International Journal of Artificial Intelligence & Applications (IJAIA)

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The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:

Design

International Journal of Artificial Intelligence & Applications (IJAIA)
ISSN: 0975-900X (Online); 0976-2191 (Print)
http://www.airccse.org/journal/ijaia/ijaia.html
Scope & Topics
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open
access peer-reviewed journal that publishes articles which contribute new results in all areas of
the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for
professionals and researchers in all fields of AI for researchers, programmers, and software and
hardware manufacturers. The journal also aims to publish new attempts in the form of special
issues on emerging areas in Artificial Intelligence and applications.
Authors are solicited to contribute to the journal by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances
in the areas of Artificial Intelligence & applications.
Topics of interest include, but are not limited to, the following:
 AI Algorithms
 Artificial Intelligence tools & Applications
 Automatic Control
 Bioinformatics
 Natural Language Processing
 CAD Design & Testing
 Computer Vision and Speech Understanding
 Data Mining and Machine Learning Tools
 Fuzzy Logic
 Heuristic and AI Planning Strategies and Tools
 Computational Theories of Learning
 Hybrid Intelligent Systems
 Information Retrieval
 Intelligent System Architectures
 Knowledge Representation
 Knowledge-based Systems
 Mechatronics
 Multimedia & Cognitive Informatics
 Neural Networks
 Parallel Processing
 Pattern Recognition
 Pervasive computing and ambient intelligence
 Programming Languages
 Reasoning and Evolution
 Recent Trends and Developments
 Robotics
 Semantic Web Techniques and Technologies

 Soft computing theory and applications
 Software & Hardware Architectures
 Web Intelligence Applications & Search
Paper Submission
Authors are invited to submit papers for this journal through E-mail: ijaiajournal@airccse.org.
Submissions must be original and should not have been published previously or be under
consideration for publication while being evaluated for this Journal.
Important Dates
 Submission Deadline : December 03, 2023
 Notification : January 03, 2024
 Final Manuscript Due : January 10, 2024
 Publication Date : Determined by the Editor-in-Chief
Current issue URL: https://airccse.org/journal/ijaia/current2022.html
Contact Us:
Here's where you can reach us : ijaiajournal@airccse.org or ijaiajournal@yahoo.com
or ijaia@aircconline.com
Submission Link: http://coneco2009.com/submissions/imagination/home.html

International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.

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As digital technology becomes more deeply embedded in power systems, protecting the communication networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3) represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities. Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network (CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to train and test our model. The results of our experiments show that our CNN-LSTM method is much better at finding smart grid intrusions than other deep learning algorithms used for classification. In addition, our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection accuracy rate of 99.50%.

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3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.

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This research tackles the challenge of manual data extraction from product labels by employing a blend of computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition (OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food Facts API (Application Programming Interface) for database population and text-only label prediction. The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set. Importantly, our approach enables visually impaired individuals to access essential information on product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep learning and OCR in automating label extraction and recognition.

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Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution, and allow bad solutions to be accepted during the search. The improved algorithm is applied to function optimization, and the simulation results show that the calculation accuracy and stability of the algorithm are improved, and the effectiveness of the improved algorithm is verified

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