IAES International Journal of Artificial Intelligence (IJ-AI)
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.
Articles
123 Documents
Search results for
, issue
"Vol 13, No 4: December 2024"
:
123 Documents
clear
<![CDATA[Deep learning approach for forensic facial reconstruction depends on unidentified skull ]]>
<![CDATA[M. Mohammed, Doaa]]>;
<![CDATA[Elgendy, Mostafa]]>;
<![CDATA[Taha, Mohamed]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp3858-3868
<![CDATA[Facial reconstruction, or facial approximation, is an essential problem in a criminal investigation involving reconstructing a victim's face from his skull to determine the victim's identification at a crime scene. Facial approximation plays a crucial part when there is a lack of clues with investigators. Investigators utilize facial approximation to guess the victims' identities. This research attempted to use computer-aided face reconstruction rather than traditional approaches. Traditional methods of face reconstruction include the use of clay or gypsum. Traditional procedures necessitate forensic professionals to rebuild the victim's face. This research uses the convolution neural network skull part with sift (CNNSPS) model is employed to reconstruct facial features from a skull image utilizing public datasets CelebAMask-HQ and MUG500+. The proposed algorithm was tested on unidentified skull databases, and celebrity faces were used. The genuine datasets are not available, which is the key issue in this research.]]>
<![CDATA[Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning ]]>
<![CDATA[H. Ali, Esraa]]>;
<![CDATA[Sadek, Sawsan]]>;
<![CDATA[F. Makki, Zaid]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp4080-4094
<![CDATA[Brain damage and deficits in interactions among brain cells are the primary causes of dementia and Alzheimer’s disease (AD). Despite ongoing research, no effective medications have yet been developed for these conditions. Therefore, early detection is crucial for managing the progression of these disorders. In this study, we introduce a novel tool for detecting AD using non invasive medical tests, such as magnetic resonance imaging (MRI). Our method employs fuzzy C-means clustering to identify features that enhance image accuracy. The standard fuzzy C-means algorithm has been augmented with fuzzy components to improve clustering performance. This enhanced approach optimizes segmentation by extracting image information and utilizing a sliding window to calculate center coordinates and establish a stable group matrix. These critical features are subsequently integrated with a two-phase watershed segmentation process. The resulting segmented images are then used to train an optimal convolutional neural network (CNN) for AD classification. Our methodology demonstrated a 98.20% accuracy rate in the detection and classification of segmented MRI brain images, highlighting its efficacy in identifying disease types.]]>
<![CDATA[A neural machine translation system for Kreol Repiblik Moris and English ]]>
<![CDATA[Pudaruth, Sameerchand]]>;
<![CDATA[Armoogum, Sheeba]]>;
<![CDATA[Kumar Betchoo, Nirmal]]>;
<![CDATA[Sukhoo, Aneerav]]>;
<![CDATA[Gooria, Vandanah]]>;
<![CDATA[Peerally, Abdallah]]>;
<![CDATA[Zafar Khodabocus, Mohammad]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp4976-4987
<![CDATA[Although Google Translate is a widely used machine translation service that supports 133 languages, it does not incorporate support for the Kreol Repiblik Moris (KRM) language. Addressing this limitation, the current research focuses on enhancing the accuracy and fluency of machine translation between KRM and English through natural language processing and deep neural machine translation techniques. In this study, a machine translation system using a transformer model trained with a dataset of 50,000 parallel corpora has been developed. The model was evaluated using manual translations and the bilingual evaluation understudy (BLEU) score. A score of 31.46 for translating from KRM to English and 28.15 for translating from English to KRM was achieved. To our knowledge, these are the highest BLEU scores for translation between these two languages. This is due to utilising the largest dataset and extensive atomic words from the KRM dictionary. This successful interdisciplinary funded project led to the setting up of a free online translation service and a smartphone app for Mauritian citizens and tourists.]]>
<![CDATA[Systematic review of artificial intelligence with near-infrared in blueberries ]]>
<![CDATA[Cayhualla Amaro, Liset]]>;
<![CDATA[Rau Reyes, Sebastian]]>;
<![CDATA[Acuña Meléndez, María]]>;
<![CDATA[Ovalle, Christian]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp3761-3771
