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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 1,893 Documents
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The role of prompt engineering in enhancing LLMs: a systematic review of applications and ethical implications Fatawi, Izzul; Bilad, Muhammad Roil; Asy'ari, Muhammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1071-1086

Abstract

Large language models (LLMs) have transformed natural language processing (NLP), demonstrating exceptional proficiency in tasks such as text generation, translation, and summarization. However, LLMs are prone to generating biased, inaccurate, or contextually irrelevant outputs, posing significant risks in high-stakes domains such as healthcare, legal reasoning, and engineering. This paper systematically investigates the role of prompt engineering as a solution to these challenges. By strategically designing inputs, prompt engineering enhances LLM performance, yielding more accurate, contextually relevant, and ethically aligned outputs. Advanced techniques, including chain-of-thought (CoT) prompting and retrieval augmented generation (RAG), are examined for their ability to improve reasoning capabilities, reduce errors, and mitigate bias. CoT prompting facilitates structured, stepwise reasoning, while RAG incorporates real-time data, ensuring output accuracy in rapidly evolving fields. In addition, we present a novel comparative perspective on these techniques, highlighting their distinct strengths and limitations across specialized applications such as healthcare diagnostics and scientific data extraction. The findings demonstrate that sophisticated prompt engineering significantly elevates the reliability and precision of LLM outputs, while addressing critical ethical concerns such as data privacy, bias, and hallucination. These insights underscore the necessity of advanced prompt design in optimizing LLMs for high-impact applications, ensuring both performance and ethical integrity.
Automated bacteria and fungi classification using convolutional neural network on embedded system Bouganssa, Tarik; Ait Moulay, Maryem; Aarabi, Samar; Lasfar, Abedelali; EL Afia, Abdelatif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1132-1142

Abstract

In this study, we created and applied novel concepts for hardware-based image identification and categorization. For artificial intelligence (AI) and image recognition applications, this includes putting algorithms for recognizing colors, textures, and shapes into practice. Our contribution uses an embedded device with a camera and a microcomputer (Raspberry-Pi4 type) to replace the optical assessment of Petri dishes. Our object recognition system processes images efficiently by using a state-of-the-art kernel function and a new neighborhood architecture. Using the well-known convolutional neural network (CNN) architecture, YOLOv8, as a pre-trained model, we evaluated the proposed CNN-based method for object recognition in a number of demanding scenarios. Several Petri plates, uncontrolled settings, and different backgrounds and illumination were used to evaluate the technology. Our dynamic mode integrates a CNN network with an attention mask to highlight the traits of bacteria and fungi, ensuring robust recognition. We implemented our algorithm on a Raspberry Pi 400, connected to a CMOS 3.0 camera sensor and a human-machine interface (HMI) for instant display of results.
An efficient approach for cyber-attack detection by using machine learning and deep learning algorithms Shakir, Yasir Hussein; Abdelhamied, Mahmoud Mohamed; Aziz Awadh AL Mandhari, Eshaq; Alkhazraji, Ali; Reda, Naglaa M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1219-1235

Abstract

Cybercrime gained traction in the late 20th century. The capabilities of cyber-attackers have improved dramatically. One of the biggest challenges facing cybersecurity developers is safeguarding consumers' security and privacy. Interest in using AI approaches in cybersecurity has grown significantly because of the incredible proficiency these techniques have demonstrated across all domains. Even while machine learning algorithms are very effective at identifying malicious activity, there are still certain issues that lower performance accuracy. This paper has the novelty of deploying the Artificial Bee Colony (ABC) meta-heuristic algorithm with the K-Nearest Neighbors (KNN) classifier to detect cyber-attacks. It proposes a variant approach called KNN+Bee that detects attacks efficiently, achieving 99.86% overall accuracy. The NSL-KDD dataset of cyberattacks has been leveraged in the training and testing phases. The proposed approach has been contrasted with the most popular machine learning. According to experimental findings, the suggested model delves deeper into the identification of cyberattacks. It achieves unprecedented performance, outperforming other models in terms of precision, Recall, F-score and MCC. Furthermore, popular deep learning models have been implemented and examined on the same dataset. Results prove that GRU is the most accurate, reaching 99.71%.
Adaptive control of ball and beam system using SNA-PID combined with recurrent fuzzy neural network identifier Le, Minh-Thanh; Nguyen, Chi-Ngon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1202-1210

