Ahmed Yahya

RESEARCH INTERESTS

Distributed Cloud Edge Computing, Vertical Testbed Architctures, LoRaWAN Networks, V2X Communications, Area-efficient instruction set synthesis for reconfigurable system-on-chip designs, and Cognitive IOT Networks

Scopus Publications

Spectrum Sensing Optimization Using De-noising and Energy Detection




A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks
Ashraf M. Etman, Mohamed S. Abdalzaher, Ahmed A. Emran, Ahmed Yahya, and Mostafa Shaaban
Institute of Electrical and Electronics Engineers (IEEE)
Smart grids (SGs) are crucial to the efficiency and sustainability of modern energy systems. As the world’s population continues to increase, so does the need for energy, and traditional energy systems are struggling to keep up. In this context, this study reviews the possibilities of deploying machine learning (ML) on wireless sensor networks (WSNs) in smart grid systems. In several ways, SGs may gain from combining WSNs with ML, including enhance system reliability, sustainability, improve fault detection, and increase energy efficiency. This paper offers an extensive review of pertinent research emphasizing the use of supervised, unsupervised, and reinforcement learning approaches. The evaluation contains 234 peer reviewed articles from highly regarded academic journals and conferences covering the years 2017 through 2024 which depict the effectiveness of supervised techniques on WSNs in the field of SGs. In addition the paper presents set of the most usable datasets in the field of WSNs and SGs, and introduces a comparison between our paper and relevant surveys. The study also analyses the opportunities and challenges related to the application of WSNs and ML in SGs and offers possible research directions. Overall, the study makes it clear that combining WSNs with ML may significantly contribute to the creation of smart grid systems that are more effective, dependable, and sustainable.


SENSING TIME IMPROVEMENT USING TWO STAGE DETECTORS FOR COGNITIVE RADIO SYSTEM
Mohamed Khalaf, Ahmed Fawzi, and Ahmed Yahya
Academy and Industry Research Collaboration Center (AIRCC)
Cognitive radio (CR) is a promising technology for both present and future telecommunications to satisfy the demand of the next generation due to inefficient use of the allocated spectrum. Due to its ability to utilize the available bandwidth of other wireless communication networks and so enhance its occupancy. Spectrum sensing (SS) is the key characteristic of the CR system that helps it identify the empty spectrum. SS has gained a lot of interest recently and it is an active research area since it offers additional opportunities to secondary users. A broad variety of analytical methods to identify the Primary User's (PUs) presence have emerged as a result of SS techniques for CRs. Although each approach has its own benefits, the drawbacks attached to them make an individual implementation of the technique impractical for usage. To mitigate the drawbacks and maximize the benefits offered by the individual methods, a two stage detector can be employed for SS. However, the stages method lengthens the time needed to sense the spectrum and produce a definitive result. In this paper, we propose a two-stage sensing approach, where the first stage is an Interval Dependent Denoising detector (IDD) and followed by a second stage is Energy Detection (ED) which provides a large decrease in the mean sensing time compared to different related two-stage SS approaches The ED method is simple and has a short sensing time, but it performs poorly when the Signal to Noise Ratio (SNR) is low, Hence it is thought that separating PU activity from noise is essential for accurate SS. Therefore, we use IDD as a noise reduction technique in the first stage before the ED stage. The simulation results have been utilized to demonstrate that this technique leads to a large savings in sensing time as compared to an existing two-stage detection approaches.


A Distributed Cloud Architecture Based on General De Bruijn Overlay Network
Osama R. S. Ramadan, Mohamed Yasin I. Afifi, and Ahmed Yahya
IGI Global
Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and coherence maintenance of these systems pose significant challenges. Recent studies reveal shortcomings in existing distributed system schemes to meet modern cloud application demands and maintain coherence among heterogeneous system elements. This paper proposes a service-oriented network architecture for distributed cloud computing networks. Using a De Bruijn network as a software-defined overlay network, the architecture ensures scalability and coherence. Through service-based addressing, requests are issued to designated service address bands, streamlining service discovery. The architecture's evaluation through extensive simulations showcases sustainable scalability and inherent load-balancing properties. The paper concludes with insights into future research directions, emphasizing the extension of the proposed architecture to emerging distributed cloud use cases and decentralized security.


