Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion

Ahmed; Alia N.

doi:https://doi.org/10.54216/FPA.080201

Journal of Neutrosophic and Fuzzy Systems | 2022

On Some Results About The Second Order Neutrosophic Differential Equations By Using Neutrosophic Thick Function

In this paper we define a novel neutrosophic differential equation by using neutrosophic thick function. In addition, we present the concept of Laplace transformation on neutrosophic thick function and apply this transformation to solve some neutrosophic differential equations. Also, we illustrate many examples to clarify the methods and algorithms.

groups

Ahmad Salama mail -

Rasha Dalla mail -

Malath Al Aswad mail -

Rozina Ali mail

link https://doi.org/10.54216/JNFS.040104

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2022

Some Results About 2-Cyclic Refined Neutrosophic Complex Numbers

This paper is dedicated to define for the first time the concept of 2-cyclic complex refined neutrosophic numbers as a direct application of 2-cyclic refined neutrosophic sets. Also, it presents some of their elementary properties such as conjugates, absolute values, invertibility, and algebraic operations. Also, we illustrate many examples to clarify the validity of our discussion

groups

Ahmad Salama mail -

Rasha Dalla mail -

Malath Al Aswad mail -

Rozina Ali mail

link https://doi.org/10.54216/JNFS.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2022

A Study of Neutrosophic Differential Equations By Using the One-Dimensional Geometric AH-Isometry Of NeutrosophicLaplace Transformation

In this paper, we study the  neutrosophic differential equation by using the one-dimensional geometric AH-Isometry of Neutrosophic Laplace Transformation.Where we use this AH-isometry to find the algebraic image of this transformation, and then to apply this image directly on the problem of finding the solutions of differential equations.

groups

Ahmad Salama mail -

Malath Al Aswad mail -

Rasha Dalla mail -

Rozina Ali mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2022

A New Data Fusion Model for Medical Image Encryption in IoT Environment

An improvement of the Internet of Things (IoT) was forecast for changing the healthcare industry and is generating the increase of the Internet of Medical Things (IoMT). The IoT revolution was surpassed the present-day human service with promise social prospects, mechanical, and financial. During this condition, it can be essential for framing an effectual approach for guaranteeing the safety and reliability of t patient’s symptomatic information which are transmitted and received in IoT criteria. This study introduces a new data fusion model in IoT environment. The proposed model is called SSOECC-MIC model focuses on the design of effective encryption scheme with optimal key generation process for IoT environment. To achieve this, the SSOECC-MIC model designs an ECC model for the encryption and decryption of medical images effectively. To further improve the security performance of the ECC model, the optimal key generation process is carried out by the use of swallow swarm optimization (SSO) algorithm. For examining the enhanced performance of the SSOECC-MIC model, a wide ranging experimental analysis is carried out. The experimental outcomes reported the betterment of the SSOECC-MIC model over recent models.

groups

Reem Atassi mail -

Fuad Alhosban mail -

Milan Dordevic mail

link https://doi.org/10.54216/FPA.080102

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2022

Intelligent Red Deer Algorithm based Energy Aware Load Balancing Scheme for Data Fusion in Cloud Environment

A cloud computing (CC) method was effectual if its sources were used in optimal way and an effectual consumption is attained by using and preserving proper management of cloud sources. Resource management can be attained through adoption of powerful source scheduling, allotment, and robust source scalability methods. The balancing of load in cloud is performed at VM level or physical machine level. A task use sources of VM and whenever a bunch of tasks reaches VM, the sources will be exhausted means no source is now existing for handling the extra task requests. This article develops an Intelligent Red Deer Algorithm based Energy Aware Load Balancing Scheme for data fusion in Cloud Environment, called IRDA-EALBS model. The presented IRDA-EALBS model majorly concentrates on the balancing of load among the virtual machines (VMs) in the cloud environment. The IRDA-EALBS model is mainly stimulated from the nature of red deers during a breading period. In addition, the IRDA-EALBS model derived an objective function to minimize energy consumption and maximize makespan. To demonstrate the enhanced performance of the IRDA-EALBS model, a wide range of experimental analyses is carried out. The simulation results highlighted the enhanced outcomes of the IRDA-EALBS model over other load balancers in the cloud environment.

groups

Abedallah Zaid Abualkishik mail -

Rasha Almajed mail -

William Thompson mail

link https://doi.org/10.54216/FPA.080103

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Journal of Cosmology, Filaments and Astrobiology | 2022

Our universe is but one page in a large book, each with different sets of physical laws and types of Consciousness

Our universe is but one page in a large book. For example, things and Beings can travel between Universes, intentionally or unintentionally. The pages of the "book" of Universe are connected at a common point and move outwards in a rotation, overlaid in a spiral manner, related to the phi ratio. Each "page" which is "touching" the next page and the previous page, has physical laws and forms of Consciousness that are only slightly different from one another.

groups

Robert N. Boyd mail

link https://doi.org/10.54216/JCFA.010204

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Journal of Cosmology, Filaments and Astrobiology | 2022

"... we lit beacons... in the Universe only we are alone ..."

