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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 8 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2024): September 2024" : 8 Documents clear
Pilot Study on Enhanced Detection of Cues over Malicious Sites Using Data Balancing on the Random Forest Ensemble Okpor, Margaret Dumebi; Aghware, Fidelis Obukohwo; Akazue, Maureen Ifeanyi; Eboka, Andrew Okonji; Ako, Rita Erhovwo; Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Binitie, Amaka Patience; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-14

Abstract

The digital revolution frontiers have rippled across society today – with various web content shared online for users as they seek to promote monetization and asset exchange, with clients constantly seeking improved alternatives at lowered costs to meet their value demands. From item upgrades to their replacement, businesses are poised with retention strategies to help curb the challenge of customer attrition. The birth of smartphones has proliferated feats such as mobility, ease of accessibility, and portability – which, in turn, have continued to ease their rise in adoption, exposing user device vulnerability as they are quite susceptible to phishing. With users classified as more susceptible than others due to online presence and personality traits, studies have sought to reveal lures/cues as exploited by adversaries to enhance phishing success and classify web content as genuine and malicious. Our study explores the tree-based Random Forest to effectively identify phishing cues via sentiment analysis on phishing website datasets as scrapped from user accounts on social network sites. The dataset is scrapped via Python Google Scrapper and divided into train/test subsets to effectively classify contents as genuine or malicious with data balancing and feature selection techniques. With Random Forest as the machine learning of choice, the result shows the ensemble yields a prediction accuracy of 97 percent with an F1-score of 98.19% that effectively correctly classified 2089 instances with 85 incorrectly classified instances for the test-dataset.
Phishing Website Detection Using Bidirectional Gated Recurrent Unit Model and Feature Selection Setiadi, De Rosal Ignatius Moses; Widiono, Suyud; Safriandono, Achmad Nuruddin; Budi, Setyo
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-15

Abstract

Phishing attacks continue to be a significant threat to internet users, necessitating the development of advanced detection systems. This study explores the efficacy of a Bidirectional Gated Recurrent Unit (BiGRU) model combined with feature selection techniques for detecting phishing websites. The dataset used for this research is sourced from the UCI Machine Learning Repository, specifically the Phishing Websites dataset. This approach involves cleaning and preprocessing the data, then normalizing features and employing feature selection to identify the most relevant attributes for classification. The BiGRU model, known for its ability to capture temporal dependencies in data, is then applied. To ensure robust evaluation, we utilized cross-validation, dividing the data into five folds. The experimental results are highly promising, demonstrating a Mean Accuracy, Mean Precision, Mean Recall, Mean F1 Score, and Mean AUC of 1.0. These results indicate the model's exceptional performance distinguishing between phishing and legitimate websites. This study highlights the potential of combining BiGRU models with feature selection and cross-validation to create highly accurate phishing detection systems, providing a reliable solution to enhance cybersecurity measures.
An Interpretable Machine Learning Strategy for Antimalarial Drug Discovery with LightGBM and SHAP Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-16

Abstract

Malaria continues to pose a significant global health threat, and the emergence of drug-resistant malaria exacerbates the challenge, underscoring the urgent need for new antimalarial drugs. While several machine learning algorithms have been applied to quantitative structure-activity relationship (QSAR) modeling for antimalarial compounds, there remains a need for more interpretable models that can provide insights into the underlying mechanisms of drug action, facilitating the rational design of new compounds. This study develops a QSAR model using Light Gradient Boosting Machine (LightGBM). The model is integrated with SHapley Additive exPlanations (SHAP) to enhance interpretability. The LightGBM model demonstrated superior performance in predicting antimalarial activity, with an ac-curacy of 86%, precision of 85%, sensitivity of 81%, specificity of 89%, and an F1-score of 83%. SHAP analysis identified key molecular descriptors such as maxdO and GATS2m as significant contributors to antimalarial activity. The integration of LightGBM with SHAP not only enhances the predictive ac-curacy of the QSAR model but also provides valuable insights into the importance of features, aiding in the rational design of new antimalarial drugs. This approach bridges the gap between model accuracy and interpretability, offering a robust framework for efficient and effective drug discovery against drug-resistant malaria strains.
Optimizing Rice Production Forecasting Through Integrating Multiple Linear Regression with Recursive Feature Elimination Ingio, Joseph Abunimye; Nsang, Augustine Shey; Iorliam, Aamo
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-17

Abstract

Rice is a staple food for most Nigerians, making accurate yield prediction is crucial for food security. This study addresses the limitations of traditional forecasting methods by employing Multiple Linear Regression (MLR) coupled with Recursive Feature Elimination (RFE) to predict rice yield in Adamawa and Cross River states, characterized by distinct agroclimatic conditions. Utilizing climatic data and historical yield records from 1990 to 2022, we trained and evaluated MLR and compared the MLR results with two other machine learning models (XGBoost, and K Nearest Neighbours). RFE-optimized feature selection identified All-sky Photosynthetically Active Radiation (PAR) as a key factor. MLR demonstrated a very stable prediction performance with R² values of 0.90 and 0.92 for Adamawa and Cross River, respectively, after RFE. This research contributes to developing advanced Agro-information systems, supporting informed agricultural decision-making, and enhancing Nigeria's food security.
A Reinforcement Learning-Based Approach for Promoting Mental Health Using Multimodal Emotion Recognition Pathirana, Amod; Rajakaruna, Dumidu Kasun; Kasthurirathna, Dharshana; Atukorale, Ajantha; Aththidiye, Rekha; Yatipansalawa, Maheshi
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-22

