Samy Ghoniemy - Academia.edu (original) (raw)

Papers by Samy Ghoniemy

International journal of online and biomedical engineering, Feb 27, 2019

Social networks and Internet of things are two paradigms when integrated a new paradigm Internet ... more Social networks and Internet of things are two paradigms when integrated a new paradigm Internet of Everything is established that has its impact on revolutionizing various fields such as engineering, industry and healthcare. Social networks became nowadays of the most important web services on which people heavily rely, thus became a major source for information extraction for rational decision making considering individuals as social or socio sensors. Furthermore, people using sensors especially biological sensors enabled the use of internet of things technology in building intelligent healthcare systems. One of the challenges facing the design of such systems is the design of an intelligent recommender system that is able to deal with such big data. For that, this paper proposes a framework to develop an enhanced intelligent expert advisorbased health monitoring and disease awareness system. The proposed framework enables the researchers to design advisory systems that are able to observe physiological signals through the use of different biosensors and integrate it with historical medical data together with the massive data collected from social networks to provide accurate alerts and recommendations for many ailments inspected. The proposed Framework is designed to facilitate generic, dynamic and scalable process of integrating different types of social networks and biosensors.

The International Conference on Electrical Engineering (Print), May 1, 2008

In this paper, feed forward controller model based on the laser's inverse model is proposed. An e... more In this paper, feed forward controller model based on the laser's inverse model is proposed. An enhanced digital control algorithm for 3rd, 5th, and 7th and possibly higher harmonics for laser frequency locking is developed. A generic diagram for the digital implementation of the proposed controller is presented. Digital implementation of the control algorithm modules is proposed and discussed. Hardware realization based on microcontrollers for the proposed digital controller is introduced and described for He-Ne laser systems. Simulation results of the full system showed that using higher harmonics in laser frequency locking yields better performance in regard to the frequency shifts, improved noise performance, and hence better reproducibility. It also showed that the frequency difference between lasers locked with higher order harmonic locking using the digital realization is better than that locked with lower order harmonic locking. The measurement results using the lab version of the realized controller and He-Ne laser system is presented and found in a very good agreement with the simulation results which assure that the proposed digital controller can be accurately used for transmitting multiple users' data over WDM communication systems with acceptable stability.

Proceedings of SPIE, May 16, 2003

Analytical expressions for the intensity and frequency/phase noise of single mode semiconductor l... more Analytical expressions for the intensity and frequency/phase noise of single mode semiconductor lasers based on quantum-mechanical rate equations are derived. Correlated photons, electrons, and phase Langevin noise sources and their auto and cross-correlation relations are presented along with a novel self-consistent normalized laser model that includes the laser's correlated noise sources. A Symbolically Defined Device (SDD) is constructed using the proposed normalized model and implemented in Agilent's Advanced Design System (ADS) CAD tool. Dynamic laser characteristics are predicted using the SDD implementation for 1300 nm InGaAsP/InP lasers. The results of time domain dynamic simulations of photons, carriers, optical output power, and phase - with and without the effects of the noise - are presented. Simulation results are used to show the effects of random noise on both the phase and optical power output of semiconductor lasers. Simulation results are analyzed to demonstrate the resonance frequency shift dependence on the bias current levels, the relation between the frequency response and the bias current and the dependence of the laser line width broadening on the frequency fluctuations. Comparison between the presented results and other published results (simulations and measurements) show good agreement while achieving simulation time enhancement. The suitability of the proposed models for the study and characterization of the performance of complete systems in both circuit and system simulations is examined.

International Conference on Aerospace Sciences & Aviation Technology, May 1, 1995

The results of a 6-Degrees-of-freedom (6DOF) simulation model of a shortrange homing missile are ... more The results of a 6-Degrees-of-freedom (6DOF) simulation model of a shortrange homing missile are presented. The missile considered is roll-rate stabilized and is guided by proportional navigation law. The missile motor is of boost-sustain type. In this paper, the effect of the boost acceleration is analyzed. During the boost phase, it is found that the presence of a relatively large incidence angle increases the demanded missile maneuver to correct for the input errors to the guidance loop. Mathematical analysis shows that this large incidence angles couples a considerable amount of the boost acceleration to the lateral plane. This can be regarded as an input acceleration bias that increases the effort done by the guidance loop. In the sustain phase; however, the sustainer acceleration is low and this effect can be neglected. To reduce the demanded effort from the guidance loop, a • delayed guidance scheme is proposed. In this scheme, the guidance and control of the missile starts few moments after launch. The results obtained via numerical computations are in complete agreement with the theoretically obtained conclusions.

