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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 2,143 Documents
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Dual-path one-dimensional convolutional BiLSTM with attention for silk cocoon price forecasting Shivananda Shivanna; Lincy Meera Mathews; Sivagnanam Rajamanickam Mani Sekhar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4679-4690

Abstract

Accurately forecasting silk cocoon market prices is very crucial for enhancing decision-making silk cocoon supply chain. This work introduces a hybrid deep learning model, the dual-path one-dimensional convolutional neural network–bidirectional long short-term memory with lightweight attention mechanism (DPCBLA), which combines temporal sequence data with static features to predict daily maximum silk cocoon prices. The model captures short-term temporal patterns by using a one-dimensional convolutional neural network (1D CNN), then passes to a bidirectional long short-term memory (BiLSTM) network that captures long-term dependencies, and a lightweight attention mechanism that highlights key time steps for better interpretability. Static inputs such as market variety, seasonal indicators, and weather conditions are processed through a dense layer and integrated with temporal features in a dual-pathway design. The model was trained using a multi-source dataset obtained from the Ramanagara Government Silk Market in Karnataka, India. The dataset comprises historical transaction records, weather information, and seasonal disease data. The proposed model DPCBLA performed better than the traditional convolutional neural network (CNN), long short-term memory (LSTM), and BiLSTM models, achieving a mean absolute error (MAE)of24.73, a root mean square error (RMSE) of 33.93, a mean absolute percentage error (MAPE) of 4.05, a coefficient of determination (R2) of 0.94, and a prediction accuracy of 95.95%.
Multimodal retrieval-augmented generation with sentiment-aware responses for depression Aluri Suguna Ratnapriya; A. Mary Sowjanya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4907-4918

Abstract

In view of the rapid growth in conversational interfaces, there is a demand for more adaptive retrieval systems. Traditional chatbots, built on pre-trained models, struggle to understand and respond to a wide range of real-world user queries. In view of this, a framework based on multimodal retrieval-augmented generation with sentiment-aware responses (MRAG-SAR) for depression that addresses the challenges and strengthens conversational interface performance has been developed. The proposed system can process unstructured data in multiple formats of inputs, such as audio, image, and text, using a multimodal late-fusion mechanism, making it suitable for managing diverse, domain-specific information. It also leverages powerful embedding models for capturing contextual similarity and scalable vector databases for efficient retrieval. The user queries are augmented to provide precise and nuanced context-related responses. The proposed framework retrieves content that captures the dimensions of the topic to enhance the relevance of the output responses. Introduction of sentiment-aware response generation allows the system to respond in a tone based on the emotional context of the user queries, which is essential in making use of personalized conversations. Relevant bidirectional encoder representations from transformers score (BERTScore) and other metrics highlight the framework's effectiveness in delivering high-quality responses.
An explainable artificial intelligence benchmarking with deep reinforcement learning for telecom battery backup prediction Promphak Boonraksa; Kedsara Palachai; Veerapatra Wangsilabatra; Terapong Boonraksa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4482-4496

Abstract

Accurate prediction of battery backup time is essential for ensuring service reliability and efficient energy management in modern telecommunication networks, particularly during power outages. This study proposes a comprehensive benchmarking framework to evaluate the performance of eleven advanced time-series forecasting models, including deep learning architectures (multi-layer perceptron (MLP), convolutional neural network 1-dimension (CNN1D), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and temporal convolutional network (TCN)), Transformer-based models (Transformer, Informer-Lite, and patch time-series Transformer-lite (PatchTST-Lite)), and linear baseline approaches (decomposition linear (DLinear) and normalized linear (NLinear)). The models are trained and validated using real-world telecom power system data under varying load and environmental conditions. To improve model transparency and reliability, an explainable artificial intelligence (XAI) framework is incorporated to interpret feature contributions and identify key factors influencing battery performance. Experimental results indicate that Transformer-based models outperform conventional deep learning and linear models in terms of prediction accuracy and robustness across different operating scenarios. Furthermore, XAI analysis highlights that load current, ambient temperature, and battery health indicators are the most influential variables affecting backup time prediction. The proposed framework provides both high accuracy forecasting and interpretability, enabling more effective decision-making and enhancing energy resilience in telecommunication infrastructure.
Autoencoder-based anomaly detection in water cyber-physical systems Sara Tahiri; Mouad Choukhairi; Youssef Fakhiri; Mohamed Amnai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4635-4644

