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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 121 Documents
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Search results for , issue "Vol 13, No 3: September 2024" : 121 Documents clear
Encoder-decoder approach for describing health of cauliflower plant in multiple languages Mondhe, Parag Jayant; Satone, Manisha P.; Wasatkar, Namrata N.
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.pp2971-2977

Abstract

Physically examining each plant to determine its state of health and determining the disease if plant is affected due to it, is challenging. The encoder - decoder approach is proposed for describing health of cauliflower plant in English, Hindi and Marathi languages from aerial images. Experiments are performed with different CNN models and LSTM combinations. The Multilanguage Cauliflower Captions Dataset (MCCD) is developed to evaluate the performance of the model. The dataset contains 1213 images where each image is described in 3 different languages. The dataset contains images of cauliflower plant affected due to bacterial spot rot, black rot and downy mildew diseases. It also contains images of healthy plant. The objective metrics such as BLEU scores and subjective criteria are used to decide the quality of the generated description.
Enhancing the English natural language processing dictionary using natural language processing++ Chikkarangaiah, Jayanth; Uday, Adarsh; De Hilster, David; Gangadhar, Shobha; Shetty, Jyoti
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.pp3466-3477

Abstract

Every natural language-based project requires the use of an English dictionary. But the current English dictionaries are not updated as the English language is constantly evolving. The English dictionary used for natural language processing (NLP) projects needs to be enhanced by adding more words and phrases. This helps in improving the accuracy of NLP applications such as machine translation, performance of text analysis, recognition, and part of speech (POS) tagging. Several approaches are proposed in this direction, this paper develops and demonstrates enhancement of the English dictionary using a more versatile and robust programming language known as NLP++, a plugin to distributed big data analytics platforms such as HPCC systems. The unique features of NLP++ language is the enabler for realization of the proposed approach. This paper also discusses key NLP techniques, dictionary refinements analysis using NLP and NLP++. The results show that the proposed approach using NLP++ has significantly improved the accuracy and comprehensiveness of the English dictionary.
Artificial intelligence in land use prediction modeling: a review Utami, Westi; Sugiyanto, Catur; Rahardjo, Noorhadi
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.pp2514-2523

Abstract

This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.
Optimizing pulmonary carcinoma detection through image segmentation using evolutionary algorithms Elavarasu, Moulieswaran; Govindaraju, Kalpana
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.pp2912-2922

Abstract

This paper’s goal is to suggest an image segmentation technique for use with medical images, specifically computer tomography scan images, to aid doctors in understanding the images. To address a variety of picture segmentation issues, it is necessary to investigate and apply novel evolutionary algorithms. The study focuses on pulmonary carcinoma, which is the cancer that affects males the most frequently across the globe. For proper treatment and life-saving measures, early identification of lung cancer is essential. To identify lung cancer, doctors frequently employ the computed tomography imaging technique. In order to extract tumours from lung scans, the study analyses the effectiveness of three optimization algorithms: k-means clustering, particle swarm optimization, and modified guaranteed convergence particle swarm optimization. The study also examines the pre-processing performance of four filters, namely the mean, bilateral, gaussian, and laplacian filters, shows that the bilateral filter is best suited for CT scans of the body. To test the proposed technique on 30 examples of lung scans. The proposed algorithm is tested on 30 sample lung images. The results show that the modified guaranteed convergence particle swarm optimization algorithm has the highest accuracy of 96.01%.
Learning methodologies towards leveraging security resiliency in internet-of-things environment Somanath, Sowmya; Ajay, Usha Banavikal
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.pp2490-2497

Abstract

The evolution of artificial intelligence (AI) has faciliated a significant contribution of machine learning and deep learning in order to improvise the security features of large internet-of-things (IoT) environment. Since last decade there has been different variants of learning-based methodologies towards leveraging security improvements among communication in IoT devices; however, it is yet to know the strength and weakness of them. Hence, this paper presents a review of security methodologies adopted in machine learning and deep learning-based techniques in IoT to understand the degree of resiliency and effectiveness of these techniques. The paper further contributes towards highlighting the current methodologies with respect to benefits and limiting factors along with exclusive highlights of research trends while the research gap explored assists in offering these insights. The distinct findings of the study assist in paving the work direction in future by harnessing better form of learning scheme.
Artificial intelligence-based learning model to improve the talents of higher education students towards the digitalization era Wahjusaputri, Sintha; Bunyamin, Bunyamin; Indah Nastiti, Tashia; Sopandi, Evi; Subagyo, Tatang; Veritawati, Ionia
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.pp3611-3620

