Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms

Ahmed; Hoda K.

doi:https://doi.org/10.54216/JISIoT.080103

Journal of Cybersecurity and Information Management | 2022

Coded DCO-OFDM Techniques in Intensity Modulation/Direct Detection (IM/DD) Systems

Optical wireless communications (OWC) are among the best alternative techniques for transmitting information-laden optical radiation across a free-space channel from one place to another. DC-biased optical OFDM (DCO-OFDM) is a technique that sacrifices the power efficiency to transmit unipolar OFDM signals. The primary drawback with DCO-OFDM is its clipping noise, which causes distortion and lowers the bit error rate (BER). Thus, in this paper, we show the performance of DCO with different coded techniques to improve the BER in additive white Gaussian noise (AWGN) for IM/DD systems. The experimental results show that the coded DCO-OFDM has the best performance. Furthermore, turbo coding has the best coding technique added to the DCO-OFDM system.

groups

Mohamed Abdelaziz mail -

Essam Abdellatef mail

link https://doi.org/10.54216/JCIM.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2022

An Enhanced Deep Learning Technique to Measure the Impact of Cryptocurrency on the World Payment system using Random Forest

Cryptocurrency is a technology that uses an encrypted peer-to-peer network to facilitate digital barter. Bitcoin, the first and most popular cryptocurrency, is paving the way as a disruptive technology to long-standing and unchanging financial payment systems. While cryptocurrencies are unlikely to replace traditional fiat currency, they have the potential to alter how Internet-connected global markets interact with one another, removing the restrictions that exist around traditional national currencies and exchange rates. Technology advances at a breakneck pace, and a technology's success is almost entirely determined by the market it tries to improve. Cryptocurrencies have the potential to change digital trade marketplaces by enabling a fee-free trading mechanism. A SWOT analysis of Bitcoin is offered, which highlights some of the recent events and movements that may have an impact on whether Bitcoin contributes to a paradigm change in economics. Cryptocurrency is a relatively new payment option, and users are naturally drawn to it because it offers privacy. To measure the impact of cryptocurrency on the world payment system, we use a Cryptocurrency extra data – Bitcoin. The proposed algorithm uses Random Forest Algorithm for prediction. The RFPA has achieved a 0.073 MSE. The RFPA has achieved the best results as it can handle huge datasets with a lot of dimensionality. It improves the model's accuracy and eliminates the problem of overfitting. When compared to other algorithms, it takes less time to train.

groups

Fatma M. Talaat mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2022

Bank Marketing Data Classification Using Optimized Voting Ensemble, Sine Cosine, and Genetic Algorithms

Nowadays, the banking industry is no exception to the general trend of massive data production in all spheres of modern life. In this research, we analyze the categorization of marketing data from banks using a variety of machine learning techniques. The term "banking" refers to the supply of services by a bank to an individual consumer. The data was first compiled from the UCI Machine Learning repository and the Kaggle website. Phone-based banking marketing statistics are the focus of this data set. Python is utilized as the language of implementation, and the Machine Learning concept is employed for statistical learning and data analysis in this work. An improved prediction is the primary goal of machine learning's model-building phase. In order to classify the results, a supervised Naive Bayes algorithm is used to the data. The primary goal of the modeling effort is to characterize whether or not the consumer has chosen a term deposit. The bank should devote substantial time to returning phone calls from prospective customers. Accuracy, precision, recall, and F1 score were all evaluated as a consequence of this study in the direction of term deposit forecasting.

groups

Marwa M. Eid mail -

El-Sayed M. El-Kenawy mail -

Abdelhameed Ibrahim mail -

Abdelaziz A. Abdelhamid mail -

Mohamed Saber mail

link https://doi.org/10.54216/AJBOR.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2022

Metaheuristic Optimized Voting Ensemble for Recognizing Daily and Sports Activities

