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1.

001-es BibID:BIBFORM101810
Első szerző:Abbass, Ahmed Abdulrudah
Cím:Efficient Eye Recognition for Secure Systems Using Convolutional Neural Network / Ahmed Abdulrudah Abbass, Hussein Lafta Hussein, Wisam Abed Shukur, Jasim Kaabi, Robert Tornai
Dátum:2022
ISSN:1735-188X
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Webology. - 19 : 1 (2022), p. 4967-4978. -
További szerzők:Hussein, Hussein Lafta Shukur, Wisam Abed Kaabi, Jasim Hussein Lafta (1992-) (informatikus) Tornai Róbert (1976-) (informatikus, matematikus)
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DOI
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2.

001-es BibID:BIBFORM101809
035-os BibID:(Scopus)85124946792
Első szerző:Abbass, Ahmed Abdulrudah
Cím:American standard code for information interchange mapping technique for text hiding in the RGB and gray images / Ahmed Abdulrudah Abbass, Salam Al-augby, Hussein Lafta Hussein, Jasim Hussein Kaabi, Robert Tornai
Dátum:2022
ISSN:2088-8708 2722-2578
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:International Journal of Electrical and Computer Engineering. - 12 : 3 (2022), p. 2812-2817. -
További szerzők:Al-augby, Salam Hussein, Hussein Lafta Kaabi, Jasim Hussein Lafta (1992-) (informatikus) Tornai Róbert (1976-) (informatikus, matematikus)
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3.

001-es BibID:BIBFORM121325
035-os BibID:(WOS)000765783100001
Első szerző:Adegoke, Nurudeen A.
Cím:Monitoring multivariate coefficient of variation for high-dimensional processes / Nurudeen A. Adegoke, Abdaljbbar Dawod, Olatunde Adebayo Adeoti, Ridwan A. Sanusi, Saddam Akber Abbasi
Dátum:2022
ISSN:0748-8017
Megjegyzések:Multivariate coefficient of variation (MCV) charts are effective tools for monitoring process relative variability. They are developed on the assumption that the process subgroup size available for monitoring the MCV parameter is larger than the number of process characteristics.Insuchacase,the unbiasedestimates of the in-control mean vector and covariance matrix are used to calculate the chartmonitoringstatistic.Here,westudytheperformanceofMCVcontrolcharts when only a small subgroup size is available for estimating the in-control mean vector and covariance matrix. We examine the use of a shrinkage estimate of the covariance matrix and propose two one-sided upward and downwardleast absolute shrinkage and selection operator (LASSO)-based MCV charts for detecting upward and downward shifts in the process MCV parameter, respectively. Our simulation study shows that the LASSO-based MCV charts outperform the classical twoone-sided MCVchartswhensmallsub group sizes a reavailable for monitoring. The improvedperformanceofthe proposed LASSO-based MCV chartsin monitoring shifts in the MCV parameter is demonstrated via an illustrative case study of carbon fiber tube application, where changes are detected earlier than the classical two one-sided MCV charts.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
high-dimensional
LASSO
likelihood ratio test
process monitoring
relative variability
shrinkage covariance matrix
Megjelenés:Quality And Reliability Engineering International. - 38 : 5 (2022), p. 2606-2621. -
További szerzők:Dawod, Abdaljbbar Babiker Abdaljbbar (1987-) (Ph.D. student) Adeoti, Olatunde Adebayo Sanusi, Ridwan A. Abbasi, Saddam Akber
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4.

