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Research Article
Space of Reasoning of Individual Common Sense in Cognitive Architecture AGICA
Sergii Kornieiev*
Issue:
Volume 9, Issue 1, June 2025
Pages:
1-15
Received:
9 February 2025
Accepted:
28 February 2025
Published:
29 April 2025
Abstract: This article examines the problem of forming basic concepts in robots within the framework of the development of Artificial General Intelligence (AGI). The theories of concept formation in infants were reviewed. There is the consensus that in this early period of human cognitive development the basic concepts and reasoning are established that was named “common sense”. In the article common sense will be seen from two perspectives: individual and collective ones. This article is devoted to the formation of individual concepts of common sense in robots. In the set of individual concepts of common sense are being discussed here spatial and temporal conceptual domains prevail. Collective concepts of common sense will be mentioned briefly. In this phase of AGI development the proposed concepts formation procedure means mostly a-priori concepts that are preliminary designed as the software procedures and only “is grounded” in robot. But these “first concepts” create the basis for further learning procedures. As the platform for conceptualization procedure the cognitive architecture AGICA was considered. Cognitive architecture AGICA was represented by the author in 2023 on the base of “axiomatic approach” in AGI development. In cognitive architecture AGICA there were used the models of AGI-Consciousness, AGI-Individual Type, AGI-Collective Type in the framework of “grounded cognition”. AGI-Individual Type is based on “instinct of self-preservation” (survival instinct). AGI-Collective Type is based on “species preservation instinct”. Given that we are now far from a general theory of concepts, the development of transport robots is viewed as an application domain. This article does not claim to be comprehensive and can be viewed as some “engineering approach” for problem solving, - thus it is mostly addressed to the developers. In the main part of the article we will consider only perceptual/modal/concrete concepts. Amodal/abstract concepts will be out of the discussion.
Abstract: This article examines the problem of forming basic concepts in robots within the framework of the development of Artificial General Intelligence (AGI). The theories of concept formation in infants were reviewed. There is the consensus that in this early period of human cognitive development the basic concepts and reasoning are established that was ...
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Research Article
Amharic Language Hate Speech Detection on Social Media
Beyene Kassa Wondie,
Ermias Melku Tadesse*,
Tarekegn Walle Yirdaw
Issue:
Volume 9, Issue 1, June 2025
Pages:
16-21
Received:
10 March 2025
Accepted:
31 March 2025
Published:
9 May 2025
Abstract: Social media platforms enable rapid communication, information sharing, and opinion expression. However, their misuse for hate speech targeting race, religion and political differences has become a growing concern. This issue is particularly sensitive for underrepresented languages like Amharic, a Semitic language with the second-largest number of speakers after Arabic and the working language of Ethiopia. This study addresses the challenge of detecting hate speech in Amharic text by analyzing posts and comments from Facebook, YouTube, and Twitter. A dataset of 7,590 labeled entries was collected using the Face pager tool, focusing on hate speech related to race, religion, politics, and neutral content. The dataset was annotated with the guidance of researchers, legal experts, and language specialists. Preprocessing techniques, including data cleaning, tokenization, and normalization, were applied, and feature extraction was performed using embedding layers. The dataset was split into training (80%), validation (10%), and testing (10%) sets. Several deep learning models LSTM, BiLSTM, GRU, BiGRU, and RoBERTa were developed and evaluated using precision, recall, F1-score, and accuracy metrics. The RoBERTa model outperformed others, achieving an accuracy of 91%. This research highlights the effectiveness of advanced deep learning techniques in detecting Amharic hate speech, offering a valuable tool for mitigating this critical issue in Ethiopian social media contexts.
Abstract: Social media platforms enable rapid communication, information sharing, and opinion expression. However, their misuse for hate speech targeting race, religion and political differences has become a growing concern. This issue is particularly sensitive for underrepresented languages like Amharic, a Semitic language with the second-largest number of ...
