Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition

Amel Ali; Marwa M.; Wei Hong

doi:https://doi.org/10.54216/JAIM.050104

International Journal of Neutrosophic Science | 2023

Algebraic Operations on Pythagorean neutrosophic sets (PNS): Extending Applicability and Decision-Making Capabilities

Pythagorean neutrosophic sets (PNS) have been recognized as a highly effective mechanism for managing situations characterized by indeterminacy and inconsistency within decision-making procedures. This paper delves into the examination of algebraic operations performed on PNS, thereby expanding their scope of application, and enhancing their utility. In this study, we put forth a set of algebraic operations that can be applied to PNS. These operations encompass addition, multiplication, scalar multiplication, and power. These operations facilitate the efficient manipulation and combination of PNS, thereby enhancing decision-making in scenarios characterized by uncertainty and vagueness. To demonstrate the efficacy of these operations, we will present several illustrative examples accompanied by corroborating proofs. The introduction of algebraic operations enhances the capabilities of PNS, thereby creating opportunities for their practical application.

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Jamiatun Nadwa Ismail mail -

Zahari Rodzi mail -

Faisal Al-Sharqi mail -

Ashraf Al-Quran mail -

Hazwani Hashim mail -

Nor Hashimah Sulaiman mail

link https://doi.org/10.54216/IJNS.210412

Volume & Issue

Vol. Volume 21 / Iss. Issue 4

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

A Comparative Analysis of Methods for Detecting and Diagnosing Breast Cancer Based on Data Mining

Breast cancer is a significant public health concern worldwide, and early detection is crucial for its treatment. Although breast cancer has been extensively studied, there is still room for improvement in its classification accuracy. This study aims to improve the classification accuracy of breast cancer by applying information gain feature selection and machine learning techniques to the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The information gain method is utilized to reduce feature characteristics, and machine learning algorithms such as support vector machine (SVM), naive Bayes (NB), and C4.5 decision tree are employed for breast cancer classification. The study also conducts a comparison analysis based on accuracy value. The proposed model achieves maximum classification accuracy (100%) and a weighted average for precision (100%) and recall (100%) using a C4.5 decision tree, while SVM accuracy (98.42%) and weighted average for precision (98.17%) and recall (98.58%) are achieved using a C4.5 decision tree. The NB algorithm attains an accuracy of 96%, with a weighted average for precision (18.57%) and recall (50%). The proposed model's results are compared to similar studies and demonstrate significant progress, indicating new opportunities for breast cancer detection.

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Ahmed T. Alhasani mail -

Hussein Alkattan mail -

Alhumaima Ali Subhi mail -

El-Sayed M. El-Kenawy mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JAIM.040201

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Utilizing Artificial Intelligence to Provide Intelligent Control of Traffic Lights

  Mathematical programming can express competency concepts in a well-defined mathematical model for a particular. As both the population and the number of cars in cities continue to grow, one of the most pressing problems is the resulting increase in congestion. Not only can traffic jams make drivers' trips longer and more stressful, but they also increase the amount of gasoline they use and contribute to pollution in the air.  Despite the fact that it appears to be present everywhere, the megacities are the ones that are most negatively impacted by it. In addition, the fact that it is always growing makes it essential to compute the road traffic density in real time in order to achieve more accurate signal control and more efficient traffic management. One of the most important aspects that determines how well traffic moves is the traffic controller. As a result, there is a growing requirement for improved traffic control that should be optimized to better meet these rising expectations. For the purpose of determining the volume of traffic at intersections, the system that we have designed will use image processing and artificial intelligence to analyze live footage captured by cameras installed there. In addition to this, it places an emphasis on the algorithm that determines when to change the color of the traffic lights based on the number of vehicles in an area. This helps to ease congestion, which in turn speeds up transit for pedestrians and reduces air pollution.

