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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 81 Documents
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Search results for , issue "Vol 14, No 1: February 2025" : 81 Documents clear
Chinese paper classification based on pre-trained language model and hybrid deep learning method Luo, Xin; Mutalib, Sofianita; Syed Aris, Syaripah Ruzaini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp641-649

Abstract

With the explosive growth in the number of published papers, researchers must filter papers by category to improve retrieval efficiency. The features of data can be learned through complex network structures of deep learning models without the need for manual definition and extraction in advance, resulting in better processing performance for large datasets. In our study, the pre-trained language model bidirectional encoder representations from transformers (BERT) and other deep learning models were applied to paper classification. A large-scale chinese scientific literature dataset was used, including abstracts, keywords, titles, disciplines, and categories from 396 k papers. Currently, there is little in-depth research on the role of titles, abstracts, and keywords in classification and how they are used in combination. To address this issue, we evaluated classification results by employing different title, abstract, and keywords concatenation methods to generate model input data, and compared the effects of a single sentence or sentence pair data input methods. We also adopted an ensemble learning approach to integrate the results of models that processed titles, keywords, and abstracts independently to find the best combination. Finally, we studied the combination of different types of models, such as the combination of BERT and convolutional neural networks (CNN), and measured the performance by accuracy, weighted average precision, weighted average recall, and weighted average F1 score.
A page rank-based analytical design of effective search engine optimization Srinivas, Vinutha Mysore; Halli Cheluvae Gowda, Padma Muthalambikasheta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp73-82

Abstract

Search engine optimization (SEO) is an important internet marketing strategy and process that facilitates maximizing an intended website’s visibility with search engine results. It is widely employed nowadays to improve traffic volume or quality from search engines to a particular website. Even though a significant number of publications imply the essential aspects of SEO, only a few provide generalized ideas to deal with the complex structure of the web. Also, the critical issues of content quality, site popularity, keyword density, and publicity factors were not much considered in the traditional ranking algorithms during SEO processes. This has negatively influenced the retrieval rate in the existing SEO techniques, and consequently, inadequate search results were obtained through search engines. Hence, the study considers web page ranking as a theoretical basis for the research and addresses these limitations in the existing system. It further improves SEO performance by introducing a unique web-page ranking strategic design to gain higher page rank results. The results of the investigational study show that the proposed system effectively contributes towards SEO with an improved page ranking strategy and also provides higher accuracy in calculating the importance score of web pages which is comparable with popular ranking algorithms such as hyperlink-induced topic search (HITS) and PageRank.
A novel ensemble-based approach for Windows malware detection Verma, Vikas; Malik, Arun; Batra, Isha; Hosen, A. S. M. Sanwar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp327-336

Abstract

The exponential growth of internet-connected devices, particularly accelerated by the COVID-19 pandemic, has brought forth a critical global challenge: safeguarding the security of transmitted information. The integrity and functionality of these devices face significant threats from various forms of malware, leading to behavioral distortions. Consequently, a vital aspect of cybersecurity entails accurately identifying and classifying such malware, enabling the implementation of appropriate countermeasures. Existing literature has explored diverse approaches for malware identification, encompassing static and dynamic analysis techniques like signature-based, behavior-based, and heuristic-based methods. However, these approaches face a key issue of inadequately identifying unknown malware variants, often resulting in misclassifications of new strains as benign. To tackle this challenge, this study introduces a novel ensemble-based approach for identifying and classifying malware on Windows platforms, with a specific focus on detecting new and previously unknown variants. The proposed approach leverages multiple machine learning schemes to identify elusive unknown malware that proves challenging for existing methods. 
Large language models-based metric for generative question answering systems Abdel Azim, Hazem; Tharwat Waheed, Mohamed; Mohammed, Ammar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp151-158

Abstract

In the evolving landscape of text generation, which has advanced rapidly in recent years, techniques for evaluating the performance and quality of the generated text lag behind relatively. Traditionally, lexical-based metrics such as bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), consensus-based image description evaluation (CIDER), and F1 have been utilized, primarily relying on n-gram similarity for evaluation. In recent years, neural and machine-learning-based metrics, like bidirectional encoder representations from transformers (BERT) score, key phrase question answering (KPQA), and BERT supervised training of learned evaluation metric for reading comprehension (LERC) have shown superior performance over traditional metrics but suffered from a lack of generalization towards different domains and requires massive human-labeled training data. The main contribution of the current research is to investigate the use of train-free large language models (LLMs) as scoring metrics, evaluators, and judges within a questionanswering context, encompassing both closed and open-QA scenarios. To validate this idea, we employ a simple zero-shot prompting of Mixtral 8x7 B, a popular and widely used open-source LLM, to score a variety of datasets and domains. The experimental results on ten different benchmark datasets are compared against human judgments, revealing that, on average, simple LLMbased metrics outperformed sophisticated state-of-the-art statistical and neural machine-learning-based metrics by 2-8 points on answer-pairs scoring tasks and up to 15 points on contrastive preferential tasks.
High body temperature detection solution through touchless machine for health monitoring Swami, Siddharth; Mohan Joshi, Lalit; Ismail Iqbal, Mohammed; Sharma, Meera; Jeet Rawat, Amar; Dev Sharma, Sameer; Singh, Rajesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp166-171