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Research Article
Integrating AI and Remote Sensing in Precision Agriculture for Advancing Sustainable Irrigation Monitoring and Management in Ethiopia
Belachew Muche Mekonen*
Issue:
Volume 9, Issue 1, June 2025
Pages:
22-29
Received:
2 April 2025
Accepted:
15 April 2025
Published:
9 May 2025
Abstract: Agriculture is the backbone of Ethiopia’s economy, yet it remains highly vulnerable to climate variability due to its heavy dependence on rainfed farming. Although the country possesses significant irrigation potential, only a small portion is utilized. This study explores the integration of Artificial Intelligence (AI) and remote sensing technologies to improve irrigation efficiency, enhance water management, and boost agricultural productivity in Ethiopia. By leveraging tools such as satellite imagery, drones, and Internet of Things (IoT) sensors alongside AI-driven models, the research aims to optimize irrigation scheduling, reduce water waste, and increase crop yields. The proposed approach combines AI techniques—such as Artificial Neural Networks (ANN) and Random Forest (RF)—with remote sensing indicators, including the Normalized Difference Vegetation Index (NDVI), Soil Moisture Index (SMI), and Land Surface Temperature (LST). These tools were used to forecast irrigation needs based on key environmental factors such as temperature, rainfall, and soil moisture while monitoring crop health and identifying water-stressed areas. This integrated system provides a predictive framework for data-driven irrigation planning, enhancing water productivity, and promoting sustainable agricultural practices. Two case studies were conducted to evaluate the effectiveness of the AI-based irrigation system. The first study, in Ethiopia’s Awash Basin, examined large-scale irrigation systems, while the second focused on traditional smallholder farming practices in the Rift Valley. Results showed that the AI-driven approach reduced water consumption by 18% and increased crop yields by 11% compared to inconsistent outcomes and water inefficiencies observed under traditional methods. Despite these promising results, several challenges were identified that limit the widespread adoption of these technologies. These include limited access to high-quality data, frequent cloud cover affecting satellite imagery, a shortage of technical expertise among farmers, and financial barriers to acquiring advanced tools. In addition, rural infrastructure deficits restrict the use of IoT sensors and real-time data collection. The study recommends targeted strategies to address these issues: investing in digital and IoT infrastructure, developing low-cost and user-friendly AI tools, and providing training programs to build local capacity. Furthermore, enhancing AI interpretability and creating mobile platforms tailored to farmers' needs can increase trust and usability. Policy support and public-private partnerships are also essential to scaling these innovations nationwide. In conclusion, integrating AI and remote sensing holds great potential to transform irrigation practices in Ethiopia, making agriculture more resilient to climate change and contributing to national food security through sustainable water use and increased productivity.
Abstract: Agriculture is the backbone of Ethiopia’s economy, yet it remains highly vulnerable to climate variability due to its heavy dependence on rainfed farming. Although the country possesses significant irrigation potential, only a small portion is utilized. This study explores the integration of Artificial Intelligence (AI) and remote sensing technolog...
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Research Article
A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images
Suresh Babu Nettur,
Shanthi Karpurapu*,
Unnati Nettur,
Likhit Sagar Gajja,
Sravanthy Myneni,
Akhil Dusi,
Lalithya Posham
Issue:
Volume 9, Issue 1, June 2025
Pages:
30-45
Received:
31 March 2025
Accepted:
9 April 2025
Published:
24 May 2025
Abstract: Early detection of COVID-19 plays a vital role in enabling timely treatment and curbing the spread of the virus. This study introduces a novel hybrid deep learning model tailored to identify COVID-19 infections from chest CT scan images, aiming to support healthcare professionals facing overwhelming diagnostic demands. Our approach integrates the strengths of three pre-trained convolutional neural networks namely VGG16, DenseNet121, and MobileNetV2, each known for their robust feature extraction capabilities. These models independently extract deep features from input CT images, capturing both low-level and high-level representations essential for accurate classification. To address potential redundancy and reduce the computational burden, Principal Component Analysis (PCA) is employed for dimensionality reduction. The refined feature vectors from all three models are then concatenated to form a comprehensive feature representation, which is subsequently passed to a Support Vector Classifier (SVC) for final classification. Our hybrid architecture enables the model to leverage the complementary strengths of each CNN while maintaining efficiency. We evaluated our proposed model on a dataset consisting of 2,108 training images and 373 test images, comprising both COVID-positive and non-COVID samples. Comparative analysis with individual CNN models showed that our hybrid model achieved superior performance, reaching an accuracy of 98.93%. It also outperformed standalone models in precision, recall, F1-score, and ROC-AUC, highlighting its potential as a highly reliable and efficient diagnostic aid.
Abstract: Early detection of COVID-19 plays a vital role in enabling timely treatment and curbing the spread of the virus. This study introduces a novel hybrid deep learning model tailored to identify COVID-19 infections from chest CT scan images, aiming to support healthcare professionals facing overwhelming diagnostic demands. Our approach integrates the s...
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Research Article
Comparative Analysis of ARIMA and LSTM Models for Temperature Forecasting in Semi-Arid Regions: A Case Study of Machakos County, Kenya
Noah Mutavi Mutiso*,
Martin Mutwiri Kithinji,
Victor Muthama Musau
Issue:
Volume 9, Issue 1, June 2025
Pages:
46-54
Received:
28 April 2025
Accepted:
16 May 2025
Published:
3 June 2025
DOI:
10.11648/j.ajai.20250901.15
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Views:
Abstract: Accurate temperature forecasting is vital for agriculture, disaster management, and climate resilience, particularly in semi-arid regions like Machakos County, Kenya. Traditional forecasting models, such as the Auto Regressive Integrated Moving Average (ARIMA), have been widely used for both short- and long-term temperature predictions. However, with advancements in machine learning, there is a growing need to evaluate how modern methods compare to traditional approaches. This research focused on comparing the predictive accuracy of ARIMA and the Long Short-Term Memory (LSTM) model, a deep learning algorithm that uses a gating mechanism to capture non-linear patterns in data. The study used Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Diebold-Mariano statistical test to evaluate and compare the performance of models. Temperature data was obtained from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resource (NASA POWER) database, using GPS coordinates to retrieve location-specific data for Machakos County. To ensure robust analysis, Python libraries such as Statsmodels, Pandas, TensorFlow, NumPy, Keras, and Matplotlib were used for data processing, fitting of models, and visualization of results. Findings showed that LSTM performed better in long-term predictions, achieving higher accuracy for 30-day forecasts, while both models performed significantly equivalently in the short term of 7 days. These findings highlight the complementary strengths of traditional and deep learning models in addressing different forecasting needs.
