An Efficient Machine Learning based Cervical Cancer Detection and Classification

Ahmed N. Al; Hamam

doi:https://doi.org/10.54216/JCIM.020203

International Journal of Neutrosophic Science | 2021

Single-valued Plithogenic graph for handling multi-valued attribute data and its context

  Recently, several researchers paid attention towards dealin with uncertainty in data with neutrosophic attributes. In this process, a problem is addressed while dealing with contradiction and its impact on decision making. It is considered as one of the major issues for data science researchers working in three-way fuzzy concept lattice. To deal with this issue, current paper tried to introduce the algebra of single-valued Plithogenic graph and its visualization based on infium and supremum. The proposed method also demonstated with an illustrative example for better understanding.  

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Prem Kumar Singh mail

link https://doi.org/10.54216/IJNS.150204

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2019

A PSPICE Fast Model for the Single Electron Transistor

Motivated by the merits of low power dissipation, ultra small size, and high speed of many nanoelectronic devices, They have been demonstrated to ensure future progress. Single electron devices became one of the most important nanoelectronic devices due to their interesting electrical characteristics and behavior. Many research efforts moved to describe their electrical characteristics to use them with conventional electronic devices. This paper deals with modeling and simulation of such new electronic devices. This paper presents a model for the Single Electron Transistor (SET) and its application in simulating hybrid SET/MOS ADC and DAC converters. This model uses the orthodox theory of single-electron tunneling and determines the average current through the transistor. The proposed model is more flexible that is valid for large range of drain to source voltage, valid for single or multi gate SET and symmetric or asymmetric SET. Finally, using this model with MOSFET transistors to realize a multi-bit Analog-to-Digital Converters (ADC) and Digital-to-Analog Converters (DAC). The hybrid n-bit DAC nano-circuits are simulated for (n=4 and 8) using Orcad Capture PSPICE. The performance of the SET/MOS hybrid n-bit ADC circuits were simulated (for n=3 and 8). The results show that the transient operation of hybrid SET/MOS circuit-based DAC could successfully operate at 1000K while ADC could operate at 144K. This performance can be compared with the pure SET circuits, the proposed converter circuits have been enhanced in the drive capability and the power dissipation. Compared with the oth`er SET/MOS hybrid circuit, the implemented converter circuits have low simulation time, high speed, high load drivability and low power dissipation.

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Lobna Osman mail

link https://doi.org/10.54216/IJWAC.000101

Volume & Issue

Vol. Volume 0 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2021

A Novel Fuzzy Clustering with Metaheuristic based Resource Provisioning Technique in Cloud Environment

Cloud Computing (CC) becomes a commonly available tool to enable quick, on-demand services from a shared pool of configurable computing resources which can be allocated and utilized. Resource provisioning is a major issue in CC environment which ensures guaranteed outcomes on the applications related to CC. This study introduces an efficient fuzzy c-means clustering (FCM) with hybrid grey wolf optimization (GWO) and differential evolution (DE) algorithm, called FCM-GWODE for resource provisioning in cloud environment. The aim of the FCM-GWODE technique is to allocate the resources in such a way that the resource utilization can be accomplished. In addition, the FCM technique with metaheuristics is applied to partition the resources and scalable searching process can be minimized. Moreover, the GWODE algorithm is derived by resolving the local optima issue of the GWO and improve the population diversity using DE. A comprehensive simulation process takes place using CloudSim tool and the results are inspected interms of several evaluation metrics. The simulation results highlighted the supremacy of the FCM-GWODE technique over the other methods.

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Ahmed N. Al-Masri mail -

Manal Nasir mail

link https://doi.org/10.54216/FPA.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2021

An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security

Due to the drastic rise in multimedia content, digital images have become a major carrier of data. Generally, images are communicated or archived via wireless communication changes, and the significance of data security gets increased. In order to accomplish security, encryption is an effective technique which is used to encrypt the images using secret keys in such a way that it is not readable by the hacker. In this view, this study focuses on the design of Teaching and Learning based Optimization (TLBO) with Multi-Key Homomorphic Encryption (MHE) technique, called MHE-TLBO algorithm. The goal of the MHE-TLBO algorithm is to optimally select multiple keys using TLBO algorithm for encryption and decryption processes. In addition, the MHE-TLBO algorithm has derived a fitness function involving peak signal to noise ratio (PSNR) and thereby ensures the superior quality of the reconstructed image. For validating the security performance of the MHE-TLBO algorithm, a comprehensive result analysis is made and the simulation results ensured the betterment of the MHE-TLBO algorithm interms of different aspects.

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Mustafa s Khalifa mail -

Ahmed N. Al-Masri mail

link https://doi.org/10.54216/JCIM.070203

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2021

Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction

In the current epidemic situations, people are facing several mental disorders related to Depression, Anxiety, and Stress (DAS). Numerous scales are developed for computing the levels for DAS, and DAS-21 is one among them. At the same time, machine learning (ML) models are applied widely to resolve the classification problem efficiently, and feature selection (FS) approaches can be designed to improve the classifier results. In this aspect, this paper develops an intelligent feature selection with ML-based risk management (IFSML-RM) for DAS prediction. The IFSML-RM technique follows a two-stage process: quantum elephant herd optimization-based FS (QEHO-FS) and decision tree (DT) based classification. The QEHO algorithm utilizes the input data to select a valuable subset of features at the primary level. Then, the chosen features are fed into the DT classifier to determine the existence or non-existence of DAS. A detailed experimentation process is carried out on the benchmark dataset, and the experimental results showcased the betterment of the IFSML-RM technique in terms of different performance measures. 