A framework for classifying breast cancer based on deep features integration and selection
Abdallah M. Hassan, Ahmed Yahya, and Ashraf Aboshosha
Springer Science and Business Media LLC
AbstractDeep convolutional neural networks (DCNNs) are one of the most advanced techniques for classifying images in a range of applications. One of the most prevalent cancers that cause death in women is breast cancer. For survival rates to increase, early detection and treatment of breast cancer is essential. Deep learning (DL) can help radiologists diagnose and classify breast cancer lesions. This paper proposes a computer-aided system based on DL techniques for automatically classify breast cancer tumors in histopathological images. There are nine DCNN architectures used in this work. Four schemes are performed in the proposed framework to find the best approach. The first scheme consists of pre-trained DCNNs based on the transfer learning concept. The second scheme performs feature extraction of the DCNN architectures and uses a support vector machine (SVM) classifier for evaluation. The third one performs feature integration to show how the integrated deep features may enhance the SVM classifiers' accuracy. Finally, in the fourth scheme, the Chi-square (χ2) feature selection method is applied to reduce the large feature size in the feature integration step. The results of the proposed system present a promising performance for breast cancer classification with an accuracy of 99.24%. The system performance shows that the proposed tool is suitable to assist radiologists in diagnosing breast cancer tumors.


A decision support healthcare system based on IoT and neural network technique
Khadeja Al_Sayed Fahmy, Ahmed Yahya, and M. Zorkany
Emerald
Purpose The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital. Design/methodology/approach AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN. Findings Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN. Originality/value AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.


Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing the Massive IoT-LoRaWAN Network Performance
Samar Adel Almarzoqi, Ahmed Yahya, Zaki Matar, and Ibrahim Gomaa
MDPI AG
Long-Range Wide Area Network (LoRaWAN) is an open-source protocol for the standard Internet of Things (IoT) Low Power Wide Area Network (LPWAN). This work’s focal point is the LoRa Multi-Armed Bandit decentralized decision-making solution. The contribution of this paper is to study the effect of the re-learning EXP3 Multi-Armed Bandit (MAB) algorithm with previous experts’ advice on the LoRaWAN network performance. LoRa smart node has a self-managed EXP3 algorithm for choosing and updating the transmission parameters based on its observation. The best parameter choice needs previously associated distribution advice (expert) before updating different choices for confidence. The paper proposes a new approach to study the effects of combined expert distribution for each transmission parameter on the LoRaWAN network performance. The successful transmission of the packet with optimized power consumption is the pivot of this paper. The validation of the simulation result has proven that combined expert distribution improves LoRaWAN network’s performance in terms of data throughput and power consumption.


Enhancement of spectrum sensing performance via cooperative cognitive radio networks at low snr
Kotb A. Kotb, Ahmed S. Shalaby, and Ahmed Yahya
University of Baghdad College of Science
     The inefficient use of spectrum is the key subject to overcome the upcoming spectrum crunch issue. This paper presents a study of performance of cooperative cognitive network via hard combining of decision fusion schemes. Simulation results presented different cooperative hard decision fusion schemes for cognitive network. The hard-decision fusion schemes provided different discriminations for detection levels. They also produced small values of Miss-Detection Probability at different values of Probability of False Alarm and adaptive threshold levels. The sensing performance was investigated under the influence of channel condition for proper operating conditions. An increase in the detection performance was achieved for cognitive users (secondary users) of the authorized unused dynamic spectrum holes (primary users) while operating in a very low signal-to-noise ratio  with the proper condition of minimum total error rate.


A Modified Particle Swarm Optimization Approach for Latency of Wireless Sensor Networks
Jannat H. Elrefaei, Ahmed Yahya, Mouhamed K. Shaat, Ahmed H. Madian, and Refaat M. Fikry
The Science and Information Organization
In time-sensitive applications, such as detecting environmental and individual nuclear radiation exposure, wireless sensor networks are employed.. Such application requires timely detection of radiation levels so that appropriate emergency measures are applied to protect people and the environment from radiation hazards. In these networks, collision and interference in communication between sensor nodes cause more end-to-end delay and reduce the network's performance. A time-division multiple-access (TDMA) media access control protocol guarantees minimum latency and low power consumption. It also overcomes the problem of interference. TDMA scheduling problem determines the minimum length conflict-free assignment of slots in a TDMA frame where each node or link is activated at least once. This paper proposes a meta-heuristic centralized contention-free approach based on TDMA, a modified particle swarm optimization. This approach realizes the TDMA scheduling more efficiently compared with other existing algorithms. Extensive simulations were performed to evaluate the modified approach. The simulation results prove that the proposed scheduling algorithm has a better performance in wireless sensor networks than the interference degree leaves order algorithm and interference degree remaining leaves order algorithm. The results demonstrate also that integrating the proposed algorithm in TDMA protocols significantly optimizes the communication latency reduction and increases the network reliability. Keywords—Wireless sensor networks; media access control protocol; scheduling algorithms; meta-heuristics; particle swarm optimization


