International Journal of Knowledge-based and Intelligent Engineering Systems - Volume Pre-press, issue Pre-press

Authors: Lu, Meiyan

Article Type: Research Article

Abstract: With the continuous development of Internet era, teaching modes are also being innovated and developed, and online and offline blended teaching (OOBT) is one of them, which is mainly achieved by combining OOBT. By combining online and offline teaching modes with college English courses, teachers not only help students cultivate independent learning habits, but also help them think alone and improve their practical skills. This is also true for vocational and technical education, and it can be said that this approach is more relevant. The OOBT evaluation in college English teaching courses is deemed to multiple-attribute decision-making (MADM). In this …study, we connected the geometric Heronian mean (GHM) technique and prioritized aggregation (PA) in line with 2-tuple linguistic neutrosophic numbers (2TLNNs) to produce the generalized 2TLNN power GHM (G2TLNPGHM) technique. Then, the G2TLNPGHM technique is implemented to process the MADM under 2TLNNs. Finally, the G2TLNPGHM technique is implemented as the numerical example of evaluating the OOBT in college English courses. Some comparative analysis of the impact of parameters are fully addressed. Show more

Keywords: MADM, 2TLNNs, G2TLNPGHM technique, OOBT effect evaluation

DOI: 10.3233/KES-230235

Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Mahmood, Tahir | ur Rehman, Ubaid

Article Type: Research Article

Abstract: Resource allocation strategy selection in 5G networks is a MADM (Multi-Attribute Decision-Making) problem, which all the methods defined so far or those used to solve it have neglected the negative aspects of attributes. This can result in the occurrence of information loss and it would be difficult to come to the right decision. Thus, in this paper, we present a MADM technique that can be used to take the negative aspects of attributes into account. This goal will be achieved by the method based on bipolar fuzzy sets (BFS) and tangent trigonometric aggregation operators (AOs). For this, in this article, …firstly, we devise the concept of tangent trigonometric bipolar fuzzy number (TT-BFN) and linked algebraic operators. Then, we deduce tangent trigonometric bipolar fuzzy weighted averaging (TT-BFWA), tangent trigonometric bipolar fuzzy ordered weighted averaging (TT-BFOWA), tangent trigonometric bipolar fuzzy weighted geometric (TT-BFWG), and tangent trigonometric bipolar fuzzy ordered weighted geometric (TT-BFOWG) operators. We also devised the related results of these operators that is idempotency, monotonicity, and boundedness. Further in this manuscript, we investigate a case study “Selection of resource allocation strategy for 5G network” by considering artificial data and employing the invented MADM approach in the environment of BFS and get that “Max-Min Fairness Allocation” is the finest resource allocation strategy in 5G network. Finally, we compare our deduced theory with a few current ones to reveal supremacy and dominance. Show more

Keywords: Resource allocation strategy, 5G network, tangent trigonometric aggregation operators, bipolar fuzzy set

DOI: 10.3233/KES-240086

Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2023

Authors: Zhang, Yanboyu

Article Type: Research Article

Abstract: In this study, the objective is to model a neuronal population using cellular automata (CA) rules that are both simple and realistic. Unlike in previous research, the primary aim is to capture a range of neuronal behaviours with minimal complexity and computational overhead. To achieve this, a two-dimensional lattice of cellular automata was implemented in the C+ ⁣ + programming language, with the lattice and cell structure, cell states, neighbourhood interactions, and governing rules being defined. Various behaviours within the neuronal population were simulated by taking into account the resting state and action potential of each …neuron. Four distinct behaviours in the model were unveiled by our observations: fixed point, rhythmic, chaotic, and dual behaviour. Importantly, the outcomes of the neuronal CA were significantly influenced by the neighbourhood configuration of a neuron. In particular, a dual behaviour also referred to as an intrinsic bifurcation, was induced when the number of neighbouring cells for a neuron was reduced. In a broader sense, the maximum and minimum neighbour counts for a cell were found to serve as low-pass and high-pass filters, respectively, for the output signal of the CA model. Show more

Keywords: Modeling, cellular automata, neuron population, nonlinear dynamics

DOI: 10.3233/KES-230154

Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Yao, Yao | Shen, Baohua

Article Type: Research Article

Abstract: The mixed online and offline mode is a new teaching mode that is gradually mature and developed based on information technology. Applying this model to English teaching will help teachers get rid of the shackles of traditional models, reconstruct teaching, and improve the flexibility and effectiveness of teaching activities. Analyze the connotation and main characteristics of the mixed online and offline mode, understand students’ English abilities and future development needs, formulate scientific teaching measures that combine online and offline, innovate and optimize teaching activities, create a good learning atmosphere for students, and thereby improve the quality of English teaching. The …blended teaching effectiveness evaluation of ESP courses in universities in an output-oriented perspective is viewed as the multiple-attribute decision-making (MADM). In this paper, Taxonomy technique is designed for MADM under interval-valued neutrosophic sets (IVNSs). Then, the interval-valued neutrosophic numbers Taxonomy (IVNN-Taxonomy) technique is formed to cope with MADM problem. Finally, a numerical example for blended teaching effectiveness evaluation of ESP courses in universities in an output-oriented perspective is given to demonstrate the IVNN-Taxonomy technique. The main contribution of this study is: (1) the Taxonomy technique is extended to IVNSs; (2) the entropy technique is employed to implement the weight values based on combined distances; (3) the IVNN-Taxonomy technique is managed to put up with the MADM under IVNSs. (4) a numerical example for blended teaching effectiveness evaluation of ESP courses in universities in an output-oriented perspective has been given. (5) some comparative algorithms are given to show the rationality of IVNN-Taxonomy technique. Show more

