Fingerprint Recognition Using Deep Learning - A Review

David

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

Journal of Sustainable Development and Green Technology | 2023

Fusion Unleashed: A Comprehensive Analysis of Egypt's Digital Potential, the Growing Digital Economy, and its Socio-Economic Implications

This research paper explores the fusion-driven dynamics of Egypt's digital economy and its socio-economic implications, with a specific focus on the growth of e-commerce, FinTech, startups, and digital skills development. It explores the transformative effects of the digital economy on entrepreneurship, job creation, and inclusive participation in Egypt's evolving digital landscape. The study examines the barriers hindering inclusive access to digital technologies, digital skills, and digital financial services, aiming to propose strategies for promoting digital inclusion. Additionally, the research explores the role of social entrepreneurship in bridging the digital divide and fostering sustainable development in Egypt's digital economy. By analyzing the impact of social entrepreneurship initiatives, the study sheds light on innovative models that empower marginalized communities and contribute to the inclusive growth of the digital economy. The findings of this research contribute to the existing body of knowledge by providing insights into the unique dynamics of Egypt's digital economy and the potential of social entrepreneurship in driving digital inclusion and sustainable development. The study concludes with recommendations for policymakers, businesses, and stakeholders to foster an enabling environment that supports equitable and inclusive participation in Egypt's digital economy.

groups

Muhammad Eid Balbaa mail

link https://doi.org/10.54216/JSDGT.020201

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2023

An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System

The article presents the design and control of the adaptive neuro fuzzy Inference system (ANFIS) for the wind-driven permanent magnet synchronous generator (PMSG) in the grid connected system. The rectifier and inverter are connected with the PMSG output and the grid for maintaining the voltage at the grid under variable wind operations. Such interconnections have many challenges, like high harmonics at the output and an improper voltage profile. The harmonics are measured in terms of total harmonic distortion (THD). Performance parameters like peak overshoot and settling time of DC link voltage and rotor speed have been measured. The control of the rectifier and inverter has been assessed with the ANFIS and PID controllers. A closed strategic mechanism has been developed for the ANFIS and PID controllers for improving the performance parameters and harmonics.. Finally, it is observed that the peak overshoot (%) and settling time (sec) of the DC link voltage with ANFIS are 5.2% and 2.9 sec, which are found to be less in comparison to the PID controller with the values of 6.1% and 3.8 sec and other existing methods. Similarly, the settling time (sec) of rotor speed with ANFIS is 1.1 sec, which is less than the settling time (2.6 sec) of the PID controller. Another advantage of ANFIS is the reduction of THD (%) of 5.1% with respect to THD (%) of PID controllers of 6.2% and other existing methods. The reduced THD shows the improved version of the voltage profile.

groups

Nirmal Kumar Agarwal mail -

Manish Prateek mail -

Neeta Singh mail -

Abhinav Saxena mail

link https://doi.org/10.54216/FPA.120216

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

Neutrosophic inverse power Lindley distribution: A modeling and application for bladder cancer patients

The inverse power Lindley distribution is employed in the realm of survival analysis to imitate human lifetime data practices. The neutrosophic inverse power Lindley distribution (NIPLD) is intended to characterize a variety of survival data with indeterminacies. The established distribution is particularly useful for modeling uncertain data that is roughly positively skewed. This work discusses the key statistical properties of the developed NIPLD, including the neutrosophic survival function, neutrosophic hazard rate, and neutrosophic moments. In addition, the neutrosophic parameters are estimated using the well-known maximum likelihood estimation approach. To find out if the predicted neutrosophic parameters were reached, a simulation study is done. Not to mention, actual data has been utilized to discuss potential NIPLD real-world applications. Real data were used to illustrate how well the proposed model performed in compared to the current distributions.

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Marwah Yahya Mustafa mail -

Zakariya Yahya Algamal mail

link https://doi.org/10.54216/IJNS.210218

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

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.

groups

Nader Behdad mail -

Mohamed Saber mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

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.

groups

S. K. Towfek mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

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.

groups

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.

groups

David Winters mail

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

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new