Intelligent Decision Technologies - Volume 18, issue 2

Authors: Pandey, Krishna | Chandra, Saurabh | Arya, Rajeev

Article Type: Research Article

Abstract: In the context of fifth generation (5G) technology, Device-to-Device (D2D) communication plays a pivotal role, requiring swift and intelligent decision-making in mode selection and device discovery. This study addresses the challenge of rapid mode selection and device discovery within 5G communication networks, focusing specifically on enhancing spectral and energy efficiency for Internet of Things (IoT) applications. A novel self-centered game theory-based algorithm is introduced to optimize spectral efficiency and support intelligent mode selection. Additionally, the utilization of the support vector machine (SVM) expedites mode selection decisions. For D2D discovery, the Frank-Wolfe method is adopted, significantly improving the differentiation between D2D …and Cellular users based on signal strength and interference, thereby enhancing spectral efficiency. The proposed approach maximizes spectral efficiency while adhering to strict power and interference constraints, intelligently partitioning bandwidth into two subparts using game theoretic principles to amplify spectral efficiency. Furthermore, the emphasis on energy efficiency is underscored through iterative calculations to achieve maximum energy-efficient spectral allocation. Numerical analyses validate the efficacy of the proposed technique, revealing substantial improvements in accurately predicted labels. As the number of devices increases, precision and recall rates experience noteworthy enhancements, ultimately leading to superior bandwidth utilization. This research presents a significant contribution to the field of 5G communication, particularly concerning energy efficiency, which is paramount for IoT applications. By accelerating D2D connectivity and optimizing energy and spectrum resources, it advances the goals of energy-efficient D2D communication within 5G-IoT networks. Show more

Keywords: Device-to-Device (D2D) communication, energy efficiency, Frank-Wolfe, machine learning, fifth generation (5G), spectral efficiency

DOI: 10.3233/IDT-240008

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 981-1000, 2024

Authors: Lazaros, Konstantinos | Koumadorakis, Dimitris E. | Vrahatis, Aristidis G. | Kotsiantis, Sotiris

Article Type: Research Article

Abstract: Advancements in computational capabilities have enabled the implementation of advanced deep learning models across various domains of knowledge, yet the increasing complexity and scarcity of data in specialized areas pose significant challenges. Zero-shot learning (ZSL), a subset of transfer learning, has emerged as an innovative solution to these challenges, focusing on classifying unseen categories present in the test set but absent during training. Unlike traditional methods, ZSL utilizes semantic descriptions, like attribute lists or natural language phrases, to map intermediate features from the training data to unseen categories effectively, enhancing the model’s applicability across diverse and complex domains. This review …provides a concise synthesis of the advancements, methodologies, and applications in the field of zero-shot learning, highlighting the milestones achieved and possible future directions. We aim to offer insights into the contemporary developments in ZSL, serving as a comprehensive reference for researchers exploring the potentials and challenges of implementing ZSL-based methodologies in real-world scenarios. Show more

Keywords: Big data transfer learning deep learning zero-shot learning

DOI: 10.3233/IDT-240297

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1001-1028, 2024

Authors: K S, Jishnu | B, Arthi

Article Type: Research Article

Abstract: In the dynamic realm of digital advancements, the persistent menace of phishing attacks continues to jeopardize the security landscape for both individuals and organizations. As cyber attacks continue to proliferate, URL-based phishing attacks are growing rapidly. This paper presents an exploratory study aimed at enhancing cybersecurity measures through the detection of phishing URLs. Our approach involves exploring the integration of Gated Recurrent Units (GRU) with various attention mechanisms to bolster accuracy in discerning between legitimate and phishing URLs. Notably, our study reveals that the implementation of the Bahdanau attention mechanism with GRU yields remarkable results, achieving an accuracy of 98.14%. …We conducted experiments on a comprehensive dataset comprising 95,913 URLs. Our primary objectives include fortifying cybersecurity defenses against phishing threats, innovating through the integration of diverse attention mechanisms with GRU, and substantiating the efficacy of our model through rigorous evaluation metrics. As the realm of cybersecurity confronts escalating challenges, our research not only offers valuable insights but also charts a promising trajectory for future advancements in cybersecurity strategies. Show more

Keywords: Phishing, GRU, attention mechanisms, neural networks, cyber security

DOI: 10.3233/IDT-240026

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1029-1052, 2024

Authors: Arif, Muhammad Usman | Haider, Sajjad

Article Type: Research Article

Abstract: Multi-Robot Task Allocation (MRTA) is a complex problem domain with the majority of problem representations categorized as NP-hard. Existing solution approaches handling dynamic MRTA scenarios do not consider the problem structure changes as a possible system dynamic. RoSTAM (Robust and Self-adaptive Task Allocation for Multi-robot teams) presents a novel approach to handle a variety of MRTA problem representations without any alterations to the task allocation framework. RoSTAM’s capabilities against a range of MRTA problem distributions have already been established. This paper further validates RoSTAM’s performance against the more conventional dynamics, such as robot failure and new task arrival, while performing …allocations against two of the most frequently faced problem representations. The framework’s performance is evaluated against a state-of-the-art online auction scheme. The results validate RoSTAM’s capability to allocate tasks across a range of dynamics efficiently. Show more

