International Journal of Artificial Intelligence & Applications (IJAIA)

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi-monthly peer-reviewed journal that publishes articles on new results in all areas of artificial intelligence and applications. The journal aims to publish new research, projects, surveys, and industrial experiences that illustrate significant advances in artificial intelligence tools, algorithms, and their applications. Topics of interest include AI technologies like machine learning, neural networks, fuzzy logic, robotics, and natural language processing. Authors are invited to submit original papers by the upcoming deadlines of December 16, 2023 for notification by January 17, 2024.

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International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi-monthly peer-reviewed journal that publishes articles on new results in all areas of artificial intelligence and applications. The journal aims to publish new research, projects, surveys, and industrial experiences that describe advances in AI tools, algorithms, and applications. Topics of interest include AI techniques like machine learning, neural networks, fuzzy logic, and intelligent systems applied to areas such as computer vision, natural language processing, robotics, and bioinformatics. Authors are invited to submit original papers for consideration in the journal.

PhotoQR: A Novel ID Card with an Encoded View

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There is an increasing interest in developing techniques to identify and assess data to allow an easy and continuous access to resources, services or places that require thorough ID control. Usually, in order to give access to these resources, different kinds of documents are mandatory. In order to avoid forgeries without the need of extra credentials, a new system –named photoQR, is here proposed. This system is based on a ID card having two objects: one person’s picture (pre-processed via blur and/or swirl techniques) and one QR code containing embedded data related to the picture. The idea is that the picture and the QR code can assess each other by a proper hash value in the QR. The QR without the picture cannot be assessed and vice versa. An open source prototype of the photoQR system has been implemented in Python and can be used both in offline and real-time environments, which effectively combines security concepts and image processing algorithms to obtain data assessment.

12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...

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12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.

International Conference on NLP, Artificial Intelligence, Machine Learning an...

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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.

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

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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%.

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

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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

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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.

May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...

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

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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.

Information Extraction from Product Labels: A Machine Vision Approach

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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.

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

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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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The 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)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

International Journal of Artificial Intelligence & Applications (IJAIA)

gerogepatton

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi-monthly peer-reviewed journal that publishes articles on new results in all areas of artificial intelligence and applications. The journal aims to publish new research, projects, surveys, and industrial experiences that describe advances in AI tools, algorithms, and applications. Topics of interest include AI techniques like machine learning, neural networks, fuzzy logic, and intelligent systems applied to areas such as computer vision, natural language processing, robotics, and bioinformatics. Authors are invited to submit original papers for consideration in the journal.

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International Journal of Artificial Intelligence & Applications (IJAIA)

The International Journal of Artificial Intelligence & Applications (IJAIA)

International Journal of Artificial Intelligence & Applications (IJAIA)

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PhotoQR: A Novel ID Card with an Encoded View

gerogepatton

12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...

gerogepatton

International Conference on NLP, Artificial Intelligence, Machine Learning an...

gerogepatton

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

gerogepatton

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

gerogepatton

Immunizing Image Classifiers Against Localized Adversary Attacks

gerogepatton

May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...

gerogepatton

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

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Information Extraction from Product Labels: A Machine Vision Approach

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

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

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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

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

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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)

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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.

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

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10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.

THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...

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This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with theories and models reviewed and expanded constructs, the writers propose a new framework called “The Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational model.

13th International Conference on Software Engineering and Applications (SEA 2...

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13th International Conference on Software Engineering and Applications (SEA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Software Engineering and Applications. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts and establishing new collaborations in these areas.

AN IMPROVED MT5 MODEL FOR CHINESE TEXT SUMMARY GENERATION

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Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model as the core framework and initial weights. Additionally, In addition, this paper reduced the model size through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method, and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on the Chinese policy text summarization dataset.

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

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International Journal of Artificial Intelligence & Applications (IJAIA)

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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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