International Journal of Intelligent Systems and Applications @ijisa
Journal articles - International Journal of Intelligent Systems and Applications
All articles: 1277
Supplier Segmentation using Fuzzy Linguistic Preference Relations and Fuzzy Clustering
Scientific article
In an environment characterized by its competitiveness, managing and monitoring relationships with suppliers are of the essence. Supplier management includes supplier segmentation. Existing literature demonstrates that suppliers are mostly segmented by computing their aggregated scores, without taking each supplier’s criterion value into account. The principle aim of this paper is to propose a supplier segmentation method that compares each supplier’s criterion value with exactly the same criterion of other suppliers. The Fuzzy Linguistic Preference Relations (LinPreRa) based Analytic Hierarchy Process (AHP) is first used to find the weight of each criterion. Then, Fuzzy c-means algorithm is employed to cluster suppliers based on their membership degrees. The obtained results show that the proposed method enhances the quality of the previous findings.
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Support-Intuitionistic Fuzzy Set: A New Concept for Soft Computing
Scientific article
Today, soft computing is a field that is used a lot in solving real-world problems, such as problems in economics, finance, banking... With the aim to serve for solving the real problem, many new theories and/or tools which were proposed, improved to help soft computing used more efficiently. We can mention some theories as fuzzy sets theory (L. Zadeh, 1965), intuitionistic fuzzy set (K Atanasov, 1986). In this paper, we introduce a new notion of support-intuitionistic fuzzy (SIF) set, which is the combination a intuitionistic fuzzy set with a fuzzy set. So, SIF set is a directly extension of fuzzy set and intuitionistic fuzzy sets (Atanassov). Then, we define some operators on support-intuitionistic fuzzy sets, and investigate some properties of these operators.
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Survey on answer validation for Indonesian question answering system (IQAS)
Scientific article
Research on Question Answering System (QAS) has been done mainly in English. Unfortunately, for Indonesian, it is still rarely explored whereas Indonesian is the official language used more than 250 million people. Research in the area of Indonesian Question Answering System (IQAS) began in 2005s, and since then only few number of IQAS have been developed. One of the important issues in IQAS is Answer Validation (AV), which is a system that can determine the correctness of QAS. To identify the future scope of research in this area, the need of comprehensive survey on IQAS and AV arises naturally. The goals of this survey are to find the cutting-edge method used in AV and to prove that AV has not been implemented on IQAS. Based on the results, we suggest new opportunities and research challenges for IQAS community.
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Swarm-Optimization-Based Affective Product Design Illustrated by a Mobile Phone Case-Study
Scientific article
This paper presents a new approach of user-oriented design for transforming users’ perception into product elements design. An experimental study on mobile phones is conducted to examine how product form and product design parameters affect consumer’s perception of a product. The concept of Kansei Engineering is used to extract the experimental samples as a data base for neural networks (NNs) with particle swarm optimization (PSO) analysis. The result of numerical analysis suggests that mobile phone makers need to focus on particular design parameters to attract specific target user groups, in addition to product forms. This paper demonstrates the advantage of using KE-PSO for determining the optimal combination of product design parameters. Based on the analysis, we can use KE-PSO to suggest customers’ preferences for mobile phone design attributes that would be considered optimal by various user groups of all surveyed. They can be used for improvement and development of new future products.
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System with Distributed Lag: Adaptive Identification and Prediction
Scientific article
Adaptive algorithms of parametric identifica-tion of discrete systems with lag variables are proposed. Adaptive algorithms (AA) in the presence of lag input variables are developed. The convergence of the AA and the boundedness of the trajectories the adaptive system is proved. Convergence domain АА depends on operating disturbance. Models with multiplicative parameters (MPM) for the decrease of a number estimated parameters are offered. The process for selection of the vector of base parameters MPM was developed. The performance of adaptive system identification for this case is proved. It is shown that parameters of system estimation at the application of multiplicative identification must be chosen from a condition of minimization of the criterion of the prediction error. Transformation of interdependence be-tween the lagged variables is offered, allowing eliminating their effect on system work. In the second part of work, the method of synthesis АА identification of the systems containing lagged output variables is offered. We consider a case of linear correlation between an output of the system and operating disturbance. For a solution of a problem, we suggest fulfilling an estimation of operating disturbance. Corresponding procedures are described and proved their efficiency. Simulation results are presented that confirm the efficiency of the adaptive methods.
