bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of a framework implementation of the transceiver performances for integrating optical technologies and wireless LAN based on OFDM-RoF
Authors: Ali Adnan Hussein , Farhood Alaa Desher , Naji Maham Kamil
Published: June 2020

The greatest advantages of optical fibers are the possibility of extending data rate transmission and propagation distances. Being a multi-carrier technique, the orthogonal frequency division multiplexing (OFDM) can be applicable in hybrid optical-wireless systems design owing to its best spectral efficiency for the interferences of radio frequency (RF) and minor multi-path distortion. An optical OFDM-RoF-based wireless local area network (W-LAN) system has been studied and evaluated in this work. The outline for integrating an optical technology and wireless in a single system was provided with the existence of OFDM-RoF technology and the microstrip patch antenna; these were applied in the Optisystem communication tool. The design of the proposed OFDM-RoF system is aimed at supporting mm-wave services and multi-standard operations. The proposed system can operate on different RF bands using different modulation schemes like 4,16 and 64QAM, that may be associated to OFDM and multidata rates up to 5 Gbps. The results demonstrate the robustness of the integrated optical wireless link in propagating OFDM-RoF-based WLAN signals across optical fibers.
Analysis and design of a compact ultra-wideband antenna with WLAN and X-band satellite notch
Authors: El Ouahabi Mohssine , Dkiouak Aziz , Zakriti Alia , Essaaidi Mohamed , Elftouh Hanae
Published: June 2020

A compact design of ultra-wideband (UWB) antenna with dual band-notched characteristics based on split-ring resonators (SRR) are investigated in this paper. The wider impedance bandwidth (from 2.73 to 11.34 GHz) is obtained by using two symmetrical slits in the radiating patch and another slit in the partial ground plane. The dual band-notch rejection at WLAN and X-band downlink satellite communication system are obtained by inserting a modified U-strip on the radiating patch at 5.5 GHz and embedding a pair of rectangular SRRs on both sides of the microstrip feed line at 7.5 GHz, respectively. The proposed antenna is simulated and tested using CST MWS high frequency simulator and exhibits the advantages of compact size, simple design and each notched frequency band can be controlled independently by using the SRR geometrical parameters. Therefore, the parametric study is carried out to understand the mutual coupling between the dual band-notched elements. To validate simulation results of our design, a prototype is fabricated and good agreement is achieved between measurement and simulation. Furthermore, a radiation patterns, satisfactory gain, current distribution and VSWR result at the notched frequencies make the proposed antenna a suitable candidate for practical UWB applications.
Multi-stage secure clusterhead selection using discrete rule-set against unknown attacks in wireless sensor network
Authors: N. Tejashwini , Kumar D. R. Shashi , Reddy K. Satyanarayana
Published: June 2020

Security is the rising concern of the wireless network as there are various forms of reonfigurable network that is arised from it. Wireless sensor network (WSN) is one such example that is found to be an integral part of cyber-physical system in upcoming times. After reviewing the existing system, it can be seen that there are less dominant and robust solutions towards mitigating the threats of upcoming applications of WSN. Therefore, this paper introduces a simple and cost-effective modelling of a security system that offers security by ensuring secure selection of clusterhead during the data aggregation process in WSN. The proposed system also makes construct a rule-set in order to learn the nature of the communication iin order to have a discrete knowledge about the intensity of adversaries. With an aid of simulation-based approach over MEMSIC nodes, the proposed system was proven to offer reduced energy consumption with good data delivery performance in contrast to existing approach.
Comparison between handwritten word and speech record in real-time using CNN architectures
Authors: Pinzón-Arenas Javier Orlando , Jiménez-Moreno Robinson
Published: June 2020

This paper presents the development of a system of comparison between words spoken and written by means of deep learning techniques. There are used 10 words acquired by means of an audio function and, these same words, are written by hand and acquired by a webcam, in such a way as to verify if the two data match and show whether or not it is the required word. For this, 2 different CNN architectures were used for each function, where for voice recognition, a suitable CNN was used to identify complete words by means of their features obtained with mel frequency cepstral coefficients, while for handwriting, a faster R-CNN was used, so that it both locates and identifies the captured word. To implement the system, an easy-to-use graphical interface was developed, which unites the two neural networks for its operation. With this, tests were performed in real-time, obtaining a general accuracy of 95.24%, allowing showing the good performance of the implemented system, adding the response speed factor, being less than 200 ms in making the comparison.
Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network
Authors: Hossain Md. Kamrul , Haque Md. Mokammel
Published: June 2020

Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.
Leveraging graph-based semantic annotation for the identification of cause-effect relations
Authors: Bhaskoro Susetyo Bagas , Hardini Inkreswari Retno
Published: June 2020

This research is related to language article in Indonesia that discuss about causality relationship research used as public health surveillance information monitoring system. Utilization of this research is suitability of feature selection, phrase annotation, paragraph annotation, medical element annotation and graph-based semantic annotation. Evaluation of system performance is done by intrinsic approach using the Naive Bayes Multinomial method. The results obtained sequentially for recall, precision and f-measure are 0.924, 0.905, and 0.910.
A new approach for content-based image retrieval for medical applications using low-level image descriptors
Authors: Shatnawi Mohammed Qassim , Alrousan Mohammad , Amareen Suzan
Published: June 2020

Content based image retrieval (CBIR) has become an important factor in medical imaging research and is obtaining a great success. More applications still need to be developed to get more powerful systems for better image similarity matching, and as a result getting better image retrieval systems. This research focuses on implementing low-level descriptors to maximize the quality of the retrieval of medical images. Such a research is supposed to set a better result in terms of image similarity matching. In this research a system that uses low-level descriptors is introduced. Three descriptors have been developed and applied in an attempt to increase the accuracy of image matching. The final results showed a qualified system in medical images retrieval specially that the low-level image descriptors have not been used yet in the image similarity matching in the medical field.
Improving the voltage quality of Abu Hummus network in Egypt
Authors: Nassar Ibrahim A. , Seif Mohammed Sh. , M. ElAttar Mahmoud
Published: June 2020

In this paper the performance of the electrical network of Egypt is studied by considering a small part on the network (Abu Hummus city). The transmission network of Abu Hummus city was created for 66 kV, 11 kV, and 0.4 kV in the digital simulation and electrical network calculation (DIgSILENT power factory software) to study the voltage profiles. The load flow operational analysis was performed to obtain the voltage magnitudes at every bus bar. The voltage magnitudes in 11 kV and 0.4 kV networks were 10% to 15% less than the nominal value due to overloading off the transmission lines and the voltage magnitudes in 66 kV was within permissible limits. By using automatic tap-changing transformer or Static VAR System, the main idea of this paper is to obtain the voltage profiles at every bus bar to improve the voltage quality of the networks, so as to achieve better voltage profiles on the low voltage side without much effect on high voltage side under various operating conditions.
Non-convex constrained economic power dispatch with prohibited operating zones and piecewise quadratic cost functions
Authors: Spea Shaimaa R.
Published: June 2020

This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms.
Data science for digital culture improvement in higher education using K-means clustering and text analytics
Authors: Maylawati Dian Sa'adillah , Priatna Tedi , Sugilar Hamdan , Ramdhani Muhammad Ali
Published: June 2020

This study aims to investigate the meaningful pattern that can be used to improve digital culture in higher education based on parameters of the technology acceptance model (TAM). The methodology used is the data mining technique with K-means algorithm and text analytics. The experiment using questionnaire data with 2887 respondents in Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung. The data analysis and clustering result show that the perceived usefulness and behavioral intention to use information systems are above the normal value, while the perceived ease of use and actual system use is quite low. Strengthened with text analytics, this research found that the EDA and K-means result in harmony with the hope or desire of academic society the information system implementation. This research also found how important the socialization and guidance of information systems, especially the new one information system, in order to improve digital culture in higher education.
The electromagnetic interferance caused by high voltage power lines along the electrical railway equipment
Authors: El Hajji Moine , Mahmoudi Hassane , Labbadi Moussa
Published: June 2020

