Journal of Intelligent & Fuzzy Systems - Volume 43, issue 5

Authors: Shukla, Shilpi | Jain, Madhu

Article Type: Research Article

Abstract: Human emotion recognition with the evaluation of speech signals is an emerging topic in recent decades. Emotion recognition through speech signals is relatively confusing because of the speaking style, voice quality, cultural background of the speaker, environment, etc. Even though numerous signal processing methods and frameworks exists to detect and characterize the speech signal’s emotions, they do not attain the full speech emotion recognition (SER) accuracy and success rate. This paper proposes a novel algorithm, namely the deep ganitrus algorithm (DGA), to perceive the various categories of emotions from the input speech signal for better accuracy. DGA combines independent component …analysis with fisher criterion for feature extraction and deep belief network with wake sleep for emotion classification. This algorithm is inspired by the elaeocarpus ganitrus (rudraksha seed), which has 1 to 21 lines. The single line bead is rarest to find, analogously finding a single emotion from the speech signal is also complex. The proposed DGA is experimentally verified on the Berlin database. Finally, the evaluation results were compared with the existing framework, and the test result accomplishes better recognition accuracy when compared with all other current algorithms. Show more

Keywords: Speech signal, emotion recognition, deep analysis, deep ganitrus algorithm, recognition accuracy

DOI: 10.3233/JIFS-201491

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5353-5368, 2022

Authors: Deshwal, Deepti | Sangwan, Pardeep | Dahiya, Naveen | Nehra, Neelam | Dahiya, Aman

Article Type: Research Article

Abstract: Good feature representation is the chief requirement for improving Language Identification (LID) system recognition performance. In this work LID system for Indian languages is proposed based on unsupervised feature learning utilizing Deep Belief Network (DBN). The proposed methodology is implemented in two parts. The first phase of this work is based on extracting MFCC features combined with SDC hybrid features. The resultant hybrid features are further stacked to Deep Belief Network (DBN). The second phase of the proposed work is investigating the performance of various Feed forward back propagation neural network models for classification using different training algorithms. Effect of …combining different activation functions and varying the hidden neurons is also investigated The performance of the resultant models is evaluated on the basis of some performance metrics such as the epochs, training time, Mean Square Error, Regression and Mean Absolute Percentage Error. Results indicate that optimal performance is achieved in model trained with Levenberg Marquardt (LM) training algorithm. The activation functions used in the hidden and output layer are “tansig” and “purelin”. Similarly, the effect of varying the number of neurons in the hidden layer is not significant in improving the performance of the derived models. FFBPNN models trained with PL and TS activation functions gave best performance indices. A user defined language database in four different languages Hindi, English, Tamil and Malayalam is used for this work. Show more

Keywords: Language identification system, deep belief network, hybrid features, learning algorithm, activation function

DOI: 10.3233/JIFS-210186

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5369-5385, 2022

Authors: Balasubramanian, Suganya | Akila, I.S.

Article Type: Research Article

Abstract: Agricultural Food Supply chain management has arisen as an area of day-to-day importance for the agricultural food sector, since stakeholders involved in the execution of decision-making processes. Quality less agricultural products are added to the market in day-to-day life which leads to usage of chemicals in the production process. These leads to the major issues that gives the impact on agricultural product’s quality as well as overall well-being of the consumers. Devices are needed to identify the quality of the food products which are highly demanded due to the lack of transparency in the recent processes. Henceforth Blockchain technology is …evolving as a decentralized and secure infrastructure which could replace involvement of a third party to verify the transactions inside the system. The purpose of the proposed work is to implement a Blockchain based solution i.e. constructing a Decentralized Application (DApp) using Hyperledger Fabric framework to verify the food quality and the cause of the agricultural supply chain. A private permissioned Blockchain concept is chosen instead of a public Blockchain in the proposed work to ensure transparency and secure transaction by consenting any person to access the network. Smart contract chain code was instantiated for the deployment of Blockchain network. All the performers who are involved in the supply chain must be able to interact with the system to achieve the transparency. Transaction and queries related to a food product are validated by peers of the Blockchain network. A Barcode & QR code-based scanning mechanism is used to indicate the customer’s satisfaction with their products. Transactions without third party gives Farmer’s reputation for their products. A unique DApp mechanism is used to identify each product within the food supply chain. Thus, the proposed system has been implemented as a prototype and validated using smart contracts. Show more

Keywords: Blockchain, agri-food supply chain, traceability, hyperledger fabric, smart contracts

DOI: 10.3233/JIFS-211265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5387-5398, 2022