Design of Multi-band Microstrip Patch Antennas for Mid-band 5G Wireless Communication
Karima Mazen, Ahmed Emran, Ahmed S. Shalaby, and Ahmed Yahya
The Science and Information Organization
Recently, the best antenna structures have considered microstrip patch antenna due to their simple construction, low cost, minimum weight, and the fact that they can be effortlessly integrated with circuits. To achieve multiband operation an antenna is designed with an etching rectangle and circle slot on the surface of the patch to achieve multi-band frequency capabilities in mid-band 5G applications. Inset-fed structure type of fed of all antenna printed and fabricated on the brow of the Rogers RT5880 substrate. Then, prototype structures of the microstrip patch antenna were acquired during the design process until achieving the desired antennas. The antenna_1 achieved tri-band characteristics covering the WiMAX band including 2.51 – 2.55 GHz, WLAN, and S-band including 3.80 – 3.87 GHz and C-and X-band including 6.19 – 6.60 GHz. The antenna_2 gives dual-band characteristics covering C-band and X-band including (6.72 – 7.92 GHz) with a peak under -45 dB suitable for mid-band 5G applications. High impedance bandwidth increases between (70 MHz-1.25 GHz) for wireless applications. The proposed microstrip patch antennas were simulated using CST MWS-2015 and were experimentally tested to verify the fundamental characteristics of the proposed design, it offers multiple-band operation with high stable gain and good directional radiation characteristics results. Keywords—Band-width; microstrip; multi-band; notch slot; rectangle slot; 5 G


Performance evaluation of IoT messaging protocol implementation for e-health systems
M Zorkany, K.Fahmy -, and Ahmed Yahya
The Science and Information Organization
Now-a-days, e-health and healthcare applications in the internet of things are growing rapidly. These applications are starting from remote monitoring of patient's parameters in home to monitoring patients during his life activities at work, transportation, etc. So we can monitor patients at any place outside of hospitals and clinical settings. By using this technology, we can save lives and reduce the number of emergency visits to hospitals. In contemporary time, there are great progress and opportunities for the internet of things (IoT) related E-health systems. Most IoT e-health platforms consist of three main parts; client nodes (patient or doctor), IoT server and IoT communication messaging protocol. One of E-health systems design over IoT challenge is choosing the most suitable IoT messaging protocol for E-health applications. In this paper, IoT remote patient and e-Health monitoring system was designed for monitoring physiological medical signals of patients based on most two famous IoT messaging protocols, MQTT and CoAP. These medical signals can be include parameters like heart rate signals, electro-cardio graph (ECG), patient temperature, blood pressure, etc. This practical comparison between CoAP and MQTT is to choose most suitable for e-health systems. The proposed approach was evaluated based on most significant protocol parameters like capability, efficiency, communication method and message delay. Practical and simulation results show the performance of the proposed E-health systems over IoT for different network infrastructure with different losses percentages.


Comparison of different backpropagation training algorithms using robust M-estimators performance functions
Ali R. Abd Ellah, Mohamed H. Essai, and Ahmed Yahya
IEEE
Artificial neural networks are one of the most popular and promising areas of artificial intelligence research. Training data containing outliers are often a problem for supervised neural networks learning algorithms that may not always come up with acceptable performance. Many robust learning algorithms have been proposed so far to improve the performance of neural networks in the presence of outliers. In this paper, we investigate the performance of four different backpropagation training algorithms, which are conjugate gradient with Fletcher - Reeves updates, conjugate gradient with Polak - Ribiére updates, resilient backpropagation, and conjugate gradient with Powell - peal restart. We compare their performance in terms of Root Mean Square Error as a merit function and the training speed in seconds. Examined neural networks trained by aforementioned backpropagation learning algorithms, which used the robust M-estimators performance functions instead of MSE one, in order to get robust learning in the presence of outliers. The study results show that Traincgf is the best algorithm in terms of mean square error, while the Traincgp is the best in terms of training speed.