The identification of risks linked to electromagnetic compatibility (EMC) in the electric railway is a major concern in identifying EMC problems and analyzing the unintentional various external disturbing sources as well as the probability of occurrence of interference, the level of interference along the railway system. The purpose of this analysis is to determine the electromagnetic interaction coupling generated by the high voltage (HV) lines located along the railway line by analyzing the voltage induced in the signaling transmission cables such as the European rail traffic management system/European train control system (ERTMS/ETCS) through the multi-conductor transmission line (MTL) theory which may have an impact on the transmitting information. Dubanton method and approximate calculation will be applied and simulated through COMSOL Multiphysics tool in order to analyze if the protection distance and coupling conditions are respected by the railway standards.
Control of the powerquality for a DFIG powered by multilevel inverters
Authors: Hachemi H. , Allali A. , Belkacem B.
Published: June 2020

This paper treats the modeling, and the control of a wind power system based on a doubly fed induction generator DFIG, the stator is directly connected to the grid, while the rotor is powered by multilevel inverters. In order to get a decoupled system of controlfor an independently transits of active and reactive power, a vector control method based on stator flux orientation SFOC is considered: Direct vector control based on PI controllers. Cascaded H-bridge CHBI multilevel inverters are used in the rotor circuit to study its effect on supply power quality. All simulation models are built in MATLAB/Simulink software. Results and waveforms clearly show the effectiveness of vector control strategy. Finally, performances of the system will tested and compared for each levels of inverter.
MPPT control design for variable speed wind turbine
Authors: Otmane Rachedi Meriem , Larbi Saidi Mohammed , Arbaoui Fayçel
Published: June 2020

Variable speed wind turbine systems (VSWT’s) have been in receipt of extensive attention among the various renewable energy systems. The present paper focuses on fuzzy fractional order proportional-integral (FFOPI) control segment for variable speed wind turbine (VSWT) directly driving permanent magnet synchronous generator (PMSG). The main objective of this study is to reach maximum power point tracking (MPPT) through combination of advanced control based on FFOPI control applied to generator side converter (turbine and PMSG). The basic idea of the FFOPI controller is to implement a fuzzy logic controller (FLC) in cascade with Fractional Order Proportional Integral controller (FOPI). A comparative study with FOPI and classical PI control schemes is made. The traditional PI controller cannot deliver a sufficiently great performance for the VSWT. However, the results found that the proposed approach (FFOPI) is more effective and feasible for controlling the permanent magnet synchronous generator to mantain maximum power extraction. The validation of results has been performed through simulation using Matlab/Simulink®.
A dynamic cruise control system (DCCS) for effective navigation system
Authors: Someswari T. , Tiwari Anil Kumar , R Nagraj
Published: June 2020

With the fast development of artificial intelligence, robotics, and embedded system along with sensor technologies, the speed control mechanism is required in various other applications such as automatic or self-piloting aircraft, auto-driven vehicles, auto driven lifts and much other robotics based automation plants, etc. For each unpredictable and progressed vehicular framework accompanies a better route that is fit for utilizing the two GPS and INS related sign. There have been a noteworthy number of research works being completed towards creating sliding mode control framework. In case of inaccurate navigational data or no availability of navigational service, the cruise control could also stop working. Hence, there is a need to evolve up with a novel system offering reliable and fault tolerant navigation system in order to minimize the dependencies on GPS-based information and maximize the utilization of INS based information. This manuscript presents a dynamic cruise control system to achieve better navigation under uncertainties. The performance of the system is analyzed by incorporating sliding mode and fuzzy logic and achieves better accuracy in tracking error, computational complexity (28 sec of simulation time) under chattering and switching action operation.
CL-SA-OFDM: Cross-layer and smart antenna based ofdm system performance enhancement
Authors: T. G. Shivapanchakshari , Aravinda H. S.
Published: June 2020

