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1.

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)
Pályázati támogatás:TKP2021
Egyéb
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2.

001-es BibID:BIBFORM096233
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Prediction and Analysis Based on Sensor Network Data Using Machine Learning Techniques / Ashraf ALDabbas, Zoltan Gal
Dátum:2021
Megjelenés:Chisinau : Eliva Press, 2021
Terjedelem:163 p.
Megjegyzések:The remote sensing of sensor data is becoming more detailed and less costly. This allows for offline or real-time event detection in applications, including planning, policymaking, environmental monitoring, and emissions monitoring and warning systems. Users may now monitor their surroundings in greater detail because of advances in wireless sensor networks and the Internet of Things. A distributed sensor network controls air quality and weather comfort. Science and development projects involving dynamic structures are often carried out in collaboration with various institutions, engineers, and scientists. Certain parts of the framework are developed by several organizations located in different geographic areas in such a collaboration. Recently, there has been a surge in interest in Machine Learning (ML)-based scientific and engineering techniques. This growing excitement comes from the collaborative development and use of effective algorithms for analysis, the enormous amounts of data available from experimental equipment and other sources, and the achievements of researchers and the academic community. Analyzing the environmental and interplanetary trajectory is an important element of the study duties as quickly connected instruments and sensory gadgets grow more prevalent in our daily lives. The sea of high-velocity information flow is increasing. This massive quantity of high-rate data produced necessitates quick insight into a variety of applications such as IoT, energy storage, and so on.
ISBN:978-1636483252
Tárgyszavak:Műszaki tudományok Informatikai tudományok szakkönyv
könyv
További szerzők:Gál Zoltán (1966-) (informatikus)
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3.

001-es BibID:BIBFORM095824
035-os BibID:(Scopus)85107901573
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Deep Learning-Based Method for Detecting Cassini-Huygens Spacecraft Trajectory Modifications / Ashraf ALDabbas, Zoltán Gál
Dátum:2021
ISSN:1613-0073
Megjegyzések:During the last 13.5 year motion cycle of the interplanetary research project, there were necessary flight path modifications of the Cassini spacecraft. In the order of signal travel time (approximatively 80 minutes) on the Earth-Cassini long sized channel, complex event detection of orbital modifications requires special investigation and analysis of the collected large trajectory dataset. This paper presents a sophisticated, in-depth learning approach for detecting Cassini spacecraft's trajectory modifications in postprocessing mode. The model uses neural networks with Long Short-Term Memory (LSTM) to extract useful data and learn the time series' inner data pattern, together with the penetrability of the LSTM layers distinguish dependencies between the long- and short-term phases.
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Megjelenés:Proceedings of the 1st Conference on Information Technology and Data Science / eds. István Fazekas, András Hajdu, Tibor Tómács. - p. 19-31. -
További szerzők:Gál Zoltán (1966-) (informatikus)
Pályázati támogatás:EFOP-3.6.3-VEKOP-16-2017-00002
EFOP
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4.

001-es BibID:BIBFORM093076
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Change Detection of the Cassini Orbit Based on Data Dissimilarity / Ashraf AlDabbas, Zoltán Gál
Dátum:2020
Megjegyzések:Remote sensing methods in change detection have influenced numerous domains also the research path we fulfill. Concerning GIS and spatial scope of study several approaches have been developed such as data remote sensing. This research paper provides a systematic approach for detecting changes among Cassini spacecraft orbit. GIS incorporate various data provenance into change detection, as the prime usefulness of utilizing status information in the scope of provisioning considerable sight of the intended domain. Mainly, change detection make practical and effective use of multi-temporal datasets to commensurately construe the temporal impacts of the observed facts. As such, our research seeks to offer a unique perspective of the substantial processes requested concerning change detection of the Cassini orbiter evaluated in the last 13.2 years of its mission around planet Saturn.
ISBN:978-963-318-886-6
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
data science
remote sensed data
planetary cartography
change detection
Megjelenés:Az elmélet és a gyakorlat találkozása a térinformatikában XI. : theory meets practice in GIS / szerk. Molnár Vanda Éva. - p. 23-29. -
További szerzők:Gál Zoltán (1966-) (informatikus)
Pályázati támogatás:EFOP-3.6.3-VEKOP-16-2017-00002
EFOP
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5.

