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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
Towards efficient knowledge extraction: Natural language processing-based summarization of research paper introductions Chaudhari, Nikita; Vora, Deepali; Kadam, Payal; Khairnar, Vaishali; Patil, Shruti; Kotecha, Ketan
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.pp680-691

Abstract

Academic and research papers serve as valuable platforms for disseminating expertise and discoveries to diverse audiences. The growing volume of academic papers, with nearly 7 million new publications annually, presents a formidable challenge for students and researchers alike. Consequently, the development of research paper summarization tools has become crucial to distilling crucial insights efficiently. This study examines the effectiveness of pre-trained models like text-to-text transfer transformer (T5), bidirectional encoder representations from transformers (BERT), bidirectional and auto-regressive transformer (BART), and pre-training with extracted gap-sentences for abstractive summarization (PEGASUS) on research papers, introducing a novel hybrid model merging extractive and abstractive techniques. Comparative analysis of summaries, recall-oriented understudy for gisting evaluation (ROUGE) and bilingual evaluation understudy (BLEU) score evaluations and author evaluation help evaluate the quality and accuracy of the generated summaries. This advancement contributes to enhancing the accessibility and efficiency of assimilating complex academic content, emphasizing the importance of advanced summarization tools in promoting the accessibility of academic knowledge.
Microarray gene expression classification: dwarf mongoose optimization with deep learning Balaraman, Shyamala Gowri; Nair, Anu H.; Kumar, Sanal
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.pp213-221

Abstract

The deoxyribonucleic acid (DNA) microarray model holds significant promise for revealing expression data from thousands of genes. It serves as a valuable tool for investigating gene expressions in diverse biological research fields. This study explores advancements in gene selection for cancer detection through artificial intelligence, with a focus on the challenge of extracting pertinent information from vast databases. The application of deep learning architecture in detecting chronic diseases and aiding medical decision-making has proven effective across various domains. Therefore, this study designs an enhanced microarray gene expression classification by utilizing a dwarf mongoose optimization with deep learning (MGEXC-DMODL) approach. The MGEXC-DMODL approach intends to classify the microarray gene expression (MGE). For this, the MGEXC-DMODL technique initially applies the wiener filtering (WF) technique to eradicate the noise. In addition, the MGEXC-DMODL technique employs a deep residual shrinkage network (DRSN) to learn feature vectors. Meanwhile, the convolutional autoencoder (CAE) model was executed for identifying and classifying the MGE data. Furthermore, the dwarf mongoose optimization (DMO)-based hyperparameter tuning is performed to enhance the detection outcomes of the CAE model. The investigational evaluation of the MGEXC-DMODL model is validated using a benchmark database. The comprehensive comparison outcome highlighted the betterment of the MGEXC-DMODL model over recent approaches. 
Machine learning methods for classification and prediction information security risk assessment Muhammad, Alva Hendi; Nasiri, Asro; Harimurti, Agung
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.pp457-465

Abstract

Information is an essential company asset that must be protected. The value of information assets depends on the type and scale of the business and its role in delivering services. One of the primary programs that can help identify areas of improvement and guide the development of security awareness programs is risk assessment. Managing cybersecurity risks is critical to protecting enterprises from developing cyber threats and promoting resilience. This includes detecting, assessing, and mitigating risks to protect sensitive data, systems, and networks. While cybersecurity risk management is challenging, organizations may improve their security posture. This paper seeks to contribute to the field of information security risk assessment by leveraging the power of machine learning to provide quick, cost-effective, and individualized risk assessments for small and medium enterprises. Specifically, we extend the evaluation for security level classification by utilizing a support vector machine, random forest, and gradient boosting algorithms. The results demonstrate how well the model detects significant cases while reducing false positives. The model’s exceptional precision ensures that its identifications are dependable, while the high recall demonstrates that it accurately detects relevant data. Precision is critical in security risk assessment because a false positive result might have profound effects.
A systematic analysis on machine learning classifiers with data pre-processing to detect anti-pattern from source code Akhter, Nazneen; Khatun, Afrina; Rahman, Md. Sazzadur; Sanwar Hosen, A. S. M.; Shahidul Islam, Mohammad
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.pp376-384

