International Journal of Hybrid Intelligent Systems - Volume 20, issue 4

Authors: Jovanovic, Luka | Zivkovic, Miodrag | Bacanin, Nebojsa | Bozovic, Aleksandra | Bisevac, Petar | Antonijevic, Milos

Article Type: Research Article

Abstract: This study explores the realm of time series forecasting, focusing on the utilization of Recurrent Neural Networks (RNN) to detect abnormal cardiovascular rhythms in Electrocardiogram (ECG) signals. The principal objective is to optimize RNN performance by finely tuning hyperparameters, a complex task with known NP-hard complexity. To address this challenge, the study employs metaheuristic algorithms, specialized problem-solving techniques crafted for navigating intricate and non-deterministic optimization landscapes. Additionally, a refined algorithm is introduced to overcome limitations inherent in the original approach. This modified algorithm exhibits significant improvements, surpassing its predecessor in identifying anomalous cardiovascular rhythms within ECG signals. The most successful …optimized model achieves an accuracy of 99.26%, outperforming models optimized by other contemporary metaheuristics assessed in the study. Further experimentation extends the initial inquiry by exploring the capabilities of Long Short-Term Memory (LSTM) models augmented by attention layers. In this extension, the best models demonstrate an accuracy of 99.83%, surpassing the original RNN models. These findings underscore the crucial importance of refining machine learning models and emphasize the potential for substantial advancements in healthcare through innovative algorithmic approaches. Show more

Keywords: Electroencephalography, medical data, diagnosis, recurrent neural networks, metaheuristic optimization

DOI: 10.3233/HIS-240005

Citation: International Journal of Hybrid Intelligent Systems, vol. 20, no. 4, pp. 275-300, 2024

Authors: Umoh, Uduak | Asuquo, Daniel | Eyoh, Imo | Murugesan, Vadivel

Article Type: Research Article

Abstract: This paper compares the effectiveness of Interval Type 2 Fuzzy Logic (IT2FL) and Machine Learning (ML) models in addressing real-world challenges. It employs four non-parametric ML algorithms (Support Vector Machine (SVM), K-Nearest Neighbor (KNN), (Random Forest (RF) and Classification and Regression Tree (CART)) and evaluates their performance against IT2FL using parameters generated through Gaussian membership functions. Initially, the IT2FL algorithm preprocesses the dataset by predicting missing values, leveraging IT2F membership to optimize decision-making and mitigate uncertainties. The study assesses the predictive performance, robustness, and interpretability of IT2F-ML models, analyzing datasets from cardiovascular disease patients to predict shock levels. After transforming …the dataset using IT2FL, it is divided into 60% training and 40% testing sets to train and test four ML algorithms, aimed at predicting shock levels in patients. The models’ performance is evaluated using various metrics, demonstrating the superior learning enhancement and performance of the IT2F-SVR method compared to other predictive approaches on the same dataset. Moreso, it is observed that the integration of the IT2-FL algorithm with machine learning models offers a promising approach for addressing data incompleteness and improving prediction accuracy. Furthermore, the study provides valuable guidance for researchers and practitioners in selecting suitable methodologies for prediction tasks. Show more

Keywords: Interval type-2 fuzzy logic systems, machine learning models, prediction problems, comparative analysis, uncertainty handling

DOI: 10.3233/HIS-240008

Citation: International Journal of Hybrid Intelligent Systems, vol. 20, no. 4, pp. 301-316, 2024