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TRAC: PCU-Weighted Traffic Control with Virtual Lanes for Unstructured Indian Traffic
Scientific article
Traffic congestion in Indian cities causes annual economic losses exceeding Rs 1.5 lakh crore. This paper proposes the TRAC (Traffic Routing and Allocation Control) system combining YOLOv3 object detection, virtual lane adaptation, and PCU-weighted scheduling for unstructured heterogeneous traffic. TRAC achieves 43.2s average waiting time across 1600 SUMO simulation cycles, reducing waiting time by 43% versus traditional methods (74.25s) and 27% versus actuated controllers (59s). Throughput increases 28% (1087 vs 862 vehicles per 10 minutes). The edge-deployable pipeline runs on NVIDIA Jetson Nano with 85ms end-to-end latency using a custom 12,500-frame Indian traffic dataset (0.87 mAP). Virtual lanes handle non-lane discipline while 8-class detection (car, bike, bus, rickshaw, etc.) enables accurate PCU weighting. This represents the first system combining PCU-weighted optimization with virtual lane adaptation specifically designed for chaotic Indian traffic conditions.
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Technology of gene expression profiles filtering based on wavelet analysis
Scientific article
The paper presents the technology of gene expression profiles filtering based on the wavelet analysis methods. A structural block-chart of the wavelet-filtering process, which involves concurrent calculation of Shannon entropy for both the filtered data and allocated noise component is proposed. Simulation of the wavelet-filtering process was performed with the use of orthogonal and biorthogonal wavelets on different levels of wavelet decomposition and with the use of various values of the thresholding coefficient. Result of the simulation has allowed us to propose the technology to determine the optimal parameters of the wavelet filter based on complex analysis of the filtered data and allocated noise component.
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Temperament and Mood Detection Using Case-Based Reasoning
Scientific article
Case-Based Reasoning (CBR) is a branch of AI that is employed to solving problems which emphasizes the use of previous solutions in solving similar new problems. This work presents TAMDS, a Temperament and Mood Detection system which employs Case-Based Reasoning technique. The proposed system is adapted to the field of psychology to help psychologists solve part of the problems in their complex domain. We have designed TAMDS to detect temperament and moods of individuals. A major aim of our system is to help individuals who are out of reach of a professional psychologist to manage their personality and moods because as humans, moods affect our perceptions, personal health, the way we view the world around us and the way we react to it.
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Scientific article
This paper presents a self-tuning method of fuzzy logic controllers. The consequence part of the fuzzy logic controller is self-tuned through the Q-learning algorithm of reinforcement learning. The off policy temporal difference algorithm is used for tuning which directly approximate the action value function which gives the maximum reward. In this way, the Q-learning algorithm is used for the continuous time environment. The approach considered is having the advantage of fuzzy logic controller in a way that it is robust under the environmental uncertainties and no expert knowledge is required to design the rule base of the fuzzy logic controller.
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Scientific article
A method for semantic graph analysis of the evolution of internet fraud is proposed based on the construction and comparative analysis of temporal semantic networks. The study is conducted using a corpus of 51,755 thematic documents retrieved from the InfoStream information system covering the period 2011–2025. A Retrieval-Augmented Generation (RAG)-based workflow was employed to retrieve relevant document contexts and support their semantic processing during network construction. Temporal semantic networks representing the conceptual structure of internet fraud at successive stages of its evolution were constructed for three five-year intervals, together with an integral semantic network characterizing the stable conceptual core of the subject domain. The topological characteristics of the networks were analyzed using the framework of graph theory—specifically degree, betweenness, and eigenvector centrality—enabling the quantitative assessment of structural changes, the identification of concepts with a high transformational role, and the tracking of thematic cluster evolution. It is demonstrated that the development of internet fraud is predominantly evolutionary in nature, occurring through the restructuring of existing semantic structures rather than their complete replacement. Building upon this foundation, a hypothesis of structural continuity for semantic networks is formulated, and a model for forecasting their evolution is proposed. This model involves estimating the structural transformation operator, predicting the emergence of new concepts and semantic relationships, and constructing the forecasted network for the subsequent time interval. The validity of the proposed approach is confirmed through retrospective validation, which entails forecasting semantic networks for the subsequent period using solely data from preceding temporal slices, followed by a comparison between the forecasted and actual structures. The obtained results demonstrate the potential of utilizing temporal semantic-graph analysis as a tool for the early detection of structural changes in the information space and the forecasting of new trends in the development of internet fraud. The retrospective validation confirmed the model's high predictive capability, achieving a Precision of 0.870, a Recall of 0.816, and an F1-score of 0.842 for the 2021–2025 forecast interval, alongside a strong rank correlation (ρ = 0.811) in preserving node centrality. These quantitative results demonstrate the practical validity of temporal semantic-graph analysis as a tool for the early detection of structural changes and the forecasting of new trends in the development of internet fraud.