Performance comparison of linear multiuser detectors and neural network detector for DS/CDMA systems in AWGN
Hassan A. Hassan, Mohamed H. Essai, and Ahmed Yahya
IEEE
The most commonly used multiple access technique in wireless communication sphere is the direct sequence code division multiple access. The main drawback of this system is multiple access interference (MAI) caused by sharing a number of users the same channel. Multiuser Detection enhances the performance of DS-CDMA system by combating MAI. In this paper, the performance of the neural network detector is compared with the linear multiuser detectors includes decorrelating (Decor.) detector, and Minimum Mean Squared Error detector (MMSE). This neural network detects the user bits after the bank of matched filter in additive white Gaussian noise channel, with using spreading code of Gold sequence (GS) type. Where these detectors work in both synchronous and asynchronous transmission modes, in this paper its performance was investigated in synchronous AWGN channel. Simulation results show that the performance of linear multiuser detectors depends mainly on the number of active users. The neural network detector is superior to the linear multiuser detectors in the terms of bit error rate (BER) performance.


Multi-Path approach for real-time service delivery optimization over HetNets
Anwar Fouad, Emad Abd-Elrahman, and Ahmed Yehya
IEEE
With the new era of smartphones, the access devices have many radio interfaces like WiFi/3G/4G. Those interfaces can work in concurrent mode in order to improve the overall network access. Meanwhile, they could enhance the service continuity and maximize the application throughput through providing uninterrupted service supply. In this paper, we introduce a Multi-Path (MP) approach using TCP over different radio interfaces for real time applications delivery. Then, a hybrid way combines MP-TCP with DASH technology is proposed as amelioration for video service delivery optimization over Internet. Our test indicates an improvement in handover delay comparing to single path TCP. Moreover, the MP-TCP with DASH enhances the quality of video transmission by rates of different radio interfaces available.


Deterministic UWB channel modeling using ray tracing approach
A. Fathy, A. Yahya, and Hani Ragai
IEEE
Two major model sets are used to model a radio wave propagation problem. The first is empirical, based on measurements and statistics. Empirical models are easy to implement and fast. They only consider Tx-Rx separation and thus are not accurate enough as different objects of the environment are not taken into account. The second is deterministic exploiting physical laws to simulate signal propagation. Deterministic propagation prediction models based on a combination of Geometrical Optics (GO) and the Uniform Theory of Diffraction (UTD) represent the unique solution for reliable estimations. The most famous of the second approach is the well-known ray tracing like model, based on the computation of the different paths according to the geometrical optical laws. Concerning the transmitted signal bandwidth (BW), the propagation channel appears differently to Ultra Wideband (UWB) wireless systems than it does to Narrow Band (NB) sine wave systems. The main objective of this paper is to develop a ray tracing Matlab code to estimate the power received as a function of frequency in a given environment. The results were experimentally validated according to time domain corporation specifications where the BW was selected to cover the 3.1-5.3 GHz band with center frequency of 4.3 GHz.


Performance analysis for new call bounding scheme with SFR in LTE-advanced networks
Mahammad A. Safwat, Hesham M. El Badawy, Ahmad Yehya, and H. El Motaafy
IEEE
The call admission control (CAC) optimizes the use of allocated channels against offered traffic maintaining the required quality of service (QoS). Provisioning QoS to user at cell-edge is a challenge where there is limitation in cell resources due to inter-cell interference (ICI). New Call bounding scheme is a Call Admission control that depends on restricting the number of new calls accepted into the cell by a threshold. In this paper, traffic analysis in cell-edge is developed based on NCB policy using three dimension Markov model. The model is based on actual resources allocated to cell-edge users according to Soft Frequency Reuse (SFR) scheme in LTE-Advanced system. In the proposed model, the performance metrics in terms of blocking and dropping probability for cell-edge and cell-core users is deduced separately, in addition, the optimal parameter settings for system performance metrics is developed.


Performance Evaluation of Blackhole Attack on VANET's Routing Protocols




Energy-aware architecture for multi-rate ad hoc networks
Ahmed Yahya
Elsevier BV

RECENT SCHOLAR PUBLICATIONS

Vibration analysis of thermoelastic micro-beams on a Pasternak foundation with two parameters using the Moore–Gibson–Thompson heat conduction model
A Zakria, A Yahya, AE Abouelregal, M Suhail
Continuum Mechanics and Thermodynamics 37 (2), 27 2025



Response of Generalized Thermoelastic for Free Vibration of a Solid Cylinder with Voids Under a Dual-Phase Lag Model
A Yahya, A Saidi
Iranian Journal of Science and Technology, Transactions of Mechanical 2025