IEEE Access, 2022

According to the World Health Organization, several factors have affected the accurate reporting ... more According to the World Health Organization, several factors have affected the accurate reporting of SARS-CoV-2 outbreak status, such as limited data collection resources, cultural and educational diversity, and inconsistent outbreak reporting from different sectors. Driven by this challenging situation, this study investigates the potential expediency of using social network data to develop reliable early information surveillance and warning system for pandemic outbreaks. As such, an enhanced framework of three inherently interlinked subsystems is proposed. The first subsystem includes data collection and integration mechanisms, data preprocessing, and hybrid sentiment analysis tools to identify tweet sentiment taxonomies and quantitatively estimate public awareness. The second subsystem comprises the feature extraction unit that identifies, selects, embeds, and balances feature vectors and the classifier fitting and training unit. This subsystem is designed to capture the most effective linguistic feature combinations with more spatial evidence by using a variety of approaches, including linear classifiers, MLPs, RNNs, and CNNs, as well as pre-trained word embedding algorithms. The last is the modeling and situational awareness evaluation subsystem, which measures temporal associations between pandemic-relevant social network activities and officially announced infection counts in the most hazardous geolocations. The proposed framework was developed and tested using a combination of static datasets and real-time scraped Twitter data. The results of these experiments showed the remarkable performance of the framework in assessing the temporal associations between public awareness and outbreak status. It also showed that the Decision Tree Classifier with Unigram+TF-IDF feature vectors outperformed other conventional models for sentiment classification and geolocation classification with an accuracy of 94.3% and 80.8, respectively. As indicated, conventional machine learning algorithms didn't achieve a precision of more than 80%, while, for instance, MLP with selfembedding layer, Word2Vec, and GloVe pre-trained word embedding resulted in very poor accuracy of 10%, 36%, and 32%, respectively. However, adding the PoS tag one-hot encoding embedding increased the validation accuracy from 36% to approximately 89%, while the best performance for the second subsystem was achieved by Bi-LSTM with RoBERTa word embedding, with an accuracy of 96%. The achieved results reveal that the proposed framework can proactively capture the potential hazards associated with the prevalence of infectious diseases as an effective early detection and info-surveillance awareness system.

International Conference on Aerospace Sciences & Aviation Technology, Apr 1, 2017

Physical quantities can be simulated using Matlab Simulink and Matlab Simscape. In this paper the... more Physical quantities can be simulated using Matlab Simulink and Matlab Simscape. In this paper the behavioral analysis of capacitive gravitational accelerometer based tilt sensor for both mechanical transducer and interface circuits is modeled and simulated. The output signal from each stage of the proposed tilt sensor circuit was measured and displayed for an input tilt angles ranging from-15 to 15. The simulated results showed a good agreement with the published results

International Conference on Aerospace Sciences & Aviation Technology, Apr 1, 1989

The rate-distortion function with a mean square error distortion criterion is studied for Gaussia... more The rate-distortion function with a mean square error distortion criterion is studied for Gaussian Markov sources .The funcpon is calculated for all distortion v4ues in the interval [O-o-The function is derived for the n-order Markov process , and is computed for two special cases : n = 1 and n = 2 , where n is the order of the process .

International Conference on Aerospace Sciences & Aviation Technology, May 1, 1997

A complete six-degrees of freedom flight simulation model for anti-aircraft command guided missil... more A complete six-degrees of freedom flight simulation model for anti-aircraft command guided missile system is developed. A computer code that solves the model is constructed with BORLANDC. Modular concept is considered in the code development. The non-linear differential equations that describe the model are solved by Runge-kutta 4 method. The integration step is chosen small enough that the numerical errors are negligible. The aerodynamic non-linear coefficients that describe the missile airframe are calculated by the aid of standard NASA curves. The missile is roll angle stabilized throughout the flight. It is controlled in the lateral planes via two pairs of rear control fins. The pitch and yaw control channels are identical except for constant gravity bias added to the pitch channel. The missile airframe is deeply investigated. The step response of the airframe to unit step fin deflection is obtained. The results indicate that the airframe can be accurately represented by a second order lag system. The weathercock natural frequency and the damping coefficient are obtained by transforming the timedomain data to the frequency domain by the Fast Fourier Transform. The obtained results show that the missile airframe is heavily underdamped. As well, the airframe bandwidth increases by increasing the missile speed. Doubling the missile speed fairly doubles the weathercock frequency at the expense of aerodynamic damping coefficient reduction.