Abstract

The ball and beam system is a nonlinear and inherently unstable single input, multiple-output (SIMO) system, which poses significant challenges for control design. Intelligent control algorithms are often applied to autonomously control complex systems when there are changes in parameters or the control environment. Therefore, in this paper, we research and develop two methods: proportional integral derivative (PID) and single neuron adaptive (SNA)-PID-recurrent fuzzy neural network identifier (RFNNI) to control the ball and beam system. Simulation results on MATLAB/Simulink show that the SNA-PID-RFNNI controller provides a more stable output signal than the traditional PID controller, with minimal overshoot and a settling time of about 15 seconds. Next, we will conduct real-time experiments on the object using the proposed algorithm through the MEGA2560 control board with an ultrasonic positioning mechanism.
Blockchain-enabled framework using diversity mutation with siberian tiger optimization for offloading in fog computing Rajini, Srikanta Murthy; Shilpa, Reginald
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1371-1380

Abstract

Fog computing has developed as a promising framework to support latency sensitive internet of things (IoT) applications for mobile devices operating in dynamic environments. During the offloading process, malicious activities interrupt the existing methods, which increases the execution time. Therefore, this research proposes a diversity mutation with siberian tiger optimization (DM-STO) for computation offloading in blockchain based fog computing. The blockchain is used to secure offload and attain quality of service (QoS) mobile users with less energy consumption and execution time. The DM-STO can balance workloads among local devices and fog servers. The diversity mutation operation improves the exploration ability to dynamic network conditions, leading to efficient computational offloading in fog computing. The execution time, service cost and energy consumption are evaluated to calculate the performance of the proposed DM-STO with varying numbers of IoT requests such as 50, 100, 200, and 300. For 50 IoT requests with a fixed fog server of 10, the DM-STO achieves an execution time of 18 s, a service cost of 10$ and energy consumption of 5 mJ compared to the BAT algorithm.
Deep learning for early detection of cardiovascular diseases via auscultation sound classification Kasture, Shreyas; Maurya, Sudhanshu; Kumar Sharma, Amit; Chitraju Gopal Varma, Santhosh; Mirza, Kashish; Sadaf Mohammad Ismail, Firdous
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1746-1761

Abstract

Heart diseases are one of the most prominent causes of death globally, which requires immediate and accurate diagnosis. The auscultation methods used in conventional medical practice, where the doctor listens to the sounds produced by the body without intervention is very ineffective because of the limitations in the actual skills and perception of the doctor. The main goal of this project will be designing a mobile-based system for the early detection of cardiovascular disease (CVD) by utilizing deep learning for auscultation sound classification. The approach involves the use of deep learning structures to classify cardiac sounds into normal and abnormal patterns on its own. Wavelet transformations, time-frequency representations, and Mel frequency cepstral coefficients (MFCC) have been used in feature extraction. The ResNet152V2 model showed high classification performance with area under the receiver operating characteristic curve (AUROC) of 0.9797 and 0.9636 on two datasets. Contrary to that, data augmentation, hyperparameter optimization, attention mechanisms, as well as input-output residual connections, led to better functionality and interpretability. This research seeks to overcome the limitations of traditional stethoscope use through the incorporation of sophisticated algorithms and the availability of mobile technology that could result in early diagnosis and prevention of CVDs, especially in underprivileged areas.
Technical analysis model for stock prediction using a grammatical evolution algorithm Setyanegara, Aditya Kusuma; Sitanggang, Imas Sukaesih; Mushthofa, Mushthofa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1236-1246

Abstract

Stocks are a popular investment instrument but carry high risks, where investors may incur losses when stocks are bought at high prices and sold at lower prices. Technical analysis is used to study past stock price behavior to predict future prices. In this study, grammatical evolution (GE) is applied as an evolutionary computing technique to discover optimal functions or programs that represent historical stock price data. This study develops GE based prediction models by utilizing objective functions and search spaces defined through grammar. The model integrates technical indicators based on complex statistical models such as autoregressive integrated moving average (ARIMA), prophet, exponential smoothing, and Fibonacci retracements. Furthermore, this study employs GE to generate ensemble weights randomly, ensuring each model contributes equitably to the final prediction formula. Experiments were conducted using multiple stock datasets, including SMAR, S&P 500, the Johannesburg Stock Exchange (JSE), the New York Stock Exchange (NYSE), and Adani Enterprises (ADANIENT), to evaluate the model’s adaptability and generalization capability. The results demonstrate that the proposed GE model effectively captures complex market patterns and produces more reliable stock price predictions compared to deep learning-based approaches. Although GE requires greater computational time, the findings suggest that GE provides a flexible and effective framework for constructing hybrid stock price forecasting models in dynamic market environments.
Sentiment-aware user-item recommendation combining weighted XGBoost and optimized similarity metrics Bhogan, Snehal; Rajpurohit, Vijay S.; Sannakki, Sanjeev S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1851-1862