The growing usage of wireless services is lacking in providing high-speed data communication in recent times. Hence, many of the modulation techniques are evolved to attain these communication needs. The recent researches have widely considered OFDM technology as the prominent modulation mechanism to fulfill the futuristic needs of wireless communication. The OFDM can bring effective usage of resources, bandwidth, and system performance enhancement in collaboration with the smart antenna and resource allocation mechanism (adaptive). However, the usage of adaptive beamforming with the OFDM leads to complication in the design of medium access layer and which causes a problem in adaptive resource allocation mechanism (ARAM). Hence, the proposed manuscript intends to design an OFDM system by considering different switched beam smart antenna (SBSA) along with the cross-layer adaptive resource allocation (CLARA) and hybrid adaptive array (HAA). In this, various smart antenna mechanism are considered to analyze the quality of service (QoS) and complexity reduction in the OFDM system. In this paper, various SA schemes are used as per the quality of service (QoS) requirement of the different users. The performance analysis is conducted by considering data traffic reduction, bit-rate reduction, and average delay.
Face recognition using selected topographical features
Authors: Naji Maitham Ali , Salman Ghalib Ahmed , Fadhil Muthna Jasim
Published: June 2020

This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier
Efficient denoising approach based eulerian videomagnification forcolour and motion variations
Authors: Shahadi Haider Ismael , Al-allaq Zaid Jabbar , Albattat Hayder Jawad
Published: June 2020

Digital video magnification is a computer-based microscope, which is useful to detect subtle changes to human eyes in recorded videos. This technology can be employed in several areas such as medical, biological, mechanical and physical applications. Eulerian is the most popular approach in video magnification. However, amplifying the subtle changes in video produces amplifying the subtle noise. This paper proposes an approach to reduce amplified noise in magnified video for both type of changes amplifications, color and motion. The proposed approach processes the resulted video from Eulerian algorithm whether linear or phase based in order to noise cancellation. The approach utilizes wavelet denoising method to localize the frequencies of distributed noise over the different frequency bands. Subsequently, the energy of the coefficients under localized frequencies are attenuated by attenuating the amplitude of these coefficients. The experimental results of the proposed approach show its superiority over conventional linear and phase based Eulerian video magnification approaches in terms of quality of the resulted magnified videos. This allows to amplify the videos by larger amplification factor, so that several new applications can be added to the list of Eulerian video magnification users. Furthermore, the processing time does not significantly increase, the increment is only less than 3% of the overall processing compare to conventional Eulerian video magnification.
Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study
Authors: Kifouche Abdessalam , Guessoum Abderrezak
Published: June 2020

Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system behavior. In this study, a recent multi-electrode technology allows the record of the simultaneous neuron activities in behaving animals. Intra-cortical data are processed according to these steps: spike detection and sorting, than desired action extraction from the rate of the obtained signal. We focus on the following important questions about (i) the possibility of linking the brain signal time events with some time-delayed mapping tools; (ii) the use of some suitable inputs than others for the decoder; (iii) a consideration of separated data or a special representation founded on multi-dimensional statistics. This paper concentrates mostly on the analysis of parallel spike train when certain critical hypotheses are ignored by the data for the working method. We have made efforts to define explicitly whether the underlying hypotheses are actually achieved. In this paper, we propose an algorithm to define the embedded memory order of NARX recurrent neural networks to the hand trajectory tracking process. We also demonstrate that this algorithm can improve performance on inference tasks.
A novel CAD system to automatically detect cancerous lung nodules using wavelet transform and SVM
Authors: Abu Baker Ayman A. , Ghadi Yazeed
Published: June 2020

A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
Design and investigations of MPPT strategies for a wind energy conversion system based on doubly fed induction generator
Authors: Rached Bouchaib , Elharoussi Mustapha , Abdelmounim Elhassane
Published: June 2020

The purpose of this work is to design and to discuss various strategies to optimize the production of a wind energy conversion chain based on the doubly fed induction generator (DFIG), by capturing the maximum power at the wind turbine, using maximum power point tracking (MPPT) and pitch control. The proposed controls allow the generator to monitor the optimal operating points of the turbines regardless of wind speed variations, system parameters disturbance, and parameters variation. Simulation of WECS based on a 1.5 MW wound rotor induction generator under MATLAB/SIMULINK is carried out using the PI controller (PIC), RST controller and fuzzy logic controller (FLC). Analysis and comparisons are made for different operating scenarios: Reference tracking, robustness under variable wind speed conditions and parameters variation. The application of FLC provides a very interesting outcome for the robustness and the dynamic challenges.
Energy efficient clustering and routing optimization model for maximizing lifetime of wireless sensor network
Authors: S. Savitha , Lingareddy S. C. , Chitnis Sanjay
Published: June 2020