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Khadija Shazly mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JAIM.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

PAPR Reduction in OFDM System Using Metaheuristic Algorithm

The advancement of technology necessitates the development of more sophisticated modulation strategies for wideband digital communication systems. The requirements for high-speed data transmissions can be effectively met by utilizing orthogonal frequency division multiplexing, which is an effective technique. However, a high peak-to-average power ratio (PAPR) is one of the key limits that OFDM systems face, both in terms of their performance and their power efficiency. The evaluation of the PAPR reduction has become a topic of widespread interest in this present decade due to the relevance it holds in the industrial and scientific communities. The purpose of this study is to show the combination of the bat algorithm with the partial transmit sequence scheme as an effective way for reducing PAPR that also eases the burden of computing work. For the purpose of providing a comparative evaluation of the PAPR reduction performance, a number of simulations using various partial transmit sequence schemes have been carried out. The findings of the simulation show that the BA-PTS scheme has the potential to provide superior PAPR reduction performance while simultaneously reducing the computational load.

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Nader Behdad mail -

Mohamed Saber mail

link https://doi.org/10.54216/JAIM.040203

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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Journal of Artificial Intelligence and Metaheuristics | 2023

Navigating the Storm: Cutting-Edge Risk Mitigation and Analysis for Volatile Markets

In volatile markets, risk mitigation and analysis play a crucial role in ensuring financial stability and profitability. This paper presents a new framework for risk mitigation and analysis tailored specifically for volatile markets. The framework combines data analysis, statistical modeling, and domain expertise to provide a inclusive and proactive approach to managing risks. The key theories and beliefs underlying the framework are discussed, with a focus on the use of logistic regression as the core risk predictor. The framework's development process, including data collection and preprocessing, feature engineering, and model selection, is outlined. Moreover, the incorporation of the Weight of Evidence (WoE) technique to enhance the interpretability and effectiveness of the logistic regression model is explained. The proposed framework aims to encourage market participants with valuable insights into risk levels and facilitate informed decision-making and effective risk mitigation strategies in volatile market environments.

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S. K. Towfek mail

link https://doi.org/10.54216/JAIM.040204

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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Journal of Artificial Intelligence and Metaheuristics | 2023

Identification of Cardiovascular Disease Risk Factors Among Diabetes Patients using ontological Data Mining Techniques

Diabetes patients face a severe health cost from cardiovascular disease (CVD). Recognising the risk factors for CVD in this group of people is critical for developing effective preventative and management measures. In this study, we use an ontological data mining approach, LightGBM, to analyze a dataset of diabetes patients and investigate the risk variables that contribute to CVD. The association between diabetes and CVD is investigated, emphasising the increased risk that diabetes patients confront. We look into the demographics, health behaviors, and physiological indicators that influence the emergence of heart disease in this population. We use LightGBM to find complicated relationships and trends within the dataset, allowing us to identify critical risk variables. Our research contributes to the field by offering a thorough examination of the diabetes-CVD link and applying an advanced machine-learning technique for information extraction. The results have implications for specific interventions, risk evaluation models, and personalised therapy approaches aimed at reducing the effect of CVD in diabetics.

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Abdelaziz A. Abdelhamid mail -

Marwa M. Eid mail -

Mostafa Abotaleb mail -

S. K. Towfek mail

link https://doi.org/10.54216/JAIM.040205

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Fingerprint Recognition Using Deep Learning - A Review

There have been efforts to address the problems with fingerprint identification systems that require physical contact by creating contactless fingerprint identification systems. Numerous studies on various aspects of contactless fingerprint processing, including the use of deep learning in various algorithmic frameworks, classical image processing, and the machine-learning pipeline, have been published. It was demonstrated that the deep learning-based solutions were more accurate than the alternatives. This effort was driven by a desire to provide a thorough assessment of these successes and their identified limitations. This study examined three approaches to contactless fingerprint recognition: (i) methods for capturing images of the fingerprint, (ii) traditional preprocessing techniques for enhancing fingerprint images for recognition tasks, and (iii) deep learning. (i) taking a picture of your finger, and (ii) using conventional image processing to get the picture ready for recognition. In total, eight research papers were found to meet both the inclusion and exclusion criteria. Based on this review's findings, we discussed the potential benefits of deep learning methods for biometrics and the challenges that still need to be overcome before these methods can be used in practical biometric settings.