001-es BibID:BIBFORM122234
035-os BibID:(Scopus)85128327615
Első szerző:Ahmed, Alaa H.
Cím:Prediction of COVID-19 disease severity using machine learning techniques / Ahmed, Alaa H. ; Al-Hamadani, Mokhaled N. A. ; Satam, Ihab A.
Dátum:2022
ISSN:2089-3191 2302-9285
Megjegyzések:A terrifying spread of COVID-19 (which is also known as severe acute respiratory syndrome coronavirus 2 or SARS-COV-2) led scientists to conduct tremendous efforts to reduce the pandemic effects. COVID-19 has been announced pandemic discovered in 2019 and affected millions of people. Infected people may experience headache, body pain, and sometimes difficulty in breathing. For older people, the symptoms can get worse. Also, it can cause death because of the huge effect on some parts of the human body, particularly for those who have chronic diseases like diabetes. Machine learning algorithms are applied to patients diagnosed with Corona Virus to estimate the severity of the disease depending on their chronic diseases at an early stage. Chronic diseases could raise the severity of COVID-19 and that is what has been proved in this paper. This paper applies different machine learning techniques such as random forest, decision tree, linear regression, binary search, and k-nearest neighbor on Mexican patients' dataset to find out the impact of lifelong illnesses on increasing the symptoms of the virus in the human body. Besides, the paper demonstrates that in some cases, especially for older people, the virus can cause inevitable death.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Chronic Diseases
Classification
Covid-19
Machine Learning
Severity
Megjelenés:Bulletin of Electrical Engineering and Informatics. - 11 : 2 (2022), p. 1069-1074. -
További szerzők:Al-Hamadani, Mokhaled N. A. (1987-) (informatics) Satam, Ihab Abdulrahman
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5.

001-es BibID:BIBFORM101166
035-os BibID:(WoS)000775913000001 (Scopus)85127426857
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Recurrent neural network variants based model for Cassini-Huygens spacecraft trajectory modifications recognition / Ashraf ALDabbas, Zoltan Gal
Dátum:2022
ISSN:1433-3058 0941-0643
Megjegyzések:Over the last 13.7 years period of the Cassini mission, amendments to the spacecraft's flight path were needed. This research is being carried out as there is a limited number of studies that use a temporal discrimination analysis to handle raw data. More complex inspection and analysis of the collected broad trajectory dataset is necessary to classify orbital events in the signal travel period (approximately 88 minutes on the Earth-Cassini travel channel length). This paper provides an innovative, in-depth learning method to identify offline modifications in the Cassini spacecraft trajectory. The models are based on variants of Recurrent Neural Networks (RNNs: Gated Recurrent Unit (GRU)/ Long Short-Term Memory (LSTM)/ Bidirectional Long Short-Term Memory (BiLSTM)) to derive valuable data and learn the inner data structure of the time sequence, along with the penetration of long-term and short-term phase-dependencies of the RNNs layers. To validate our models, we used a variety of statistical approaches in our analysis. A considerable number of tests have been carried out, and the findings obtained have shown that the GRU and LSTM give a substantial boost to increasing the efficiency of the detection mechanism. The proposed model would consolidate potential exploration in outer space exploration to accommodate massive databases, search for correlations, and recognize complex events and outliers with an accuracy that exceeds 99 %. This method can be utilized for similar detection processes within the future outer space expedition. The results show that binary classifications of Matthews Correlation Coefficient (MCC) are more accurate than F1 score.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Cassini-Huygens interplanetary project
Artificial intelligence
Knowledge representation
Sensory data
Megjelenés:Neural Computing & Applications. - 2022 (2022), p. 13575-13598. -
További szerzők:Gál Zoltán (1966-) (informatikus, villamosmérnök)
Pályázati támogatás:TKP2021
Egyéb
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6.

001-es BibID:BIBFORM136209
Első szerző:Aldarwish, Abdulla J. Y.
Cím:Design a sturdy and secure authentication scheme capable of early detection of COVID-19 patients using WBANs / Abdulla J. Y. Aldarwish, Ali A. Yassin, Abdullah Mohammed Rashid, Hamid Ali Abed Alasadi, Aqeel Adel Yaseen, Eman Thabet Khalid
Dátum:2022
ISSN:2502-4752 2502-4760
Megjegyzések:COVID-19 was first reported in China Wuhan and rapidly grown up to more than 58 countries based on the World Health Organization (WHO). Well ahead of any health emergency, the health care server has the ability to access these data via authorization and then s/he performs necessary actions. In order to protect medical data from malicious activities, authentication is the starting point for this. Authentication systems represent a network support factor to reduce ineffective users and radically eliminate phishing because authentication determines the identity of the real user. Many schemes and technologies have been suggested for authentication in wireless body area networks (WBANs). In this paper, we suggest a strong dynamic password authentication system for WBANs. We adopt a (different/new) way to calculate a password and make it coherent and dynamic for each login session. Our work also provides additional security properties to get rid of hub node impersonation attacks and resolve key escrow issues. Our scheme resist fishing attach which keep patient from any illegal change of drugs. By comparison, the proposed scheme is considered active and has strong security based on formal security analysis tools such as AVISPA.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Authentication
AVISPA
COVID-19
Health care
Wireless body area networks
Megjelenés:Indonesian Journal of Electrical Engineering and Computer Science. - 27 : 2 (2022), p. 900-910. -
További szerzők:Yassin, Ali A. Al-Bazooni, Abdullah Mohammed Rashid (1986-) (mérnök, informatikus) Al-Asadi, Hamid Ali Abed Yaseen, Aqeel A. Khalid, Eman Thabet
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7.

