Journal of Intelligent & Fuzzy Systems - Volume 45, issue 1

Authors: Sun, Shuwan | Bian, Weixin | Xie, Dong | Xu, Deqin | Huang, Yi

Article Type: Research Article

Abstract: With the development of wireless communication technology and the rapid increase of user data, multi-server key agreement authentication scheme has been widely used. In order to protect users’ privacy and legitimate rights, a two-factor multi-server authentication scheme based on device PUF and users’ biometrics is proposed. The users’ biometrics are combined with the physical characteristics of the Physically Unclonable Functions (PUF ) as authentication factors, which not only ensures the security of the scheme, but it also is user-friendly without a password. The proposed scheme can be applied to telemedicine, smart home, Internet of Vehicles and other fields to …achieve mutual authentication and key agreement between users and servers. In order to prove the security of the proposed scheme, the widely accepted ROR model and BAN logic are used for formal security analysis. The scheme can effectively resist various security attacks, and the comparison with existing schemes shows that it has better performance in terms of communication cost and computational complexity. Show more

Keywords: Multi-server, physical unclonable function, password-free, mutual authentication, biometric security and privacy

DOI: 10.3233/JIFS-221354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 911-928, 2023

Authors: Nishy Reshmi, S. | Shreelekshmi, R.

Article Type: Research Article

Abstract: In this paper, we propose a method exploiting syntactic structure, semantic relations and word embeddings for recognizing textual entailment. The sentence pairs are analyzed using their syntactic structure and categorization of sentences in active voice, sentences in passive voice and sentences holding copular relations. The main syntactic relations such as subject, verb and object are extracted and lemmatized using a lemmatization algorithm based on parts-of-speech. The subject-to-subject, verb-to-verb and object-to-object similarity is identified using enhanced Wordnet semantic relations. Further similarity is analyzed using modifier relation, number relation, nominal modifier relation, compound relation, conjunction relation and negative relation. The experimental evaluation …of the method on Stanford Natural Language Inference dataset shows that the accuracy of the method is 1.4% more when compared to the state-of-the-art zero shot domain adaptation methods. Show more

Keywords: GloVe, natural language processing, textual entailment, Wordnet

DOI: 10.3233/JIFS-223275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 929-939, 2023

Authors: Liu, Boting | Guan, Weili | Yang, Changjin | Fang, Zhijie

Article Type: Research Article

Abstract: Word vector is an important tool for natural language processing (NLP) tasks such as text classification. However, existing static language models such as Word2vec cannot solve the polysemy problem, leading to a decline in text classification performance. To solve this problem, this paper proposes a method for making Chinese word vector dynamic (MCWVD). The part of speech (POS) is used to solve the ambiguity problem caused by different POS. The POS structure graph is constructed and the syntactic structure information of POS features is extracted by GCN (Graph Convolutional Network). POS vector and word vector are concatenated into PW (POS-Word) …vector. Parametric matrix is added to improve the fusion effect of POS and word features. Multilayer attention is used to distinguish the importance of different features and further update the vector expression of word vectors about the current context. Experiments on Chinese datasets THUCNews and SogouNews show that MCWVD effectively improves the accuracy of text classification and achieves better performance than CoVe (Context Vectors) and ELMo (Embeddings from Language Models). MCWVD also achieves similar performance to BERT and GPT-1 (Generative Pre-Training), but with a much lower computational cost and only 4% of BERT parameters. Show more

Keywords: Word vector, Word2vec, part of speech, Graph Convolutional Network, multi-layer attention

