Intelligent Decision Technologies - Volume 15, issue 2

Article Type: Book Review

DOI: 10.3233/IDT-210005

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 173-174, 2021

Article Type: Book Review

DOI: 10.3233/IDT-210006

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 175-176, 2021

Article Type: Book Review

DOI: 10.3233/IDT-210007

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 177-178, 2021

Authors: Prajapati, Harshadkumar B. | Vyas, Ankit S. | Dabhi, Vipul K.

Article Type: Research Article

Abstract: Face Expression Recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could …achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN. Show more

Keywords: FER, face expression recognition, CNN, JAFFE dataset, deep learning, convolutional neural network

DOI: 10.3233/IDT-190181

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 179-187, 2021

Authors: Anoop, V.S. | Deepak, P. | Asharaf, S.

Article Type: Research Article

Abstract: Online social networks are considered to be one of the most disruptive platforms where people communicate with each other on any topic ranging from funny cat videos to cancer support. The widespread diffusion of mobile platforms such as smart-phones causes the number of messages shared in such platforms to grow heavily, thus more intelligent and scalable algorithms are needed for efficient extraction of useful information. This paper proposes a method for retrieving relevant information from social network messages using a distributional semantics-based framework powered by topic modeling. The proposed framework combines the Latent Dirichlet Allocation and distributional representation of phrases …(Phrase2Vec) for effective information retrieval from online social networks. Extensive and systematic experiments on messages collected from Twitter (tweets) show this approach outperforms some state-of-the-art approaches in terms of precision and accuracy and better information retrieval is possible using the proposed method. Show more

Keywords: Social networks, distributional semantics, information retrieval, topic modeling, Phrase2Vec, latent dirichlet allocation

DOI: 10.3233/IDT-200001

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 189-199, 2021

Authors: Samal, Sidharth | Dash, Rajashree

Article Type: Research Article

Abstract: In recent years Extreme Learning Machine (ELM) has gained the interest of various researchers due to its superior generalization and approximation capability. The network architecture and type of activation functions are the two important factors that influence the performance of an ELM. Hence in this study, a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) oriented multi-criteria decision making (MCDM) framework is suggested for analyzing various ELM models developed with distinct activation functions with respect to sixteen evaluation criteria. Evaluating the performance of the ELM with respect to multiple criteria instead of single criterion can help in …designing a more robust network. The proposed framework is used as a binary classification system for pursuing the problem of stock index price movement prediction. The model is empirically evaluated by using historical data of three stock indices such as BSE SENSEX, S&P 500 and NIFTY 50. The empirical study has disclosed promising results by evaluating ELM with different activation functions as well as multiple criteria. Show more

Keywords: ELM, MCDM, TOPSIS, time series forecasting

DOI: 10.3233/IDT-200013

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 201-220, 2021

Authors: Lin, Chia-Hung | Kan, Chung-Dann | Chen, Wei-Ling | Mai, Yi-Chen | Chen, Ying-Shin

Article Type: Research Article

Abstract: Extracorporeal membrane oxygenation (ECMO) is employed to treat critical patients for one to a few days of life support in intensive care units. Venovenous (VV) and venoarterial (VA) ECMO configurations are the most commonly used rescue strategies for temporary cardiac and respiratory function support. However, both ECMO modes sometimes cannot meet a patient’s demands because of (a) less oxygenated blood in either the upper body or lower body, or (b) a deterioration in the patient’s hemodynamic status. Veno-Venoarterial (VVA) ECMO is an upgraded system that provides sufficiently oxygenated blood to the systemic and pulmonary circulation systems. Drainage cannulas and gas …flow exchanges are determined to provide the maximum drainage blood flow required by the patient through a servo-regulator that adjusts the motor speed. A generalized regression neural network (GRNN) based estimator is created to automatically estimate the desired pump speed and then provide sufficient drainage flow for temporary life support. To achieve stability flow in an ECMO circuit, a bisection approach algorithm (BAA) is employed to improve the performance of transient responses in step controls and steady state controls. Experimental studies are used to validate the proposed model and it is compared with conventional controllers to indicate good performance in clinical VVA ECMO applications. Show more

