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Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2
(VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY, 2020) Alsharif, Mohammed H.; Alsharif, Yahia H.; Albreem, Mahmoud A. M.; Jahid, Abu; Solyman, Ahmad Amin Ahmad; Yahya, Khalid O. Moh.; Alomari, Osama Ahmad; Hossain, Md. Sanwar
Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARSCoV-19 through existing data that reveal the SARS’s cause. Artificial intelligence (AI) learning models can be created to predict drug structures that can be used to treat COVID-19. Despite the effectively demonstrated repurposed drugs, more repurposed drugs should be recognized. Furthermore, technological advancements have been helpful in the battle against COVID-19. Machine intelligence technology can support this procedure by rapidly determining adequate and effective drugs against COVID-19 and by overcoming any barrier between a large number of repurposed drugs, laboratory/clinical testing, and final drug authorization. This paper reviews the proposed vaccines and medicines for SARSCoV-2 and the current application of AI in drug repurposing for COVID-19 treatment.
Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach
(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2021) Deif, Mohanad A.; Solyman, Ahmad Amin Ahmad; Alsharif, Mohammed H.; Uthansakul, Peerapong
The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.
Bit and Packet Error Rate evaluations for Half-Cycle stage cooperation on 6G wireless networks
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2021) Attar, Hani H.; Solyman, Ahmad Amin Ahmad; Khosravi, Mohammad R.; Qi, Lianyong; Alhihi, Mohammad; Tavallali, Pooya
The excellent features of the sixth generation (6G) of wireless technology enthuses researchers to apply advanced and complex techniques on mobile communication networks, which is what this paper explores. As a result of the ability of 6G to exchange vast data rates and network programming, the Cooperative Network Coding (CoNC) technique can be implemented to improve the connectivity and diversity within 6G applications. Though CoNC is usually implemented at the second stage, this paper proposes dividing the first stage into two Half-Cycle stages, and then applying CoNC at the second Half-Cycle stage. The resulting Bit Error Rate (BER) behaviour is investigated on the physical layer for direct data exchange in 6G local mobile networks over an Additive White Gaussian Noise (AWGN) channel. Partial Unit Turbo Code (PUTC) (4,2,1,4) and (8,4,3,8) are used by each mobile node as the forward error correction technique, which means that each mobile acts as a Base Station (BS) for other mobiles in the local network by applying CoNC on the received packets, and then each mobile node (or BS), either Amplify-and-Forward (AF), or Decode-re-encode-amplify and Forward (DF), acts. When full connectivity is not achieved at the end of the first two Half-Cycle stages, new Half-Cycle stage transmissions follow, and the BER behaviour for all additional Half-Cycle stages is obtained. The results illustrate that applying CoNC at the second Half-Cycle stage of the first general stage produces a limited BER loss. To mitigate the damage in the BER, a soft-decision PUMTC decoder is also proposed.
A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences
(AMER INST MATHEMATICAL SCIENCES-AIMS, PO BOX 2604, SPRINGFIELD, MO 65801-2604, 2021) Deif, Mohanad A.; Solyman, Ahmad Amin Ahmad; Kamarposhti, Mehrdad Ahmadi; Band, Shahab S.; Hammam, Rania E.
In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyperparameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.
Design of Biodegradable Mg Alloy for Abdominal Aortic Aneurysm Repair (AAAR) Using ANFIS Regression Model
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141, 2022) Hammam, Rania E.; Solyman, Ahmad Amin Ahmad; Alsharif, Mohammed H.; Uthansakul, Peerapong; Deif, Mohanad A.
