparison of the current techniques with preceding methods is made. The trial results showed that an efficient hybrid bert model for brain tumor classification suggested strategy provided precision of 98.65%, F1-score of 98.25%, recall of 99.25%, and accuracy of 99.75%. Show more
Keywords: Brain tumor, BERT, Bi-LSTM, grabcut algorithm, classification, feature extraction
DOI: 10.3233/JIFS-237653
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7241-7258, 2024
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