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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 1,722 Documents
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A multi-algorithm approach for phishing uniform resource locator’s detection Sree Guddanti, Devi; Chiramdasu, Rupa; Gayathri Uppuluri, Mahalakshmi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp358-367

Abstract

Nowadays, the internet is used to organise a wide range of cybersecurity risks. Threats to cybersecurity include a broad spectrum of malevolent actions and possible hazards that affect data, networks, and digital systems. Cybersecurity dangers that are commonly encountered are distributed denial-of-service (DDoS) attacks, phishing, and malware. Phishing attempts frequently use text messages, email, and uniform resource locators (URLs) to target specific people while impersonating trustworthy sourcesin an effort to trick the victim. Consequently, machine learning plays a critical role in stopping cybercrimes, especially those that involve phishing assaults. The suggested model is based on a well constructed dataset that has been enhanced with 32 features. By combining the features of several machine learning methods, such as random forest, CatBoost, AdaBoost, and multilayer perceptron, the suggested model greatly increases the precision of phishing URL detection. Evaluation indicators that highlight the model's effectiveness in defending against cyber threats include precision, recall, accuracy, and F1-score. These metrics also highlight the urgent need for proactive cybersecurity measures.
Detection of location-specific intra-cranial brain tumors Usharani, Shola; Lakshmanan, Rama Parvathy; Rajakumaran, Gayathri; Basu, Aritra; Nandam, Anjana Devi; Depuru, Sivakumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp428-438

Abstract

Mutations or abnormalities in genes can occasionally cause cells to grow uncontrolled, resulting in a tumor, which is very dangerous. These are the most prevalent cancer causes. They are caused by significant damage to genes in a specific cell during a person's existence. Brain tumors are increasing rapidly, majorly brain tumor cases in the US are projected to rise from 27,000 in 2020 to 31,000 in 2023 at an annual growth rate of 1.5%, all the cases are rising because of the detection of the tumors in the late phase. Thus, it needs the hour to create something which can solve this anomaly and help us detect the tumor rapidly and efficiently. While major research papers on brain tumor detection mainly focus on the detection and classification of the tumors, the presented research aims to first detect the tumor using pre-recognized photos using machine learning object detection models. Then after successful detection of the tumor, the study team plans to determine its precise coordinates and display the tumor and its location in the picture.
Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning H. Ali, Esraa; Sadek, Sawsan; F. Makki, Zaid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4080-4094

Abstract

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.
A neural machine translation system for Kreol Repiblik Moris and English Pudaruth, Sameerchand; Armoogum, Sheeba; Kumar Betchoo, Nirmal; Sukhoo, Aneerav; Gooria, Vandanah; Peerally, Abdallah; Zafar Khodabocus, Mohammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4976-4987

Abstract

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.
Systematic review of artificial intelligence with near-infrared in blueberries Cayhualla Amaro, Liset; Rau Reyes, Sebastian; Acuña Meléndez, María; Ovalle, Christian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3761-3771

Abstract

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.
Improving the performance of the fuzzy-internet of things joint system by using an efficient service deployment algorithm Razzaq, Ali Ahmed; Rao, Kunjam Nageswara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp172-181

Abstract

The aim of this paper to present research proposes a quality of service (QoS)-fog service placement algorithm, which is in the context of providing better performance when there are requests for high-priority (delay-sensitive) services. Our proposed algorithm attempts to maintain the implementation of delay-sensitive services within a fog environment. Its performance was evaluated by comparing it with two placement strategies (cloud only, edge-ward), using the MobFogSim simulator. The results showed that the proposal achieved better performance than the two mentioned strategies, from two perspectives (according to the scenarios). The first is at the service level, where the algorithm was able to achieve a lower average response time and the other is at the system level, as it was able to reduce the total energy consumption by adopting a mechanism to save energy when there is a low network load.
Unsupervised hindi word sense disambiguation using graph based centrality measures Jha, Prajna; Agarwal, Shreya; Abbas, Ali; Singh, Satyendr; Jahan Siddiqui, Tanveer
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4957-4964

Abstract

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.
Parallel rapidly exploring random tree method for unmanned aerial vehicles autopilot development using graphics processing unit processing Mochurad, Lesia; Davidekova, Monika; Mitoulis, Stergios-Aristoteles
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp712-723

Abstract

Autonomous air movement systems hold great potential for transforming various industries, making their development essential. Autopilot design involves advanced technologies like artificial intelligence, machine learning, and big data. This paper focuses on developing a parallel rapidly-exploring random tree (RRT) algorithm using compute unified device architecture (CUDA) technology for efficient processing on graphics processing units (GPUs). The study evaluates the algorithm's performance in automated trajectory planning for unmanned aerial vehicles (UAVs). Numerical experiments show that the parallel algorithm outperforms the sequential central processing unit (CPU)-based version, especially as task complexity and state space dimensions increase. In scenarios with numerous obstacles, the parallel algorithm maintains stable performance, making it well-suited for various applications. Comparisons with CPU-based methods highlight the advantages of GPU use, particularly in terms of speed and efficiency. Additionally, the performance of two GPU models, NVIDIA RTX 2070 and T4 is compared, with the T4 demonstrating superior performance for similar tasks. Future research should explore integrating multiple algorithms for a more comprehensive UAV autopilot system. The proposed approach stands out for its stability and practical applicability in real-world autopilot implementations.
Implications of artificial intelligence chatbot models in higher education Khandakar, Hissan; Ali Fazal, Syed; Fattah Afnan, Kazi; Kamrul Hasan, Khandakar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3808-3813

Abstract

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.
Multi platforms fake accounts detection based on federated learning Azer, Marina; H. Zayed, Hala; A. Gadallah, Mahmoud E.; Taha, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3837-3848

Abstract

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.

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