Abstract

User-item recommendation systems play a vital role in enhancing personalized digital experiences across e-commerce and social media platforms. Traditional recommendation approaches, such as collaborative filtering (CF) and content-based filtering (CBF), often suffer from challenges like data sparsity, cold-start issues, and limited contextual understanding. Sentiment-aware recommendation systems have emerged as a promising solution by incorporating emotional insights extracted from user reviews, thereby improving recommendation accuracy and personalization. This study proposes a novel sentiment-aware user-item recommendation system (SAUIRS) framework that integrates optimized term frequency inverse document frequency (O-TF-IDF), parameterized bidirectional encoder representations from transformers (P-BERT), weighted extreme gradient boosting (WXGBoost), and an optimized similarity metrics model. The optimized TF-IDF enhances feature selection, reducing dimensionality while preserving relevant textual information. P-BERT, a fine-tuned BERT model, improves sentiment classification accuracy by leveraging deep contextual embeddings. WXGBoost further refined sentiment predictions, addressing class imbalance and enhancing model robustness. The extracted sentiment information is incorporated into an optimized similarity metrics model to improve recommendation precision by aligning user preferences with sentiment-driven insights. Extensive experiments conducted on Amazon benchmark datasets demonstrate the superior performance in terms of accuracy, root mean square error (RMSE), and mean absolute error (MAE) of the proposed framework compared to state-of-the-art recommendation models.
Accurate stroke area classification using extreme gradient boosting with multi-feature extraction Praveen Kumar Rao, Kavikondala; Bondla, Maha Lakshmi; Srinivasa Rao, Bommaraju; Naveena, Ambidi; Balaramakrishna, K. V.; Goda, Srinivasarao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1390-1401

Abstract

Stroke, one of the most common neurological disorders leading to long-term disability and mortality, requires accurate detection of affected brain regions for timely treatment planning. However, conventional deep learning models face challenges in achieving precise segmentation and robust classification due to noisy inputs, weak feature representation, and poor generalization. To address these gaps, this study introduces a hybrid framework that integrates the ConvNeXt architecture for stroke region segmentation with XGBoost based classification, strengthened through three complementary feature extraction methods: local binary patterns (LBP), adaptive threshold directional binary gradient matrix (AT-DBGM), and wavelet packet transform (WPT). These methods capture textural, directional, and multi resolution features, which are concatenated into a stacked vector and classified using XGBoost. Preprocessing steps, including normalization and resizing, ensure improved input consistency. Experimental evaluations on benchmark stroke imaging datasets show that the proposed framework achieves 98.56% Dice similarity coefficient (DSC), 12.96 mm Hausdorff distance (HD), 99.12% accuracy, 98.69% sensitivity, 99.06% specificity, 98.98% precision, and 98.85% F1-score.
A hybrid model for enhanced aspect-based sentiment analysis using large language models Ziaulla, Mohammed; Biradar, Arun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1825-1838

Abstract

Aspect-based sentiment analysis (ABSA) is a crucial task within natural language processing (NLP), enabling fine-grained opinion mining by identifying sentiments associated with specific aspects of a product or service. While transformer-based models like bidirectional encoder representations from transformers (BERT) have improved sentiment classification, they still struggle with limited contextual adaptability, especially in customer reviews containing complex expressions. Most existing approaches rely heavily on benchmark datasets such as semantic evaluation (SemEval) and multi-aspect multi-sentiment (MAMS), which do not fully capture the diversity of real-world review scenarios. Hence, this research addresses these limitations by proposing a novel hybrid model, called as hybrid-BERT (H-BERT), that integrates span-aware BERT (SpanBERT) with bidirectional long short-term memory (BiLSTM), conditional random field (CRF), and large language models (LLMs). The objective is to enhance aspect extraction and sentiment classification performance using both annotated and synthetic data. The methodology includes preprocessing, hybrid model training, and evaluation using the SemEval 2014 dataset. Experimental results show that H-BERT achieved 90.58% accuracy and 90.56% F-score in the laptop domain and 91.21% accuracy with a 92.03% F-score in the restaurant domain. These results outperform existing models, confirming H-BERT’s robustness and effectiveness. In conclusion, H-BERT improves sentiment understanding in customer reviews.

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