ABSTRACT Abdominal aortic aneurysm (AAA) is among the most widespread and dangerous diseases that may cause death. Recently, Endovascular Aneurysm Repair outperformed open aortic surgery, since it is a safe and reliable technique where a stent graft system is placed within the aortic aneurysm. It was aimed to design an Mg biodegradable alloy with bio-friendly alloying elements that enhance the corrosion resistance and mechanical properties of the alloy for the design of stents for Abdominal Aortic Aneurysm (AAA) repair. Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed for the design of the Mg alloy and compared to other traditional machine learning regression models (Multiple Linear Regression (MLR) and Gradient Boosting (GB). The dataset utilized in this work consisted of 600 samples of Mg alloys that were collected from the mat web database and additional papers from Google Scholar. The results revealed the superior prediction capability of the ANFIS model since it attained maximum R 2 scores of 0.926, 0.958, and 0.988 for the prediction of UTS, YS, and Ductility respectively. Furthermore, the ANFIS model was capable of designing an Mg biodegradable alloy having a UTS, YS, and Ductility of 346.148 Mpa, 230.8 Mpa, and 15.4% respectively which are excellent mechanical properties satisfying vascular stents requirements The ANFIS model can be further applied to speed up the design of other alloys in the future for various medical applications, reducing the time, cost, and effort of large searching space.
Deterministic cooperative hybrid ring-mesh network coding for big data transmission over lossy channels in 5G networks
(SPRINGER, ONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES, 2021) Attar, Hani H.; Solyman, Ahmad Amin Ahmad; Alrosan, Ayat; Chakraborty, Chinmay; Khosravi, Mohammad R.
Wired and wireless communication data is getting bigger and bigger at such a high pace. Accordingly, the big data (BD) communication networks should be developed as quickly as the quick increase in the exchanging data size is. Based on this regard, this paper proposes a wired and wireless protocol that applies cooperation Network coding (CoNC) in a wired ring topology (WRT) to improve exchanging the BD signifcantly in wireless mesh network (WMN). The paper presents a solution for distributed nodes to deal with big data over 5G by proposing Hybrid Ring-Mesh Protocols (HRMP) that exploit the CoNC technique at distributed nodes. The proposed protocol (X-ORING) deterministically combines the data that is received at a base station (BS), where the BS wirelessly retransmits the combined data to the WMN members, instead of just forwarding them to the WMN members. Moreover, all members of the WMN are connected by wired optical fbre channels in a WRT and directly to the BS. The results show that applying CoNC in the proposed protocols exploits the advantages of the WRP between the WMN members, and consequently, the WMN packet error rate is signifcantly improved. Moreover, using optical fbre wires between the mesh network members and the BS increases the WMN coverage region considerably, and allows the BS to receive all members’ packets correctly. Finally, the results show that applying CoNC on the WRT improves the entire network maintenance and reliability greatly, simply because the proposed HRMP can continue broadcasting even if one of the direct optical fbre goes out of serves, i.e. the fbre link between one of the N member and the BS lost the connectivity.
Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach
(HINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLAND, 2022) Deif, Mohanad A.; Attar, Hani; Amer, Ayman; Elhaty, Ismail A. M.; Khosravi, Mohammad R.; Solyman, Ahmad Amin Ahmad
Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.
Efficient equalisers for OFDM and DFrFT-OCDM multicarrier systems in mobile E-health video broadcasting with machine learning perspectives
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2020) Attar, Hani H.; Solyman, Ahmad Amin Ahmad; Mohamed, Abd-Elnaser Fawzy; Khosravi, Mohammad R.; Menon, Varun G.; Bashir, Ali Kashif; Tavallali, Pooya
Recently, the orthogonal frequency-division multiplexing (OFDM) system has become an appropriate technique to be applied on the physical layer in various requests, mainly in wireless communication standards, which is the reason to use OFDM within mobile wireless medical applications. The OFDM with cyclic prefix (CP) can compensate lacks for the time-invariant multi-path channel effects using a single tap equaliser. However, for mobile wireless communication, such as the use of OFDM in ambulances, the Doppler shift is expected, which produces a doubly dispersive communication channel where a complex equaliser is needed. This paper proposes a low-complexity band LDLH factorisation equaliser to be applied in mobile medical communication systems. Moreover, the discrete fractional Fourier transform (DFrFT) is used to improve the communication system’s performance over the OFDM. The proposed low-complexity equaliser could improve the OFDM, and the DFrFT-orthogonal chirpdivision multiplexing (DFrFT-OCDM) system’s performance, as illustrated in the simulation results. This proves that the recommended system outperforms the standard benchmark system, which is an essential factor as it is to be applied within mobile medical systems.