Applications of conformable double Sumudu-Elzaki transform
SA Ahmed, AA Hassan, HE Dargail, A Zakria, IE Ahmed, A Yahya
prospects 9, 12 2025



A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks
MS ASHRAF M. ETMAN1 , MOHAMED S. ABDALZAHER , AHMED A. EMRAN , AHMED YAHYA
IEEE ACCESS 13, 2604-2627 2025



A Predictable performance Multi-controller-based Monitoring Framework for SDN
UAF Mahmoud Eissa1, Ahmed Yahya 2
International Journal of Communication Networks and Information Security 16 2024



13 Lipid-Based Nanocarriers for Brain
KM Kotb, MN Sharafeldin, H Hassan, A Boutalbi, A Yahya, HF Niazi, ...
Nanocarriers in Neurodegenerative Disorders: Therapeutic Hopes and Hypes, 173 2024



Optimizing Multiple-Target CFAR Detection Efficacy through Advanced Intelligent Clustering Algorithms within K-Distribution Sea Clutter Environments
MM Al-dabaa, AA Emran, A Yahya, M El-Mashade, A Aboshosha
Journal of Al-Azhar University Engineering Sector 19 (72), 250-269 2024



Deep Learning Mitigation of Sea Clutter for Enhanced Radar Target Detection
MM Al-dabaa, AA Emran, A Yahya, A Aboshosha
Journal of Al-Azhar University Engineering Sector 19 (72), 289-302 2024



Suitability of Egyptian ornamental stone calcite wastes as non-conventional aggregates in concrete
W Ogala, A Yahya, A Gamal, F Abd-EL-Raoof, AM Soltan
Egyptian Journal of Chemistry 67 (7), 275-287 2024



Clays to lightweight aggregates: Thermochemical modeling and industrial validation
N Abdel-Kader, F Abd EL-Raoof, A Sharaf-Eldin, A Elmasry, A Yahya, ...
Construction and Building Materials 432, 136580 2024



Spectrum Sensing Optimization Using De-noising and Energy Detection.
M Khalaf, A Fawzi, A Yahya
Iranian Journal of Electrical & Electronic Engineering 20 (1) 2024



A Distributed Cloud Architecture Based on General De Bruijn Overlay Network
AY Osama R. S. Ramadan, Mohamed Yasin I. Afifi
International Journal of Cloud Applications and Computing Volume 14 • Issue 2024



A Distributed Cloud Architecture Based on General De Bruijn Overlay Network
ORS Ramadan, MYI Afifi, A Yahya
International Journal of Cloud Applications and Computing (IJCAC) 14 (1), 1-19 2024



SENSING TIME IMPROVEMENT USING TWO STAGE DETECTORS FOR COGNITIVE RADIO SYSTEM
AFAY Mohamed Khalaf
International Journal of Computer Networks & Communications 16 (1), 15 2024



A SCALABLE MONITORING SYSTEM FOR SOFTWARE DEFINED NETWORKS
UG Mahmoud Eissa, Ahmed Yahya
International Journal of Distributed and Parallel systems 15 (1), 22 2024



Electrical, microstructural and physical characteristics of talc-based cordierite ceramics
A Yahya, AM Soltan, R Mahani, B El-Kaliouby, S Kenawy, EMA Hamzawy
Silicon 15 (6), 2901-2919 2023



Pharmaceutical polymers and P-glycoprotein: Current trends and possible outcomes in drug delivery
MS Attia, MT Elsebaey, G Yahya, H Chopra, MA Marzouk, A Yahya, ...
Materials Today Communications 34, 105318 2023



Mesoporous silica nanoparticles: Their potential as drug delivery carriers and nanoscavengers in Alzheimer's and Parkinson's diseases
MS Attia, A Yahya, NA Monaem, SA Sabry
Saudi Pharmaceutical Journal 31 (3), 417-432 2023



A framework for classifying breast cancer based on deep features integration and selection
AM Hassan, A Yahya, A Aboshosha
Neural Computing and Applications, 9 2023



Generalized thermoelastic heat conduction model involving three different fractional operators
A Saidi, AMH Yahya, AE Abouelregal, HE Dargail, A Ibrahim-Elkhalil, ...
Advances in Materials Science 23 (2), 25-44 2023

MOST CITED SCHOLAR PUBLICATIONS

Comparison of different backpropagation training algorithms using robust M-estimators performance functions
AR Abd Ellah, MH Essai, A Yahya
2015 Tenth International Conference on Computer Engineering & Systems (ICCES 2015
Citations: 25