DOI: 10.3233/JIFS-224052

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 941-952, 2023

Authors: Tong, Shekun | Peng, Jie

Article Type: Research Article

Abstract: In this work, with the aim of separating the genuine and forgery samples of the signature, we developed a new dual-path architecture using deep neural network and a traditional descriptor for feature extraction toward an automatic offline signature recognition. The proposed approach is an extended version of VGG-16, which is enhanced using our two paths architecture. In the first path, we explore features using a deep convolutional neural network, and in the second path, we discover global features using a traditional heuristic approach. For classical feature extraction, an innovative idea is presented, in which the descriptor is stable for some …common changes, such as magnification and epoch, in the signature samples. Our traditional approach extracts global features that are stable with rotation and scaling. The proposed method was analyzed and compared with three well-known databases of CEDAR, UTsig, and GPDS signature images. A dual-patched model architecture is significantly more accurate than the basic model when compared to the basic model. In agreement with the proposed method, the best signature recognition accuracy on the CEDAR database is in the range of 98.04-99.96%, while the best recognition accuracy on the GPDS and UTsig databases is 98.04% and 99.56%, respectively. Furthermore, this technique has been compared with four popular methods such as VGG-S, VGG-M, VGG-16, and LS2Net. The presented approach achieved a recognition rate of 99.96% using a diverse signature database. Experimental results demonstrate that the proposed VGG-16 based signature recognition system is superior over texture-based and deep-learning methods and also outperforms the existing state-of-the-art results in this regard. It is expected that the proposed system will provide fresh acumen to the researchers in developing offline signature verification and recognition systems in other scripts. Show more

Keywords: Signature recognition, offline, deep learning, VGG 16-layer neural network, feature extraction

DOI: 10.3233/JIFS-224326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 953-964, 2023

Authors: Devika, M. | Shaby, Maflin

Article Type: Research Article

Abstract: One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing …protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime. Show more

Keywords: Wireless sensor network, enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA), low-energy adaptive clustering hierarchy (LEACH), stable election protocol, zonal stable election protocol

DOI: 10.3233/JIFS-224380

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 965-973, 2023

Authors: Yao, Zhuangkai | Zeng, Bi | Hu, Huiting | Wei, Pengfei

Article Type: Research Article

Abstract: In recent mathematical reasoning tasks, self-attention has achieved better results in public datasets. However, self-attention performs poorly on more complex mathematical problems due to the lack of capacity to capture local features and the ill-conditioned training after deepening the number of layers. To tackle the problem and enhance its ability of extracting local features while learning the global contexts, we propose an implicit mathematical reasoning model that improves Transformer by combining self-attention and convolution to achieve joint modeling of global and local context. Also, by introducing Reweight connection and adversarial loss function, we prevent the model gradient from disappearing or …exploding in a deep neural network while ensuring the convergence speed and avoiding overfitting. Experimental results show that the proposed model improves the accuracy by 4.47% on average for complex mathematical problems compared to the best existing results. In addition, we verify the validity of our model using ablation analysis and further demonstrate the interpretability of the model by attention mapping and task role analysis. Show more

Keywords: Implicit mathematical reasoning, self-attention, depth separable convolution, causal language model, adversarial loss

DOI: 10.3233/JIFS-224598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 975-988, 2023

Authors: Wang, Wei | Zhang, Ning | Peng, Weishi | Liu, Zhengqi

Article Type: Research Article

Abstract: Intonation evaluation is an important precondition that offers guidance to music practices. This paper present a new intonation quality evaluation method based on self-supervised learning to solve the fuzzy evaluation problem at the critical intonations. Firstly, the effective features of audios are automatically extracted by a self-supervised learning-based deep neural network. Secondly, the intonation evaluation of the single tones and pitch intervals are carried out by combining with the key local features of the audios. Finally, the intonation evaluation method characterized by physical calculations, which simulates and enhances the manual assessment. Experimental results show that the proposed method achieved the …accuracy of 93.38% which is the average value of multiple experimental results obtained by randomly assigning audio data, which is much higher than that of the frequency-based intonation evaluation method(37.5%). In addition, this method has been applied in music teaching for the first time and delivers visual evaluation results. Show more

Keywords: Music practice, intonation evaluation, self-supervised learning, deep neural network, audio feature extraction

DOI: 10.3233/JIFS-230165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 989-1000, 2023