001-es BibID:BIBFORM103759
035-os BibID:(Scopus)85134039519
Első szerző:Alexan, Alexandru Iulian
Cím:Smartwatch activity recognition feature comparison using ML.net / Alexandru Alexan, Anca Alexan, Stefan Oniga
Dátum:2022
ISBN:978-1-6654-7933-2
Tárgyszavak:Műszaki tudományok Villamosmérnöki tudományok könyvfejezet
könyvrészlet
Megjelenés:2022 23rd IEEE International Conference on Automation, Quality and Testing, Robotics (THETA, AQTR) / Szilard Enyedi. - p. 1-6. -
További szerzők:Alexan, Anca Oniga István László (1960-) (villamosmérnök)
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8.

001-es BibID:BIBFORM103765
035-os BibID:(Scopus)85134012695
Első szerző:Alexan, Anca
Cím:Activity recognition using unsupervised learning / Anca Alexan, Alexandru Alexan, Stefan Oniga
Dátum:2022
ISBN:978-1-6654-7933-2
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Megjelenés:2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR) / Szilard Enyedi. - p. 1-6. -
További szerzők:Alexan, Alexandru Iulian Oniga István László (1960-) (villamosmérnök)
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9.

001-es BibID:BIBFORM136838
Első szerző:Ali Adil Ali, Ali (phd student)
Cím:A smart water grid network for water supply management systems / Adil Ali, Ali; Mohammed Saadi, Saadi; Mohammed Mahmood, Tameem; A. Mostafa, Salama
Dátum:2022
ISSN:2089-3191 2302-9285
Megjegyzések:This paper proposes a smart water grids network (SWGN) architecture that combines the advantages of fog computing, internet of things (IoT), long range wide area network (LoRaWAN), and Software-defined networking (SDN). The main aims of the SWG architecture are to optimize data routing and monitor water supply and quality in real-time. SWGN handles a vast amount of data that is collected by IoT devices from different points related to water supply and quality. The data is processed in a distributed way by a number of fog servers that are located at the edge of the network. The fog controllers are deployed at the fog layer in order to take action locally for frequent events. The cloud layer has a cloud controller to take actions globally for infrequent events. The LoRaWAN provides communication technology that allows devices to operate regularly. The SDN technology decouples network traffic to control data routing decisions efficiently. A primitive evaluation under the Mininet emulator, focusing on SDN, shows the feasibility and efficiency of the architecture.
Tárgyszavak:idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Cloud computing
Fog computing
Smart water grid
Water quality monitoring internet of things
Megjelenés:Bulletin of Electrical Engineering and Informatics. - 11 : 3 (2022), p. 1706-1714. -
További szerzők:Saadi, Mohammed Saadi Mahmood, Tameem Mohammed Mostafa, Salama A.
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10.