Abstract

This research aims to enhance mental well-being by addressing symptoms of anxiety and depression through a personalized, culturally specific multimodal emotion prediction system. It employs an emotionally aware Reinforcement Learning (RL) agent to suggest tailored Cognitive Behavioral Therapy (CBT) activities. The study focuses on developing precise, individualized emotion prediction models using facial expressions, vocal tones, and text, and integrates these models with the RL agent for emotionally aware CBT recommendations. The mHealth approach combines deep learning models with RL, achieving accuracies of 72% for facial expressions, 73% for vocal tones, and 86% for text, all fine-tuned for the Sri Lankan context. Validation through real-world use and user feedback consistently demonstrated that each model exceeds 70% accuracy, fulfilling the objective of precise emotion prediction. A weighted algorithm was introduced to refine the emotion prediction experience and personalize forecasts across the three modalities to enhance mental well-being. The RL-enabled agent suggests CBT activities approved by mental health professionals, tailored based on predicted emotions, and delivered through the same mHealth application. The effectiveness of these interventions was assessed using the DASS-21 questionnaire, revealing significant reductions in depression scores (from 21.08 to 13.54) and anxiety scores (from 19.85 to 10.46) in the study group compared to the control group. The study concludes that integrating multimodal emotion prediction models with RL-based CBT suggestions positively impacts mental well-being and contributes to personalized mental health interventions.
AI4CRC: A Deep Learning Approach Towards Preventing Colorectal Cancer Fanijo, Samuel
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-28

Abstract

Each year, more than 1.9 million cases of colorectal cancer (CRC) are diagnosed worldwide. By 2040, the burden of colorectal cancer is estimated to reach 3.2 million new cases per year and 1.6 million deaths per year worldwide. As of 2024, it ranks as the third most prevalent form of cancer, contributing to over 10% of all new cancer cases annually, with a 5-year survival rate of only 65%. With effective early detection mechanisms in place, the survival rate could potentially increase to 90%. However, current detection mechanisms are manual and error-prone. This study presents a deep learning-based approach to automating the detection of polyps, the tumor that causes colorectal cancer, in the human colon. Various state-of-the-art deep learning models – including VGG, ResNet, DenseNet, and EfficientNet were trained and tested on a publicly available dataset. The findings of this study show that deep learning models can significantly automate the early diagnosis process of colorectal cancer with high accuracy, especially the DenseNet and EfficientNet models – attaining 99% and 99.4% respectively for both accuracy and F1 score metrics on the test dataset. This study validates the potential of deep learning to enhance the accuracy and reliability of colorectal cancer detection and prevention, ultimately contributing to better quality of diagnosis and patient outcomes.
Exploring Machine Learning and Deep Learning Techniques for Occluded Face Recognition: A Comprehensive Survey and Comparative Analysis Muhamada, Keny; Setiadi, De Rosal Ignatius Moses; Sudibyo, Usman; Widjajanto, Budi; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-30

Abstract

Face recognition occluded by occlusions, such as glasses or shadows, remains a challenge in many security and surveillance applications. This study aims to analyze the performance of various machine learning and deep learning techniques in face recognition scenarios with occlusions. We evaluate KNN (standard and FisherFace), CNN, DenseNet, Inception, and FaceNet methods combined with a pre-trained DeepFace model using three public datasets: YALE, Essex Grimace, and Georgia Tech. The results show that KNN maintains the highest accuracy, reaching 100% on two datasets (Essex Grimace and YALE), even in the presence of occlusions. Meanwhile, CNN shows strong performance, with accuracy remaining 100% on YALE, both with and without occlusions, although its performance drops slightly on Essex Grimace (94% with occlusion). DenseNet and Inception show a more significant drop in accuracy when faced with occlusion, with DenseNet dropping from 81% to 72% on Essex Grimace and Inception dropping from 100% to 92% on the same dataset. FaceNet + DeepFace excels on more large dataset (Georgia Tech) with 98% accuracy, but its performance drops dramatically to 53% and 70% on Essex Grimace and YALE with occlusion. These findings indicate that while deep learning methods show high accuracy under ideal conditions, machine learning methods such as KNN are more flexible and robust to occlusion in face recognition.
A Hybrid Approach for Efficient DDoS Detection in Network Traffic Using CBLOF-Based Feature Engineering and XGBoost Dhahir, Zainab Sahib
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-33

Abstract

This is one of the greatest challenges in computer network security and cannot be dealt with without a set of most recent detection techniques. This paper lays down a new hybrid technique that combines Clustering-Based Local Outlier Factor (CBLOF) and Extreme Gradient Boosting (XGBoost) to enhance accuracy while detecting Distributed Denial of Service (DDoS) from network traffic. The proposed hybrid model utilizes a CBLOF for outlier detection as feature engineering. Over the detected anomalies, classification is to be done using XGBoost classification to attain the objective. The proposed hybrid model was tested extensively on CICIDS 2017 and CICIDS 2018 datasets Compared with traditional ones, the proposed model outperformed the traditional ones with an accuracy rate of 99.99%, precision of 100%, and F1 score reflecting perfection. These results confirm this model's efficiency in terms of known and novel attack patterns and introduce a further reliable framework for the timely detection of DDoS attacks. Even if it is computation-heavy, optimization could be made towards real-time large-scale data.

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