Authors: Ellouze, Mourad | Belguith, Lamia Hadrich

Article Type: Research Article

Abstract: In this paper, we present an intelligent methodology for assisting decision-makers in both understanding the structure of a data warehouse model and making decisions. The support module proposed by our method comprises three operations: (i) transforming a data warehouse model into an ontology, allowing for the display of the different terminology related to a specific domain as well as the different semantic relationships between them, (ii) recommending a series of queries to the decision-maker that enables an understanding of the reasoning logic based on the ontology’s structure, (iii) enriching the different results obtained from some analysis tools through the use of …advanced machine learning techniques. The originality of our proposed methodology lies in its ability to influence a decision-maker’s thinking in order to encourage him to take full advantage of the services provided by the data warehouse model. We apply our proposed methodology to an extended data warehouse model that enables the analysis of social media data related to people with personality disorders (PD). The primary goal of this model is to provide decision-makers with suitable services that allow them to make meaningful decisions for people with personality disorders around the world. This task was carried out by analyzing the activities and content of people on social media. In addition, one of the main advantages of this model is the use of various artificial intelligence (AI) and natural language processing (NLP) techniques. Our proposed methodology is implemented and the results achieved are evaluated using both quantitative and qualitative methods. Show more

Keywords: Data warehouse, ontology, machine learning, personality disorders, social media, content analysis

DOI: 10.3233/HIS-240010

Citation: International Journal of Hybrid Intelligent Systems, vol. 20, no. 4, pp. 317-332, 2024

Authors: Pendhari, Amjad | Pendhari, Nazneen | Singh, Santosh

Article Type: Research Article

Abstract: The use of social media is becoming increasingly important in our day-to-day activities. Platforms for social media are utilised on a daily basis, and it has been seen that young adults make regular use of social media, even while they are in the midst of an emergency scenario. For the purpose of communication, individuals, businesses, and governments all make use of various social media platforms. Through their efforts to establish communication with their loved ones who are residing in regions that have been impacted by disasters, a great number of people are demonstrating their profound concern for the well-being of …those individuals. The individuals are looking for a variety of necessities, including food, help, pharmaceuticals, lodging, transportation, and other necessities. It is possible for telecommunication networks to experience a breakdown or become incapable of adequately accommodating a sudden spike in the number of users attempting to connect to the network during times of crisis. There is a widespread use of short messaging service (SMS) mobile text messages in the modern communication landscape. Platforms for social media websites that are accessible online have the potential to effectively regulate the flow of communication. The existence of social media networks that are technologically scalable makes it possible for this to be a feasible option. The usage of a platform that enables communication in both directions has the potential to outperform the efficiency of conventional channels that only transmit information in one direction, such as radio and television, when it comes to crisis situations. The proliferation of network technologies has resulted in an increased emphasis on the examination of the features of network components, the mitigation of the affects of these components, and the rapid restoration of operations in the event of disasters. It is possible to improve the efficiency, dependability, and participatory nature of emergency communication by making use of various social media platforms. Consequently, it is possible to make the observation that crises have become an integral part of the ecosystem of social media in the modern day. Show more

Keywords: Social media, twitter, simple vector machine (SVM), machine learning (ML), classification, POS, NER, bag of words

DOI: 10.3233/HIS-240011

Citation: International Journal of Hybrid Intelligent Systems, vol. 20, no. 4, pp. 333-353, 2024

Authors: Gasmi, Salwa | Mezghani, Anis | Kherallah, Monji

Article Type: Research Article

Abstract: In the last decade, the world has witnessed remarkable technological development, especially in artificial intelligence, which helps researchers find solutions to problems of concern to the individual and society, mainly, the huge propagation of hate speech with the increased use of social media platforms. In this study, we aim to enhance the detection of Arabic hate speech on social media by addressing challenges related to imbalanced datasets through data augmentation techniques. Several machine learning algorithms and the DziriBert, a pre-trained transformer model, are implemented on the Tunisian Hate Speech and Abusive Dataset (T-HSAB). The proposed approach achieves good results, improving …the detection of hateful comments on Arabic social media using the Synthetic Minority Over-sampling Technique (SMOTE). Notably, the DziriBert model exhibits remarkable proficiency in detecting hate speech, achieving an accuracy of 82%. Random Forest (RF) and Linear SVC outperform the state of the art approaches, achieving the best result. Show more

Keywords: Tunisian hate speech, social media, SMOTE, machine learning, DziriBert, NLP

