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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 1,808 Documents
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Parallel multivariate deep learning models for time-series prediction: A comparative analysis in Asian stock markets Widiputra, Harya; Juwono, Edhi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp475-486

Abstract

This study investigates deep learning models for financial data prediction and examines whether the architecture of a deep learning model and time-series data properties affect prediction accuracy. Comparing the performance of convolutional neural network (CNN), long short-term memory (LSTM), Stacked-LSTM, CNN-LSTM, and convolutional LSTM (ConvLSTM) when used as a prediction approach to a collection of financial time-series data is the main methodology of this study. In this instance, only those deep learning architectures that can predict multivariate time-series data sets in parallel are considered. This research uses the daily movements of 4 (four) Asian stock market indices from 1 January 2020 to 31 December 2020. Using data from the early phase of the spread of the Covid-19 pandemic that has created worldwide economic turmoil is intended to validate the performance of the analyzed deep learning models. Experiment results and analytical findings indicate that there is no superior deep learning model that consistently makes the most accurate predictions for all states' financial data. In addition, a single deep learning model tends to provide more accurate predictions for more stable time-series data, but the hybrid model is preferred for more chaotic time-series data.
Adaptive Bayesian contextual hyperband: A novel hyperparameter optimization approach Swaminatha Rao, Lakshmi Priya; Jaganathan, Suresh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp775-785

Abstract

Hyperparameter tuning plays a significant role when building a machine learning or a deep learning model. The tuning process aims to find the optimal hyperparameter setting for a model or algorithm from a pre-defined search space of the hyperparameters configurations. Several tuning algorithms have been proposed in recent years and there is scope for improvement in achieving a better exploration-exploitation tradeoff of the search space. In this paper, we present a novel hyperparameter tuning algorithm named adaptive Bayesian contextual hyperband (Adaptive BCHB) that incorporates a new sampling approach to identify best regions of the search space and exploit those configurations that produce minimum validation loss by dynamically updating the threshold in every iteration. The proposed algorithm is assessed using benchmark models and datasets on traditional machine learning tasks. The proposed Adaptive BCHB algorithm shows a significant improvement in terms of accuracy and computational time for different types of hyperparameters when compared with state-of-the-art tuning algorithms.
Recommendation mobile antivirus for Android smartphones based on malware detection Saputra, Hendra; Zahra, Amalia; Faldi, Faldi; Fadzlul Rahman, Ferry; Harits, Sayekti; Joko Pranoto, Wawan; Rahman, Fathur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3559-3566

Abstract

The proliferation of smartphone malware attacks due to a lack of vigilance in app selection raises serious concerns. Built-in smartphone security features often must be improved to protect devices from these threats. Although numerous articles recommend top-tier antivirus solutions, there need to be more reliable data sources that raise suspicions about undisclosed promotional motives. This research endeavors to establish a ranking of antivirus efficacy to provide optimal recommendations for Android smartphone users. The research methodology entails a meticulous comparison of malware detection and labeling outcomes between various antivirus programs within Virustotal and the labeling system employed by the Euphony application. The comparative results are categorized into three groups: antivirus solutions proficient in identifying specific malware types, those detecting malware presence without categorization, and antivirus software failing to detect malware effectively. The experimental findings present the five leading antivirus solutions, ranked from the highest to lowest scores, as Ikarus, Fortinet, ESET-NOD32, Avast-Mobile, and SymantecMobileInsight. Based on the comprehensive assessment conducted in this study, these solutions are recommended as the top antivirus choices. These recommendations are poised to significantly aid users in selecting the most suitable antivirus protection for their Android smartphones.
Personalized E-commerce based recommendation systems using deep-learning techniques Nagraj, Shruthi; Palayyan, Blessed Prince
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp610-618

Abstract

As technology is surpassing each day, with the variation of personalized drifts relevant to the explicit behavior of users using the internet. Recommendation systems use predictive mechanisms like predicting a rating that a customer could give on a specific item. This establishes a ranked list of items according to the preferences each user makes concerning exhibiting personalized recommendations. The existing recommendation techniques are efficient in systematically creating recommendation techniques. This approach encounters many challenges such as determining the accuracy, scalability, and data sparsity. Recently deep learning attains significant research to enhance the performance to improvise feature specification in learning the efficiency of retrieving the necessary information as well as a recommendation system approach. Here, we provide a thorough review of the deep-learning mechanism focused on the learning-rates-based prediction approach modeled to articulate the widespread summary for the state-of-art techniques. The novel techniques ensure the incorporation of innovative perspectives to pertain to the unique and exciting growth in this field.
Structure tensor-based Gaussian kernel edge-adaptive depth map refinement with triangular point view in images Shalma, H.; Selvaraj, P.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1945-1953

