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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 83 Documents
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Search results for , issue "Vol 14, No 6: December 2025" : 83 Documents clear
Classification system of banana types and ripeness levels based on convolutional neural network Jambola, Lucia; Darlis, Arsyad Ramadhan; Malaha, Windi; Aryanta, Dwi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4891-4901

Abstract

Recently, the availability of bananas in supermarkets has been relatively abundant. However, most buyers experience problems categorizing the type and level of ripeness of bananas, so the level of purchases of this fruit decreases. This study implements a system that can automatically classify bananas based on type and level of ripeness so that buyers can choose them based on their needs. In this study, the proposed system could classify the types and degrees of banana ripeness using a Convolutional Neural Network (CNN) where the system was implemented in real-time using the hardware of the Jetson Nano as a processing unit and a camera system as a sensor. The methodology adopted in this research involves implementing CNN architectures, i.e., ResNet-18 and ResNet-50, under various conditions. The training phase comprises 60 epochs, while testing considers illumination parameters from LED lights with power of 6 watts, 12 watts, and 22 watts under distances ranging from 10 to 100 cm. The results show that the system could classify the type and level of ripeness of bananas in real-time with an accuracy of 93% that is achieved using the 22-watt power for all type and ripeness levels. 
Sentiment classification using gradient modulation and layered attention Natarajan, Bagiyalakshmi; Veeramakali, T.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5193-5200

Abstract

Sentiment analysis is a technique for evaluating text to ascertain whether a statement is positive, negative, or neutral. Currently, transformer-based models capture the contextual relationships among words in a phrase and accomplish sentiment analysis in a nuanced manner via multi-head attention. This approach, with a fixed number of layers and heads, struggles to find the complex relationships between phrases and their semantic structures. To mitigate this issue, the suggested technique incorporates the graded multi head attention model (GMHA) at the base of the distilled bidirectional encoder representations from transformers (DistilBERT) model. It is employed to augment the layers and heads progressively, capturing the relationships between sentences in a sophisticated manner. By increasing the layers and heads the proposed model extracts long-term and hierarchical relationships from the sentence. Additionally, the attention sentient optimization technique is introduced, which improves model learning by giving more weight to important words in a sentence. During training, the process checks to see which words (“amazing" or "worst") get more attention and gives them more weight in the model update. This makes it easier for the model to understand important emotions. Our suggested model enhances performance in sentiment exploration, with an accuracy of 96.53%. This interpretation includes a comparison analysis with another contemporary framework.
Enhancing waste management through municipal solid waste classification: a convolutional neural network approach Tarequzzaman, Md.; Akash, Mojahidul Alom; Nayon, Zakir Hossain; Sabbir Reza, Md.; Haque, Shajjadul
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4775-4786

Abstract

The escalation of population, economic expansion, and industrialization has resulted in an increase in waste production. This has made waste management more challenging and has resulted in environmental deterioration, negatively impacting the quality of life. Recycling, reducing, and reusing are viable methods to eradicate the escalating waste issue, requiring the appropriate classification of municipal solid waste. This study focuses on comparing six advanced waste classification systems that employ a pre-trained convolutional neural network (CNN) designed to recognize twelve distinct categories of municipal waste. It has been determined that DarkNet53 is the most effective classifier among these six models. To assess the effectiveness of each waste classifier, the confusion matrix, precision, recall, F1 score, the area under the receiver operating characteristic curve, and the loss function are examined. It has been found that DarkNet53 has an F1 score of 98.7% and validation accuracy of 99%, respectively. The suggested approach will be useful in promoting garbage recovery and reuse in the direction of a circular and sustainable economy.
Greywater treatment system based on fuzzy logic control Pratama, I Putu Eka Widya; Ridha, Muhammad Rasyid; Chafsah, Anis Mahmuda; Hija, Akhmad Ibnu; Zaine, Siti Nur Azella
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4643-4651