001-es BibID:BIBFORM121327
035-os BibID:(WOS)000786184000001
Első szerző:Al-Momani, Marwan
Cím:Model Selection and Post Selection to Improve the Estimation of the ARCH Model / Marwan Al-Momani, Abdaljbbar B. A. Dawod
Dátum:2022
ISSN:1911-8066 1911-8074
Megjegyzések:The Autoregressive Conditionally Heteroscedastic (ARCH) model is useful for handling volatilities in economical time series phenomena that ARIMA models are unable to handle. The ARCHmodelhasbeenadopted in many applications that contain time series data such as financial market prices, options, commodity prices and the oil industry. In this paper, we propose an improved post-selection estimation strategy. We investigated and developed some asymptotic properties of the suggested strategies and compared with a benchmark estimator. Furthermore, we conducted a Monte Carlo simulation study to reappraise the relative characteristics of the listed estimators. Our numerical results corroborate with the analytical work of the study. We applied the proposed methods on the S&P500 stock market daily closing prices index to illustrate the usefulness of the developed methodologies.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
ARCH
heteroscedastic
financial markets
residuals bootstrapping
pretest
shrinkage
Megjelenés:Journal of Risk and Financial Management. - 15 : 4 (2022), p. 1-17-. -
További szerzők:Dawod, Abdaljbbar Babiker Abdaljbbar (1987-) (Ph.D. student)
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11.

001-es BibID:BIBFORM127456
Első szerző:Alyousify, Ahmed Loqman Mustafa (computer scientist)
Cím:AR-assisted children book for smart teaching and learning of Turkish alphabets / Ahmed L. Alyousify, Ramadhan J. Mstafa
Dátum:2022
ISSN:2096-5796
Megjegyzések:Augmented reality (AR), virtual reality (VR), and remote-controlled devices are driving the need for a better 5G infrastructure to support faster data transmission. In this study, mobile AR is emphasized as a viable and widespread solution that can be easily scaled to millions of end-users and educators because it is lightweight and low-cost and can be implemented in a cross-platform manner. Low-efficiency smart devices and high latencies for real-time interactions via regular mobile networks are primary barriers to the use of AR in education. New 5G cellular networks can mitigate some of these issues via network slicing, device-to-device communication, and mobile edge computing. Methods In this study, we use a new technology to solve some of these problems. The proposed software monitors image targets on a printed book and renders 3D objects and alphabetic models. In addition, the application considers phonetics. The sound (phonetic) and 3D representation of a letter are played as soon as the image target is detected. 3D models of the Turkish alphabet are created by using Adobe Photoshop with Unity3D and Vuforia SDK. Results The proposed application teaches Turkish alphabets and phonetics by using 3D object models, 3D letters, and 3D phrases, including letters and sounds.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Human computer interaction
5G
Augmented Reality
IOT
Turkish alphabet
Smart teaching
AR assisted book
Megjelenés:Virtual Reality & Intelligent Hardware. - 4 : 3 (2022), p. 263-277. -
További szerzők:Mstafa, Ramadhan J.
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12.

001-es BibID:BIBFORM104000
035-os BibID:(WOS)000906815200001 (Scopus)85145750125
Első szerző:Al-Zaidi, Mustafa Majid Hayder (informatikus)
Cím:Benchmarking Redis and HBase NoSQL Databases Using Yahoo Cloud Service Benchmarking Tool / Mustafa Alzaidi, Aniko Vagner
Dátum:2022
ISSN:1787-5021 1787-6117
Megjegyzések:Not Structured Query Language (NoSQL) databases have become more relevant to applications developers as the need for scalable and flexible data storage for online applications has increased. Each NoSQL database system provides features that fit particular types of applications. Thus, the developer must carefully select according to the application's needs. Redis is a key-value NoSQL database that provides fast data access. On the other hand, the Apache Hbase database is a column-oriented database that offers scalability and fast data access, is a promising alternative to Redis in some types of applications. In this research paper, the goal is to use the Yahoo Cloud Serving Benchmark (YCSB) to compare the performance of two databases (Redis and HBase). The YCSB platform has been developed to determine the throughput of both databases against different workloads. This paper evaluates these NoSQL databases with six workloads and varying threads (0 - 10).
Tárgyszavak:Műszaki tudományok Informatikai tudományok konferenciacikk
folyóiratcikk
Redis
Hbase
YCSB
benchmarking
nosql database
Megjelenés:Annales Mathematicae et Informaticae. - 56 (2022), p. 1-9. -
További szerzők:Vágner Anikó (1978-) (informatikus)
Internet cím:DOI
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