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All Journal Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Iorliam, Aamo
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Computer Science & IT Decision Sciences, Operations Research & Management

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Design and Validation of Structural Causal Model: A Focus on EGRA Dataset Ayem, Gabriel Terna; Asilkan, Ozcan; Iorliam, Aamo
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9304

Abstract

Designing and validating structural causal model (SCM) correctness from a dataset whose background knowledge is obtained from a research process is not a common phenomenon. Studies have shown that in many critical areas, such as healthcare and education, researchers develop models from direct acyclic graphs (DAG), a component of an SCM, without testing them. This phenomenon is worrisome and is bound to cast a shadow on the inference estimates that may arise from such models. In this study, we have designed a novel application-based SCM for the first time using the background knowledge obtained from the Early Grade Reading Assessment (EGRA) program called the Strengthen Education in Northeast Nigeria (SENSE-EGRA), which is an educational intervention program of the American University of Nigeria (AUN), Yola, on the letter identification subtask. This project was sponsored by the United States Agency for International Development (USAID). We employed the conditional independence test (CIT) criteria for the validation of the SCM’s correctness, and the results show a near-perfect SCM.
A Comparative Analysis of Generative Artificial Intelligence Tools for Natural Language Processing Iorliam, Aamo; Ingio, Joseph Abunimye
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.9447

Abstract

Generative artificial intelligence tools have recently attracted a great deal of attention. This is because of their huge advantages, which include ease of usage, quick generation of answers to requests, and the human-like intelligence they possess. This paper presents a vivid comparative analysis of the top 9 generative artificial intelligence (AI) tools, namely ChatGPT, Perplexity AI, YouChat, ChatSonic, Google's Bard, Microsoft Bing Assistant, HuggingChat, Jasper AI, and Quora's Poe, paying attention to the Pros and Cons each of the AI tools presents. This comparative analysis shows that the generative AI tools have several Pros that outweigh the Cons. Further, we explore the transformative impact of generative AI in Natural Language Processing (NLP), focusing on its integration with search engines, privacy concerns, and ethical implications. A comparative analysis categorizes generative AI tools based on popularity and evaluates challenges in development, including data limitations and computational costs. The study highlights ethical considerations such as technology misuse and regulatory challenges. Additionally, we delved into AI Planning techniques in NLP, covering classical planning, probabilistic planning, hierarchical planning, temporal planning, knowledge-driven planning, and neural planning models. These planning approaches are vital in achieving specific goals in NLP tasks. In conclusion, we provide a concise overview of the current state of generative AI, including its challenges, ethical considerations, and potential applications, contributing to the academic discourse on human-computer interaction.  
A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends Jillahi, Kamal Bakari; Iorliam, Aamo
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10350

Abstract

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.
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.
Exploring Explainability in Multi-Category Electronic Markets: A Comparison of Machine Learning and Deep Learning Approaches Adamu, Suleiman; Iorliam, Aamo; Asilkan, Özcan
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.3048-3719-58

Abstract

Artificial intelligence can change many industries as a global phenomenon. Over the years, this transformation has supported Electronic Markets in reengineering the processes and activities that take place in traditional markets, focusing on improving transaction effectiveness and efficiency. While our dependence on intelligent machines continues to grow, the demand for more transparent and interpretable models equally grows. Thus, explanations for machine decisions and predictions are needed to justify their reliability, which requires greater interpretability and often elaborates the need to understand the algorithms' underlying mechanism. This paper, therefore, proposed models based on Decision Tree (DT), Long Short-Term Memory (LSTM), and an ensemble of the two aforementioned models for improving CLV accuracy, interpretability, and explainability of AI-based models in the multi-category electronic market. An open-source e-commerce Behavior Data from a multi-category store, previously used by similar studies on XAI and CLV, was used in this experiment, ensuring the robustness of the product prediction and explanations and fair comparison. From the results, the models from this study demonstrated remarkable performance in terms of minimal error rates of MAE, MSE, and RMSE, with LSTM outperforming the other models. Regarding explainability and interpretation, the begin_time is ranked as the most relevant feature in CLV prediction.
Co-Authors Adamu, Suleiman Asilkan, Ozcan Ayem, Gabriel Terna Ingio, Joseph Abunimye Jillahi, Kamal Bakari Nsang, Augustine Shey