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David Winters mail

link https://doi.org/10.54216/JAIM.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Detection of Breast Cancer Based on Feature Extraction Using WPSO in Conjunction with CNN

According to cancer reports from the past few years in India, thirty percent of instances are breast cancer, and furthermore, it is possible that this percentage would increase in the near future. In addition, one woman is given a diagnosis every two minutes, and another woman passes away every nine minutes as a result of her condition. People who are diagnosed with cancer at an earlier stage have a better chance of survival. Micro calcifications are one of the most important symptoms to look out for when trying to diagnose breast cancer in its earlier stages. Several scientific investigations have been carried out in an effort to combat this illness, for which techniques related to machine learning can be utilized to a significant extent. Particle swarm optimization, often known as PSO, is acknowledged as one of several effective and promising methods for identifying breast cancer. This method helps medical professionals administer treatment that is both timely and appropriate. The weighted particle swarm optimization (WPSO) approach is utilized in this work for the purpose of extracting textural information from the segmented mammography picture for the purpose of classifying micro calcifications as normal, benign, or malignant, hence increasing the accuracy. A portion of the cancerous growth is removed from the breast region using optimizing techniques. In this article, Convolutional Neural Networks (CNNs) are presented for the purpose of identifying breast cancer in order to cut down on the amount of manual overhead. The CNN framework is built in order to extract features as effectively as possible. This algorithm was developed to identify areas in mammograms (MG) that are suspicious for cancer and to classify those areas as normal or abnormal as quickly as possible. This model makes use of MG pictures that were gathered from a variety of hospitals in the surrounding area.

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Mohamed Saber mail -

Nader Behdad mail -

Ehsaneh khodadadi mail

link https://doi.org/10.54216/JAIM.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Data Mining Techniques in Predictive Medicine: An Application in hemodynamic prediction for abdominal aortic aneurysm disease

Due to its potential to enhance patient outcomes and ease individualized therapy, predictive medicine has received considerable interest in recent years. In this article we examine the use of data mining in predictive medicine, with a particular emphasis on hemodynamic prediction for abdominal aortic aneurysm (AAA) disease. In AAA, the abdominal aortic wall becomes weakened and may rupture, putting the patient's life in danger. Clinical decision making and treatment planning for AAA rely heavily on accurate hemodynamic prediction. For developing these predictive models for hemodynamic assessment, we use the well-known data mining techniques of Random Forest (RF) and AdaBoost. To capture complicated interactions, the RF approach employs a collection of decision trees, while AdaBoost iteratively improves the model by giving more weight to examples that were incorrectly classified. The experimental evidence shows that these methods are effective in providing reliable estimates of the hemodynamics of AAA. This research adds to the expanding field of predictive medicine by providing new understanding of the potential of data mining methods to improve the quality of care for patients with AAA illness.

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Doaa Sami Khafaga mail -

Abdelhameed Ibrahim mail -

S. K. Towfek mail -

Nima Khodadadi mail

link https://doi.org/10.54216/JAIM.050103

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Advancing Communication for the Deaf: A Convolutional Model for Arabic Sign Language Recognition

For the deaf population that speaks Arabic, Arabic Sign Language (ArSL) is an essential means of communication. This research presents a convolutional model for recognizing Arabic sign language because of the importance of clear communication. We hope to improve the deaf community's access to communication and broaden its sense of belonging by harnessing deep learning's power and fine-tuning the model to ArSL's particularities. To represent the complex hand movements and visual patterns that are characteristic of ArSL, the proposed model makes use of a variety of carefully made architectural decisions, such as the number of layers, the size of the kernels, the activation functions, and the pooling approaches. Our model outperforms state-of-the-art machine learning techniques, as shown by experimental findings on a large dataset. These results not only lay the groundwork for future developments in sign language recognition, but also demonstrate the promise of our technique in improving communication for the Arabic-speaking deaf community.  

groups

Amel Ali Alhussan mail -

Marwa M. Eid mail -

Wei Hong Lim mail

link https://doi.org/10.54216/JAIM.050104

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new