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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 1,722 Documents
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Novel preemptive intelligent artificial intelligence-model for detecting inconsistency during software testing Govinda, Sangeetha; Prasanthi, B. G.; Vincent, Agnes Nalini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1781-1789

Abstract

The contribution of artificial intelligence (AI)-based modelling is highly significant in automating the software testing process; thereby enhancing the cost, resources, and productivity while performing testing. Review of existing AI-models towards software testing showcases yet an open-scope for further improvement as yet the conventional AI-model suffers from various challenges especially in perspective of test case generation. Therefore, the proposed scheme presents a novel preemptive intelligent computational framework that harnesses a unique ensembled AI-model for generating and executing highly precise and optimized test-cases resulting in an outcome of adversary or inconsistencies associated with test cases. The ensembled AI-model uses both unsupervised and supervised learning approaches on publicly available outlier dataset. The benchmarked outcome exhibits supervised learning-based AI-model to offer 21% of reduced error and 1.6% of reduced processing time in contrast to unsupervised scheme while performing software testing.
Depression and post traumatic stress disorder analysis with multi-modal data Sivanaiah, Rajalakshmi; Suseelan, Angel Deborah; Thannickal, Krupa Elizabeth; Pirabahar, Sanmati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2358-2366

Abstract

With an increasing global population and more people living to the age when major depressive disorder (MDD) or post traumatic stress disorder (PTSD) commonly occurs, the number of those who suffer from such disorders is rising. Studies have also shown a high likelihood of comorbidity between these 2 disorders. This comorbidity can worsen symptoms, increase the risk of chronicity, and complicate treatment, significantly impacting patients’ emotional wellbeing and social and occupational functioning. There is a need to enable faster and reliable diagnosis methods, while taking into account the subjectivity of individuals and the role of behavioural cues. The proposed approach analyses the combination of audio, video and text input features (multi-modal data) of the subject to determine the severity class of MDD and PTSD. The DistilBERT transformer is used for learning and building a model with the textual modality and random forest classifiers for the audio and video modalities. An ensemble of these 3 models from 3 modalities performs better in the final classification of MDD and PTSD when compared to individual models. This work also covers a comparison of the models with different splits on the dataset. This ensembled system shows an improved accuracy of 2% to 7% for the MDD and PTSD multi class classification over the models tested on individual modalities.
Artificial intelligence multilingual image-to-speech for accessibility and text recognition Rosalina, Rosalina; Fahmi, Hasanul; Sahuri, Genta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1743-1751

Abstract

The primary challenge for visually impaired and illiterate individuals is accessing and understanding visual content, which hinders their ability to navigate environments and engage with text-based information. This research addresses this problem by implementing an artificial intelligence (AI)-powered multilingual image-to-speech technology that converts text from images into audio descriptions. The system combines optical character recognition (OCR) and text-to-speech (TTS) synthesis, using natural language processing (NLP) and digital signal processing (DSP) to generate spoken outputs in various languages. Tested for accuracy, the system demonstrated high precision, recall, and an average accuracy rate of 0.976, proving its effectiveness in real-world applications. This technology enhances accessibility, significantly improving the quality of life for visually impaired individuals and offering scalable solutions for illiterate populations. The results also provide insights for refining OCR accuracy and expanding multilingual support.
Enhancing e-commerce personalization with review-based adaptive feature matching: a real-time approach Zareena, Noorbasha; Rao Balaga, Tarakeswara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2178-2184