Keywords: Computational intelligence, evolutionary algorithm, multi-robot task allocation, scheduling, multi-robot systems, multi-agent systems

DOI: 10.3233/IDT-230693

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1053-1076, 2024

Authors: Al-Obaidi, Sumia Abdulhussien Razooqi | Lighvan, Mina Zolfy | Asadpour, Mohammad

Article Type: Research Article

Abstract: With the surging prominence of digital communication platforms, there has been an intensified emphasis on ensuring robust security and privacy measures. Against this backdrop, image steganalysis has emerged as a critical discipline, employing advanced methods to detect clandestine data within image files. At the core of our research is an innovative exploration into image steganalysis using an amalgamation of enhanced reinforcement learning techniques and online data augmentation. This methodology ensures the meticulous identification of concealed data within images. Our design integrates triple parallel dilated convolutions, enabling concurrent extraction of feature vectors from the input images. Once extracted, these vectors are …synthesized, paving the way for subsequent classification tasks. To substantiate the efficacy of our approach, we conducted tests on a comprehensive dataset sourced from BossBase 1.01. Furthermore, to discern the influence of transfer learning on our proposed model, the BOWS dataset was employed. Notably, these datasets present a challenge due to its inherent imbalance. To counteract this, we incorporated an advanced Reinforcement Learning (RL) framework. Herein, the dataset samples are envisioned as states in a sequence of interrelated decisions, with the neural network playing the role of the decision-making agent. This agent is then incentivized or reprimanded based on its accuracy in discerning between the minority and majority classes. To bolster our classification capabilities, we innovatively employed data augmentation using images generated by a Generative Adversarial Network (GAN). Concurrently, a regularization mechanism was instituted to alleviate prevalent GAN-related challenges, such as mode collapse and unstable training dynamics. Our experimental outcomes underscore the potency of our methodology. The results highlight a remarkable capability to discern between pristine and steganographic images, registering an average accuracy rate of 85%. Show more

Keywords: Image steganalysis, reinforcement learning, generative adversarial network, imbalanced classification, transfer learning

DOI: 10.3233/IDT-240075

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1077-1100, 2024

Authors: Gkioka, Georgia | Dominguez, Monica | Mentzas, Gregoris

Article Type: Research Article

Abstract: In the realm of modern urban mobility, automatic incident detection is a critical element of intelligent transportation systems (ITS), since the ability to promptly identify unexpected events allows for quick implementation of preventive measures and efficient response to the situations as they arise. With the growing availability of traffic data, Machine Learning (ML) has become a vital tool for enhancing traditional incident detection methods. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process; however the application of AutoML for incident detection has not been widely explored in scientific research In this paper, we propose and apply …an AutoML-based methodology for traffic incident detection and compare it with state-ofthe-art ML approaches. Our approach integrates data preprocessing with AutoML, and uses Tree-based Pipeline Optimization Tool (TPOT) to refine the process from raw data to prediction. We have tested the efficiency of our approach in two major European cities, Athens and Antwerp. Finally, we present the limitations of our work and outline recommendations for application of AutoML in the incident detection task and potentially in other domains. Show more

Keywords: Incident detection, automated machine learning (AutoML), artificial intelligence (AI), intelligent transport systems (ITS), big data

DOI: 10.3233/IDT-240231

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1101-1122, 2024

Authors: Shaik, Allabaksh | Basha, Shaik Mahaboob

Article Type: Research Article

Abstract: Anomaly detection is a branch of behavior understanding in surveillance scenes, where anomalies represent a deviation in the behavior of scene entities (viz.,humans, vehicles, and environment) from regular patterns. In pedestrian walkways, this plays a vital role in enhancing safety. With the widespread use of video surveillance systems and the escalating video volume, manual examination of abnormal events becomes time-intensive.Hence, the need for an automated surveillance system adept at anomaly detection is crucial, especially within the realm of computer vision (CV) research. The surge in interest towards deep learning (DL) algorithms has significantly impacted CV techniques, including object detection and …classification. Unlike traditional reliance on supervised learning requiring labeled datasets, DL offers advancements in these applications. Thus, this study presents an Optimal Deep Transfer Learning Enabled Object Detector for Anomaly Recognition in Pedestrian Ways (ODTLOD-ARPW) technique. The purpose of the ODTLOD-ARPW method is to recognize the occurrence of anomalies in pedestrian walkways using a DL-based object detector. In the ODTLOD-ARPW technique, the image pre-processing initially takes place using two sub-processes namely Wiener filtering (WF) based pre-processing and dynamic histogram equalization-based contrast enhancement. For anomaly detection, the ODTLOD-ARPW technique employs the YOLOV8s model which offers enhanced accuracy and performance. The hyperparameter tuning process takes place using a root mean square propagation (RMSProp) optimizer. The performance analysis of the ODTLOD-ARPW method is tested under the UCSD anomaly detection dataset. An extensive comparative study reported that the ODTLOD-ARPW technique reaches an effective performance with other models with maximum accuracy of 98.67%. Show more