Superluminal contact with extraterrestrial civilizations can be carried out either by observing the appearance   of  regular gaps in the CMB relic microwave background or by manipulating the state of  quantum   fluctuation using the dynamic Casimir effect; potentially, it is also possible to observe patterns in the relic background of Goldstone bosons – axions. It is more correct to evaluate the search for a  signal on an axion background of noise as a spectrum of intermediate NeutralA - gaps in various relict  backgrounds that form a recognizable pattern of NonA formed NeutralA and AntiA, which is the neutrosophic signal of NonA for us. Observation of the NonA neutrosophic signal for axions is possible               with scalar/vector potential detectors based on the Aharonov-Bohm effect or on the basis of the Wolf-Bragg`s condition for X-ray diffraction/interference. The creation of axion telescopes with matrices of  nano- or micron-sized pixels for observing the cosmic NonA for Rφ and RA will make it possible to establish superluminal Contact of Civilizations.  

groups

Alexander B. Ilin mail

link https://doi.org/10.54216/JCFA.010205

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2022

Machine Learning Data Fusion for Plant Disease Detection and Classification

  It is crucial to quickly identify plant diseases since they impede the development of affected plants. Despite the widespread use of Machine Learning (ML) models for this purpose, the recent advances in a subset of ML known as Deep Learning (DL) suggest that this field of study has much room for improvement in terms of detection and classification accuracy. To identify and categorize plant diseases, a wide variety of established and customized DL architectures are deployed with several visual analysis methods. In this study, we use deep learning techniques to create a model for a convolutional neural network that can identify and diagnose plant diseases using very basic photos of healthy and sick plant leaves. The models were trained using an open library of 20639 photos that included both healthy and diseased plants across 15 different classifications. Some model architectures were trained, with the highest performance obtaining a success rate of 97.70% in detecting the correct [plant, illness] pair (or healthy plant). Due to its impressive success rate, the model is a valuable advising or early warning tool, and its technique might be developed to help an integrated plant disease diagnosis system function in actual production settings.

groups

El Mehdi Cherrat mail -

Amine Saddik mail

link https://doi.org/10.54216/FPA.080104

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2022

Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion

Among women, breast cancer has a high incidence and high fatality rate. Due to a lack of early detection facilities and barriers to accessing technological improvements in battling this illness, mortality rates are disproportionately greater in underdeveloped countries. Biopsies done by trained pathologists are the only certain approach to diagnosing cancer. With the use of computer-aided diagnostic algorithms, pathologists may improve their efficiency, objectivity, and consistency in making diagnoses. A key goal of this research is to create an accurate automated system for diagnosing breast cancer that can function in the current clinical setting. In this work, we offer an algorithm for the identification of breast cancer that uses asymmetric analysis as the basic choice and decision-level fusion. Fusion of local nuclei features extracted using convolutional neural network (CNN) models pre-trained on the database constitutes the picture feature representation. The dataset is accessible for public use, and the results are evaluated by running 25 random trials with an 80%-20% split between train and test. Overall, the suggested framework was 86%. The proposed framework is shown to outperform numerous current methods and to provide results on par with the state-of-the-art techniques without requiring extensive computing resources. Breast cancer detection from histological pictures may be greatly aided by the use of this qualitative approach based on transfer learning.

groups

Surinder Kaur mail -

Javalkar Dinesh Kumar mail -

Gopal Chaudhary mail -

Manju Khari mail

link https://doi.org/10.54216/FPA.080105

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2022

Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion

Among the most frequent forms of cancer, skin cancer accounts for hundreds of thousands of fatalities annually throughout the globe. It shows up as excessive cell proliferation on the skin. The likelihood of a successful recovery is greatly enhanced by an early diagnosis. More than that, it might reduce the need for or the frequency of chemical, radiological, or surgical treatments. As a result, savings on healthcare expenses will be possible. Dermoscopy, which examines the size, form, and color features of skin lesions, is the first step in the process of detecting skin cancer and is followed by sample and lab testing to confirm any suspicious lesions. Deep learning AI has allowed for significant progress in image-based diagnostics in recent years. Deep neural networks known as convolutional neural networks (CNNs or ConvNets) are essentially an extended form of multi-layer perceptrons. In visual imaging challenges, CNNs have shown the best accuracy. The purpose of this research is to create a CNN model for the early identification of skin cancer. The backend of the CNN classification model will be built using Keras and Tensorflow in Python. Different network topologies, such as Convolutional layers, Dropout layers, Pooling layers, and Dense layers, are explored and tried out throughout the model's development and validation phases. Transfer Learning methods will also be included in the model to facilitate early convergence. The dataset gathered from the ISIC challenge archives will be used to both tests and train the model.

groups

Ahmed Abdelaziz mail -

Alia N. Mahmoud mail

link https://doi.org/10.54216/FPA.080201

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new