Recently, the wide adoption of WSNs (Wireless-Sensor-Networks) is been seen for provision non-real time and real-time application services such as intelligent transportation and health care monitoring, intelligent transportation etc. Provisioning these services requires energy-efficient WSN. The clustering technique is an efficient mechanism that plays a main role in reducing the energy consumption of WSN. However, the existing model is designed considering reducing energy- consumption of the sensor-device for the homogenous network. However, it incurs energy-overhead (EO) between cluster-head (CH). Further, maximizing coverage time is not considered by the existing clustering approach considering heterogeneous networks affecting lifetime performance. In order to overcome these research challenges, this work presents an energy efficient clustering and routing optimization (EECRO) model adopting cross-layer design for heterogeneous networks. The EECRO uses channel gain information from the physical layer and TDMA based communication is adopted for communication among both intra-cluster and inter-cluster communication. Further, clustering and routing optimization are presented to bring a good trade-off among minimizing the energy of CH, enhancing coverage time and maximizing the lifetime of sensor-network (SN). The experiments are conducted to estimate the performance of EECRO over the existing model. The significant-performance is attained by EECRO over the existing model in terms of minimizing routing and communication overhead and maximizing the lifetime of WSNs.
Noise uncertainty effect on multi-channel cognitive radio networks
Authors: Osama Amira , El-Dien Heba A. Tag , El-Banna Ahmad A. Aziz , El-Dien Adly S. Tag
Published: June 2020

Achieving high throughput is the most important goal of cognitive radio networks. The main process in cognitive radio is spectrum sensing that targets getting vacant channels. There are many sensing methods like matched filter, feature detection, interference temperature and energy detection which is employed in the proposed system; however, energy detection suffers from noise uncertainty. In this paper a study of throughput under noise fluctuation effect is introduced. The work in this paper proposes multi-channel system; the overall multi-channel throughput is studied under noise fluctuation effect. In addition, the proficiency of the network has been examined under different number of channels and sensing time with noise uncertainty.
Analysis of back propagation and radial basis function neural networks for handover decisions in wireless communication
Authors: Mahajan Payal , Zaheeruddin Zaheeruddin
Published: June 2020

In mobile systems, handoff is a vital process, referring to a process of allocating an ongoing call from one BS to another BS. The handover technique is very important to maintain the Quality of service. Handover algorithms, based on neural networks, fuzzy logic etc. can be used for the same purpose to keep Quality of service as high as possible. In this paper, it is proposed that back propagation networks and radial basis functions may be used for taking handover decision in wireless communication networks. The performance of these classifiers is evaluated on the basis of neurons in hidden layer, training time and classification accuracy. The proposed approach shows that radial basis function neural network give better results for making handover decisions in wireless heterogeneous networks with classification accuracy of 90%.
A new approach to image classification based on a deep multiclass AdaBoosting ensemble
Authors: Asil Hasan , Bagherzadeh Jamshid
Published: June 2020

In recent years, deep learning methods have been developed in order to solve the problems. These methods were effective in solving complex problems. Convolution is one of the learning methods. This method is applied in classifying and processing of images as well. Hybrid methods are another multi-component machine learning method. These methods are categorized into independent and dependent types. Ada-Boosting algorithm is one of these methods. Today, the classification of images has many applications. So far, several algorithms have been presented for binary and multi-class classification. Most of the above-mentioned methods have a high dependence on the data. The present study intends to use a combination of deep learning methods and associated hybrid methods to classify the images. It is presumed that this method is able to reduce the error rate in images classification. The proposed algorithm consists of the Ada-Boosting hybrid method and bi-layer convolutional learning method. The proposed method was analyzed after it was implemented on a multi-class Mnist data set and displayed the result of the error rate reduction. The results of this study indicate that the error rate of the proposed method is less than Ada-Boosting and convolution methods. Also, the network has more stability compared to

the other methods.
An energy optimization with improved QOS approach for adaptive cloud resources
Authors: Sudha Danthuluri , Chitnis Sanjay
Published: June 2020