Abstract

Greywater from households and public facilities represents a major source of untreated wastewater, carrying high microbial loads and variable chemical composition that threaten environmental and public health. Conventional treatment systems often lack adaptive control mechanisms capable of handling the dynamic fluctuations of greywater quality. This study presents the design and validation of an intelligent greywater treatment system that integrates real–time sensing with a Sugeno fuzzy logic controller to regulate pump and solenoid valve operation. The system continuously monitors pH, total dissolved solids (TDS), dissolved oxygen (DO), and ammonia (NH3), and dynamically adjusts treatment cycles based on sensor feedback. Experimental deployment demonstrated significant improvements in effluent quality, with pH reduced from 9.04 to 8.08, TDS from 611.04 ppm to 393.96 ppm, and NH₃ from 0.52 ppm to 0.19 ppm, while DO increase from 2.52 mg/L to 6.07 mg/L. These results confirm that fuzzy logic–based control enhances system responsiveness and ensures effluent compliance under variable influent conditions. The proposed framework provides a scalable, cost-effective solution for decentralized wastewater management, advancing the development of intelligent treatment technologies for sustainable urban water systems.
Catalysing precision in bone x-ray analysis for image detection and classification: the triple context attention model advancement Sultana, Tabassum N.; Hegde, Nagaratna P.; Parveen, Asma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4957-4970

Abstract

Accurate detection and classification of fractures in bone x-ray images are crucial for effective medical diagnosis and treatment. In this study, we propose the triple context attention model (TCAN) as a novel approach to address the challenges in this domain. TCAN offers several key contributions that significantly enhance the accuracy and efficiency of bone x-ray image recognition and classification. Firstly, TCAN introduces the coordination attention mechanism, which considers both horizontal and vertical positional data during the recognition process. Secondly, TCAN mitigates the common issue of mislabelling fractures in bone x-ray images, particularly in the you only look once (YOLO) model, due to the absence of positional data during training. Thirdly, TCAN efficiently enhances positional data by focusing on weights, and increasing feature dimension while maintaining a manageable model size. This allows for effective utilization of positional data without computational overhead. Lastly, TCAN combines the visual attention network (VAN) with its capabilities, resulting in a comprehensive system that can handle diverse image dimensions and accurately classify various types of fractures across different body regions. Overall, TCAN presents a promising advancement in medical image analysis, improving fracture detection accuracy and classification efficiency in bone x-ray images, thus aiding in more effective clinical decision-making.
A review on long short-term memory combination development Riyadi, Ahmad; Rokhman, Nur; Heryawan, Lukman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4427-4441

Abstract

Long short-term memory (LSTM) has continued to develop since it was proposed in 1997. LSTM has optimized solutions to various problems. The LSTM cell, architecture, and memory model have been reviewed. A review of LSTM implementation has been carried out in various problem domains. There are combinations of LSTM with other methods to optimize solutions. However, there is no review on the development of LSTM combination (LC). This research reviews the development of the LC model on nine research questions, namely: development framework, data, preprocessing, learning process, tasks, optimization and evaluation, domain problems, trends, and challenges. The results show that the LC model is increasingly widespread in solving problems. The LC model has completed 26 types of tasks. Prediction, detection, forecasting, classification, and recognition are the most frequently performed tasks. LC model development trends show that LSTM is increasingly collaborative with other methods on a wider scope. The challenges identified include research areas, data, model developments, the area of implementation, performance, and efficiency.
Designing a squeeze-and-excitation-capsule BiLSTM transformer for plant leaf disease recognition Suksukont, Aekkarat; Naowanich, Ekachai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5069-5080

Abstract

Deep learning (DL) is critical in plant disease recognition and classification with precision like those of expert human evaluators. However, development of effective systems is often disrupted due to the complexity and variability of disease pathogenesis. To address these challenges, this research applies to a hybrid DL architecture that integrates spatial encoding, sequential modelling, and attention for visual recognition. This proposed model can incorporate squeeze-and-excitation (SE) with residual blocks, capsule network (CapsNet), bidirectional long short-term memory (BiLSTM), and transformer network (TransNet)-based attention to realize spatial relationships and long-range dependencies for improving recognition accuracy. The proposed model is assessed on the corn leaf disease dataset (CLDD) and rice leaf diseases dataset (RLDD), and its performance is compared to leading-edge models. CLDD and RLDD achieved 99.88 and 99.10% training accuracy respectively. The area under the curve (AUC) reached almost ceiling recognition on CLDD, with 99.73, 99.96, 99.96, and 99.98% for blight (BL), common rust (CR), gray leaf spot (GL), and healthy (HE) result. RLDD results were also high, with 94.98, 93.70, 97.66, 84.57, 99.58, and 98.85% for bacterial leaf blight (BLB), brown spot (BS), HE, leaf blast (LB), leaf scald (LS), and narrow brown spot (NBS), respectively. The results of these tests show the remarkable promise and performance of the proposed model in plant disease recognition applications.
Computer vision syndrome prevention: detection of expression and eye distance with monitor screens Frisky, Aufaclav Zatu Kusuma; Azrien, Elang Arkanaufa; Sumiharto, Raden; Hartati, Sri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4533-4540