Abstract

Cyber-physical systems (CPS) are at the core of enabling services in the critical infrastructures, such as a smart water network, hence the need for effective anomaly detection to ensure operational safety and security. This paper proposes deep water anomaly detection (DeepWAD), a big data-based multiple purpose anomaly detection tool that leverages deep learning, supports big data, and internet of things (IoT) data collection and analysis. In a unified processing pipeline, it estimates and contrasts three kinds of autoencoders, viz adversarial autoencoder (AAE), variational autoencoder (VAE), and classical autoencoder (CAE). These models are then applied to a practical water treatment system, namely the secure water treatment (SWaT) dataset. Using reconstruction errors, DeepWAD analyzes time-series data generated by IoT sensors in order to discover anomalies including, for example, equipment malfunctions, sensor errors, and cyber threats. The framework also demonstrates the performance of various architectures in terms of data complexity, training consistency, and reconstruction accuracy. Due to its generality, DeepWAD can also be applied in other CPS domains such as the energy grid or industrial control systems. It serves as a decision-support tool to select the most appropriate anomaly detection model depending on system constraints and detection requirements.
A real-time drone detection framework for variable environmental conditions based on web-based deep learning Yuliani Indrianingsih; Astika Ayuningtyas; Anton Yudhana; Haruno Sajati; Anggraini Kusumaningrum; Fajar Khanif Rahmawati; Imam Riadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4780-4791

Abstract

The rapid proliferation of unmanned aerial vehicles (UAVs) in civilian, industrial, and military domains has intensified challenges related to airspace security, particularly in restricted and sensitive areas. Unauthorized drone operations pose risks including illegal surveillance, disruption of air traffic, and threats to critical infrastructure. This research proposes a lightweight, web-based real-time drone detection framework utilizing a convolutional neural network (CNN) integrated with a fast application programming interface (FastAPI) server to support continuous visual surveillance. The system is trained and evaluated using a custom dataset of 23,118 annotated drone images, enhanced through data augmentation to improve robustness under varying environmental conditions. Experimental results demonstrate real-time inference performance of 20–25 frames per second across multiple camera resolutions, including 1080p, 2K, and digital single-lens reflex (DSLR) Canon 600D. Detection accuracy varies between 53% and 74%, revealing a strong dependency on illumination and imaging conditions, with optimal performance achieved under high illumination levels (45,000 lux). Performance degradation is observed in low-light environments, particularly for DSLR-based detection. The proposed system enables live video streaming and static image analysis through a user-friendly web interface without reliance on external services. Overall, this study presents a practical and deployable prototype for automated drone monitoring, emphasizing the trade-offs between accuracy, illumination, and camera resolution in real-world airspace security applications.
Automated modeling of leaf mangrove by optimizing convolutional neural networks Paranita Asnur; Rifki Kosasih; Sarifuddin Madenda; Dewi Agushinta R.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4447-4458

Abstract

Accurate mangrove species identification is fundamental to biodiversity conservation, ecological monitoring, and climate change mitigation because mangrove ecosystems provide essential ecological services and function as significant carbon sinks. This study proposes a systematic optimization framework for mangrove species identification by jointly investigating dataset size, convolutional depth, and hyperparameter configuration within a convolutional neural network (CNN). Leaf images of seven mangrove species collected in Kolaka Regency, Southeast Sulawesi, were augmented, resized to 128 × 128 pixels, and processed using the NVIDIA DGX A100 supercomputer. The framework systematically optimized the number of convolutional layers, learning rate (LR), batch size, number of epochs, kernel size, activation function (AF), and optimizer, followed by a comprehensive evaluation of their combined effects on classification performance. The optimized CNN achieved 98.93% testing accuracy, 98.71% precision, 98.71% recall, and a 98.86% F1-score. The results demonstrate that the integrated optimization of CNN architecture, dataset size, and hyperparameters substantially improves model robustness and generalization over conventional fixed CNN configurations, providing a practical deep learning framework for automated mangrove monitoring and biodiversity assessment.
Impact of feature selection on the performance of machine learning models for distributed denial of service detection Jaimin Shroff; Sanjay Shah; Jigna Jadav; Nakul Dave
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4325-4334