Keywords: Multiple-attribute decision-making (MADM), interval-valued neutrosophic sets (IVNSs), taxonomy technique, blended teaching effectiveness evaluation

DOI: 10.3233/KES-230209

Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: H, Anila Glory | V S, Shankar Sriram

Article Type: Research Article

Abstract: Brain Computer Interface (BCI) – one of the recent advancements in the field of Bioinformatics which offers a real-time support for the people, who are affected by chronic neurological disorders. Owing to the rapid progression of Electroencephalogram (EEG) – based BCI system, the detection of epileptic seizures has become much simpler. However, accurate detection through visual inspection is tedious, time-consuming and prone to error. Thus, automation has become inevitable and for automating the epileptic seizure detection, entropies are appropriate as the nature of EEG signals are complex, arrhythmic, ephemeral, and non-stationary. Several renowned entropies are widely applied, nevertheless, the existing …models fail to identify the optimal parameters of the entropies which greatly influences the performance of the Machine Learning models that could make better predictions. Hence to address the aforementioned issue, this paper presents a parallel machine learning based farmland fertility algorithm which optimizes the parameters of various entropies thereby detecting Epileptic Seizures in a systematic way. A novel weighted fitness function has been designed based on Kullback-Leibler Divergence (KLD). The extracted features are further classified using state-of-the-art classifiers. The overall performance of the proposed algorithm was evaluated using the EEG dataset obtained from University of Bonn, Germany, University of Bern and Indian EEG, New Delhi and the results show the supremacy of the proposed model in terms of sensitivity, specificity, precision, F1-score, G-mean and classification accuracy. Show more

Keywords: Epileptic seizure detection, kullback-leibler divergence, farmland fertility optimization algorithm

DOI: 10.3233/KES-230059

Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2023

Authors: Depczyński, Radosław | Wątróbski, Jarosław

Article Type: Research Article

Abstract: The recent focus on sustainability and its three pillars (economic, environmental, and social) brought new issues to be addressed, especially for manufacturing companies. One of the areas of sustainable manufacturing is to work with material suppliers as part of sustainable supply chain management (SSCM). The main hypothesis of the research is that choosing the right supplier, characterized by a high level of sustainable development, is able to positively influence the evaluation result of the sustainability of the supplied enterprise. Considering the above, the aim of the paper is to present the process of selecting and assessing a sustainable supplier based …on the identified sustainability criteria (economic, environmental, social), which should be taken into account when deciding to cooperate with a selected supplier. The analysis uses the multi-criteria decision-making analysis (MCDM) Preference Ranking Organization METHod for Enrichment Evaluation II group decision support system (PROMETHEE II GDSS) method. The research was carried out in a steel industry enterprise in Poland. Show more

Keywords: Supplier selection, sustainability, MCDM, PROMETHEE II, PROMETHEE II GDSS

DOI: 10.3233/KES-230563

Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024

Authors: Haffar, Nafaa | Zrigui, Mounir

Article Type: Research Article

Abstract: This paper introduces a novel neural network architecture for classifying temporal relationships among events in Arabic sentences. Our model integrates a deep learning pipeline that combines multiple techniques. Initially, the Bidirectional Encoder Representations from Transformers (BERT) model is employed to obtain the contextual representation of each word. Furthermore, the model integrates the part-of-speech (POS) representation, the position of events, and the output from a convolutional neural network (CNN) based at the sentence’s head. Collectively, these features capture the complex relationships between words in their context. The architecture also incorporates two sequential Bidirectional Long Short-Term Memory (BiLSTM) layers. These are complemented …by an attention mechanism, which assesses the significance of each word in terms of temporal relationship types. Another CNN layer processes the entire sentence, and finally, a fully connected layer with Softmax determines the temporal relation category, drawing on vectors from the BiLSTM layers, the attention mechanism, and the CNN. Experimental results on the Ara-TimeBank corpus reveal that our model achieves an 89% F1-score, outperforming prior work in this domain. Show more

Keywords: BERT, temporal relation, ordering events, arabic language, TimeML standard, attention model, CNN, BiLSTM

DOI: 10.3233/KES-230066

Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. Pre-press, no. Pre-press, pp. 1-25, 2024

Authors: Heidari, Ali | Khalilzadeh, Mohammad | Pamucar, Dragan

Article Type: Research Article

Abstract: This paper presents an intelligent intrusion detection system using fuzzy logic based on particle swarm optimization algorithm. The main goal of this research is to survey the convergence capability of the particle swarm optimization algorithm using fuzzy logic in intelligent intrusion detection of a designable system. In order to simulate intelligent attacks on a system, KDD99 data are used. Based on the findings, the Particle Swarm Optimization (PSO) algorithm is highly capable of detecting an intelligent attack on a system. In this study, we considered 1800 times attack, in which the PSO algorithm was capable of repelling attacks in 7.24 …seconds and converged. The best convergence occurred at stage 775, and then all attacks were eliminated from the system. Results showed that the stability and convergence of the system improved after each attack. Also, the number of attacks increased to 2500 times to investigate unpredictable intrusions and converge accrued at the attack 771st. Finally, the results obtained by the PSO algorithms were compared to the results obtained by the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The findings indicate that the PSO algorithm is highly capable of detecting intelligent intrusions into a system. It is also suggested to employ this algorithm in cloud computing systems because of its high capability of repelling smart attacks. Show more

Keywords: Attack detection, intelligent intrusion, fuzzy logic, particle swarm optimization, genetic algorithm, simulated annealing

DOI: 10.3233/KES-240436

Citation: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023