Abstract

Artificial intelligence (AI) technology is a hallmark of the 4.0 revolution. The two main issues in Indonesia are infrastructure that needs to be equipped with technology and intelligence-based curriculum integrated with business and industry programs, and lecturers as educators who do not want to use and develop AI technology in applying guided learning models. This research aims to create a learning model based on AI that will help college students develop their talents while maintaining the Pancasila principles in the age of digitization. This study contains four stages: data collection, data analysis, research analysis outcomes, and validation of research analysis results. This research developed an AI-based learning model for use in higher education consisting of four dimensions: input, process, output, and outcome. The input dimension includes components such as students, lecturers, organizations, and infrastructure ready to adopt AI-based learning models. The process dimension consists of the elements that influence the operation of the AI-based learning model system and the functionality provided by the learning model. The output dimension includes characteristics that may be directly measured and process feedback. Finally, the outcome comprises the predicted outputs from the AI-based learning model.
Chatbot with ChatGPT technology for mental wellbeing and emotional management Andrade Arenas, Laberiano; Yactayo-Arias, Cesar
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.pp2635-2644

Abstract

There is a growing concern among the world's population about mental health in work, academics, and other contexts where stress, anxiety, and depression are common problems that negatively impact mental health. This study evaluates a chatbot powered by ChatGPT, offering a novel perspective on emotional intervention and mental well-being. It highlights the urgency of this approach in a context where mental health is critical, providing value by combining advanced technologies with emotional management. A multi-faceted approach was implemented to evaluate both usability and technical performance. The usability of the chatbot was evaluated by users using the System Usability Scale (SUS), while the technical performance was evaluated by experts. The active participation of 15 users provided a detailed perspective, resulting in an average usability of 83, reflecting a positive experience in interacting with the system. At the same time, five experts, through technical metrics, assigned an average technical performance of 4.28, indicating solid operational effectiveness. In conclusion, although more research is needed to customize and optimize chatbots over the long term, this approach holds promise for addressing mental health issues in a variety of settings and represents the integration of artificial intelligence to the benefit of those seeking help managing emotional disorders.
A survey of detecting leaf diseases using machine learning and deep learning in various crops Thangamuthu, Thilagraj; Kareem, Abdul; Kumara, Varuna; Udesh Naik, Utkrishna; Poojary, Sanjana; R, Bharath
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.pp2498-2505

Abstract

For agricultural productivity and food security to be guaranteed, early detection and treatment of illnesses are crucial. Machine learning (ML) and deep learning (DL) approaches can be used to precisely and successfully identify plant leaf diseases. A heterogeneous dataset comprising photos of both healthy and diseased leaves such as bacterial blights, fungal infections, and viral manifestations provides the foundation for the model building and training. Accuracy, precision, recall, and F1-score are the measures used to assess the model's performance. ML techniques are helpful in the identification and extraction of pertinent information from plant leaf pictures, whereas DL techniques in general, and convolutional neural networks (CNN), in particular, are remarkable at learning complex hierarchical representations. Therefore, DL architectures like CNN are utilized in conjunction with ML approaches like support vector machines (SVM), decision trees, and random forests to extract complicated patterns and attributes from leaf pictures. This research provides an extensive analysis of the performance and application of DL and ML approaches recently applied to the early identification of leaf diseases in different crops.
Intelligent automation computational modelling for contextual consulting services using Industry 4.0 Pandey, Vijay Kumar; Rathore, Neeraj; Bhosale, Narayan P
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.pp2557-2565

Abstract

The methodology towards operational management and service delivery associated with consulting firms, irrespective of any business domain, are less revisited in perspective from the automation-based process management. Adoption of Industry 4.0 has been attempted by various researchers from the business process management; however, there are less evidence of any computational model towards it. Apart from this, existing models are accompanied by various loopholes which makes its further challenging to analyze it on practical environment. Hence, the proposed study introduces a novel computational and analytical framework which is capable of performing the predictive modelling in order to meet the contextual service development and delivery demands in distributed environment. The novelty of this model is its inclusion of contextual data aggregation, contextual constraint analysis, predictive maintenence, and self-adjusting machine which are core attributes of Industry 4.0 automation standards. The study outcome shows that proposed system offers 20% cost reduction and 89% of minimized service delivery time in contrast to existing related work. Same has been also observed in benchmarked outcomes with state-of-art models.
A framework of attribute extraction and dependable aspect term selection from reviews of hospital websites Mohammed Basha, Nasreen Taj; Gowdra Shivappa, Girisha
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.pp3456-3465

Abstract

Online reviews found on hospital websites and external platforms constitute user-generated content where patients and their families share their firsthand encounters. As patients increasingly rely on online platforms to share their experiences, understanding the importance of their feedback is paramount for healthcare providers. The novelty of this research lies in the development of advanced frameworks that not only extract relevant information but also offer a more sophisticated and coherent analysis of the multifaceted aspects embedded in patient reviews. Hence, this work involves collecting data from various hospital websites, followed by data pre-processing to ensure accuracy and consistency. Subsequently, two distinct frameworks are proposed. The first framework aims to extract specific attributes (topics) mentioned in reviews, enhancing the granularity of information derived from the collected data. The second framework addresses the efficient extraction of aspect terms from pre-processed data, utilizing a coherence score-based approach called as modified latent dirichlet allocation term frequency-inverse document frequency (M-LDA TF-IDF). The M-LDA TF-IDF has achieved better a coherence score of 0.478 which is much better in comparison with other topic modelling approaches.

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