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Journal of Future Artificial Intelligence and Technologies
Published by Future Techno Science
ISSN : -     EISSN : 30483719     DOI : 10.62411/faith
Core Subject : Science,
Computer Science & IT
Journal of Future Artificial Intelligence and Technologies E-ISSN: 3048-3719 is an international journal that delves into the comprehensive spectrum of artificial intelligence, focusing on its foundations, advanced theories, and applications. All accepted articles will be published online, receive a DOI from CROSSREF, and will be OPEN ACCESS. The RAPID peer-reviewed process is designed to provide the first decision within approximately two weeks. The journal publishes papers in areas including, but not limited to: Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Quantum Computing in AI, AI in Image Processing, AI in Security, AI in Signal Processing, and Various other AI Applications Special emphasis is given to recent trends related to cutting-edge research within the domain. If you want to become an author(s) in this journal, you can start by accessing the About page. You can first read the Policies section to find out the policies determined by the FAITH. Then, if you submit an article, you can see the guidelines in the Author Guidelines section. Each journal submission will be made online and requires prospective authors to register and have an account to be able to submit manuscripts.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 37 Documents
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Hypertension Detection via Tree-Based Stack Ensemble with SMOTE-Tomek Data Balance and XGBoost Meta-Learner Odiakaose, Christopher Chukwufunaya; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Eboka, Andrew Okonji; Binitie, Amaka Patience; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Ibor, Ayei Egu; Ako, Rita Erhovwo; Geteloma, Victor Ochuko; Ugbotu, Eferhire Valentine; Aghaunor, Tabitha Chukwudi
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-43

Abstract

High blood pressure (or hypertension) is a causative disorder to a plethora of other ailments – as it succinctly masks other ailments, making them difficult to diagnose and manage with a targeted treatment plan effectively. While some patients living with elevated high blood pressure can effectively manage their condition via adjusted lifestyle and monitoring with follow-up treatments, Others in self-denial leads to unreported instances, mishandled cases, and in now rampant cases – result in death. Even with the usage of machine learning schemes in medicine, two (2) significant issues abound, namely: (a) utilization of dataset in the construction of the model, which often yields non-perfect scores, and (b) the exploration of complex deep learning models have yielded improved accuracy, which often requires large dataset. To curb these issues, our study explores the tree-based stacking ensemble with Decision tree, Adaptive Boosting, and Random Forest (base learners) while we explore the XGBoost as a meta-learner. With the Kaggle dataset as retrieved, our stacking ensemble yields a prediction accuracy of 1.00 and an F1-score of 1.00 that effectively correctly classified all instances of the test dataset.
AI-Based Detection Techniques for Skin Diseases: A Review of Recent Methods, Datasets, Metrics, and Challenges Jaiyeoba, Oluwayemisi; Jaiyeoba, Oluwaseyi; Ogbuju, Emeka; Oladipo, Francisca
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-46

Abstract

The identification and early treatment of skin diseases are crucial to mitigate serious health risks. The growing attention on researching skin disease analysis stems from the transformative impact of artificial intelligence (AI) in dermatology. In this systematic review, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to comprehensively assess recent approaches for skin disease detection. Our study addressed four key research questions exploring the methods for skin disease detection, the evaluation techniques employed to measure the effectiveness of skin disease detection models, the datasets utilized, and the challenges encountered in applying machine learning and deep learning techniques for skin disease detection. We screened studies from 2019 to 2023 from reputable databases, including IEEE Explore, Science Direct, and Google Scholar. Our findings revealed that the CNN model outperformed other deep learning models. Additionally, our analysis identified the ISIC public dataset as the most frequently used dataset. The studies reviewed employed evaluation metrics such as accuracy, recall, precision, sensitivity, and F1 score to evaluate model performance. We identified several limitations in the studies we reviewed, including the use of limited datasets, challenges in distinguishing between diseases with similar features, and other related limitations. Overall, we provided a comprehensive overview of the current state-of-the-art techniques in skin disease detection and highlighted the future directions.
Integrating HCI Principles in AI: A Review of Human-Centered Artificial Intelligence Applications and Challenges Sharma, Shristi; Shrestha, Sushil
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-47