Abstract

Image reconstruction is the process of restoring the image resolution. In 3D image reconstruction, the objects in different viewpoints are processed with the triangular point view (TPV) method to estimate object geometry structure for 3D model. This work proposes a depth refinement methodology in preserving the geometric structure of objects using the structure tensor method with a Gaussian filter by transforming a series of 2D input images into a 3D model. The computation of depth map errors can be found by comparing the masked area/patch with the distribution of the original image's greyscale levels using the error pixel-based patch extraction algorithm. The presence of errors in the depth estimation could seriously deteriorate the quality of the 3D effect. The depth maps were iteratively refined based on histogram bins number to improve the accuracy of initial depth maps reconstructed from rigid objects. The existing datasets such as the dataset tanks and unit (DTU) and Middlebury datasets, were used to build the model out of the object scene structure. The results of this work have demonstrated that the proposed patch analysis outperformed the existing state of the art models depth refinement methods in terms of accuracy.
Insights of learning approach towards determination of potentially objectional communication in social networking Nanjundappa, Praneetha Garagadakuppe; Naganna, Kamalakshi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2506-2513

Abstract

Over the last decade, sentiment analysis has evolved significantly towards extracting the contextual knowledge associated with the communication exchanged in social networks. Irrespective of various approaches to natural language processing and constantly evolving machine learning, sentiment analysis has inherent shortcomings, which further act as an obstacle to determining hateful and offensive speech exchanged in social networks. Therefore, this paper offers a compact yet granular insight into the effectiveness of existing sentiment analysis approaches used distinctly for determining hateful and offensive speech with particular emphasis on machine learning-based methodologies. The paper further contributes towards research trend analysis followed by distinct highlights of the research gap. The paper offers a learning outcome that significantly benefits future researchers investigating the same field.
HybridTransferNet: soil image classification through comprehensive evaluation for crop suggestion Raju, Chetan; Davanageri Virupakshappa, Ashoka; Basappa Vijaya, Ajay Prakash
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1702-1710

Abstract

Soil image classification is a critical task within the realms of agriculture and environmental applications. In recent years, the integration of deep learning has sparked significant interest in image-based soil classification. Transfer learning, a well-established technique in image classification, involves finetuning a pre-trained model on a specific dataset. However, conventional transfer learning methods typically focus solely on fine-tuning the final layer of the pre-trained model, which may not suffice to attain high performance on a new task. HybridTransferNet, a unique hybrid transfer learning approach designed for soil classification based on images is proposed in this paper. HybridTransferNet goes beyond the conventional approach by finetuning not only the final layer but also a select number of earlier layers in a pre-trained ResNet50 model. This extension results in substantially enhanced ability to classify when compared to standard transfer learning methods. Our evaluation of HybridTransferNet, conducted on a soil classification dataset, encompasses the reporting of various performance indicators, such as the F1 score, recall, accuracy, and precision. Our findings from experiments highlight HybridTransferNet's advantages over conventional transfer learning strategies, establishing it as a state-of-the-art solution in the domain of soil classification.
BahanbaKu: intuitive mobile application for buying recipe ingredients Brata, Komang Candra; Ramadhan, Rigel Vibi; Nugraha, Irvan Rizki; Zhafrant, Rifqi Hilmy; Putra, Ananda Ilyasa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2566-2573

Abstract

Many solutions were proposed to help people get their food or recipe ingredients easily without having to go to the market in person. Especially for people who have limited time and energy to go out and buy groceries. However, the existing solutions still lack the intuitiveness for users to get the ingredient information of a food recipe and still can’t facilitate the customers to have an all-in-one solution to get the needed ingredients. This study aims to combine the machine learning modalities and image recognition approach to provide a mobile app that can help people find food recipes intuitively and provide a digital ecosystem that can accommodate collaboration between various business actors including the MSMEs (Micro, Small, and Medium Enterprises). Instead of users buying items separately, the unique value of the proposed app (BahanbaKu) is that each of the ingredients needed by the user will be delivered in a single package. The preliminary evaluation result reveals that the accuracy of the proposed app is promising to assist people while getting recipe information for a particular food. The proposed app is also considered intuitive to the users with 81.7 SUS score.
You only look once model-based object identification in computer vision Reddy, Shiva Shankar; Maheswara Rao, Venkata Rama; Voosala, Priyadarshini; Nrusimhadri, Silpa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp827-838

Abstract

You only look once version 4 (YOLOv4) is a deep-learning object detection algorithm. It is used to decrease parameters and simplify network structures, making it suited for mobile and embedded device development. The YOLO detector can foresee an object's Class, bounding box, and probability of that Object's Class being found inside that bounding box. A probability value for each bounding box represents the likelihood of a given item class in that bounding box. Global features, channel attention, and special attention are also applied to extract more compelling information. Finally, the model combines the auxiliary and backbone networks to create the YOLOv4's entire network topology. Using custom functions developed upon YOLOv4, we get the count of the objects and a crop around the objects detected with a confidence score that specifies the probability of the thing seen being the same Class as predicted by YOLOv4. A confidence threshold is implemented to eliminate the detections with low confidence. 
Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio Fahmi, Faisal; Fajar, Rizqon; Atmaja, Sigit Tri; Erwandi, Erwandi; Rahuna, Daif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1969-1979

Abstract

Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.

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