Keywords: Anomaly detection, pedestrian ways, hyperparameter tuning, deep learning, wiener filtering

DOI: 10.3233/IDT-240040

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1123-1138, 2024

Authors: Linardatos, Pantelis | Papastefanopoulos, Vasilis | Kotsiantis, Sotiris

Article Type: Research Article

Abstract: Time series forecasting is the process of predicting future values of a time series based on its historical data patterns. It is a critical task in many domains, including finance, supply chain management, the environment, and more as accurate forecasts can help businesses and organizations make better decisions and improve their metrics. Although there have been significant advances in time series forecasting systems, thanks to the development of new machine learning algorithms, hardware improvements, and the increasing availability of data, it remains a challenging task. Common pitfalls, especially of single-model approaches include susceptibility to noise and outliers and inability to …handle non-stationary data, which can lead to inaccurate and non-robust forecasts. Model-combining approaches, such as averaging the results of multiple predictors to produce a final forecast, are commonly used to mitigate such issues. This work introduces a novel application of Cascade Generalization or Cascading for time series forecasting, where multiple predictors are used sequentially, with each predictor’s output serving as additional input for the next. This methodology aims to overcome the limitations of single-model forecasts and traditional ensembles by incorporating a progressive learning mechanism. We adapt Cascade Generalization specifically for time series data, detailing its implementation and potential for handling complex, dynamic datasets. Our approach was systematically evaluated against traditional two-model averaging ensembles across ten diverse datasets, employing the Root Mean Square Error (RMSE) metric for performance assessment. The results revealed that cascading tends to outperform voting ensembles in most cases. This consistent trend suggests that cascading can be considered a reliable alternative to voting ensembles, showcasing its potential as an effective strategy for improving time series forecasting across a wide range of scenarios. Show more

Keywords: Time series, forecasting, machine learning, cascade generalization, stacking generalization, stacking, cascade, cascading, ensemble, regressor

DOI: 10.3233/IDT-240224

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1139-1156, 2024

Authors: Bäßmann, Felix N. | Lier, Sarah K. | Werth, Oliver | Krage, Astrid | Breitner, Michael H.

Article Type: Research Article

Abstract: Today, access to real-time information from emergency scenes is still limited for emergency medical services, fire departments, and their professionals, also called first responders. Emergency Response Information Systems (ERISs) have recently been discussed in the literature as a potential solution to this problem. Using the Design Science Research (DSR) paradigm, we present a novel 5G-enabled ERIS (5G-ERIS) design that leverages 5G mobile network technologies to offer diverse real-time information. We provide a user-centered examination of design specifications for a 5G-ERIS based on a smart city digital twin. Based on literature and qualitative expert interviews with several first responders in Germany, …we derive how emergency medical services and fire departments can improve their decision-making with this 5G-ERIS. Based on existing 5G application architectures, we structure our identified design specifications into four system layers. Our findings provide an essential knowledge base for the successful development, deployment, and long-term use of 5G-ERISs. We stimulate a broader discussion on the design objectives and specifications of 5G-ERISs in theory and practice. Show more

Keywords: 5G, design science research, ERIS, emergency, digital twin

DOI: 10.3233/IDT-230476

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1157-1179, 2024

Authors: Zarkadoulas, Dimitrios | Virvou, Maria

Article Type: Research Article

Abstract: Emotional feeling is a phase of neurobiological activity that plays an important role in cognitive thinking and learning, although largely overlooked in complex tutoring fields like Mathematics. This paper introduces an innovative e-learning Mathematics course integrating emojis as a feedback mechanism to express students’ emotional responses towards mathematical challenges. By providing a platform for intuitive emotional expression, this approach aims to strengthen engagement and comprehension. Through empirical investigation, emotional reactions of online mathematics students are explored, with attention to gender-related differences in emoji usage. A survey administered to 100 students prompts them to select emojis conveying their sentiments towards mathematical problems. …Statistical analyses reveal that emojis effectively capture students’ emotions, with an emphasis on gender-based variations in selection. These insights illuminate the dynamics of emotional expression and hold implications for fostering comprehensive learning environments that mitigate negative emotions such as mathematical anxiety. By empowering educators to monitor students’ emotional reactions and adapt teaching strategies accordingly, this approach has the potential to cultivate confident and proficient learners essential for STEM (Science, Technology, Engineering, Mathematics) advancement. Show more

Keywords: E-learning, online education, gender studies, teaching techniques, secondary school students, user modelling, affective computing, sentiment analysis, emojis, mathematics, STEM, mathematics anxiety

DOI: 10.3233/IDT-240170

Citation: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1181-1201, 2024