2020 International Conference on Advanced Science and Engineering (ICOASE)最新文献

{"title":"Computerized morphometric characteristics of Gebel Watershed","authors":"Jihad I. Salim","doi":"10.1109/ICOASE51841.2020.9436619","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436619","url":null,"abstract":"The study has been achieved on the Gebel watershed in the Kurdistan region (north of Iraq). The watershed drains on an area of 404.65 Km2, this area is located between 43°49'45.899”E – 44°6'10.86”E longitude and 36°35'20.221 “N – 36°50'59.986”N latitude. The axial length and width of the watershed were 30.76 and 13.16 km respectively. Watershed elevations ranged from 322m to 1583m (m.s.l). The study included the estimation of morphometric parameters for the main watershed and other sub-watersheds, besides the network analysis. Remote sensing (RS) data and the implementation of the Geographic Information System (GIS) are combined for the watersheds morphometric characteristics study. Three groups of morphometric aspects (linear, areal, and relief) are categorized. The results showed that the values of aerial aspects (Form factor, Elongation Ratio, Circularity Ratio. Compactness Factor, Lemniscate Ratio, Drainage density, Constant of, Channel Maintenance, Stream Frequency, Infiltration Number, Drainage Texture), for Gebel watershed were (0.43, 0.74, 0.36, 2.80, 0.58, 0.89, 0.82, 0.89, 1.08, 3.02) respectively, Also, some variable such as streams order, streams length, bifurcation ratio were included in the Linear features. Gebel watershed stream orders ranged from 1 to 6, and for all orders, the total number of stream order recorded was (360), whereas the values of relief aspects (Watershed Relief, Relief Ratio, Relative Relief, Ruggedness Number, Hypsometric Integral) were (1.261, 40.99, 1.06, 1.53, 0.5) respectively. The current study will help manage the water resource and preserve the ecosystem of the Gebel watershed.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128290455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering Documents based on Semantic Similarity using HAC and K-Mean Algorithms","authors":"Karwan Jacksi, Rowaida Kh. Ibrahim, Subhi R. M. Zeebaree, R. Zebari, M. A. Sadeeq","doi":"10.1109/ICOASE51841.2020.9436570","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436570","url":null,"abstract":"The continuing success of the Internet has greatly increased the number of text documents in electronic formats. The techniques for grouping these documents into meaningful collections have become mission-critical. The traditional method of compiling documents based on statistical features and grouping did use syntactic rather than semantic. This article introduces a new method for grouping documents based on semantic similarity. This process is accomplished by identifying document summaries from Wikipedia and IMDB datasets, then deriving them using the NLTK dictionary. A vector space afterward is modeled with TFIDF, and the clustering is performed using the HAC and K-mean algorithms. The results are compared and visualized as an interactive webpage.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124539061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voltage Build-Up Behavior of Self-Excited Induction Generator Under Different Loading Conditions","authors":"Mohammed M. Khalaf, A. M. Ali","doi":"10.1109/ICOASE51841.2020.9436546","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436546","url":null,"abstract":"This paper studies the behavior of voltage building in the self-excited induction generator (SEIG) under different loading conditions: No-load, R-Ioad, and RL-Ioad. The induction generator is operated at a constant speed that does not exceed 5 percent of the synchronous speed to achieve the working state of the generator. The value of the excitation capacitance required for the SEIG was calculated to give a suitable saturation level to assure self-excitation and to avoid heavy saturation levels. Using MATLAB / SIMULINK software the methodology of this work was adopted on 15 kW, 400V, 50Hz, 1460rpm, three-phase induction machine. The simulation results show that the generated voltage is increased with an increase in R-Ioad with a decrease in stator current, whereas decrease voltage and there is no frequency effect in the case of RL-Ioad. The results provided a good illustration about SEIG working under different loading conditions.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive Radio Network Security Enhancement Based on Frequency Hopping","authors":"A. A. Kadhim, S. Sadkhan","doi":"10.1109/ICOASE51841.2020.9436593","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436593","url":null,"abstract":"Cognitive Radio (CR) is the technology of used free spectrum band based on the main element as the primary, secondary users, through the structure can sense the surrounding environment and adapt to the different operating parameters to enhance the communication quality. A flexible and adaptable physical layer implementation is needed to achieve a better enhancement cognitive radio system. In this paper, the proposed system based on the most beneficial spread spectrum technique based on the used parameters determined as the Frequency-hopping spread spectrum (FHSS) to attend the Physical layer requirements of Cognitive Radio. The used system based on the Throughput, Data Drop Rate, Detection Time and Delay Time simulation parameters. The used method provides high throughput performance for CR implementation with simulation parameters used and sent/received data messages, So the number of data transmission without errors increased. Besides, we simulate Noise-Jamming attack in cognitive radio network Environment. Moreover, the proposed system has been done using OMNET++ simulation tool and. net Framework C# tool.