Abstract: Accurate temperature forecasting is vital for agriculture, disaster management, and climate resilience, particularly in semi-arid regions like Machakos County, Kenya. Traditional forecasting models, such as the Auto Regressive Integrated Moving Average (ARIMA), have been widely used for both short- and long-term temperature predictions. However, wi...
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Research Article
AI Work Quantization Model: Closed-System AI Computational Effort Metric
Aasish Kumar Sharma,
Michael Bidollahkhani*,
Julian Martin Kunkel
Issue:
Volume 9, Issue 1, June 2025
Pages:
55-67
Received:
12 March 2025
Accepted:
1 April 2025
Published:
21 June 2025
DOI:
10.11648/j.ajai.20250901.16
Downloads:
Views:
Abstract: The rapid adoption of AI-driven automation in IoT environments, particularly in smart cities and industrial systems, necessitates a standardized approach to quantify AI’s computational workload. Existing methodologies lack a consistent framework for measuring AI computational effort across diverse architectures, posing challenges in fair taxation models and energy-aware workload assessments. This study introduces the Closed-System AI Computational Effort Metric, a theoretical framework that quantifies real-time computational effort by incorporating input/output complexity, execution dynamics, and hardware-specific performance factors. The model ensures comparability between AI workloads across traditional CPUs and modern GPU/TPU accelerators, facilitating standardized performance evaluations. Additionally, we propose an energy-aware extension to assess AI’s environmental impact, enabling sustainability-focused AI optimizations and equitable taxation models. Our findings establish a direct correlation between AI workload and human productivity, where 5 AI Workload Units equate to approximately 60-72 hours of human labor-exceeding a full-time workweek. By systematically linking AI computational effort to human labor, this framework enhances the understanding of AI’s role in workforce automation, industrial efficiency, and sustainable computing. Future work will focus on refining the model through dynamic workload adaptation, complexity normalization, and energy-aware AI cost estimation, further broadening its applicability in diverse AI-driven ecosystems.
Abstract: The rapid adoption of AI-driven automation in IoT environments, particularly in smart cities and industrial systems, necessitates a standardized approach to quantify AI’s computational workload. Existing methodologies lack a consistent framework for measuring AI computational effort across diverse architectures, posing challenges in fair taxation m...
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Review Article
Clinical Research and Artificial Intelligence: How AI Is Changing Clinical Research
Zomana Majid,
Md Shahidul Islam*
Issue:
Volume 9, Issue 1, June 2025
Pages:
68-79
Received:
5 April 2025
Accepted:
27 April 2025
Published:
26 June 2025
DOI:
10.11648/j.ajai.20250901.17
Downloads:
Views:
Abstract: Medicine is quickly transitioning as artificial intelligence (AI) adopts the new and improved type of machine learning for better diagnosis and treatment of diseases in the various sub-specialties of practice. The enhancement of computation rate raises the potential of AI algorithms and their value for multiple domains like radiology, where some experts suppose that AI can replace radiologists. These questions are essential when determining whether specific AI applications will eventually replace doctors or only assist them in their work within specific medical specialties. This paper ponders the iridescent role of AI in clinical trials and how its drug invention process will result from innovative learning in the future. The technology engages the role of AI in clinical decision support, the latest developments in precision medicine, and the prediction of drug properties and active ingredients. The paper showcases AI contributions to remodeling clinical trial designs and model exchange and using AI to carry out the intervention. Among other things, it speaks about AI in the acquisition of the EHR, in the course of authorization of the trial, and in addressing some of the significant challenges such as data availability and perpetual vigilance. In addition, a few discussions concerning clinical AI algorithm errors and the defects of conventional trial procedures are also incorporated at the end of the paper to portray the role of AI in present clinical research more sharply.
Abstract: Medicine is quickly transitioning as artificial intelligence (AI) adopts the new and improved type of machine learning for better diagnosis and treatment of diseases in the various sub-specialties of practice. The enhancement of computation rate raises the potential of AI algorithms and their value for multiple domains like radiology, where some ex...
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