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Temporal Weather Prediction using Back Propagation based Genetic Algorithm Technique
Scientific article
Hybrid back propagation based genetic algorithm approach is a popular way to train neural networks for weather prediction. The major drawback of this method is that weather parameters were assumed to be independent of each other and their temporal relation with one another was not considered. So in the present research a modified time series based weather prediction model is proposed to eliminate the problems incurred in hybrid BP/GA technique. The results are very encouraging; the proposed temporal weather prediction model outperforms the previous models while performing for dynamic and chaotic weather conditions.
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Temporal community-based collaborative filtering to relieve from cold-start and sparsity problems
Scientific article
Recommender systems inherently dynamic in nature and exponentially grow with time, in terms of interests and behaviour patterns. Traditional recommender systems rely on similarity of users or items in static networks where the user/item neighbourhood is almost same and they generate the same recommendations since the network is constant. This paper proposes a novel architecture, called Temporal Community-based Collaborative filtering, which is an association of recommendation and the dynamic community algorithm in order to exploit the temporal changes in the community structure to enhance the existing system. Our framework also provides solutions to common inherent issues of collaborative filtering approach such as cold-start, sparsity and compared against static and traditional collaborative systems. The outcomes indicate that the proposed system yields higher values in quality standards and minimizes the drawbacks of the traditional recommender system.
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Text Classification based on Discriminative-Semantic Features and Variance of Fuzzy Similarity
Scientific article
Due to the rapid growth of the Internet, large amounts of unlabelled textual data are producing daily. Clearly, finding the subject of a text document is a primary source of information in the text processing applications. In this paper, a text classification method is presented and evaluated for Persian and English. The proposed technique utilizes variance of fuzzy similarity besides discriminative and semantic feature selection methods. Discriminative features are those that distinguish categories with higher power and the concept of semantic feature takes into the calculations the similarity between features and documents by using only available documents. In the proposed method, incorporating fuzzy weighting as a measure of similarity is presented. The fuzzy weights are derived from the concept of fuzzy similarity which is defined as the variance of membership values of a document to all categories in the way that with some membership value at the same time, the sum of these membership values should be equal to 1. The proposed document classification method is evaluated on three datasets (one Persian and two English datasets) and two classification methods, support vector machine (SVM) and artificial neural network (ANN), are used. Comparing the results with other text classification methods, demonstrate the consistent superiority of the proposed technique in all cases. The weighted average F-measure of our method are %82 and %97.8 in the classification of Persian and English documents, respectively.
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Scientific article
In this article an investigation into search operations for the multiplicative inverse in the ring of integers modulo m for Error Control Coding tasks and for data security is shown. The classification of the searching operation of the multiplicative inverse in the ring of integers modulo m is provided. The best values of parameters for Joye-Paillier method and Lehmer algorithm were also found. The improved Bradley modification for the extended Euclidean algorithm is also offered, which gives the operating speed improvement for 10-15%. The integrated experimental research of basic classes of searching methods for multiplicative inverse in the ring of integers modulo m is conducted for the first time and the analytical formulas for these calculations of random access memory necessary space when operated at k-ary RS-algorithms and their modifications are shown.