Abstract

Automatic detection of anti-patterns from source code can reduce software maintenance costs massively. Nowadays, machine learning approaches are very commonly used to identify anti-patterns. Hence, it is very crucial to choose a classifier that can be useful for detecting anti-patterns. This work aims to help practitioners to choose a suitable classifier to detect anti-patterns. In this paper, we highlight 16 classifiers in four different categories to detect anti-patterns. Furthermore, the performance of these classifiers is identified with the data pre-processing (DPP) to detect four commonly occurring anti-patterns from the three commonly used open-source Java projects’ source code. The accuracy of Dagging classifiers is 98.4%. Kernel logistic regression (KLR) also performs well i.e., 97%. In the case of time complexity, naive Bayes (NB), decision trees (DT), support vector machines (SVM), library for support vector machines (LibSVM), logistic, and LightGBM (LB) have less time complexity to build a model in all the projects.
A Fletcher-Reeves conjugate gradient algorithm-based neuromodel for smart grid stability analysis Ojo, Adedayo Olukayode; Eyitayo, Aiyedun Olatilewa; Onibonoje, Moses Oluwafemi; Gbadamosi, Saheed Lekan
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.pp159-165

Abstract

Interest in smart grid systems is growing around the globe as they are getting increasingly popular for their efficiency and cost reduction at both ends of the energy spectrum. This study, therefore, proposes a neuro model designed and optimized with the Fletcher-Reeves conjugate gradient algorithm for analyzing the stability of smart grids. The performance results achieved with this algorithm was compared with those obtained when the same network was trained with other algorithms. Our results show that the proposed model outperforms existing techniques in terms of accuracy, efficiency, and speed. This study contributes to the development of intelligent solutions for smart grid stability analysis, which can enhance the reliability and sustainability of power systems.
Machine learning for the detection of soil pH, macronutrients, and micronutrients with crop and fertilizer recommendations Montañez, John Joshua; Sarmiento, Jeffrey
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.pp439-446

Abstract

The study aims to determine the levels of soil parameters such as soil pH, macronutrients, and micronutrients. After determining said parameters, the system appropriately recommends crops and fertilizers suitable for the soil samples. For soil pH and macronutrient levels, i.e., nitrogen, phosphorus, and potassium, these parameters can be detected using the soil test kit. Meanwhile, for soil micronutrients, i.e., copper, iron, and zinc, there is a need for the development of appropriate assays for colorimetric processes that can be done for the appropriate determination of said micronutrients. Comparison of available machine learning such as support vector machine algorithm, naïve Bayes algorithms, and K-nearest neighbor algorithm is a must to determine the well-fit algorithm that is considered fast and has high predictive power in classification and regression. The outputs of the colorimetric and spectrometric processes are the inputs in the machine learning activities intended for crop and fertilizer recommendation.
Efficient reconfigurable parallel switching for low-density parity-check encoding and decoding Venkatesh, Divyashree Yamadur; Mallikarjunaiah, Komala; Srikantaswamy, Mallikarjunaswamy
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.pp260-269

Abstract

In the evolution of next-generation communication systems, the demand for higher data integrity and transmission efficiency has brought low-density parity-check (LDPC) codes into focus, particularly for their error-correcting prowess. Traditional LDPC encoding and decoding techniques, such as the belief propagation (BP), Min-Sum, and Sum-Product algorithms, are hampered by high computational complexity and latency. Our research introduces a groundbreaking approach: an efficient, reconfigurable highspeed parallel switching operation for a complexity-optimized low-density parity-check encoding and decoding model (CoLDPC-EC). This method leverages advanced parallel processing and reconfigurable computing to drastically enhance operational speed and efficiency. It significantly outperforms conventional algorithms by optimizing key parameters like decoding throughput and power consumption, ensuring swift, energy-efficient error correction ideal for cutting-edge communication technologies. Our comparison with traditional methods underscores our solution's superior speed, flexibility, and efficiency, promising a leap forward in reliable, highspeed data transmission for next-generation networks. As per the simulation analysis, the proposed system shows better performance compared to conventional methods by 10.35%, 3.56%, and 2.36% in terms of decoding throughput, power consumption, and energy efficiency error correction, respectively.
Advancing integrity and privacy in cloud storage: challenges, current solutions, and future directions Shrinivasa, Shrinivasa; Beturpalya Muddaraju, Chandrakala; Prashanth Patil, Annapurna
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.pp12-18