001-es BibID:BIBFORM093075
035-os BibID:(Scopus)85101319393
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:On the constructive knowledge-based event intelligent identification mechanism / Ashraf AlDabbas, Zoltan Gal
Dátum:2020
ISSN:1992-8645 1817-3195
Megjegyzések:The tenor of Complex Event Processing (CEP) is overly exploited to notice endurable information from the latent knowledge flow. Within this research, we provide associate degree creative methodology of CEP so as to achieve a fully developed level of process patterns or events counting on the constructive feature computing. We presented constructive event detection technique to find situations when the event's statute has transferred over a special event to a complex event by generating an emphasis centerpiece to components of complex interconnection by utilizing stacked bidirectional Long Short-Term Memory (LSTM) networks. The Constructive Knowledge-based Event (CKE) detection method tested on the data set provides over 90% of the special events hit rate of the Saturn/Cassini-Huygens interplanetary project. This approach empowers analyses of vast volumes of data within a small-time interval.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Artificial Intelligence
Complex Event
Data Analytics
Constructive Knowledge-Based Event
Megjelenés:Journal of Theoretical and Applied Information Technology. - 98 : 24 (2020), p. 4169-4180. -
További szerzők:Gál Zoltán (1966-) (informatikus)
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6.

001-es BibID:BIBFORM093074
035-os BibID:(Scopus)85101242423
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Cassini-Huygens mission images classification framework by deep learning advanced approach / Ashraf AlDabbas, Zoltan Gal
Dátum:2021
ISSN:2088-8708 2722-2578
Megjegyzések:Developing a deep learning (DL) model for image classification commonly demands a crucial architecture organization. Planetary expeditions produce a massive quantity of data and images. However, manually analyzing and classifying flight missions image databases with hundreds of thousands of images is ungainly and yield weak accuracy. In this paper, we speculate an essential topic related to the classification of remotely sensed images, in which the process of feature coding and extraction are decisive procedures. Diverse feature extraction techniques are intended to stimulate a discriminative image classifier. Features extraction is the primary engagement in raw data processing with the purpose of data classification; when it comes across the task of analysis of vast and varied data, these kinds of tasks are considered as time-consuming and hard to be treated with. Most of these classifiers are either, in principle, quite intricate or virtually unattainable to calculate for massive datasets. Stimulated by this perception, we put forward a straightforward, efficient classifier based on feature extraction by analyzing the cell of tensors via layered MapReduce framework beside meta-learning LSTM followed by a SoftMax classifier. Experiment results show that the provided model attains a classification accuracy of 96.7%, which makes the provided model quite valid for diverse image databases with varying sizes.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Deep learning
Machine learning
Meta-learning
Remote sensing datasets
Saturn images classification
Megjelenés:International Journal of Electrical and Computer Engineering. - 11 : 3 (2021), p. 2457-2466. -
További szerzők:Gál Zoltán (1966-) (informatikus)
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7.

001-es BibID:BIBFORM092439
035-os BibID:(WoS)000631194900001 (Scopus)85102656412
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft / Ashraf Aldabbas, Zoltan Gal, Khawaja Moyeezullah Ghori, Muhammad Imran, Muhammad Shoaib
Dátum:2021
ISSN:2169-3536
Megjegyzések:There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale 1.3 hour of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, complex event detection in the orbit modifications requires special investigation and analysis of the collected big data. The technologies for space exploration warrant a high standard of nuanced and detailed research. The Cassini mission has accumulated quite huge volumes of science records. This generated a curiosity derives mainly from a need to use machine learning to analyze deep space missions. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The proposed model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates, Matthews correlation coefficient, and F1 score to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98% accuracy among the last 13.35 years of the Cassini spacecraft life.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Cassini-Huygens interplanetary project
complex event
sensory data
big data
neural network
pattern processing
knowledge representation
Megjelenés:IEEE Access. - 9 (2021), p. 39111-39125. -
További szerzők:Gál Zoltán (1966-) (informatikus) Ghori, Khawaja Moyeezullah (1982-) (informatikus) Imran, Muhammad (1981-) (informatikus) Shoaib Muhammad (1971-) (mérnök, informatikus)
Pályázati támogatás:FIKP-20428-3/2018/FEKUTSTRAT
FIKP
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8.