A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area
(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2022) Deif, Mohanad A.; Solyman, Ahmad Amin Ahmad; Alsharif, Mohammed H.; Jung, Seungwon; Hwang, Eenjun
Temperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.
A Low-Complexity Equalizer for Video Broadcasting in Cyber-Physical Social Systems Through Handheld Mobile Devices
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA, 2020) Solyman, Ahmad Amin Ahmad; Attar, Hani; Khosravi, Mohammad R.; Menon, Varun G.; Jolfaei, Alireza; Balasubramanian, Venki; Selvaraj, Buvana; Tavallali, Pooya
In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels.
MIMO-OFDM/OCDM low-complexity equalization under a doubly dispersive channel in wireless sensor networks
(SAGE PUBLICATIONS INC, 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA, 2020) Solyman, Ahmad Amin Ahmad; Attar, Hani; Khosravi, Mohammad R.; Koyuncu, Baki
In this article, three novel systems for wireless sensor networks based on Alamouti decoding were investigated and then compared, which are Alamouti space-time block coding multiple-input single-output/multiple-input multiple-output multicarrier modulation (MCM) system, extended orthogonal space-time block coding multiple-input single-output MCM system, and multiple-input multiple-output system. Moreover, the proposed work is applied over multiple-input multiple-output systems rather than the conventional single-antenna orthogonal chirp division multiplexing systems, based on the discrete fractional cosine transform orthogonal chirp division multiplexing system to mitigate the effect of frequency-selective and time-varying channels, using low-complexity equalizers, specifically by ignoring the intercarrier interference coming from faraway subcarriers and using the LSMR iteration algorithm to decrease the equalization complexity, mainly with long orthogonal chirp division multiplexing symbols, such as the TV symbols. The block diagrams for the proposed systems are provided to simplify the theoretical analysis by making it easier to follow. Simulation results confirm that the proposed multiple-input multiple-output and multiple-input single-output orthogonal chirp division multiplexing systems outperform the conventional multiple-input multiple-output and multiple-input single-output orthogonal frequency division multiplexing systems. Finally, the results show that orthogonal chirp division multiplexing exhibited a better channel energy behavior than classical orthogonal frequency division multiplexing, thus improving the system performance and allowing the system to decrease the equalization complexity.
Modeling and simulation of dye-sensitized solar cell: Model verification for different semiconductors and dyes
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2022) Tayeb, Aghareed M.; Solyman, Ahmad Amin Ahmad; Hassan, Mohamed; Abu el-Ella, Tamer M.
In this article, the use of MATLAB/SIMULINK interface to realize a generalized photovoltaic simulation model is introduced. The model was created utilizing the photovoltaic (PV) cell fundamental circuit equations, including the effects of solar radiation and variations in temperature. This modeling approach enables the I–V and P–V curve of PV cells to be understood. It could also be used as a tool to forecast the behavior of any solar PV cell under differing environmental circumstances (e.g., temperature, irradiation conditions). These effects are simultaneously added in real-time. Due to their nonlinear features, they must be modeled to design and simulate the maximum power point of solar cells. This model applies to dye-sensitized solar cells with three different semiconductors, namely, TiO2, ZnO, and SnO2; use N3 dye. According to changes in atmospheric parameter values such as solar radiation, temperature, and operating parameter values like semiconductor type, dye concentration, and particles, the characteristic dimensions of photovoltaic systems such as power supply voltage (PV) and current–voltage (I-V) characteristics are drawn; in the MATLAB/SIMULINK interface observed. The simulation results reveal that these elements and the respective photovoltaic model affect the maximum operating performance of PV modules. The battery made of TiO2 semiconductor and N3 dye showed the greatest consistency with the model battery, followed by the battery made of ZnO, and finally, the battery made of SnO2 with the same dye N3.