International journal of computer applications, May 16, 2014

Computer Vision algorithms are considered computationally intensive problems. Face detection is o... more Computer Vision algorithms are considered computationally intensive problems. Face detection is one of the most complex objects to detect due to its variations. The objective is to enhance the face detection time (compared with other approaches) to reach a real-time application that will be later on used in augmented reality applications such as telepresence. The experiments with NVIDIA GTX 560 show that detecting the faces in an image of size [640x480] can process up to 33 frames per second, also this paper shows how the researcher's approach can be generalized to support larger image sizes. This in turn reflects back the achieved speed that exceeds FPGA.

As a case study for our research, COVID-19, that was caused by a unique coronavirus, has substant... more As a case study for our research, COVID-19, that was caused by a unique coronavirus, has substantially affected the globe, not only in terms of healthcare, but also in terms of economics, education, transportation, and politics. Predicting the pandemic's course is critical to combating and tracking its spread. The objective of our study is to evaluate, optimize and fine-tune state of the art prediction models in order to enhance its performance and to automate its function as possible. Therefore, a comparison between statistical versus compartmental methods for time series-based modeling and forecasting of infectious disease progression was conducted. The comparison included several classical univariate time series statistical models, including Exponential Smoothing, Holt, Holt-Winters, and Seasonal Auto Regressive Integrated Moving Average (SARIMA), as opposed to an optimized version of the compartmental multivariate epidemiological model SEIRD, which is referred to in our study, as, Non-Linear L-BFGS-B Fitted SEIRD. The mentioned methods were implemented and fine-tuned to model and forecast COVID-19 outbreak situation represented by confirmed cases, recoveries, and fatalities in (Australia, Canada, Egypt, India, United States of America and United Kingdom). Through the implementing and tuning of both types of models, we have observed that while univariate time series forecasting models such as SARIMA produce highly accurate predictions due to their ease of use and procedure, as well as their ability to deal with seasonality and cycles in time series, multivariate epidemiological models are more powerful and extendible. Despite their complexity, epidemiological models have aided extensively in understanding the spread and severity of infectious disease pandemics such as the COVID-19 global pandemic. Using our optimized SEIRD, we have obtained a Mean Squared Log Error of 10−3 order, demonstrating the forecasts' elevated accuracy and reliability. In addition to forecasting the course of the pandemic for a 3 months season in all countries under investigation, we were able to estimate the transmission potential of COVID-19 represented by its effective reproduction number Rt. With mathrmRmathrmt=1\mathrm{R}_{\mathrm{t}}=1mathrmRmathrmt=1 is considered as the pandemic control threshold, it is evident that all of the countries under investigation are hovering just above the control threshold. This study might be relieving since it can demonstrate that the world is on the right track in terms of putting an end to the pandemic as soon as possible. The whole study shows how powerful is compartmental methods compared to classical statistical methods when used to model and forecast an infectious disease outbreak which encourages our further related research concerning the study of implementing advanced compartmental models considering additional parameters and controls.

2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Oct 21, 2021

(online advertising of products has garnered substantial interest across a variety of channels, i... more (online advertising of products has garnered substantial interest across a variety of channels, including search engines, third-party websites, social media platforms, and mobile apps. The success of online campaigns is a challenge in online marketing, and it is typically measured by user response via various measures such as clicks on ad creatives, product subscriptions, item purchases or specific user input via online surveys. In recent years, business analysts have played an increasingly important role in opinion mining before launching a new product. Opinion mining is primarily concerned with detecting and extracting relevant information from various opinion-rich resources such as review sites, discussion forums, blogs, and news corpora, among others, and putting it together into a logical structure. Because the data obtained from these sources is highly unstructured in nature and extremely large in volume, data pre-processing and sentiment analysis are critical processes. Pushing researcher to find a more robust ¬and dynamic opinion data collection, integration, and information extraction techniques. This paper presents an enhanced intelligent recommender for products ads and community detection for viral market campaigns. The proposed solution is based on using an enhanced opinion mining algorithm, correlation, and ads discovery techniques, modified Naïve Bayes, collaborative filters, feature extraction techniques, ontology modeling, and semantic networks. The performance of the proposed system has been evaluated by utilizing social networks and mining services to classify products and reviewers and to discover prominent communities for viral market campaigns. Results of extensive experiments performed on real datasets. These results are a compilation of over 7,000 online reviews for 50 electrical items from websites such as Amazon and Best Buy that Datafiniti's Product Database has compiled. and it reveals that the proposed system can recommend a suitable list of product advertisements and has the capacity to predict influential communities for viral marketing campaigns. Experimental results show that the invented solution determines the right community in a timely manner)