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122262390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harmonic Elimination of D.C to A.C Converters Using Embedded Design Techniques","authors":"M. R. Khalil, Laith A. Mohammeed, Luluwah A. Y. Al. Hbeti","doi":"10.1109/ICOASE51841.2020.9436598","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436598","url":null,"abstract":"This paper develops an efficient scheme to eliminate harmonics in D.C to A.C converters (inverters). The main advantages of the proposed method are its simplicity and high flexibility to adapt the pulse width according to the target. The proposed approach utilized embedded design techniques to construct a dual-processor system with a distributed memory model to be configured on Spartan-6E Field Programmable Gate Arrays (FPGAs) slice. The designed system is programmed to generate pulse width modulation (PWM) signals to drive PWM inverter suitable for Photovoltaic (PV) system. The duty cycle of the pulses produced by the designed system can be varied and optimized to reduce the distortion in the output waveform of the inverter circuit. The generated signals are applied on a prototype inverter circuit in real-time operation. The system performance is simulated using MATLAB -Simulink environment. From simulation results, it was found that the proposed system yields harmonics distortion as low as 3.85% at the inverter circuit output. The real-time inverter output matches very accurately the simulation results.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127234850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAARSNet: A Deep Neural Network for COVID-19 Cases Diagnosis","authors":"Hawre Kh. Abdulla, Z. Ahmed, Nigar M. Shafiq Surameery, R. Rashid, Shadman Q. Salih","doi":"10.1109/ICOASE51841.2020.9436536","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436536","url":null,"abstract":"The global spread of the COVID-19 is a continuously evolving situation and it is still a major risk on the health of people around the world. A huge number of people are infected by this deadly virus and the number is still getting increased day by day. At this time, no specific vaccines or treatments of COVID-19 are found. Numerous ways are offered to detect COVID-19 such as swab test, CDC and RT-PCR tests. All of them can detect corona virus in different ways but they are not recommended by the reason of their limited availability, inaccurate results, high false-negative rate predicates, high cost and time consuming. Hence, medical radiography and Computer Tomography (CT) images were suggested as the next best alternative of RT -PCR and other tests for detecting Covid-19 cases. Recent studies found that patients with COVID-19 cases are present abnormalities in chest X-Ray images. Motivated by this, many researchers propose deep learning systems for COVID-19 detection. Although, these developed AI systems have shown quite promising results in terms of accuracy, they are closed source and unavailable to the research community. Therefore, in the present work, we introduced a deep convolutional neural network design (SAARSNet) designed to detect COVID-19 cases from chest X-Ray images. 1292 X-Ray images have been used to train and test the proposed model. the images have been collected from two open-source datasets. The input images are progressively resized into (220 by 150 by 3) in order to decrease the training time of the system and improve the performance of the SAARSNet architecture. Furthermore, we also investigate how SAARSNet makes predictions under three different scenarios with the aim of distinguishing COVID-19 class from both Normal and Abnormal classes as well as gaining deeper perceptions into critical factors related to COVID-19 cases. We also used the confusion metrics for evaluating the performance of SAARSNet CNN in an attempt to measure the true and false identifications of the classes from the tested images. With the proposed architecture promising results has been achieved in all of the three different scenarios. Although, there are some misclassified cases of COVID-19, the corresponding performance was best in detecting both Normal and Abnormal cases correctly. Furthermore, in the three classes scenario, normal class has been achieved 100% positive predictive value while optimistic results have been investigated in detecting COVID-19 and abnormal classes.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131367144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 Diagnosis Systems Based on Deep Convolutional Neural Networks Techniques: A Review","authors":"Hivi I. Dino, Subhi R. M. Zeebaree, D. A. Hasan, M. Abdulrazzaq, Lailan M. Haji, Hanan M. Shukur","doi":"10.1109/ICOASE51841.2020.9436542","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436542","url":null,"abstract":"The rapidly spreading of the viral disease “COVID-19” causes millions of infections and deaths worldwide. It causes a devastating impact on the lifestyle, public health, and the global economy. This motivates the researchers to invent and develop innovative and automated methods to detect COVID-19 at its early stages. It is necessary to isolate the positive cases quickly to prevent this epidemic and treat affected patients. Many diagnosis methods are proposed to perform accurate and fast detection for COVID-19, such as Reverse Transcription-Polymerase Chain Reaction (RT -PCR). The clinical studies indicate that the severity of COVID-19 cases depends on the virus's amount within infected lungs. Chest X-ray (CXR) and Computed Tomography (CT) images are useful imaging methods for diagnosing COVID-19 cases. Deep Convolutional Neural Network (DCNN) is a machine learning technique usually used in computer vision applications. This review focuses on utilizing the DCNN methods for building an automated Computer-Aided Diagnosis (CADs) system to detect and classify the infected cases of the COVID-19 disease accurately and fast. These techniques are used to extracts valuable information by analyzing a massive amount of CXR and CT images that can critically impact on screening of Covid-19. DCNN techniques proved their robustness, potentiality, and advancement by comparing them among the other learning algorithms. It is worth noting that DCNN is an essential tool for supporting the physicians' clinical decisions.