Optimization Analysis of Sustainable Solar Power System for Mobile Communication Systems
(TECH SCIENCE PRESS, 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052, 2022) Alsharif, Mohammed H.; Kannadasan, Raju; Hassan, Amir Y.; Tawfik, Wael Z.; Kim, Mun-Kyeom; Khan, Muhammad Asghar; Solyman, Ahmad Amin Ahmad
Cellular mobile technology has witnessed tremendous growth in recent times. One of the challenges facing the operators to extend the coverage of the networks to meet the rising demand for cellular mobile services is the power sources used to supply cellular towers with energy, especially in remote. Thus, switch from the conventional sources of energy to a greener and sustainable power model became a target of the academic and industrial sectors in many fields; one of these important fields is the telecommunication sector. Accordingly, this study aims to find the optimum sizing and technoeconomic investigation of a solar photovoltaic scheme to deploy cellular mobile technology infrastructure cleanly and sustainably. The optimal solarpowered system is designed by employing the energy-balance procedures of the HOMER software tool. The problem objective is considered in terms of cost, but the energy system is constrained to meet the power demand reliably. Process simulations were performed to determine the optimum sizing, performance and monetary cost of the power system, using long-term meteorological datasets for a case study site with defined longitude (31? 25 E) and latitude (30? 06 N). From the observed results, the total net present cost (NPC) of the proposed system is $28,187. Indeed, these outcomes can provide profound economic, technical, and ecological benefits to cellular operators. It also ensures a sizeable reduction in greenhouse gas that supports sustainable green wireless network (WN) deployment in remote areas.
Simple Mathematical and Simulink Model of Stepper Motor
(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2022) Iqteit, Nassim A.; Yahya, Khalid O. Moh.; Makahleh, Firas M.; Attar, Hani; Amer, Ayman; Solyman, Ahmad Amin Ahmad; Qudaimat, Ahmad; Tamizi, Khaled
This paper presents a simple mathematical and Simulink model of a two-phase hybrid stepper motor, where ignoring the permeance space harmonics of the hybrid stepper motor is regarded as the main physical assumption in this article. Moreover, the dq transformation method is adopted as the main mathematical approach for the derivation of the proposed model, where simple voltages, currents, and torque equations are obtained and used to build the proposed Simulink and circuit model of the stepper motor. The validity and the effectiveness of the proposed model are examined by comparing its results with the results collected from the Simulink model in the library of Matlab. The obtained simulation results showed that the proposed model achieved a high simplicity and high accuracy when compared with conventional models.
Toward 6G Communication Networks: Terahertz Frequency Challenges and Open Research Issues
(TECH SCIENCE PRESS, 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052, 2021) Alsharif, Mohammed H.; Albreem, Mahmoud A. M.; Solyman, Ahmad Amin Ahmad; Kim, Sunghwan
Future networks communication scenarios by the 2030s will include notable applications are three-dimensional (3D) calls, haptics communications, unmanned mobility, tele-operated driving, bio-internet of things, and the Nanointernet of things. Unlike the current scenario in which megahertz bandwidth are sufficient to drive the audio and video components of user applications, the future networks of the 2030s will require bandwidths in several gigahertzes (GHz) (from tens of gigahertz to 1 terahertz [THz]) to perform optimally. Based on the current radio frequency allocation chart, it is not possible to obtain such a wide contiguous radio spectrum below 90 GHz (0.09 THz). Interestingly, these contiguous blocks of radio spectrum are readily available in the higher electromagnetic spectrum, specifically in the Terahertz (THz) frequency band. The major contribution of this study is discussing the substantial issues and key features of THz waves, which include (i) key features and significance of THz frequency; (ii) recent regulatory; (iii) the most promising applications; and (iv) possible open research issues. These research topics were deeply investigated with the aim of providing a specific, synopsis, and encompassing conclusion. Thus, this article will be as a catalyst towards exploring new frontiers for future networks of the 2030s.

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