In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (ACRR) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model ACRR in terms of average run time, power consumption and average power required than any other state-of-art techniques.
An innovative IoT service for medical diagnosis
Authors: Abbas Safia
Published: June 2020

Due to the misdiagnose of diseases that increased recently in a scarily manner, many researchers devoted their efforts and deployed technologies to improve the medical diagnosis process and reducing the resulted risk. Accordingly, this paper proposed architecture of a cyber-medicine service for medical diagnosis, based internet of things (IoT) and cloud infrastructure (IaaS). This service offers a shared environment for medical data, and extracted knowledge and findings between patients and doctors in an interactive, secured, elastic and reliable way. It predicts the medical diagnosis and provides an appropriate treatment for the given symptoms and medical conditions based on multiple classifiers to assure high accuracy. Moreover, it entails different functionalities such as on-demand searching for scientific papers and diseases description for unrecognized combination of symptoms using web crawler to enrich the results. Where such searching results from crawler, are processed, analyzed and added to the resident knowledge base (KB) to achieve adaptability and subsidize the service predictive ability.
A prior case study of natural language processing on different domain
Authors: J. Shruthi , Swamy Suma
Published: June 2020

In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model.
Integrating IS success model with cybersecurity factors for e-government implementation in the Kingdom of Saudi Arabia
Authors: AL-Zahrani Mohammed
Published: June 2020

The electronic government (E-Gov) Systems are currently getting recognized as an authentic strategically tool in delivering E-services. Considering the development of information system (IS) as well as the expanding of the internet-based applications in KSA, E-Gov has always been a significant aspect in delivering governmental services. This research has adopted the (IS) success model by both DeLone and McLean (D&M), moreover, it adopted technology acceptance model (TAM) with cybersecurity factors, both models were implemented to discover the status of the IS success then investigate cybersecurity aspects that impact the service efficiency and effectiveness in KSA. Consequently, this research aims to create a model to investigate the IS success model along with cybersecurity factors that influence E-Gov services effectiveness and usage. Therefore, a survey has been applied as the major data gathering approach; the survey has been distributed among 211 users of E-Gov services consistently. Moreover, all research findings were attained through a quantitative method using the structural equation modelling (SEM). Findings revealed that the constructs fundamental of the (IS) success model are strongly influencing users' satisfaction (US) of the E-Gov services; correspondingly, the fundamental constructs factors of cybersecurity with TAM appear to have a strong impacts on perceived risk (PR), in addition, both which affect the E-Gov services towards usage and effectiveness.
A novel secure routing scheme using probabilistic modelling for better resistivity against lethal attacks
Authors: Neeli Jyoti , Cauvery N. K.
Published: June 2020

Study towards Wireless Adhoc Network dates two decades back with various researchers evolving up with new solutions towards addressing its problems. Irrespective of various other problems, the issues related to the secure routing is yet unsolved owing to massively increasing fatal strategies of the adversary. Review of existing literature shows that the existing secure routing scheme can only govern over the stated attacks reducing the applicability in case of dynamic attacks. Therefore, this manuscript introduces a novel probabilistic model which offers the capability to wireless nodes to identify the malicious behavior and react accordingly. Different from existing intrusion prevention system, the proposed system allows the malicious node to participate in the data forwarding process and exhaust its resources with no chance of launching an attack. The simulated outcome of the study shows that the proposed secure routing scheme offers better data forwarding characteristic in contrast to the existing system in the aspect of intrusion detection and secure data transmission.
An improved smooth-windowed Wigner-Ville distribution analysis for voltage variation signal
Authors: Manap Mustafa , Abdullah Abdul Rahim , Nikolovski Srete , Sutikno Tole , Jopri Mohd Hatta
Published: June 2020