Abstract

Distributed denial-of-service (DDoS) attacks significantly compromise network availability. Distributed reflection denial-of-service (DrDoS) is a variant of DDoS that exploits intermediary servers to reflect and amplify traffic toward the victim. In software-defined networking (SDN) contexts, machine learning (ML)-based detection has proven to be an efficient countermeasure; nevertheless, high-dimensional traffic features frequently diminish detection performance. This study examines the influence of sequential feature selection on the efficacy of ML models for DDoS detection utilizing the CIC-DDoS2019 dataset. A two-stage feature selection method integrating mutual information (MI) score and joint entropy analysis is suggested to diminish feature dimensionality while maintaining discriminative efficacy. The diminished feature sets are assessed utilizing random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), gradient boosting algorithm (GBA), and neural network classifiers. Experimental findings indicate that the suggested strategy markedly enhances computational efficiency and detection accuracy, with RF attaining over 97% accuracy and F1-score across several DDoS attack types. The results affirm that efficient feature selection is essential for scalable and precise DDoS detection in SDN systems.
Mesh router node placement using somersault based giant armadillo optimization Kaata Ranjith Kumar; Bala K.; Kalyanapu Srinivas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4159-4174

Abstract

Wireless mesh networks (WMNs) have rapidly advanced in the last decade because of their ease of experimentation at minimum cost, simple network maintenance, and consistent provision coverage. However, the placement of router nodes remains a critical research challenge for network operators, as it directly impacts the overall performance and effectiveness of the WMN. Thus, this research proposes a novel approach of the somersault fraction strategy (SFS)-based giant armadillo optimization (GAO) for addressing an issue of mesh router (MR) node placement in WMN. A significance of the proposed SFS-GAO approach was estimated in terms of different scenarios under diverse settings, considering network connectivity as well as client coverage metrics. The simulation outcomes demonstrate that the proposed SFS-GAO method achieves better results in each performance metric like coverage, connectivity, and fitness, based on a varied count of mesh clients (MC), MR, and coverage radius. The findings show that the proposed function offers higher client coverage and optimal network connectivity with minimum computation power.
Context-aware micro-escrow for agentic systems with language model validation Ayman Nait Cherif; Mohamed Youssfi; Omar Bouattane
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4039-4052

Abstract

Autonomous agents transacting with each other need an escrow they can trust without a human in the loop. This study propose a context-aware micro-escrow framework for this setting: large language models (LLMs), the model context protocol (MCP), agent-to-agent (A2A) communication, and blockchain smart contracts together support automated, auditable micro-transactions among heterogeneous resource holders such as live application programming interfaces (APIs), internet of things (IoT) sensors, and service providers. Traditional escrow relies on rigid condition checks or a trusted intermediary. This study instead runs a deterministic, checklist-based semantic validation pipeline that emits signed binary verdicts and commits rationale hashes on-chain, so any verdict can be replayed off-chain while on-chain settlement stays minimal. Context travels in cryptographically stamped MCP envelopes; negotiation and evidence streaming run over authenticated A2A channels. An auditing layer computes risk signals from recent settlement metadata and applies a bounded fee surcharge that internalizes monitoring effort and performance drift. The framework is evaluated with a reproducible simulator under benign and adversarial mixes, reporting latency, decision quality (precision and recall under a binary-classifier view), and economic side effects (fee collection and slashing). The results show strong tolerance to malicious providers. Coordinated validator compromise, however, marks a clear trust boundary, motivating validator-side hardening as future work.
Assessing corn quality classification with deep-learning module Monther Yousef; Zamani Md Sani; Ahmad Fauzan Kadmin; Mohammed Ahmed Salem; Masrullizam Mat Ibrahim; Hatem T. M. Duhair
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4096-4104

Abstract

This study presents a new classification framework for corn quality assessment using the you only look once version 8 (YOLOv8) deep learning architecture. Traditional corn-sorting methods are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study proposes the corn quality classification (CQC) algorithm, an artificial intelligence (AI)-driven approach for automatically recognizing and categorizing pest- and disease-related defects. The proposed model uses YOLOv8, which integrates a convolutional neural network (CNN) backbone for feature extraction and object detection. The system was trained on labelled corn images to classify them into three categories: healthy, water rot, and bug. The model achieved an overall classification accuracy of 92.7%, with class-specific accuracies of 88.9% for healthy, 70% for water rot, and 100% for bug. Key performance metrics included a recall of 93%, an F1-score of 62%, a mean average precision (mAP) of 92.7%, and an intersection over union (IoU) of 52.3%. Additionally, the F1-confidence and recall-confidence curves provided further insights into the model’s performance. The proposed classification framework and CQC algorithm demonstrate the feasibility of AI-powered agricultural monitoring systems, offering a reliable and efficient approach to corn quality assessment. By enabling consistent and accurate classification, the proposed system has the potential to reduce labor requirements and support precision agriculture practices for improved crop quality management.

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