DOI: 10.3233/HIS-240012

Citation: International Journal of Hybrid Intelligent Systems, vol. 20, no. 4, pp. 355-368, 2024

Authors: Bozhenyuk, Alexander | Knyazeva, Margarita | Kosenko, Olesya | Kosenko, Eva

Article Type: Research Article

Abstract: Monitoring equipment wear is an important problem that requires constant attention, since this process can lead to a decrease in its efficiency, accidents or breakdown. The problem of replacing equipment is a production problem, in which it is necessary to consider many factors that influence the efficiency of the enterprise itself. This paper examines various formulations of the equipment replacement problem: in the classical formulation of dynamic programming, in the stochastic formulation and fuzzy graph representation. The idea to solve the problem using periodic fuzzy graphs is proposed. We also consider the age of equipment when determining the wear coefficient, …determined by the degree of belonging to a particular class of parameters defined in the formulation of the problem. Periodic fuzzy graphs allow considering the problem of replacing equipment not for one type of equipment, but for a complex of machines, which ensures the scalability of the classical dynamic programming problem under uncertain initial data. When determining the membership function, it is possible to consider those factors that may influence the solution of the optimization production problem. Setting the problem in a fuzzy form makes it possible to forecast and plan the activities of an enterprise for future periods. Show more

Keywords: Production process, optimization, equipment replacement, fuzzy set, membership function, periodic fuzzy graph

DOI: 10.3233/HIS-240013

Citation: International Journal of Hybrid Intelligent Systems, vol. 20, no. 4, pp. 369-384, 2024

Authors: Chandrabanshi, Vishnu | Domnic, S.

Article Type: Research Article

Abstract: Visual Speech Recognition (VSR) is a popular area in computer vision research, attracting interest for its ability to precisely analyze lip motion and seamlessly convert them into textual representation. VSR systems leverage visual features to augment the understanding of automated speech and predict text. VSR finds various applications, including enhancing speech recognition in scenarios with degraded acoustic signals, aiding individuals with hearing impairments, bolstering security by reducing reliance on text based passwords, facilitating biometric authentication for liveness detection, and enabling underwater communications. Despite the various techniques proposed for improving the resilience and precision of automatic speech recognition, VSR still has …challenges like homophones of words, gradient descent issues with varying sequence lengths, and lip reading demands accounting for short and long range correlations between consecutive video frames. We have proposed a hybrid network (HNet) with multilayered three dimensional dilated convolution neural network (3D-CNN). The spatio-temporal feature extraction process will be facilitated by dilated 3D-CNN. HNet integrates two bidirectional recurrent neural networks (BiGRU and BiLSTM) to process the feature sequences bidirectionally to establish the temporal relationship. The fusion of BiGRU-BiLSTM capabilities allows the model to process feature sequences more comprehensively and effectively. The proposed work focuses on face based biometric authentication for liveness detection using the VSR model to boost security against face spoofing. The existing face based biometric systems are widely used for individual authentication and verification but are still vulnerable to 3D masks and adversarial attacks. The VSR system can be added to existing face based verification systems as a second level authentication technique to identify a person with liveness. The working ideology of the VSR system will be based on the challenge response technique, where a person has to pronounce the passcode silently displayed on the screen. The VSR model assesses its effectiveness using word error rate (WER), which matches the pronounced passcode to the one presented on the screen. Overall, the proposed work aims to enhance the accuracy of VSR so that it can be combined with existing face based authentication systems. The proposed system outperforms the existing VSR system and obtained 1.3% WER. The significance of the proposed hybrid model is that it efficiently captures temporal dependencies, enhancing context embedding, improving robustness to input variability, reducing information loss, and enhancing performance and accuracy in modeling and analyzing passcode pronunciation patterns. Show more

Keywords: VSR, Deep Learning, Dilated 3D-CNN, BiLSTM, BiGRU

DOI: 10.3233/HIS-240014

Citation: International Journal of Hybrid Intelligent Systems, vol. 20, no. 4, pp. 385-401, 2024