This paper outlines research conducted using bilinear time-frequency distribution (TFD), a smooth-windowed wigner-ville distribution (SWWVD) used to represent time-varying signals in time-frequency representation (TFR). Good time and frequency resolutions offer superiority in SWWVD to analyze voltage variation signals that consist of variations in magnitude. The separable kernel parameters are estimated from the signal in order to get an accurate TFR. The TFR for various kernel parameters is compared by a set of performance measures. The evaluation shows that different kernel settings are required for different signal parameters. Verification of the TFD that operated at optimal kernel parameters is then conducted. SWWVD exhibits a good performance of TFR which gives high peak-to-side lobe ratio (PSLR) and signal-to-cross-terms ratio (SCR) accompanied by low main-lobe width (MLW) and absolute percentage error (APE). This proved that the technique is appropriate for voltage variation signal analysis and it essential for development in an advanced embedded system.
A modified particle swarm optimization algorithm to enhance MPPT in the PV array
Authors: A. P. Yoganandini , G. S. Anitha
Published: June 2020

Due to the growing demand for electrical power, the researchers are trying to fulfill this demand by considering different ways of renewable energy resource as existing energy resources failed to do so. The solar energy from the sun is freely available, and by using photovoltaic (PV) cell power can be generated. However, it depends on rays fall on the PV cell, climatic condition. Thus, to enhance the efficiency of the photovoltaic (PV) systems, maximum power point tracking (MPPT) of the solar arrays is needed.The output of solar arrays mainly depends on solar irradiance and temperature. The mismatch phenomenon takes place due to partial shade, and it causes to the power output, which brings the incorrect operation of traditional MPP tracker. In this shaded condition, PV array exhibits multiple extreme points. In general, under this scenario, the MPPT approaches fail to judge the MPP, and it leads to low efficiency. The conventional approaches of PSO based algorithms can able to track the MPP under shading condition. However, the optimization process leads to issues in tracking speed. Thus, there a need for an efficient MPPT system which can track MPPT effectively in shaded condition? Hence, the proposed manuscript presents a modified Particle Swarm Optimization (PSO) algorithm is introduced to enhance the tracking speed as well as performance. The outcomes of the proposed system are compared with the traditional PSO system and are found that the tracking speed of MPP, accuracy, and efficiency is improved.
Electric distribution network reconfiguration for power loss reduction based on runner root algorithm
Authors: Nguyen Thuan Thanh
Published: June 2020

This paper proposes a method for solving the distribution network reconfiguration (NR) problem based on runner root algorithm (RRA) for reducing active power loss. The RRA is a recent developed metaheuristic algorithm inspired from runners and roots of plants to search water and minerals. RRA is equipped with four tools for searching the optimal solution. In which, the random jumps and the restart of population are used for exploring and the elite selection and random jumps around the current best solution are used for exploiting. The effectiveness of the RRA is evaluated on the 16 and 69-node system. The obtained results are compared with particle swarm optimization and other methods. The numerical results show that the RRA is the potential method for the NR problem.
Smart management system for monitoring and control of infant baby bed
Authors: Kaittan Ali Sachit , Hameed Siraj Manhal , Ali Nisreen K. , Ali Mohammed Hasan
Published: June 2020

Step by step the innovation likewise becomes exceptionally quick and the human makes it. Thus, it is imperative to take care of the people to come, a unique consideration ought to be appeared to them particularly indulges. This paper manages plan and usage of intelligent child support framework which is extraordinary blessing to guardians in this century In this work a baby bed with intelligent system was be designed and implemented . many sensors where be used to monitor the baby behavior . the component of this project consist of a smart camera , moisture sensor , sensitive Dc Motor and WiFi system.
Optimum reactive power compensation for distribution system using dolphin algorithm considering different load models
Authors: Shakir Al-Jubori Waleed Khalid , Hussain Ali Nasser
Published: June 2020