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Mohamed Abdel-Basset mail -

Mohamed Elhoseny mail

link https://doi.org/10.54216/JCIM.080101

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2021

Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

In recent years, a massive amount of genomic DNA sequences are being created which leads to the development of new storing and archiving methods. There is a major challenge to process, store or transmit the huge volume of DNA sequences data. To lessen the number of bits needed to store and transmit data, data compression (DC) techniques are proposed. Recently, DC becomes more popular, and large number of techniques is proposed with applications in several domains. In this paper, a lossless compression technique named Arithmetic coding is employed to compress DNA sequences. In order to validate the performance of the proposed model, the artificial genome dataset is used and the results are investigated interms of different evaluation parameters. Experiments were performed on artificial datasets and the compression performance of Arithmetic coding is compared to Huffman coding, LZW coding, and LZMA techniques. From simulation results, it is clear that the Arithmetic coding achieves significantly better compression with a compression ratio of 0.261 at the bit rate of 2.16 bpc.

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Mahmud Alosta mail -

Alireza Souri mail

link https://doi.org/10.54216/FPA.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2021

Modeling of Deep Learning based Intrusion Detection System in Internet of Things Environment

The Internet of Things (IoT) has become a hot popular topic for building a smart environment. At the same time, security and privacy are treated as significant problems in the real-time IoT platform. Therefore, it is highly needed to design intrusion detection techniques for accomplishing security in IoT. With this motivation, this study designs a novel flower pollination algorithm (FPA) based feature selection with a gated recurrent unit (GRU) model, named FPAFS-GRU technique for intrusion detection in the IoT platform. The proposed FPAFS-GRU technique is mainly designed to determine the presence of intrusions in the network. The FPAFS-GRU technique involves the design of the FPAFS technique to choose an optimal subset of features from the networking data. Besides, a deep learning based GRU model is applied as a classification tool to identify the network intrusions. An extensive experimental analysis takes place on KDDCup 1999 dataset, and the results are investigated under different dimensions. The resultant simulation values demonstrated the betterment of the FPAFS-GRU technique with a higher detection rate of 0.9976.

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Mohammad Hammoudeh mail -

Saeed M. Aljaberi mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2021

An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification

Sentimental Analysis (SA) becomes a familiar topic among business people, which is commonly applied for the classification of sentiments from online reviews. It is generally treated as a sentiment classification (SC) problem where the online reviews are categorized into positive or negative polarities using the words that exist in the online reviews. With this motivation, this paper presents a new K-means clustering with hybrid metaheuristic algorithm (KMC-HMA) for SA and classification. The proposed KMC-HMA technique initially performs data preprocessing to remove the unwanted words from the product reviews. In addition, K-means clustering technique is used for the clustering of the massive quantity of the applied product reviews. Moreover, the clustered data are fed into the classification model based on hybrid ant colony optimization (ACO) with dragonfly algorithm (DFA).  The ACO algorithm is used for the classification of product reviews and the performance of the ACO algorithm can be optimally tuned by the use of DFA. The performance validation of the KMC-HMA technique is validated using two datasets such as Canon and ipod. The experimental values pointed out the superior performance of the KMC-HMA technique over the recent state of art techniques.

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Mohammed K. Hassan mail -

Dina K. Hassan mail -

Ahmed K. Metawee mail -

Bassem Hassan mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2021

Intelligent Optimal Deep Learning based Customer Churn Prediction Model in Telecom Industry

Customer churn prediction (CCP) is a crucial problem in telecom industry which helps to improve the revenue of the company and prevent the loss of customers. Customer churn is an important issue in service sector with highly competitive services. At the same time, the prediction of users who are probably leaving the company can be identified at an earlier stage to prevent loss of revenue. Several works have used machine learning (ML) techniques for predicting the existence of customer churn in different industries. With this motivation, this paper presents an optimal long, short-term memory with stacked autoencoder (OLSTM-SAE) technique for CCP in telecom industry. The OLSTM-SAE technique encompasses three subprocesses namely preprocessing, classification, and parameter optimization. The OLSTM-SAE technique classifies the preprocessed data into churn and non-churn customers. In addition, the grey wolf optimization (GWO) technique is used to adjust the variables involved in the LSTM-SAE model.  For examining the enhanced performance of the OLSTM-SAE technique, an extensive simulation analysis takes place, and the outcomes are inspected with respect to various measures. The experimental results highlighted the betterment of the OLSTM-SAE technique in terms of different evaluation parameters. 

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Taif Khalid Shakir mail -

Ahmed N. Al Masri mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2020

An Efficient Machine Learning based Cervical Cancer Detection and Classification

Cervical cancer (CC) is the fourth commonly occurring cancer among females over the globe. It accounts for 7.9% of woman cancer as identified by world health organization (WHO). The most important reason for increased mortality due to cervical cancer is the deficiency of effective initial treatment. The asymptomatic nature is a main problem faced in the analysis of CC from initial stage. Recently, computer aided diagnosis (CAD) model has gained significant attention in the disease diagnostic process. At the same time, machine learning (ML) finds its use in several medical applications and is utilized as classifier for the initial detection of cancerous cells occurs from cervix area of uterus. With this motivation, this study introduces an intelligent ML based CAD (IML-CAD) technique to classify cervix cancer. The IML-CAD technique involves different stages of operations to detect and classify the cancerous cervix cells. In addition, the IML-CAD technique involves histogram based segmentation to determine the affected regions. Moreover, local binary patterns (LBP) based feature extractor and least squares support vector machine (LS-SVM) based classifier is designed for CC classification. To showcase the better performance of the IML-CAD technique, a series of simulations is performed and the experimental results highlighted the superior performance of the IML-CAD technique over the other techniques.

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Ahmed N. Al Masri mail -

Hamam Mokayed mail

link https://doi.org/10.54216/JCIM.020203

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new