Mesoporous silica nanoparticles: Their potential as drug delivery carriers and nanoscavengers in Alzheimer's and Parkinson's diseases
MS Attia, A Yahya, NA Monaem, SA Sabry
Saudi Pharmaceutical Journal 31 (3), 417-432 2023
Citations: 23



Generalized thermoelastic responses in an infinite solid cylinder under the thermoelastic-diffusion model with four lags
AE Abouelregal, H Ahmad, AMH Yahya, A Saidi, H Alfadil
Chinese Journal of Physics 76, 121-134 2022
Citations: 23



Design of Multi-band Microstrip Patch Antennas for Mid-band 5G Wireless Communication
ahmed yahya karima mazen, ahmed emran, ahmed salah shalaby
International Journal of Advanced Computer Science and Applications (IJACSA 2021
Citations: 21



Pharmaceutical polymers and P-glycoprotein: Current trends and possible outcomes in drug delivery
MS Attia, MT Elsebaey, G Yahya, H Chopra, MA Marzouk, A Yahya, ...
Materials Today Communications 34, 105318 2023
Citations: 19



Optimization of microstructure of basalt-based fibers intended for improved thermal and acoustic insulations
M Farouk, AM Soltan, S Schlter, E Hamzawy, A Farrag, A El-Kammar, ...
Journal of Building Engineering 34, 101904 2021
Citations: 16



Performance Evaluation of IoT Messaging Protocol Implementation for E-Health Systems
a yahya zorkany, fahmy
International Journal of Advanced Computer Science and Applications (IJACSA 2019
Citations: 16



A decision support healthcare system based on IoT and neural network technique
KAS Fahmy, A Yahya, M Zorkany
Journal of Engineering, Design and Technology 20 (3), 727-748 2022
Citations: 14



Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing The Massive IoT-LoRaWAN Network Performance
G samar adel almarzoqi, Ahmed Yahya, Zaki Matar
Sensor, communication 22 (1603) 2022
Citations: 14



Thermoelastic responses in rotating nanobeams with variable physical properties due to periodic pulse heating
AMH Yahya, AE Abouelregal, KM Khalil, D Atta
Case Studies in Thermal Engineering 28, 101443 2021
Citations: 14



Performance evaluation of blackhole attack on vanet’s routing protocols
EF Ahmed, RA Abouhogail, A Yahya
International Journal of Software Engineering and Its Applications 8 (9), 39-54 2014
Citations: 14



Keterangan dokumen dalam bentuk digital di Mahkamah Syariah: Analisis berkaitan definisi serta kebolehterimaannya di sisi prinsip Syariah di Malaysia
A Yahya, A Azam, A Hassan
UKM Journal Article Repository 1, 1-12 2017
Citations: 13



Pulse Shape Discrimination Techniques based on Cross-correlation and Principal Component Analysis.
MA A yahya, H saleh, M sayed
International Journal of Computer Applications (0975–8887) 38 (5), 6-11 2012
Citations: 13



Energy-aware architecture for multi-rate ad hoc networks
A Yahya
Egyptian Informatics Journal 11 (1), 33-38 2010
Citations: 12



Robust backpropagation learning algorithm study for feed forward neural networks
ARA Ellah, MH Essai, A Yahya
Master’s Thesis 2016
Citations: 11



Optimized Image Compression Techniques for the Embedded Processors
AA Al-hamid, A Yahya, RA El-Khoribi
International Journal of Hybrid Information Technology 9 (1), 319-328 2016
Citations: 11



A framework for classifying breast cancer based on deep features integration and selection
AM Hassan, A Yahya, A Aboshosha
Neural Computing and Applications, 9 2023
Citations: 10



Performance assessment for LTE-advanced networks with uniform fractional guard channel over soft frequency reuse scheme
MA Safwat, HM El-Badawy, A Yehya, H El-Motaafy
Wireless Engineering and Technology 4 (4), 161-170 2013
Citations: 9



A Tamper proofing text watermarking shift algorithm for copyright protection
AE Afify, A Emran, A Yahya
Arab Journal of Nuclear Sciences and Applications 52 (3), 126-133 2019
Citations: 7



DST and DCT-based depth of interaction (DOI) determining techniques for LSO and LuYAP scintillation detectors in PET
H Saleh, A Yahya, M Ashour, M Sayed
International Journal of Computer Applications 28 (7) 2011
Citations: 7