Abstract

The rapid expansion of cloud computing has steered in an era where cloud storage is increasingly prevalent, offering significant advantages in terms of reducing local storage burden. However, this technological shift has also introduced complex security challenges, including data integrity and privacy concerns. In response to these challenges, various data integrity auditing (DIA) protocols have been developed, aiming to enable efficient and secure verification of data stored in cloud environments. This survey paper provides a comprehensive analysis of existing DIA mechanisms, focusing on methods like homomorphic linear authentication, dynamic hash tables, and watermarking techniques for integrity and privacy preservation. It critically evaluates these methods in terms of their advantages, limitations, and the unique challenges they face in practical applications, such as scalability, efficiency in multi-owner contexts, and real-time auditing. Furthermore, the paper identifies key research gaps, including the need for optimizing largescale data handling, balancing watermarking imperceptibility with embedding capacity, and developing comprehensive solutions for decentralized public auditing. The survey serves as a critical resource for researchers to understand the current background of cloud data integrity auditing and the future directions in this evolving field.
Improved adaptive multi-threshold method for automatic identification of rhinosinusitis in paranasal sinus images Putra, Ondra Eka; Sumijan, Sumijan; Tajuddin, Muhammad
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.pp119-129

Abstract

Rhinosinusitis, characterized by inflammation of the mucosa or mucous membrane within the paranasal sinuses, anatomical cavities situated in the facial bones, is the focus of this investigation. This study employs computed tomography (CT)-scan images comprising sagittal slices of the paranasal sinuses, acquired through a CT device featuring a Philips Ingenuity CT model MRC880 tube type, identified by tube serial number 163889, with a pixel value resolution of 0.24 mm. The primary objective of this research is to automatically identify and delineate rhizosinusitis-affected areas. This involves the application of multi-threshold values during the segmentation process, utilizing the improved adaptive multi-threshold (IAMT) segmentation method. The research dataset encompasses 380 slices of CTscans derived from 10 patients displaying indications of rhinosinusitis. Analysis of the test results reveals that the smallest observed rhinosinusitis size in this study is 0.05 cm2 on the right side, while the largest size measures 1.81 cm2 , yielding an accuracy rate of 96.66%. The magnitude of rhinosinusitis sizes serves as an indicative measure of the extent of inflammation within the paranasal sinus region, thereby suggesting a potential need for more intensive treatment interventions for the affected patients.
Accurate prediction of chronic diseases using deep learning algorithms Cordova, Ronald S.; Maata, Rolou Lyn R.; Jawarneh, Malik; Alshar'e, Marwan I.; Agustin, Oliver C.
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.pp570-583

Abstract

In this paper, the researchers studied the effects of different activation functions in hidden layers and how they impact the overfitting or underfitting of the model in the multiclass prediction of chronic diseases. This paper also evaluated the effects of varying the number of layers, and hyperparameters and its impact on the accuracy of the model and its generalization capabilities. It was found that exponential linear unit (ELU) does not have a significant advantage over rectified linear unit (ReLU) when used as an activation function in the hidden layer. Additionally, the performance of softmax function, when used in the output layer, is the same as a classic sigmoid output activation function. In terms of the ability of the model to generalize, the researchers achieved a classification accuracy of 100% when the trained model was used to predict unseen data. Through this research, the researchers should be able to assist medical professionals and practitioners in Oman in the validation and diagnosis of chronic diseases in clinics and hospitals.

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