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"1139 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113988444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of synthesis of granular ZSM-5 with a hierarchical porous structure","authors":"R. Kuvatova, O. Travkina, K. Ahmed, V. Zaripov, A. Ishkildina, B. Kutepov","doi":"10.1109/ICOASE51841.2020.9436623","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436623","url":null,"abstract":"Most of the methods described in the literature for the preparation of pentasil-containing catalysts involves the synthesis of highly dispersed zeolite ZSM-5 in the required cation-decationized forms and subsequent molding in a mixture with a binder material into granules. Information on available and promising for industrial implementation methods for the synthesis of granular materials based on high crystallinity ZSM-5 zeolite with a hierarchical porous structure is absent in the literature. As a result of the studies, a method for the synthesis of granular zeolite ZSM-5 of high phase purity and degree of crystallinity of −100% using tetrabutylammonium bromide as a template was proposed. The method includes the stages of mixing powdered zeolite ZSM-s5 and amorphous alumino silicate, moistening the mixture with water and mechanical granulation, drying and crystallizing the resulting granules. Chemical and X-ray diffraction examination XRD, low temperature N 2 adsorption and SEM (scanning electron microscopy) were utilized to determine the chemical and phase compositions, characteristics of the porous structure, and the morphology of granular ZSM-5 zeolites with a crystallinity of-100%.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130691287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biometric Signature based Public Key Security System","authors":"Huda M Therar, E. Mohammed, Ahmed J Ali","doi":"10.1109/ICOASE51841.2020.9436615","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436615","url":null,"abstract":"Digital signature is a technique that is utilized to check the authenticity of a message transmitted electronically. The digital signature technique is based on a public key method. The meant transmitter signs his / her message of his / her private key and the meant receiver checks that with the public key of the transmitter. Digital signatures also ensure authentication of messages, integrity verification, and nonrepudiation systems. This paper presents the concept of Biometric Signature: a recent technique to digitally sign a message utilizing biometrics- associated with digital signature key creation, so, merging the benefits of Public Key Infrastructure (PKI), through the utilize of biometric-based digital signature creation that is secure, reliable, quickly-comfortable, non-invasive, and clearly describes the transaction creator. It also proposes biometric signature algorithms utilizing the commonly utilized RSA digital signature technique and shows the problems related to them. This paper analyses the security of the modulus and the size of the keys used and computes the strength of RSA based on the number of bits in the key (iris template). The time of the scheme is calculated and it is extremely small (in sec).","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126645503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Security Awareness Approaches With Special Emphasis on Gamification","authors":"K. Sharif, S. Ameen","doi":"10.1109/ICOASE51841.2020.9436595","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436595","url":null,"abstract":"since the cybersecurity needs have increased, many companies have implemented awareness-raising campaigns to guarantee their workers are educated about and aware of its risks and safe operation. Technological solutions can not necessarily provide full defense on their own, since employees pose a huge threat to information security and also play an essential part in the willingness of companies to meet security protection criteria. Rising employee knowledge about the security policy of the organization plays a critical role in the successful establishment of policies. The development of diverse and useful security awareness programs remains the best way to increase security awareness. Several approaches have been used to increase security awareness and behavior among users. However, the effectiveness of these approaches remains an open question. Concepts of gamification have become more popular recently and have been used to teach people a wide variety of topics such as security awareness. This paper investigates the most recent researches on cybersecurity awareness. The paper highlights the major obstacles in successfully implementing security awareness among users together with the main factors required to aware of different peoples most effectively. Finally, the paper shows how gamification being used for security awareness among other techniques with the gaps in those methods and suggests recommendations on how to deliver information security awareness effectively.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130707087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}