Keywords: Extracorporeal membrane oxygenation (ECMO), Veno-VenoArterial (VVA), Generalized Regression Neural Network (GRNN), Bisection Approach Algorithm (BAA)

DOI: 10.3233/IDT-200018

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 221-230, 2021

Authors: Varzaneh, Zahra Asghari | Kuchaki Rafsanjani, Marjan

Article Type: Research Article

Abstract: Intrusion can compromise the integrity, confidentiality, or availability of a computer system. Intrusion Detection System (IDS) is a type of security software designed to monitor network traffic and identify network intrusions. In this paper, A Fuzzy Rule – Based classification system is used to detect intrusion in a computer network. In order to improve the classification rate, a new method is proposed based on Genetic Algorithm (GA) for rule weights specification. The proposed method is tested on KDD99 dataset. Experimental results show the proposed method improves the performance of the fuzzy rule-based classification systems in terms of detection rate and false …alarm rate. Show more

Keywords: Intrusion detection, fuzzy rule-based, rule weighting, genetic algorithm

DOI: 10.3233/IDT-200036

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 231-237, 2021

Authors: Rouba, Baroudi

Article Type: Research Article

Abstract: Detecting outliers in multicriteria decision aid (MCDA) field is a new research direction that has not been enough explored. This paper tackles this problem by proposing a statistical approach based on PROMETHEE method net-flow. To consider the multicriteria character of the problem, the net-flow of each object is computed by applying PROMETHEE method. According to the normal distribution of the net-flow values, the standard deviation (SD) or interquartile range (IQR) statistical methods are used to detect outliers. To prove its applicability, the proposed approach is evaluated on real life problem.

Keywords: Multicriteria decision aid, outlier detection, PROMETHEE, net-flow, standard deviation method, interquartile range method

DOI: 10.3233/IDT-200046

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 239-250, 2021

Authors: Jaiswal, Rajesh | Ramanna, Sheela

Article Type: Research Article

Abstract: In this paper, we have proposed a novel overlapping community detection algorithm based on an ensemble approach with a distributed neighbourhood threshold method (EnDNTM). EnDNTM uses pre-partitioned disjoint communities generated by the ensemble mechanism and then analyzes the neighbourhood distribution of boundary nodes in disjoint communities to detect overlapping communities. It is a form of seed-based global method since boundary nodes are considered as seeds and become the starting point for detecting overlapping communities. A threshold value for each boundary node is used as the minimum influence by the neighbours of a node in order to determine its belongingness to …any community. The effectiveness of the EnDNTM algorithm has been demonstrated by testing with five synthetic benchmark datasets and fifteen real-world datasets. The performance of the EnDNTM algorithm was compared with seven overlapping community detection algorithms. The F1-score , normalized mutual information ONMI and extended modularity Q o ⁢ v metrics were used to measure the quality of the detected communities. EnDNTM outperforms comparable algorithms on 4 out of 5 synthetic benchmarks datasets, 11 out of 15 real world datasets and gives comparable results with the remaining datasets. Experiments on various synthetic and real world datasets reveal that for a majority of datasets, the proposed ensemble-based distributed neighbourhood threshold method is able to select the best disjoint clusters produced by a disjoint method from a collection of methods for detecting overlapping communities. Show more

Keywords: Community detection, social networks analysis, overlapping communities, graph clustering

DOI: 10.3233/IDT-200059

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 251-267, 2021

Authors: Sharma, Akash | Kumar, Neeraj | Kumar, Ayush | Dikshit, Karan | Tharani, Kusum | Singh, Bharat

Article Type: Research Article

Abstract: In modern day Psychiatric analysis, Epileptic Seizures are considered as one of the most dreadful disorders of the human brain that drastically affects the neurological activity of the brain for a short duration of time. Thus, seizure detection before its actual occurrence is quintessential to ensure that the right kind of preventive treatment is given to the patient. The predictive analysis is carried out in the preictal state of the Epileptic Seizure that corresponds to the state that commences a couple of minutes before the onset of the seizure. In this paper, the average value of prediction time is restricted …to 23.4 minutes for a total of 23 subjects. This paper intends to compare the accuracy of three different predictive models, namely – Logistic Regression, Decision Trees and XGBoost Classifier based on the study of Electroencephalogram (EEG) signals and determine which model has the highest rate of detection of Epileptic Seizure. Show more