001-es BibID:BIBFORM087893
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Learning and Reasoning with structured Prediction Based on Revealing Event Complexity / Ashraf AlDabbas, Zoltán Gál
Dátum:2020
ISSN:2005-4238 2207-6360
Megjegyzések:We provide a state of the art speculative with the capacity to gain an accurate and deep intuitive understanding of structured prediction within the terms of which it can be fully understood and assessed by competent which is necessary for the sake of logic and accuracy. we gaze to big boundary speculation in a wide domain of prediction patterns wheresoever reasoning encompasses puzzle out complementary idealization. The target is to grasp parameters with a method that reaches to the deduction based on evidence and reasoning. The task of revealing complex events encompasses modeling special events that are structurally associated. The put forward algorithm is intended to learn the prospective functions of reasoning with structured prediction based on revealing event complexity which is an amalgamation of ideal conceptual nonlinear features conveyed by regression models. A sophisticated and pliable approach is accomplished via structured prediction in which could be effectively utilized in several enforcements. We apply a regularized framework to the issue of learning nonlinear regression functions. The framework parameters of the networks are assimilated then learned by evidence-based risk decreasing and complexity manipulation and regularization.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Predictive Analytics
Process Adaptation and Optimization
Structured Prediction
Artificial Intelligence
Data Science
Megjelenés:International Journal of Advanced Science and Technology. - 29 : 3 (2020), p. 13816-13828. -
További szerzők:Gál Zoltán (1966-) (informatikus)
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9.

001-es BibID:BIBFORM082728
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Complex Event Processing Based Analysis of Cassini-Huygens Interplanetary Dataset / Ashraf ALDabbas, Zoltán Gál
Dátum:2020
Megjegyzések: A complex event is an assortment of data observations that chimed to captivating of remarkable patterns in the implicit incident that are captured via the sensing devices. An innovative method is introduced in this research for the purpose of detecting complex events by analysing a huge dataset of images and other metadata that have been collected using remote sensing approach in Cassini-Huygens mission interplanetary expedition. The proposed method is competent to explore a big volume of data that is influenced by the indexed time-series manner using score array and the variation score of the target time intervals via the Weighted Complex Event Level (WCEL), allowing to convert the sensed data and images into spectacular detected events. It has been tested by applying it on all the batches which forming the included dataset and the results showed that almost in every batch there was more than a single complex event, within the 5 analysed phases of the mission, which include: Approach science phase, Extended phase, Extended-Extended phase, Tour, and Tour pre-Huygens phase.
ISBN:978-3-030-38500-2
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Special event detection
Extreme event
Weighted complex event processing
Classification
Sensory data
Big data
Pattern processing
Megjelenés:Intelligent Computing Paradigm and Cutting-edge Technologies / ed. Lakhmi C. Jain, Sheng-Lung Peng, Basim Alhadidi, Souvik Pal. - p. 51-66. -
További szerzők:Gál Zoltán (1966-) (informatikus)
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10.