Abstract

The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.
Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification Mara, Muhlasah Novitasari; Hidayat, Sidiq Syamsul; Putri, Farika Tono; Rahmawati, Dwi; Wahyuni, Sri; Prabowo, Muhamad Cahyo Ardi; Kabir, Noer Ni'mat Syamsu; Indra, Ragil Tri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp62-72

Abstract

This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
Performance analysis of a neuromodel for breast histopathology decision support system Ojo, Adedayo Olukayode; Sola, Adetoro Mayowa; Ojo, Florence Omolara; Onibonoje Oluwafemi, Moses
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp102-108

Abstract

Breast cancer detection and diagnosis are crucial in reducing mortality rates among women globally. This research article explores an artificial intelligence technique for early breast cancer detection, aiding doctors in making informed decisions for improved patient management. The study employs histopathological analysis of breast tissue microscopically to detect abnormalities, with the aim of categorizing normal tissue, benign lesions, in situ carcinoma, and invasive carcinoma. The proposed technique utilizes an artificial neural network trained using the resilient backpropagation algorithm (RP_ANN). The study further compares the observed performance with those of three other algorithms, including gradient descent algorithm (GDA_ANN), Levenberg-Marquardt algorithm (LM_ANN), and layer sensitivity-based (LSB_ANN) algorithm based on various evaluation metrics. RP_ANN and LSB_ANN demonstrated superior performance, with high validation and training variance accounted for (VAF) and low root mean squared error (RMSE). The results underscore the potential of deep learning-based algorithms for improving breast cancer detection, promising better patient outcomes and enhanced diagnostic accuracy.
An ontology-based knowledge modeling for the rite of Bai Sri Su Kwan: a ritual of the Greater Mekong Subregion Hoaihongthong, Suwannee; Chaichuay, Vispat; Kwiecien, Kanyarat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp788-797

Abstract

The development of ontologies is crucial in digital humanities research. This study focuses on creating a system to extract meaning from knowledge related to the Bai Sri Su Kwan ritual. Addressing semantic gaps, the second phase of our research outlines methods for developing an ontology for Bai Sri Su Kwan rituals. To fully understand this significant ritual in the Mekong Basin, we employed a theoretical framework with seven ontology development steps, using the Hozo Ontology Editor. Our ontology includes nine main classes: Bai Sri Su Kwan (A ritual subclass), persons, chants, belief, purpose, wish, literature, locations, and equipment. The Bai Sri Su Kwan subclass connects with all other classes in the ontology. This ontology forms the basis for a meaning search system for the Bai Sri Su Kwan ceremony in future research stages. The ontology was evaluated syntactically through human assessment and the OOPS! Ontology Pitfall Scanner. Validation results for the Bai Sri Su Kwan Ontology show no pitfalls in critical dimensions, indicating high integrity and reliability.
The crucial role of artificial intelligence in addressing climate change Andrade-Arenas, Laberiano; Hernández Celis, Domingo; Yactayo-Arias, Cesar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp1-11

Abstract

Addressing climate change is one of the fundamental priorities at a global level, given its significant impact on both the environment and society. This systematic literature review explores the role of artificial intelligence (AI) in addressing climate change. It identified applications, contributions to predicting extreme events, techniques used, ethical challenges, and associated biases. The rapid systematic literature review (RSL) was conducted using databases such as Scopus, Dimensions, directory of open access journals (DOAJ), and IEEE Xplore. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was used to ensure the completeness and transparency of the analysis. 40 articles were selected that were published between 2018 and 2023 and addressed AI in climate change. The findings show that AI is being used to predict and mitigate extreme climate events, estimate the greenhouse effect, and predict temperatures. In addition, innovative techniques such as hybrid machine learning models, convolutional neural networks, artificial neural networks, support vector machines, and logistic regression. In conclusion, AI offers a promising approach to addressing climate change, with transformative potential in predicting and mitigating its effects. However, continuous ethical considerations are required to guarantee its conscientious and efficient utilization.
Imitation of the human upper limb by convolutional neural networks Useche Murillo, Paula; Jimenez Moreno, Robinson; Martinez Baquero, Javier Eduardo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp193-203

Abstract

The paper outlines the development of an algorithm focused on imitating movements of a human arm and replicating strokes generated by the user's hand within a working environment. The algorithm was crafted to discern the position of either the user's left or right arm, tracking each section (fingers, wrist, elbow, and shoulder) through a detection and tracking system. These movements are then replicated onto a virtual arm, simulating the actions of a cutting tool, generating strokes as it moves. Convolutional neural networks (CNNs) were employed to detect and classify each arm section, while geometric analysis determined the rotation angles of each joint, facilitating the virtual robot's motion. The stroke replication program achieved an 84.2% accuracy in stroke execution, gauged by the closure of the polygon, distance between initial and final drawing points, and generated noise, which was under 10%, with a 99% probability of drawing a closed polygon. A Fast region-based convolutional neural network (Fast R-CNN) network detected each arm section with 60.2% accuracy, producing detection boxes with precision ranging from 17% to 59%. Any recognition shortcomings were addressed through mathematical estimation of missing points and noise filters, resulting in a 90.4% imitation rate of human upper limb movement.

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