<![CDATA[The fruit quality has a direct impact on how the fruit looks and how tasty the fruit is. The correct use of tools to determine fruit quality is essential to offer the best product for the final consumer. This study has used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The study objective was elaborate a systematic literature review (SLR) about research of the application of techniques based on artificial intelligence to analyze indicators obtained by near infrared spectroscopy (NIRS) and chemometrics to determine the quality of fruits, including blueberries. The most frequently addressed indicator is the soluble solids concentration (SSC) which was used in several studies with techniques such as support vector machines (SVM) and convolutional neural networks (CNN). According to the results obtained, it is possible to use these techniques to predict blueberry quality indicators. There was an acceptable performance and high accuracy of these models. However, future research could cover other techniques and help to provide better quality control of products in food industries.]]>
<![CDATA[Unsupervised hindi word sense disambiguation using graph based centrality measures ]]>
<![CDATA[Jha, Prajna]]>;
<![CDATA[Agarwal, Shreya]]>;
<![CDATA[Abbas, Ali]]>;
<![CDATA[Singh, Satyendr]]>;
<![CDATA[Jahan Siddiqui, Tanveer]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp4957-4964
<![CDATA[The task of word sense disambiguation (WSD) plays a key role in multiple applications of natural language processing. In this paper, we propose a novel unsupervised method for targeted Hindi WSD task. First, we create a weighted graph where the nodes correspond to various synsets of the target word and the neighboring context words. The edges in the graph represent the semantic relations between these synsets in the Hindi WordNet hierarchy. A path-based similarity measure, namely Leacock-Chodorow similarity measure, is used to assign weights to edges. An unsupervised weighted graph-based centrality algorithm is used to identify the correct sense of a target word in a given context. The performance of the proposed algorithm is measured on 20 ambiguous Hindi nouns using four different graph-based centrality measures. We observed a maximum accuracy of 66.92% using PageRank centrality measure which is significantly better than earlier reported graph-based Hindi WSD algorithmsevaluated on the same dataset.]]>
<![CDATA[Implications of artificial intelligence chatbot models in higher education ]]>
<![CDATA[Khandakar, Hissan]]>;
<![CDATA[Ali Fazal, Syed]]>;
<![CDATA[Fattah Afnan, Kazi]]>;
<![CDATA[Kamrul Hasan, Khandakar]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp3808-3813
<![CDATA[Artificial intelligence (AI) is becoming increasingly influential in the academic sector, which is why it is important to explore the ethical dilemmas and concerns surrounding AI chatbots’ design, development, and deployment in educational contexts. Conducted as a thematic literature review, this paper explores existing research on AI in education, AI chatbots, and their integration with higher education to gather evidence and insights that discuss ethical implications and challenges. The study has analyzed several articles on AI chatbots and their integration into academic fields. Significant gaps have been identified, such as the need for more practical implications and the recognition of AI chatbots as a collaborative tool for academic purposes. More AI chatbots should be explicitly trained on data relevant to the learners’ study to examine their usefulness properly. The paper discusses the ethical dilemmas and concerns about the design, development, and deployment of AI chatbots in higher education. It seeks to provide insights and recommendations to ensure the ethical use of AI chatbots in higher education by identifying significant gaps in the existing literature and providing scenarios to expect in the development of AI in education.]]>
<![CDATA[Multi platforms fake accounts detection based on federated learning ]]>
<![CDATA[Azer, Marina]]>;
<![CDATA[H. Zayed, Hala]]>;
<![CDATA[A. Gadallah, Mahmoud E.]]>;
<![CDATA[Taha, Mohamed]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp3837-3848
<![CDATA[Identifying and mitigating fake profiles is an urgent issue during the age of widespread integration with social media platforms. this study addresses the challenge of fake profile detection on major social platforms-Facebook, Instagram, and X (Twitter). Employing a two-sided approach, it compares stacking model of machine learning algorithms with the federated learning. The research extends to four datasets, two Instagram datasets, one X dataset, and one Facebook dataset, reporting impressive accuracy metrics. Federated learning stands out for it is effectiveness in fake profile detection, prioritizing user data privacy. Results reveal Instagram fake/real dataset achieves 96% accuracy while Instagram human/bot dataset reaches 95% accuracy with federated learning. using the stacking model X’s fake/real dataset achieves 99.4% accuracy, and Facebook fake/real dataset reaches 99.8% accuracy using the same model. The study underscores the pivotal role of data privacy, positioning federated learning as an ethical choice. It compares the time efficiency of stacking and federated learning, with the former providing good performance in less time and the latter emphasizing data privacy but consuming more time. Results are benchmarked against related works, showcasing superior performance. The study contributes significantly to fake profile detection, offering adaptable solutions and insights.]]>