Authors: Magdin, Martin | Sulka, Timotej | Fodor, Kristián

Article Type: Research Article

Abstract: The paper deals with the issue of classification of emotional state from speech. Due to the applied k-NN algorithm, the original solution achieved an overall classification success in the range of 20 to 35%, depending on the used audio sample input data database. In the original application, we have used the Praat program to extract the characteristics. In the current version of the application, the use of Praat has been eliminated and we have developed our solution based on neural networks. Therefore, 3 experiments with forward, 1 and 2D convolutional neural networks were performed to determine the overall success of …the classification. Their common feature is that the prediction success was always highest in tests with a test subset of the RAVDESS database, with the best result being obtained using a 1D convolutional network (78.93%). Tests with the EMO-DB database were successful at 35.76%, 31.75% and 25.49%. In all three experiments, the worst results were obtained in tests with the SAVEE database - 20.24%, 18.45% and 22.02%. Show more

Keywords: EmoRec2, real time classification, databases, speech, neural nets

DOI: 10.3233/JIFS-211402

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5399-5415, 2022

Authors: Chen, Lei | Han, Jun | Tian, Feng

Article Type: Research Article

Abstract: The registration of the infrared (IR) image and the low-light-level (LLL) image remains a challenging problem due to poor dispersion of feature points, low correlation of structure and texture information. In this paper, we propose a method based on neighbourhood difference chain code to address the challenge. First we extracted the feature points of the images with the binary eight or sixteen-neighborhood information. And then construct the descriptor of the feature point by neighborhood difference chain code. At last we use the Euclidean distance to match the feature points. We adopt TNO and INO data sets to verify our method, …and by comparing with four objective evaluation parameters obtained by other three methods. The result demonstrated that the proposed algorithm performs competitively, compared to the state-of-arts such as Harris, SIFT and SURF, in terms of accuracy of registration and speed. Show more

Keywords: IR and LLL images, feature points, chain code, neighborhood difference chain code

DOI: 10.3233/JIFS-211503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5417-5430, 2022

Authors: Lu, Jun | Liu, Yangyang

Article Type: Research Article

Abstract: Since the implementation of the Private Education Promotion Law in China, reasonably evaluating the competence of private higher-learning institutions (PHLIs) has become an urgent issue. Based on an analysis of the advantages and disadvantages of the existing evaluation methods and index system, this paper proposes a comprehensive method for evaluating the competitiveness of private colleges and universities. The evaluation index system is constructed, and private colleges and universities are then evaluated by means of the best worst method (BWM) and vague set theory. Finally, S university in Zhejiang Province is evaluated as an example. The results show that the university …has strong competitiveness in operating its schools, but the quality of the schools and its ability to operate them need to be strengthened. Compared with other experimental approaches, this method can be used for effective and reasonable evaluation of the competitiveness of private universities. Show more

Keywords: Competitiveness evaluation, vague set theory, best worst method, private higher-learning institutions

DOI: 10.3233/JIFS-211612

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5431-5441, 2022

Authors: Yang, Jun | Yao, Wei

Article Type: Research Article

Abstract: By a fuzzy preorder, it means a (0, 1]-binary relation on a nonempty set which is self-reflective and transitive. For a fuzzy preorder on a universe, this paper constructs a kind of fuzzy rough set model based on the multiplication and division of real numbers. The definable sets and the related fuzzy topology are studied.

Keywords: Fuzzy preorder, rough set, approximation operator, definable set

DOI: 10.3233/JIFS-211709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5443-5451, 2022

Authors: Dung, Ngo Q. | Viet, Le H.

Article Type: Research Article

Abstract: Nowadays, the number and types of IoT devices are increasing rapidly, which leads to an expansion in the attack surface of this kind of device. Besides, the number of Botnet malware on IoT devices also grows with a lot of new variants. This context leads to an urgent demand for an effective solution in detecting new variants of IoT Botnet malware. There have been many studies focusing on IoT Botnet malware detection using static and dynamic analysis. In particular, the combination of the dynamic method with machine learning has shown outstanding advantages to detect IoT Botnet variants. However, the preprocessing …of behavioral data originated from malware is still complicated, and the number of input vector dimensions of the machine learning model is still huge. In addition, these models also consume a lot of resources and have limited detection capabilities. Besides, dynamic analysis studies based on system calls mostly use call frequency characteristics and have not effectively exploited IoT Botnet malware’s life cycle characteristics. In this paper, we propose the Directed System Call Graph (DSCG) feature to sequentially structure the system calls. This DSCG graph will be vectorized and used as an input for building a malware analysis model based on popular machine learning classifiers such as KNN, SVM, Decision Tree, etc. Experiments on the datasets demonstrate that the features extracted from this graph have low complexity but still ensure high accuracy in detecting IoT Botnets, especially with newly emerged IoT Botnet families. The proposed model was evaluated with ACC  = 98.01 % , TPR  = 97.93 % , FPR  = 1.5 % , AUC  = 0.9961 on a dataset of 5023 IoT Botnets and 3888 benign samples. Show more

Keywords: IoT Botnet, features extraction, system calls, machine learning, malware detection

DOI: 10.3233/JIFS-211882

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5453-5470, 2022

Authors: Abbas, Qaisar

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5471-5486, 2022

Authors: Kumar, Prashant | Shakti, Shivam | Datta, Naireet | Sinha, Shashwat | Ghosh, Partha

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5487-5500, 2022