This research analyzes the effectiveness of several methods for categorizing human actions captured by inertial and magnetic sensor units worn on the chest, arms, and legs. Each device has tri-axial sensors, including a gyroscope, accelerometer, and magnetometer. Voting ensemble classification models, where votes are weighted and optimized with a new optimization technique, are offered as a means to actualize this classification problem. The optimization technique is a combination of the sine cosine and particle swarm optimization algorithms, and the ensemble model is made up of three classifiers: support vector machines, decision trees, and multilayer perceptron. The classifiers are checked for accuracy using three distinct cross-validation strategies. Classifiers' proper differentiation rates and computational costs are compared to help you choose the best one for your needs. When it comes to body location, sensor devices worn on the legs provide the most valuable data. From a comparison of the various sensor modalities, we can deduce that magnetometers, followed by accelerometers and gyroscopes, provide the best classification results when only a single sensor type is employed. Furthermore, the study contrasts three machine learning models—support vector machines, decision trees, and multilayer perceptron —with respect to their usability, controllability, and classifier performance. Results reveal that the suggested method performs well in categorizing both typical daily activities and athletic endeavors.

groups

El-Sayed M. El-Kenawy mail -

Abdelhameed Ibrahim mail -

Abdelaziz A. Abdelhamid mail -

Mohamed Saber mail -

Marwa M. Eid mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2022

Literature Review and Novel Trends of Mobile Edge Computing for 5G and Beyond

Because of the rapid evolution of communications technologies, such as the Internet of Things (IoT) and fifth generation (5G) systems and beyond, the latest developments have seen a fundamental change in mobile computing. Mobile computing is moved from central mobile cloud computing to mobile edge computing (MEC). Therefore, MEC is considered an essential technology for 5G technology and beyond. The MEC technology permits user equipment (UEs) to execute numerous high-computational operations by creating computing capabilities at the edge networks and inside access networks. Consequently, in this paper, we extensively address the role of MEC in 5G networks and beyond. Accordingly, we first investigate the MEC architecture, the characteristics of edge computing, and the MEC challenges. Then, the paper discusses the MEC use cases and service scenarios. Further, computations offloading is explored. Lastly, we propose upcoming research difficulties in incorporating MEC with the 5G system and beyond.

groups

Germien G. Sedhom mail -

Alshimaa H. Ismail mail -

Basma M. Yousef mail

link https://doi.org/10.54216/JAIM.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2022

Smart Garbage Monitoring System using IoT

The Internet of Things (IoT) will be able to integrate a wide range of various and heterogeneous end systems transparently and smoothly, while also offering open access to chosen subsets of data for the creation of a wide range of digital services. Solid waste management, which apart upsetting the ecological balance also has negative consequences on societal health, has been one of the key environmental issues. One of the main issues in the modern era is the identification, monitoring of wastes. The conventional method of manually checking the wastes in bins requires more human labor, takes longer, and costs more money. It is in no way compatible with modern technologies. This is a cutting-edge approach to automated garbage management. This project IoT Garbage Monitoring system is a very innovative system which will help greatly in preserving the environment and also makes us benefit from garbage and sell it on form raw materials to be recycled again by recycling factories. System is linked to an android application to show us the current level of each type of garbage using Ultrasonic Sensor and give a warning when the level of any of them is filled.

groups

Marwa Eid mail -

Omnia M. Osama mail -

Mariam H. Amin mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

Enhanced Active Queue Management‑Based Green Cloud Model for 5G system using K-Means

The most unique and important design considerations in 5G cloud computing are the delay, energy consumption, and throughput. Therefore, most recent studies focused on boosting delay and energy consumption, and throughput using edge computing. The active queue management-based green cloud model (AGCM) is one of the most recent green cloud models that decreases the delay and sustains a stable throughput. Also, Mobile edge computing (MEC) is an essential cloud computing model for mobile users to meet the continuous growth of data requests. Thus, we offer a handoff scenario between the AGCM and MEC to assess the possible benefits of such collaboration and enhance its effects on the fundamental cloud restrictions such as delay and throughput. Accordingly, the proposed algorithm is named Enhanced Active queue management-based green cloud model (EAGCM). The proposed EAGCM regards incorporation between Kmeans and AGCM. The simulation results indicate that the proposed EAGCM serves mobile users efficiently, enhances the throughput, and reduces latency compared to AGCM and the cloud for 5G systems.