Abstract

This review explores the integration of Human-Computer Interaction (HCI) principles in AI to advance Human-Centered Artificial Intelligence (HCAI). It highlights how these fields intersect to create user-friendly AI systems that enhance human capabilities and align with human values. Given the recent interest of HCI in user-centered design and AI in technical innovation, this paper bridges this divide by infusing principles from HCI into AI systems. Relevant peer-reviewed articles, conference papers, and case studies have been selected from leading databases like IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar, encompassing publications from 2017 to 2024. The inclusion criteria for the review focus on interdisciplinary approaches, real-world applications, and challenges of HCAI, while studies that do not have a clear methodology or lack relevance to HCAI were excluded. This paper identifies some of the key gaps, highlights the successful applications of HCAI across healthcare, edu-cation, and entertainment, and discusses various challenges that have arisen, such as bias, transparency, and balancing automation with human control. Findings reveal that iterative design and hu-man-centered frameworks will lead to better usability and ethical fit for HCAI, but significant challenges remain. This study proposes an integrative framework for bringing HCI principles into AI design through interdisciplinary collaboration in developing systems that will enhance human capabilities while considering ethical aspects. Future directions include responsible AI, personalized healthcare, and effective human-AI collaboration.
Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification Tomal, S.M. Yousuf Iqbal; Shafin, Abdullah Al; Afaf, Afrida; Bhattacharjee, Debojit
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-48

Abstract

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achievinga perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterizedgate selection, and the integration of the quantum layer with classical neural network components.This work contributes to the growing body of research on hybrid quantum-classical models and theirapplicability to real-world datasets.
Comparative Analysis of Modified Q-Learning and DQN for Autonomous Robot Navigation Khlif, Nessrine; Khraief, Nahla; Belghith, Safya
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-49

Abstract

Autonomous mobile robot navigation integrates localization, mapping, and path planning to enable effective operation in complex environments. This study compares a modified Q-learning algorithm with a Deep Q-Network (DQN) in a simulated gym environment, focusing on convergence speed, success rate, and computational efficiency. The modified Q-learning algorithm converged after 44 episodes, outperforming the DQN, which required 400 episodes. It achieved a success rate of 69.6% with a cumulative reward that surpassed the DQN in fewer episodes, while completing simulations in just 9 minutes compared to 400 minutes for the DQN. These results demonstrate the modified Q-learning’s efficiency in addressing the exploration-exploitation trade-off and navigating complex environments. This study highlights the potential of the modified Q-learning algorithm for real-world applications in robotics and autonomous navigation, providing a foundation for future research in intelligent path planning
Comprehensive Exploration of Ensemble Machine Learning Techniques for IoT Cybersecurity Across Multi-Class and Binary Classification Tasks Çetin, Aziz; Öztürk, Sıtkı
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-51

Abstract

This study aimed to predict and detect cyberattacks using hybrid machine-learning models. The CICIoT2023 dataset was utilized for attack prediction and detection, and model performance was evaluated separately by performing thirty-four class (33+1), eight class (7+1), and binary (1+1) classifications according to the types of attacks in the dataset. Voting and stacking hybrid machine learning models were employed in this study, with Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Random Forest (RF) algorithms selected as sub-models. Data preprocessing steps were applied to enhance model performance, and hyperparameter optimization was performed. As a result, this study achieved an accuracy of 98% in thirty-four class classifications, 88% in eight class classifications, and 99% in binary classifications with the Voting hybrid machine learning model. In contrast, the Stacking hybrid machine learning model reached an accuracy of 98% in both thirty-four class and eight class classifications and 99% in binary classifications. This study presents a significant innovation in the cybersecurity field by introducing an innovative approach to eliminating the disadvantages of single-model methods.
Enhancing Hybrid Course Recommendation with Weighted Voting Ensemble Learning San, Kyawt Kyawt; Win, Hlaing Hlaing; Chaw, Khin Ei Ei
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-55

Abstract

Course recommendation aims to find suitable and attractive courses for students based on their needs, playing a significant role in the curricula-variable system. However, with the abundant available courses, students often face cognitive overload when selecting the most appropriate ones. This research proposes a course recommendation system called the Enhanced Hybrid Course Recommender to address this challenge. This system uses an ensemble learning approach to combine and leverage the power of multiple machine learning classifiers, including Random Forest, Naive Bayes, and Support Vector Machine. By utilizing TF-IDF vectorization for text data transformation and label encoding for target label compatibility, this experiment significantly enhances recommendation precision and relevance, easing students' decision-making process and improving the overall quality of course recommendations. A hybrid approach is applied to improve the recommendation quality by combining predictions from all three classifiers through weighted voting. This ensemble method improves overall robustness and accuracy. This approach not only mitigates the cognitive overload faced by students but also significantly improves the quality of recommendations. Our hybrid model represents a substantial advancement in personalized course recommendation technology by demonstrating superior performance across key evaluation metrics such as accuracy, precision, recall, F1-score, ARHR, and NDCG.
The AI and Quantum Era: Transforming Project Management Practices Aliyev, Ali
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-59