Nowadays service providers are facing serious competition and are looking for new ways to address... more Nowadays service providers are facing serious competition and are looking for new ways to address their customers' very high-bandwidth demands and emerging trends as immersive video communications and ubiquitous cloud computing. Optical access successfully proved that it is a crucial broadband technology that is proposed to meet these challenges. In this paper, the wavelength division multiplexed (WDM) PON together with TDMA as a hybrid PON technology will be proposed and investigated for enhancing the system reachability, capability, and accommodation of users. For this purpose, fiber to the home (FTTH) networks using PONs, Gigabit-capable Passive Optical Networks (GPONs) and 10G-PONs that are designed using generic architectures and implemented using OptiSystem. Simulation results based on a broad set of performance measures such as bit rate, power losses, transmission distance and number of users showed that the proposed generic transmission architecture is suitable for up- and down-stream transmission of triple play traffic over optical links of up to 10 Gbps and the ability to accommodate 32 ONUs. The second part of this paper presents the development of a wavelength division multiplexing PON (WDM-PON) model in which array waveguide gratings (AWGs) are used to MUX and DEMUX wavelengths to or from Optical Network Units (ONUs). The proposed design addresses the real-time consistency between the wavelengths of optical transceivers and the connecting AWG port; and between the wavelengths of the port on the AWG that is located in the central office and the port on the remote AWG. The experimental tests using 100 or 200 GHz channel spacing and several tens of nm between up- and down-stream channel clusters showed the ability of multiplexing up- and downstream of 40-channels based on the commercially available AWG, providing the ability to accommodate 40 ONUs. The last part in this paper discusses the designs of a symmetric 4×40Gbps TWDM-PON network for the next generation passive optical networks to improve the network capacity. In this design a distributed feedback (DFB) laser diode is used for downstream transmission and a tunable intensity modulated grating laser is used for upstream transmission. The experimental results of this design showed that TWDM-PON achieved successfully 4×40Gbps along 20km fiber at 1:128 splitting providing the ability to accommodate up 128 ONUs. To the best of my knowledge, the proposed system provides two times larger number of ONUs than currently available systems.

Journal of Advances in Information Technology, 2019

The integration between Social networks and Internet of things established a paradigm that is exp... more The integration between Social networks and Internet of things established a paradigm that is expected to revolutionize various fields such as engineering, industry and healthcare. Social networks became of the most important web applications on which not only people heavily rely, but also where information extracted became a major source for rational decision making considering individuals as social or socio sensors. Furthermore, being socio who is using sensors especially biological sensors enabled the use of internet of things technology in building intelligent healthcare systems. One of the highly challenging problems facing the design of such systems is the design of the suitable intelligent recommender system that is able to deal with such big data. As such, this paper proposes a framework to develop an enhanced intelligent expert advisor-based health monitoring and disease awareness system. The proposed framework enables the researchers to design such advisory systems that are able to observe physiological signals through the use of different bio sensors and the integration of historical medical data with the massive data collected from social networks to provide accurate alerts and recommendations for many ailments inspected. The proposed Framework is designed to facilitate generic, dynamic and scalable process of integrating different types of social networks and bio sensors.

2021 10th International Conference on Software and Information Engineering (ICSIE)

Constructing an energy efficient harvesting platform along with a resource management system (RMS... more Constructing an energy efficient harvesting platform along with a resource management system (RMS) for energy harvesting wireless body area networks (EH-WBAN) for monitoring the health condition of the patients, and detecting arising diseases in the early stages, is becoming today's global evolution. The usage of sensors in constructing intelligent wireless body area networks (WBAN) health monitoring and disease awareness systems especially biosensors is quite a challenge. The reason for this is that such a design needs a continuous power source, therefore, a reliable management system is required to manage the power consumption, and the power needed to get the system going, to ensure extending the lifetime of the sensor nodes while providing the wireless body area networks (WBAN) with efficient energy. Energy harvesting is the process of capturing, collecting, and storing such energy for later use. These small amounts of energy are from one or more naturally occurring energy sources also known as ambient energy sources. Based on the needs and requirements of the network's applications. The process of designing the most suitable energy harvesting system in addition to its matching energy and power management system is extremely important.