Abstract

The widespread evolution of e-commerce platforms necessitates advanced personalization techniques to enhance user experience and satisfaction. Our paper introduces the review-based adaptive feature matching (R-AFM) algorithm, an innovative approach to real-time personalization in e-commerce settings. Leveraging the rich data from user reviews and product metadata available in the Amazon product review dataset, R-AFM dynamically adapts to user preferences and behaviors through a sophisticated feature matching process. The methodology encompasses data collection, feature extraction, user preference modeling, real-time recommendation generation, and an adaptive feedback loop. By analyzing historical review data alongside real-time user interactions, R-AFM updates preference weights for product features, thereby refining the personalization mechanism. This process culminates in the generation of highly personalized product recommendations. Comparative analysis with existing personalization methods-collaborative filtering (CF), content-based filtering (CBF), hybrid recommender systems (Hybrid RS), and deep learning-based recommender systems (DL-RS)-demonstrates R-AFM's superior performance improvement varying between 2 to 8% in terms of accuracy, precision, recall, and F1-score. The algorithm's unique capability to incorporate real-time feedback significantly enhances the e-commerce personalization landscape, offering promising avenues for future research and practical application.
Optimizing bioinformatics applications: a novel approach with human protein data and data mining techniques Thareja, Preeti; Singh Chhillar, Rajender
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2328-2337

Abstract

Biomedicine plays a crucial role in medical research, particularly in optimizing techniques for disease prediction. However, selecting effective optimization methods and managing vast amounts of medical data pose significant challenges. This study introduces a novel optimization technique, integrated bioinformatics optimization model (IBOM) for disease diagnosis, incorporating data mining to efficiently store large datasets for future analysis. Various optimization algorithms, such as whale optimization algorithm (WOA), multi-verse optimization (MVO), genetic algorithm (GA), and ant colony optimization (ACO), were compared with the proposed method. The evaluation focused on metrics like accuracy, specificity, sensitivity, precision, F-score, error, receiver operating characteristic (ROC), and false positive rate (FPR) using 5-fold cross-validation. Results indicated that the 5-fold cross-validation method achieved superior performance with metrics: 98.61% accuracy, 96.59% specificity, 88.63% sensitivity, 99.30% precision, 92.31% F-score, 10.80% error, 92.61% ROC, and a 3.00% FPR. This method was found to be the most effective, achieving an accuracy of 0.92 in disease diagnosis compared to other optimization techniques.
A review of recent deep learning applications in wood surface defect identification Ali, Martina; Hashim, Ummi Raba’ah; Kanchymalay, Kasturi; Wibawa, Aji Prasetya; Salahuddin, Lizawati; Rahiddin, Rahillda Nadhirah Norizzaty
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1696-1707

Abstract

Wood is widely used in construction, art, and home applications due to its aesthetic appeal and favorable mechanical properties. However, environmental factors significantly affect the growth and preservation of wood, often leading to defects that can reduce its performance and ornamental value. Researchers have introduced machine vision and deep learning methods to address the challenges of high labor costs and inefficiencies in identifying wood defects. Deep learning has shown great success in image recognition tasks, yielding impressive results. This paper reviews previous work on deep-learning strategies for identifying wood surface defects. It also discusses data augmentation techniques to address limited defect data and explores transfer learning to enhance classification accuracy on small datasets. Finally, the paper examines the potential limitations of deep learning for defect identification and suggests future research directions.
Ledger on internet of things: a blockchain framework for resource-constrained devices Jaganathan, Suresh; Veeramani, Karthika
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2506-2518

Abstract

The increasing use of resource-constrained devices such as the internet of things (IoT) in various applications has led to the need for an optimized blockchain framework for these devices. Blockchain-based IoT networks allow businesses to access and share IoT data within their organization without centralized authority. However, existing frameworks are not designed for IoT applications and lack features like decentralization, scalability, and network overhead. To overcome these limitations, a new blockchain framework is proposed: ledger on internet of things (LIoT), which has a new consensus-based leader election algorithm to address the challenges of existing algorithms with high block creation time and communication overhead. Moreover, a novel data structure has been developed to reduce the storage size of the ledger effectively. The proposed framework also employs a docker for deployment, which provides an efficient and easy setup of blockchain nodes without requiring the individual configuration of each machine, increases the efficiency of the consensus process, and enables convenient deployment and management of the blockchain framework on resource-constrained devices. Furthermore, the performance of the proposed consensus method is analyzed using various performance parameters, including CPU usage, memory usage, transaction execution time, and block generation time.
Multilayer stacking for polycystic ovary syndrome diagnosis Abu Taher, Kazi; Ahmed, Samia; Ferdous Esha, Jannatul; Rahman, Md. Sazzadur; Sanwar Hosen, A. S. M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1968-1975