Abstract

Project management is changing drastically due to the integration of artificial intelligence (AI) and quantum computing (QC), redefining traditional methods. This study explores Quantum AI (QAI) and AI-driven solutions to tackle enduring issues, including resource inefficiencies, schedule delays, and budget overruns. These technologies significantly enhance project outcomes by leveraging predictive analytics, dynamic scheduling, and high-dimensional optimization. A comparative analysis of prominent case studies, including the Crossrail Project, East Side Access, and the Montreal Olympics, highlights the superior performance of AI and QAI techniques compared to conventional methods. The study shows that QAI can cut delays by 60%, optimize resource allocation with 83% efficiency, and eliminate cost overruns by up to 40% using Monte Carlo simulations and Failure Mode Effects Analysis. These results demonstrate that quantum artificial intelligence is a ground-breaking tool for handling intricate, interconnected project settings. Additionally, this study emphasizes how QAI is scalable and applicable across industries, especially in fields that need real-time optimization and high-dimensional data processing. The proposed hybrid quantum-classical paradigm provides practical solutions and sets a benchmark for efficiency, scalability, and risk mitigation in project management.
Leveraging GANs for Synthetic Data Generation to Improve Intrusion Detection Systems Rahman, Md. Abdur; Francia, Guillermo A.; Shahriar, Hossain
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-52

Abstract

This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from the UNSW-NB15 dataset to create a more balanced training set. By leveraging synthetic data augmentation, our approach mitigates issues related to class imbalance and enhances the generalization capability of the classifier. Extensive experiments demonstrate that RF trained on the combined dataset of real and synthetic data achieves superior detection performance compared to models trained exclusively on real data. Specifically, RF trained solely on the original dataset achieves an accuracy of 97.58%, whereas integrating GAN-generated synthetic data improves accuracy to 98.27%. The proposed methodology is further evaluated through comparative analysis against alternative classifiers, including Support Vector Machine (SVM), XGBoost, Gated Recurrent Unit (GRU), and related studies in the field. Our findings indicate that GAN-augmented training significantly enhances detection rates, particularly for rare attack types, while maintaining computational efficiency. Furthermore, RF outperforms other classifiers, including deep learning models, demonstrating its effectiveness as a lightweight yet robust classification method. Integrating GANs with RF offers a scalable and adaptable framework for intrusion detection, ensuring improved resilience against evolving cyber threats.
High-Performance Face Spoofing Detection using Feature Fusion of FaceNet and Tuned DenseNet201 Zuama, Leygian Reyhan; Setiadi, De Rosal Ignatius Moses; Susanto, Ajib; Santosa, Stefanus; Gan, Hong-Seng; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-62

Abstract

Face spoofing detection is critical for biometric security systems to prevent unauthorized access. This study proposes a deep learning-based approach integrating FaceNet and DenseNet201 to enhance face spoofing detection performance. FaceNet generates identity-based embeddings, ensuring robust facial feature representation, while DenseNet201 extracts complementary texture-based features. These features are fused using the Concatenate function to form a more comprehensive representation for im-proved classification. The proposed method is evaluated on two widely used face spoofing datasets, NUAA Photograph Imposter and LCC-FASD, achieving 100% accuracy on NUAA and 99% on LCC-FASD. Ablation studies reveal that data augmentation does not always enhance performance, particularly on high-complexity datasets such as LCC-FASD, where augmentation increases the False Rejection Rate (FRR). Conversely, DenseNet201 benefits more from augmentation, while the proposed method performs best without augmentation. Comparative analysis with previous studies further confirms the superiority of the proposed approach in reducing error rates, particularly Half Total Error Rate (HTER), False Acceptance Rate (FAR), and FRR. These findings indicate that combining identity-based embeddings and texture-based feature extraction significantly improves spoofing detection and enhances model robustness across different attack scenarios. This study advances biometric security by introducing an efficient feature fusion strategy that strengthens deep learning-based spoof detection. Future research may explore further optimization strategies and evaluate the approach on more diverse datasets to enhance generalization.

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