<![CDATA[Improving the risk profile of Indonesian enterprise taxpayers using multilabel classification ]]>
<![CDATA[Prasetyo, Teguh]]>;
<![CDATA[Susetyo, Budi]]>;
<![CDATA[Kurnia, Anang]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp4323-4333
<![CDATA[Optimizing tax revenues is difficult in Indonesia due to obstacles such as tax evasion and tax avoidance. It is closely related to an organization's compliance with tax regulations, known as the taxpayers risk profile. However, this mechanism does not accurately detect tax avoidance and tax evasion risks. To overcome this limitation, we use a multilabel classification machine learning method in this study, which classifies a single observation into one or more labels at once. The approach involves problem transformation (binary relevance and label powerset), algorithm adaptation (multilabel k-nearest neighbor (ML-kNN) and multilabel-adaptive resonance associative map (ML ARAM)), and ensemble (label space partitioning and random k-label sets with disjoint (RAkELd)). Based on the model performance comparisons, we discovered that the ML-ARAM method based on deep learning is the best, with an average F1-score of 95.5% and a hamming loss of 7.4%. We also examine the feature importance of the best model to reduce the dimensions of features so that we can identify the dominant factors that encourage a taxpayer entity to engage in tax avoidance or tax evasion. The findings of this study improve the accuracy of tax avoidance risk detection and tax evasion risk profiles using machine learning methods, ensuring maximum tax revenues in Indonesia.]]>
<![CDATA[A multi-core makespan model for parallel scientific workflow execution in cloud computational framework ]]>
<![CDATA[Naaz, Farha]]>;
<![CDATA[Banu, Sameena]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp3849-3857
<![CDATA[Researchers have shown a lot of potential in optimizing cloud-based workload-scheduling over the past few years. However, executing scientific workloads inside the cloud is time-consuming and costly, making it inefficient from both a financial and productivity standpoint. As a result, there are many investigations conducted, with the general trend being to speed up the rate of processing and establish a cost-effective system, whereby customers are billed according to their actual use. In addition, energy-consumption is capable of being reduced, especially if the available resources are heterogeneous; however, few investigations have optimized multi-core with analyzing makespan parameters collectively to fulfill the quality of service (QoS) and service level agreement (SLA) of the workload task. In this research, we introduce an optimal scheduling for a heterogeneous distributed cloud computing environment called task aware makespan optimized scheduler (TAMOS) that guarantees requirements across the task levels of scientific workflows. The energy and time required to carry out specific workflows are significantly reduced by using this TAMOS strategy. The TAMOS framework was studied using the scientific workflows namely, inspiral and sipht. When compared to the conventional method of scheduling work, our methodology used less energy and makespan.]]>
<![CDATA[Sarcasm detection on social data: heuristic search and deep learning ]]>
<![CDATA[Palaniammal, Arumugham]]>;
<![CDATA[Anandababu, Purushothaman]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v13.i4.pp4695-4702
<![CDATA[Due to the significant surge in online activity, sarcasm detection (SD) has attracted major attention in social media networks. Sarcasm is a lexical item of negative sentiments or dislikes by utilizing exaggerated language constructs. SD has created a natural language processing (NLP) procedure focused on the intricate and unclear aspects of sarcasm, primarily used in sentiment analysis (SA), human-computer interaction, and various NLP applications. Concurrently, advancements in machine learning (ML) approaches facilitate the creation of effective SD systems. This manuscript presents the future search algorithm with deep learning assisted sarcasm detection and classification on social networking data (FSADL-SDCSND) approach. The major intent of the FSADL-SDCSND approach is in the effective and automated recognition of sarcastic text. In the presented FSADL-SDCSND technique, several data pre-processing stages are achieved to transform the data into a compatible format. Besides, the FSADL-SDCSND approach applies a bidirectional serial-parallel long short-term memory (BS-PLSTM) approach for SD and classification. The hyperparameter tuning process is accomplished by employing the future search algorithm for improving the recognition of the BS-PLSTM model. For superior output of the FSADL-SDCSND model, a sequence of simulations can be applied. The investigational outputs highlighted the improved solutions of the FSADL-SDCSND model with other approaches under diverse performance measures.]]>