2022 5th International Conference on Computing and Informatics (ICCI)

Knowledge graphs have emerged as a powerful dynamic knowledge representation model for predicting... more Knowledge graphs have emerged as a powerful dynamic knowledge representation model for predicting hidden patterns and relationships in medical and healthcare domains for medical diagnosis and disease prediction. However, generating, constructing, and integrating knowledge graphs for this domain is still challenging research area for such heterogeneous domain. In this paper, a framework for automatic disease knowledge graph (KG) construction and intelligent ontology integration with standard human disease ontology (DO) is developed. A major component of this framework is developing an enhanced diseases' knowledge graph that is based on collecting medical facts from medical platforms and social networks, including symptoms, causes, risk factors and prevention factors. This knowledge graph represents a major base for intelligent diagnosis and disease prediction systems. The developed disease knowledge graph includes diseases' symptoms, causes, risk factors and prevention factors and integrated with DO by more than 400 diseases. The knowledge graph presented is a step not only towards building an enriched knowledge graph for professional staff and normal users. The graph is also a step towards integrating two standard ontologies human disease and symptom ontologies that are not linked or integrated till now.

2019 IEEE 13th International Conference on Semantic Computing (ICSC), 2019

In recent years, social networks have been the gate for the explosion of thousands of data sets. ... more In recent years, social networks have been the gate for the explosion of thousands of data sets. Interrelations and influence between participants expand dramatically due to the success of online social networks such as Facebook, Foursquare, Twitter and others. The challenge of identifying influential nodes, has been playing a significant part in the scientific community revealing new research opportunities in many application domains such as recommendation systems, viral marketing and others. Most of the research done considered only the network structure with its geospatial information ignoring the importance of semantics underlying these datasets. In this paper, we propose a semantic-influence measurement based algorithm (SIMBA) that efficiently detects influential nodes on social networks. SIMBA is based on both the geospatial information as well as semantic information associated with each user. SIMBA is used for location recommendation purposes and also in examining the power of influential nodes in directing the recommendation to other connected nodes. Different experiments have been conducted under different conditions and varying parameters to verify and validate the proposed algorithm against the latest published algorithms. Two real location based social networks namely; Brightkite11https://snap.stanford.edu/data and weeplaces22http://www.yongliu.org/datasets are used in the experiments.

Normalized laser rate equations suitable for large signal analyses are discussed. A large signal ... more Normalized laser rate equations suitable for large signal analyses are discussed. A large signal laser model suitable for system level simulations is presented. Predicted laser modulation response characteristics are given for Fabry-Perot lasers and compared to measured results. The dependence of the laser modulation response on the laser relaxation-oscillation peak frequency and laser biasing is predicted and compared to measured values. It is demonstrated that the small-signal carrier modulation bandwidth is limited mainly by the intensity dependence of the modulation frequency and the laser's damping rate of oscillation. Simulated and measured results demonstrate that the relaxation-oscillation peak frequency is a reasonable estimate for the modulation bandwidth, and the variation of the modulation response with the output power is nearly flat at fixed modulation frequencies.

2017 13th International Conference on Semantics, Knowledge and Grids (SKG), 2017

Social network has lately shown an important impact in both scientific and social societies and i... more Social network has lately shown an important impact in both scientific and social societies and is considered a highly weighted source of information nowadays. Due to its noticeable significance, several research movements were introduced in this domain including: Location-Based Social Networks (LBSN), Recommendation Systems, Sentiment Analysis Applications, and many others. Location Based Recommendation systems are among the highly required applications for predicting human mobility based on users' social ties as well as their spatial preferences. In this paper we introduce a trust based recommendation algorithm that addresses the problem of recommending locations based on both users' interests as well as social trust among users. In our study we use two real LBSN, Gowalla and Brightkite that include the social relationships among users as well as data about their visited locations. Experiments showing the performance of the proposed trust based recommendation algorithm are also presented.

Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2019

Process Mining is a research field that aims to develop new techniques to discover, monitor and i... more Process Mining is a research field that aims to develop new techniques to discover, monitor and improve real processes by extracting knowledge from event logs. This relatively young research discipline has evidenced efficacy in various applications, especially in application domains where a dynamic behavior needs to be related to process models. Process Model Discovery is presumably the most important task in Process Mining since the discovered models can be used as an objective starting points for any further process analysis to be conducted. There are various quality dimensions the model should consider during discovery such as Replay-Fitness, Precision, Generalization, and Simplicity. It becomes evident that Process Model Discovery, with its current given settings, is a Multi-Objective Optimization Problem. However, most existing techniques does not approach the problem as a Multi-Objective Optimization Problem. Therefore, in this work we propose the use of one of the most robust and widely used Multi-Objective Optimizers in Process Model Discovery, the NSGA-II algorithm. Experimental results on a real life event log shows that the proposed technique outperforms existing techniques in various aspects. Also this work tries to establish a benchmarking system for comparing results of Multi-Objective Optimization based Process Model Discovery techniques. Recently, several ABPD techniques have been developed. Discovering process models which can be graphically represented in different process modelling