The distribution system represents the connection between consumers and the entire power network. The radial structure is preferred for distribution system due to its simple design and low cost. The electrical distribution system suffers from problems of rising power losses higher than the transmission system and voltage drop. One of the important solutions to improve the voltage profile and to reduce the electrical distribution system losses is the reactive power compensation which is based on the optimum choice of position and capacitor size in the network. In this paper, different models of electrical loads such as constant power(P), constant current(I), constant impedance(Z), and composite (ZIP) model are implemented with comparisons between them in order to identify the most effective load type that produces the optimal settlement for alleged loss reduction ,enhancement of the voltage profile, and cost savings. To minimize search space, Dolphin Optimization Algorithm (DOA) is applied for selecting the size and location of capacitors. Two case studies (IEEE 16- bus and 33- bus) are employed to evaluate the different load models with optimal reactive power compensation. The results of comparison between the different load models show that ZIP model is the best to produce the optimum solution for capacitor position and size. In addition, comparison of results with literature works are done and showed that DOA is the most robust among other algorithms to achieve the optimum solution for voltage profile enhancement significant reduction of losses, and saving cost.
Harvesting in electric vehicles: combining multiple power tracking and fuel-cells
Authors: Parada-Salado Juan-Gerardo , Gaona-Cárdenas Luis-Fernando , Rodríguez-Licea Martín-Antonio , Pérez-Pinal Francisco-Javier
Published: June 2020

Exploitation of green energy sources is essential to diminish the deterioration of our environment. The energy harvesting, represents an alternative to achieve greater range in electric and hybrid vehicles. An energy management strategy (EMS) must be optimized to obtain the best beneﬁts in such vehicles, which is not a trivial task. If harvesting or energy recovery devices are added, the EMS becomes a dual-purpose algorithm: minimizing fuel consumption and maximizing energy harvest through maximum

power point tracking (MPPT) controllers. Known studies consider separate EMS, one for traction and another for regenerative braking, without considering harvest devices such as solar panels, regenerative suspension, thermal generators, among others. Furthermore, the electronic power converters used, are not designed to handle such unequal power levels. In this article, an electronic platform to include multiple energy harvesting devices in a fuel-cell hybrid electric vehicle, was presented together with a multiple MPPT-EMS. The EMS is easily implementable, and considers quasiconstant cell energy extraction and ﬁltering of current transients to the battery bank ensuring the longevity of the devices. A new mathematical model of the platform, a closed loop stability analysis, and numerical and Hardware-in-the-Loop (HIL) validations were presented. Some experimental validation results were also provided.
Integrated compensation model using a three-phase neutral point clamped inverter
Authors: Thuyen Chau Minh , Thuong Nguyen Hoai , Tung Truong Khac
Published: June 2020

Normally, when research on active compensation models, previous studies only assumed that the source of harmonics is nonlinear load. The nonlinear load here is fixed and balanced, the supply voltage is considered ideal, i.e. the three-phase source is balanced and there is no distortion. However, in reality, the above assumption is difficult to achieve. Therefore, this paper aims to design an integrated compensation model for different types of harmonic sources. The types of harmonic sources considered here include: harmonic sources generated from nonlinear load and source. The requirement of the integrated compensation model is to create a balanced three-phase voltage at the terminal of the load and the supply current must be a sinusoidal wave in phase with the source voltage. In order to reduce the loss caused by the inverter switching, this paper uses a three-level Neutral Point Clamped inverter. The simulation results performed on Psim software have demonstrated the effectiveness of the proposed integrated compensation model compared to the traditional integrated compensation model in reducing harmonics and stabilizing DC-bus voltage.
Comparison between neural network and P&O method in optimizing MPPT control for photovoltaic cell
Authors: Abdullah ِِِAhmed G. , Aziz Mothanna sh. , Hamad Bashar Abdullah
Published: June 2020

The demand for renewable energy has increased because it is considered a clean energy and does not result in any pollution or emission of toxic gases that negatively affect the environment and human health also requiring little maintenance, and emitting no noise, so it is necessary to develop this type of energy and increase its production capacity. In this research a design of maximum power point tracking (MPPT) control method using Neural Network (NN) for photovoltaic system is presented. First we design a standalone PV system linked to dc boost chopper with MPPT by perturbation and observation P&O technique, and then a design of MPPT by using ANN for the same system is presented. Comparative between two control methods are studied. The results explained in constant and adjustable weather settings such as irradiation and temperature. The results exposed that the proposed MPPT by ANN control can improve the PV array efficiency by reduce the oscillation around the MPP that accure in P&O method and so decreases the power losses. As well as decrease the the overshot that accure in transient response, and hence improving the performance of the solar cell.