Abstract

Computer vision syndrome (CVS) is a vision-related complaint caused by computer usage. CVS can be analyzed through facial expressions detected by a camera. Expression detection is categorized into two groups: safe and dangerous. The safe category comprises happy, neutral, disgusted, sad, angry, and surprised, while the dangerous category includes sad and fearful emotions. This division is based on the similarity of CVS symptoms to facial emotion characteristics. Additionally, an additional feature is implemented to detect the distance between the screen and the user's eyes using the FaceMeshModule to prevent the user's eyes from getting too close to the screen. Both detections will provide warning notifications when a dangerous category expression is detected ≥70% every minute, and when the distance between the screen and the eyes is ≤40 cm. Notifications in this program use the Tkinter library as a graphical user interface (GUI) message box. In this research, facial expressions are detected using the CascadeClassifier for face detection and the extreme inception (Xception) as the facial expression classifier. The results of expression detection achieved an accuracy of 94%, an F1-score of 94%, precision of 95%, and recall of 94%.
Optimizing robotic motion in dynamic manufacturing environments Abiodun Salawu, Ganiyat; Bright, Glen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4590-4599

Abstract

The field of robotics has been a trending technology over the years due to its ability to revolutionize industries. This study highlights the role of optimized robotic motion in enhancing productivity in dynamic manufacturing environments using MATLAB simulations. By modeling the arrival of manufactured parts in batches via a conveyor system governed by a negative exponential distribution in a Poisson process, MATLAB is employed to design optimal robotic trajectories for pick-and-place operations. The research carefully analyzes parameters such as arrival rates and cycle times to manage the stochastic nature of part delivery. The result reveals a significant improvement in operational efficiency, with throughput increasing by up to 20% due to real-time optimization of robotic motion. The non-linear relationship between throughput and arrival rates highlights the system’s complexity, with optimal conditions observed at specific arrival rates, such as 0.16 s for peak efficiency. MATLAB’s Polynomial Trajectory Planning tool generates smooth, continuous paths, ensuring that robotic operations dynamically adapt to changing conditions. This foundation supports future innovations in robotic system integration and automated production lines, offering a significant step forward in the application of advanced simulation tools an advanced manufacturing environment.
Machine and deep learning classifiers for binary and multi-class network intrusion detection systems Aloqaily, Ahmad; Abdallah, Emad Eddien; Abu Elsoud, Esraa; Hamdan, Yazan; Jallad, Khaled
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4814-4827

Abstract

The rapid proliferation of the internet and advancements in communication technologies have significantly improved networking and increased data vol ume. This phenomenon has subsequently caused a multitude of novel attacks, thereby presenting significant challenges for network security in the intrusion detection system (IDS). Moreover, the ongoing threat from authorized entities who try to carry out various types of attacks on the network is a concern that must be handled seriously. IDS are used to provide network availability, confidentiality, and integrity by employing machine learning (ML) and deep learning (DL) algorithms. This research aimed to study the impacts of the binary and multi-attack instances label by establishing IDS that leverages hybrid algorithms, including artificial neural networks (ANN), random forest (RF), and logistic model trees (LMTs). The paper addresses challenges such as data pre processing, feature selection, and managing imbalanced datasets by applying synthetic minority oversampling technique (SMOTE) and Pearson’s correlation methodologies. The IDS was tested using network security laboratory knowledge discovery datasets (NSL-KDD) and catalonia independence corpus intrusion detection system (CIC-IDS-2017) datasets, achieving an average F1-score of 96% for binary classification on NSL-KDD and 85% for binary classification on CIC-IDS-2017, while for multi-classification, the proposed model achieved an average F1-score of 82% and 96% for NSL-KDD and CIC-IDS-2017 successively.

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