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Scientific article
Many protection applications are based upon the Phasor Measurement Units (PMUs) technology. Therefore, PMUs have been increasingly widespread throughout the power network, and there are several researches have been made to locate the PMUs for complete system observability. This paper introduces an important application of PMUs in power system protection which is the detection of single line outage. In addition, a detection of the out of service line is achieved depending on the variations of phase angles measured at the system buses where the PMUs are located. Hence, a protection scheme from unexpected overloading in the network that may lead to system collapse can be achieved. Such detections are based upon an artificial intelligence technique which is the support Vector Machine (SVM) classification tool. To demonstrate the effectiveness of the proposed approach, the algorithm is tested using offline simulation for both the 14-bus IEEE and the 30-bus IEEE systems. Two different kernels of the SVM are tested to select the more appropriate one (i.e. polynomial and Radial Basis Function (RBF) kernels are used).
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The Application of Sparse Antenna Array Synthesis Based on Improved Mind Evolutionary Algorithm
Scientific article
Mind Evolutionary Algorithm (MEA) imitates the human mind evolution by using similartaxis and dissimilation operations, which overcomes the prematurity and improves searching efficiency. But the generation of the initial population is blind and the addition of naturally washed out temporary subpopulations is random. This paper improved MEA by introducing chaos and difference into it, which brought adequate diversity to the initial population and saved the excellent genes in the evolution. Then the improved MEA is used in the synthesis of sparse antenna arrays. The excellent results of computer simulation show the advantage of array antenna patterns synthesis using the improved MEA.
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The Complement of Normal Fuzzy Numbers: An Exposition
Scientific article
In this article, our main intention is to revisit the existing definition of complementation of fuzzy sets and thereafter various theories associated with it are also commented on. The main contribution of this paper is to suggest a new definition of complementation of fuzzy sets on the basis of reference function. Some other results have also been introduced whenever possible by using this new definition of complementation.
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Scientific article
Evolutionary algorithms (EAs) are a range of problem-solving techniques based on mechanisms inspired by biological evolution. Nowadays, EAs have proven their ability and effectiveness to solve combinatorial problems. However, these methods require a considerable time of calculation. To overcome this problem, several parallelization strategies have been proposed in the literature. In this paper, we present a new parallel agent-based EC framework for solving numerical optimization problems in order to optimize computation time and solutions quality.
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Scientific article
Mating preferentialism among animals is the natural form of elitism that has a higher genetic variance and a shorter number of interactions. This concept refers to fact that most animals cannot breed indefinitely – this is the case of elitism - and suffer DNA degradation. In this paper, two types of preferentialism were analyzed (mutation and second best); in both cases we found evidence of improvements over no-preferentialism or elitism. The best number of generations for preferentialism was determined to be 5, from a group of 3 to 20, with the smallest average of iterations and the most consistent average fitness. A sequencing of 0 to 7 was selected and used in association with mutation preferentialism in order to determine the best number of generations. In the case of BinaryF6, mutation preferentialism has a higher average best fitness (ABF) (0.9986) and a lower number of interactions (2259). Second best preferentialism has a better average last fitness (ALF) (0.6070) and a little higher number of interactions (3956). These results reveal that the two suggested form of preferentialism exhibit significant improvements in terms of time and result quality when they are compared with elitism (ABF of 0.9981, ALF of 0.6005 and an average number of interactions of 18197) or with no-preferentialism (ABF of 0.9982, ALF of 0.5177 and average number of interactions of 181088.
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The Effects of Beta-I and Fractal Dimension Neurofeedback on Reaction Time
Scientific article
In this paper, we evaluate the effects of neurofeedback training protocols of the relative power of the beta-I band and the fractal dimension on the reaction time of human by the Test of Variables of Attention (TOVA) to show which of these two protocols have the great ability for the improving of the reaction time. The findings of this research show that both protocols have a good ability (p < 0.01) to improving of the reaction time and can create the significant difference (as mean dRT = 37.3 ms for the beta-I protocol and dRT = 19.6 ms for the fractal protocol) in the reaction time. Of course, we must express, the Beta-I protocol has the more ability to improving of the reaction time and it is able to provide a faster reaction time.
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