Keywords: Epileptic seizures, electroencephalogram (EEG) signals, logistic regression, decision trees, XGBoost classifier, true positive prediction rate, preictal state, prediction time

DOI: 10.3233/IDT-200091

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 269-279, 2021

Authors: Farag, Wael

Article Type: Research Article

Abstract: In this paper, based on the fusion of Lidar and Radar measurement data, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The lidar and radar devices are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is used for their data fusion. Lidars are accurate in determining objects’ positions but significantly less accurate on measuring their velocities. However, Radars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. Therefore, the merits of both sensors are combined using the proposed fusion …approach to provide both pose and velocity data for objects moving in roads precisely. The Grid-Based Density-Based Spatial Clustering of Applications with Noise (GB-DBSCAN) clustering algorithm is used to detect objects and estimate their centroids from the lidar and radar raw data. Then, the estimation of the object’s velocity as well as determining its corresponding geometrical shape is performed by the RANdom SAmple Consensus (RANSAC) algorithm. The proposed technique is implemented using the high-performance language C+ ⁣ + and utilizes highly optimized math and optimization libraries for best real-time performance. The performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Show more

Keywords: Sensor fusion, Kalman filter, object detection, object tracking, ADAS, autonomous driving

DOI: 10.3233/IDT-200106

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 291-304, 2021

Authors: Tsoni, Rozita | Sakkopoulos, Evangelos | Panagiotakopoulos, Christos T. | Verykios, Vassilios S.

Article Type: Research Article

Abstract: This work is aiming to contribute to the field of Distance Learning through Learning Analytics. We propose a methodological framework based on network analysis metrics to provide multiple indicators for Course Learning Analytics. Social Network Analysis is proposed for this purpose due to its capacity to provide an integrated representation of students’ interaction, where individual behavior is expressed within the context of a learning community. We perform experimental evaluation on real-life data from anonymized forum posts of postgraduate students and their tutors in the School of Science and Technology at the Hellenic Open University. Initially, we create and examine two-mode …networks (participant-discussion) for two different modules. Subsequently, these networks are transformed into one-mode networks. Key measures are estimated and compared and the differences between their pedagogical interpretations are highlighted. We conclude that the choice between working with a bimodal network or projecting it into a unimodal one is determined by the nature of the research questions because of the distinct features that each one of them exhibits. Show more

Keywords: Distance education and online learning, social network analysis, forum participation

DOI: 10.3233/IDT-200137

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 305-319, 2021

Authors: Papadimitriou, Spyros | Kamitsios, Margaritis | Chrysafiadi, Konstantina | Virvou, Maria

Article Type: Research Article

Abstract: Nowadays, the use of digital games for educational purposes becomes increasingly popular. The immersive environment of a digital game causes pleasant feelings to players motivating them to participate more actively in the learning process. However, there is a variety of educational games in terms of graphics and mechanics, and each player prefers to play different game categories. For maximizing the learner’s engagement and educational results, the incorporation of the same educational content into multiple games’ categories is the solution. Nevertheless, the update of the educational content in all games increases complexity. In this paper, a mechanism that uses web services …and achieves the portability of the same educational content in two completely different categories of mobile games (a point-and-click game and a virtual reality game) is presented. The gain of this innovative work is that the same educational content and mechanism can be accessible by any game’s category and platform, taking advantage of the game’s characteristics, in order to motivate the student to participate in the learning process and increase her/his engagement in it. The evaluation of the presented system indicates that the integration of the same educational content to different games has a positive effect on educational results. Show more

Keywords: Serious games, portability, web services, point-and-click, virtual reality

DOI: 10.3233/IDT-200204

Citation: Intelligent Decision Technologies, vol. 15, no. 2, pp. 321-332, 2021