groups

Alshimaa H. Ismail mail -

Germien G. Sedhom mail -

Zainab H. Ali mail

link https://doi.org/10.54216/IJWAC.060206

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

Photonic Crystal Circuitry and its Impact on Wireless Networks

Wireless networks are considered a hot topic in dealing with data without need to routers or other infrastructures. Each node has a part of routing responsibility. This result to a huge of data in forwarding to other nodes and will need high speed to process.  Photonic crystal applications come to solve the necessity for such speed with small circuitry area. One of the main factors that affect their operation is the structure topology. Ring resonator, cavity based structures, self-collimation, and waveguide approaches are some of these topologies.  OR gate is proposed in this paper to be simulated and evaluated as one of the basic element block. This design is built on a square lattice- photonic crystal construction on a ring resonator basis. Rotation of 90, 180, and 270 degrees are applied in clockwise direction. Sensitivity analysis, and carefully rod locations are considered to obtain remarkable performance. Minimum size and highly data rate are two characteristics that discriminates this design. The minimum size of 51.48 μm2 is obtained. The bit rates of 1.35, 6.35, 3.2, and 2.53 Tb/s are calculated with the 0, 90, 180, and 270 degrees, respectively. Comparison table is well organized for the recently published photonic crystal OR-gate that based on ring resonator. Finite difference time domain and Plan wave expansion method are used to analyze the proposed structure at 1.55μm wavelength to verify OR- gate operation.    

groups

Tamer S. Mostafa mail -

Shaimaa A. Kroush mail -

El- Sayed M. El- Rabaie mail

link https://doi.org/10.54216/IJWAC.060106

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Classification of Diabetic Foot Thermal Images Using Deep Convolutional Neural Network

Diabetic foot (DF) is one of the most common chronic complications of poorly controlled diabetes mellitus (DM). Early diagnosis of DF and effective treatment is usually difficult by traditional approaches. Lately, it has been found a strong relationship between temperature variation and diabetic foot ulcer emergence. Thus, the current study focused on monitoring the temperature of feet using thermal images and its analysis techniques. The proposed system was based on employing a deep convolutional neural network (CNN) on thermal foot images. Experimental results showed that the proposed CNN has a maximum accuracy of 99.3% with minimum losses. When comparing the proposed system to other relevant systems, the proposed system approved greater accuracy, lower elapsed and testing time, which offers an automatic diagnostic tool for the diabetic foot and differentiates between its types. Thus, a simple, cost-effective, and accurate computer aided design (CAD) system could be presented to get a valuable system for the clinicians in hospitals.

groups

Reem N. Yousef mail -

Marwa M. Eid mail -

Mohamed A. Mohamed mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms

Melanoma is the kind of skin cancer that poses the greatest risk to one's life and has the maximum mortality rate within the group of skin cancer disorders. Even so, the automated placement and classification of skin lesions at initial phases remains a complicated task due to the lack of contrast melanoma molarity and skin fraction and a greater level of color similarity among melanoma-affected and -nonaffected areas. Contemporary technological improvements and research methods enabled it to recognize and distinguish this type of skin cancer more successfully. A clustering technique called neutrosophic c-means clustering (NCMC) is presented in this research to group ambiguous data in the detection of skin cancer. This algorithm takes its cues from both fuzzy c-means and the neutrosophic set structure. To arrive at such a structure, an appropriate objective function must first be created and then minimized. The clustering issue must then be stated as a restricted minimization problem, the solution of which is determined by the objective function. This paper made a comparison between NCMC and fuzzy c-means clustering (FCMC). The results show that the NCMC is more suitable than the FCMC.

groups

Ahmed Abdelhafeez mail -

Hoda K. Mohamed mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new