call for papers - International Journal of Artificial Intelligence & Applications (IJAIA)

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gerogepatton

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.
AIRCC Publishing Corporation
International Journal of Artificial Intelligence & Applications
(IJAIA)
ISSN : 0975-900X ( Online ); 0976-2191 (Print)

• 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
AIRCC Publishing Corporation
International Journal of Artificial Intelligence & Applications
(IJAIA)
ISSN : 0975-900X ( Online ); 0976-2191 (Print)

• 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
AIRCC Publishing Corporation
International Journal of Artificial Intelligence & Applications
(IJAIA)
ISSN : 0975-900X ( Online ); 0976-2191 (Print)

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.
AIRCC Publishing Corporation
International Journal of Artificial Intelligence & Applications
(IJAIA)
ISSN : 0975-900X ( Online ); 0976-2191 (Print)

AIRCC Publishing Corporation
International Journal of Artificial Intelligence & Applications
(IJAIA)
ISSN : 0975-900X ( Online ); 0976-2191 (Print)
For other details please visit http://www.airccse.org/journal/ijaia/ijaia.html
Important Dates
Submission Deadline : October 26, 2019
Notification : November 26, 2019
Final Manuscript Due : December 04 ,2019
Publication Date : Determined by the Editor-in-Chief
Contact us E-mail: ijaiajournal@airccse.org

International Journal of Advanced Information Technology (IJAIT) is a bi monthly open access peer-reviewed journal, will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced information technology applications and services. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of advanced IT. Core areas of advanced IT and multi-disciplinary and its applications will be covered during in this journal. We expect that the journal and its publications will be a trigger for further related research and technology improvements in this important subject The topics suggested by this issue can be discussed in term of concepts, surveys, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.

International Journal of Advances in Artificial Intelligence (IJAAI)

ijfcst journal

International Journal of Advances in Artificial Intelligence (IJAAI) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Artificial Intelligence. The journal focuses on all technical and practical aspects of Artificial Intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced Artificial Intelligence and establishing new collaborations in these areas.

Ijaca

ijfcst journal

International Journal of Automation & Computer Applications(IJACA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Automation & Computer Applications . The journal focuses on all technical and practical aspects of Automation & Computer Applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced Automation & Computer Applications and establishing new collaborations in these areas.

International Journal of Advances in Artificial Intelligence (IJAAI)

ijfcst journal

International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

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:

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

gerogepatton

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%.

10th International Conference on Artificial Intelligence and Applications (AI...

gerogepatton

10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Immunizing Image Classifiers Against Localized Adversary Attacks

gerogepatton

This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.

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gerogepatton

3rd International Conference on Artificial Intelligence Advances (AIAD 2024)

gerogepatton

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.

Information Extraction from Product Labels: A Machine Vision Approach

gerogepatton

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.

10th International Conference on Artificial Intelligence and Applications (AI...

gerogepatton

Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...

gerogepatton

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

10th International Conference on Artificial Intelligence and Soft Computing (...

gerogepatton

10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology, and applications of Artificial Intelligence, Soft Computing. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

Employee attrition refers to the decrease in staff numbers within an organization due to various reasons. As it has a negative impact on long-term growth objectives and workplace productivity, firms have recognized it as a significant concern. To address this issue, organizations are increasingly turning to machine-learning approaches to forecast employee attrition rates. This topic has gained significant attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims to fill this gap by providing an overview of research conducted on applying machine learning to predict employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted, summarized, and classified. Most studies agree on conducting comparative experiments with multiple predictive models to determine the most effective one.From this literature survey, the RF algorithm and XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms. Additionally, the application of deep learning to employee attrition prediction issues also shows promise. While there are discrepancies in the datasets used in previous studies, it is notable that the dataset provided by IBM is the most widely utilized. This study serves as a concise review for new researchers, facilitating their understanding of the primary techniques employed in predicting employee attrition and highlighting recent research trends in this field. Furthermore, it provides organizations with insight into the prominent factors affecting employee attrition, as identified by studies, enabling them to implement solutions aimed at reducing attrition rates.

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International Journal of Artificial Intelligence & Applications (IJAIA)

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International Journal of Artificial Intelligence & Applications (IJAIA)

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International Journal of Artificial Intelligence & Applications (IJAIA)

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International Journal of Artificial Intelligence & Applications (IJAIA)

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International Journal of Artificial Intelligence & Applications (IJAIA)

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International Journal of Artificial Intelligence & Applications (IJAIA)

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International Journal of Artificial Intelligence & Applications (IJAIA)

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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

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AN IMPROVED MT5 MODEL FOR CHINESE TEXT SUMMARY GENERATION

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A Machine Learning Ensemble Model for the Detection of Cyberbullying

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The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms. Motivated by this necessity, we present this paper to contribute to developing an automated system for detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to previous experiments on the same dataset. We employed the stacking ensemble machine learning method, utilizing four various feature extraction techniques to optimize performance within the stacking ensemble learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we achieved superior results compared to traditional machine learning classifier models. The stacking classifier achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.

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