Authors: Lin, Fucai | Wu, Tingyi | Cao, Xiyan | Li, Jinjin

Article Type: Research Article

Abstract: The theory of knowledge spaces (KST) which is regarded as a mathematical framework for the assessment of knowledge and advices for further learning. Now the theory of knowledge spaces has many applications in education. From the topological point of view, we discuss the language of the theory of knowledge spaces by the axioms of separation and the accumulation points of pre-topology respectively, which establishes some relations between topological spaces and knowledge spaces; in particular, we show that the language of the regularity of pre-topology in knowledge spaces and give a characterization for knowledge spaces by inner fringe of knowledge states. …Moreover, we study the relations of Alexandroff spaces and quasi ordinal spaces; then we give an application of the density of pre-topological spaces in primary items for knowledge spaces, which shows that one person in order to master an item, she or he must master some necessary items. In particular, we give a characterization of a skill multimap such that the delineated knowledge structure is a knowledge space, which gives an answer to a problem in [14 ] or [18 ] whenever each item with finitely many competencies; further, we give an algorithm to find the set of atom primary items for any finite knowledge space. Show more

Keywords: Knowledge space, knowledge structure, learning space, pre-topological space, skill multimap, quasi ordinal space, Alexandroff space, separation of axiom, primary item, Primary 54A05, secondary 54A25, 54B05, 54B10, 54D05, 54D70

DOI: 10.3233/JIFS-230498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1001-1013, 2023

Authors: Wang, Encheng | Liu, Xiufeng | Wan, Jiyin

Article Type: Research Article

Abstract: Received Signal Strength Indication (RSSI) fluctuates with the change of indoor noise, resulting in a large positioning error of the trained Back Propagation Neural Network (BPNN). An adaptive indoor positioning model based on Cauchy particle swarm optimization (Cauchy-PSO) BPNN is proposed to solve the problem. In the off-line training phase, the signal with less noise intensity acquired in a good environment is selected as the original training set in the localization phase. The variance of the received set of signals is used as a measure of the noise intensity of the current environment. In the localization phase, the variance of …each set of signals received is calculated at equal intervals. If the variance of adjacent intervals differs significantly, the system adjusts the original training set data according to the current noise intensity and re-trains the BP model online. Meanwhile, the particle swarm optimization algorithm using Cauchy variance to optimize the BP network tends to fall into the disadvantage of local optimum. Considering that the collected fingerprint database may generate “high-dimensional disasters”, Principal Component Analysis (PCA) is used to select and downscale the features of the wireless Access Point (AP). The proposed adaptive localization model can be trained online. The improved Cauchy-PSO algorithm and data dimensionality reduction can further improve the localization accuracy and training speed of the BP model. The experimental results show that the adaptive indoor localization model has strong adaptive capability in a noise-varying environment. Show more

Keywords: RSSI, adaptive BP model (AI-BP), BPNN, PCA, Cauchy-PSO

DOI: 10.3233/JIFS-231082

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1015-1027, 2023

Authors: Qiao, Wenbao

Article Type: Research Article

Abstract: Computer network security evaluation is a basic work to determine the security performance of the network system and implement the network security management. It involves organizational management, network technology, personnel psychology, social environment and other factors. In recent years, with the rapid development of information technology in China, the problem of computer network security has become increasingly prominent. Although domestic and foreign scholars have sought effective methods of network security evaluation from different aspects and using different methods, many factors involved in network security are difficult to quantify, so far, there is no relatively mature quantitative evaluation method of network …security. The computer network security evaluation is classical multiple attribute decision making (MADM) problems. In this article, based on projection measure, we shall introduce the projection models with q-rung orthopair fuzzy information. First of all, the definition of q-rung orthopair fuzzy sets (q-ROFSs) is introduced. In addition, to fuse overall q-rung orthopair fuzzy evaluation information, two aggregation operators including q-ROFWA and q-ROFWG operators is introduced. Furthermore, combine projection with q-ROFSs, we develop the projection models with q-rung orthopair fuzzy information. Based on developed weighted projection models, the multiple attribute decision making model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons between the new proposed models and q-ROFWA and q-ROFWG operators are also conducted to illustrate advantages of the new built method. Show more

Keywords: Multiple attribute decision making (MADM) problems, q-rung orthopair fuzzy sets (q-ROFSs), q-rung orthopair fuzzy projection model, computer network security evaluation

DOI: 10.3233/JIFS-231351

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1029-1038, 2023