001-es BibID:BIBFORM081719
035-os BibID:(WoS)000582418600005 (Scopus)85085546898
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:On the Complex Event Identification Based on Cognitive Classification Process / Aldabbas Ashraf Khaled Abd Elkareem, Gál Zoltán
Dátum:2019
Megjegyzések:The concept of Complex Event Processing (CEP) is excessively utilized to detect bearable knowledge from the implicit data flow. CEP has stood out as a consolidating scope for the application of scientific knowledge that needs processing and linking raw sensory data. In this paper, we offer an innovative method of CEP in order to reach a mature level of processing patterns or events depending on the cognitive computing. The Cognitive Knowledge-based Event (CKE) model is an event processing and identification approach with cognitive aptness. There is a mounting necessity for methodologies to provide an interactive response for data and events stream around us at the time that CEP approach has scarcity in some scopes. We integrate cognitive reasoning with CEP and put forward the concept of classification by our proposed model and we test this method on the multidimensional Big Data set captured during thirteen years of the Cassini-Huygens space exploratory project. There is a pivotal necessity to develop an identification and training method for all scales in any functional systems that include a part of cognitive reasoning and learning process.
ISBN:978-1-7281-4793-2
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
complex cognitive modeling
complex event
data analytics
cognitive knowledge-based event
Megjelenés:10th IEEE International Conference on Cognitive Infocommunications CogInfoCom 2019 : Proceedings / ed. Péter Baranyi, Anna Esposito, Nelson Mauro Maldonato, Carl Vogel. - p. 29-34. -
További szerzők:Gál Zoltán (1966-) (informatikus)
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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11.

001-es BibID:BIBFORM078790
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Getting facts about interplanetary mission of Cassini-Huygens spacecraft / Ashraf Aldabbas, Zoltán Gál
Dátum:2019
Megjegyzések:The accessibility to a huge quantity of data gathered by remote sensing of planetary missions vindicate the utilization of Geographic Information Systems (GIS) to study and analyze outer space planets and trajectories to reach them. As data size raise, the exploitation of GIS methods provide the scientists with approach for rapid data retrieval, while detailed examination of heterogeneous data grant the possibility of making perceptible differentiation analysis along various batches of data which in a different state or situation could be complicated to be implemented. The work introduced here characterizes the mission of Cassini-Huygens Spacecraft with its phases and the related mission parameters besides providing analyze visualization and conception of a remotely sensed dataset with a GIS scope that conferring the possibility to carry out deep analysis for the mission path and the related planetary data.
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
remote sensed data
planetary cartography
data analytics
Cassini-Huygens mission
Megjelenés:Az elmélet és a gyakorlat találkozása a térinformatikában X. = theory meets practice in GIS / szerk. Molnár Vanda Éva. - p. 27-34
További szerzők:Gál Zoltán (1966-) (informatikus)
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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12.

001-es BibID:BIBFORM076622
Első szerző:Aldabbas, Ashraf Khaled Abd Elkareem (informatikus)
Cím:Neural Network Estimation of Tourism Climatic Index (TCI) Based on Temperature-Humidity Index (THI)- Jordan Region Using Sensed Datasets / Ashraf AlDabbas, Zoltan Gal, Buchman Attila
Dátum:2018
ISSN:1844-9689 2343-8908
Megjegyzések:Jordan which is located in the heart of the world contains hundreds of historical and archaeological locations that have a supreme potential in enticing visitors. The impact of clime is important on many aspects of life such as the development of tourism and human health, tourists always wanted to choose the most convenient time and place that have appropriate weather circumstances. The goal of this study is to specify the preferable months (time) for tourism in Jordan regions. Neural network has been utilized to analyze several parameters of meteorologist (raining, temperature, speed of wind, moisture, sun radiation) by analyzing and specify tourism climatic index (TCI) and equiponderate it with THI index. The outcomes of this study shows that the finest time of the year to entice tourists is " April" which is categorized as to be "extraordinary" for visitors. TCI outcomes indicates that conditions are not convenient for tourism from July to August because of high temperature.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
TCI index
THI index
neural network
analysis of historical sensed data set
Megjelenés:Carpathian Journal of Electronic and Computer Engineering. - 11 : 2 (2018), p. 50-55. -
További szerzők:Gál Zoltán (1966-) (informatikus) Buchman Attila (1957-) (villamosmérnök)
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