Abstract

Polycystic ovary syndrome (PCOS) is a complicated hormonal condition that is experienced by women. Despite extensive research, the precise reason be hind PCOS remains unknown, and effective treatments are still lacking. Thus, early diagnosis and treatment have a significant positive impact on the health of women. Recently, there has been remarkable performance demonstrated by machine learning (ML)-based detection models for PCOS identification. They are fast and low cost compared to the traditional processes. In this work, a multi stacking PCOS detection model is proposed using K-fold cross validation. The model uses three different ML algorithms namely: na¨ıve Bayes (NB), ran dom forest (RF), and logistic regression (LR) as base classifiers and a neural network, multi-layer perception (MLP) as meta model. This approach utilizes two feature selection techniques and compares the performances on the stack ing methods. Among the two feature selection techniques, Pearson correlation approach performed better with average 98.79% accuracy, 99.17% sensitivity, 98.40% specificity, and 98.79% f1-score.
Application of the adaptive neuro-fuzzy inference system for prediction of the electrical energy production in Jakarta Nugraha, Yoga Tri; Cahyadi, Catra Indra; Rida, Rizkha; Ningsih, Margie Subahagia; Sholeha, Dewi; Roza, Indra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1790-1798

Abstract

Jakarta, as a rapidly growing urban area, faces challenges in balancing energy demand with supply while addressing environmental concerns associated with traditional energy sources. Electrical energy production prediction in urban environments like Jakarta is crucial for effective energy management, ensuring stable supply, and promoting sustainable development. The prediction of electrical energy production in Jakarta is critical for ensuring stable and sustainable energy supply. This research proposed the application of the adaptive neuro-fuzzy inference system (ANFIS) as a predictive tool specifically tailored for Jakarta's energy production prediction context. The research methodology used in this study is the ANFIS. Five levels make up the architecture of the ANFIS model: output, normalization, defuzzification, rule evaluation, and fuzzification. The fuzzification layer converts input variables into linguistic terms using membership functions, while the rule evaluation layer calculates the activation strength of each rule based on the input values. The predicted results of Jakarta electrical energy production from 2023 to 2028 are 65,288 GWh and there is an annual increase of 5.25%. The error contained in ANFIS is with a root mean square error (RMSE) value of 0.0001058% and a mean absolute percentage error (MAPE) value of 0.00875%.
Semantic based medical visual question answering with explainable artificial intelligence Noor Mohamed, Sheerin Sitara; Srinivasan, Kavitha; Gopalsamy, Raghuraman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2169-2177

Abstract

The medical visual question answering (MVQA) system takes the advantage of both computer vision (CV) and natural language processing (NLP) to accept the medical image and corresponding question as input and generates the respective answer as output. One step further, the MVQA system capable of generating the answer based on the semantics has a distinct place and hence semantic based medical visual question answering (SMVQA) system is proposed in this research. In SMVQA, the semantics for input image and question are generated using layerwise relevance propagation explainable artificial intelligence (LRP XAI) technique and the answer is derived using deductive reasoning method. For this, seven MVQA datasets are used for model creation, testing and validation. The training phase of the SMVQA system is implemented using VGGNet, long short-term memory (LSTM), LRP XAI, ResNet and bidirectional encoder representations from transformers (BERT) to generate a model file. Then the inference is derived in the testing phase based on the generated model file for the test set. Finally, the answer is derived from the inference using natural language toolkit (NLTK) library, term frequency-inverse document frequency (TF-IDF), cosine similarity, best match25 (BM25) techniques along with deductive reasoning. As a result, the proposed SMVQA system gives improved performance then the existing MVQA system especially for abnormality type samples.

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