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91.
Abbas Waseem Khan Muhammad Fakhir Taj Murtaza Mahmood Arif 《Neural computing & applications》2021,33(19):12921-12938
Neural Computing and Applications - Autonomous driving research is an emerging area in the machine learning domain. Most existing methods perform single-task learning, while multi-task learning... 相似文献
92.
Khan Muhammad Hussain Saleem Zainab Ahmad Muhammad Sohaib Ahmed Ayaz Hamail Mazzara Manuel Raza Rana Aamir 《Neural computing & applications》2021,33(21):14507-14521
Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For... 相似文献
93.
Aziz Romila Anwar Muhammad Waqas Jamal Muhammad Hasan Bajwa Usama Ijaz 《Neural computing & applications》2021,33(21):14707-14721
Neural Computing and Applications - Detecting and correcting misspelled words in a written text are of great importance in many natural language processing applications. Errors can be broadly... 相似文献
94.
Khan Muhammad Umer Arif Shukla Sanjay Kumar Raja Muhammad Nouman Amjad 《Neural computing & applications》2021,33(21):14861-14885
Neural Computing and Applications - Marston’s load theory is commonly used for understanding the soil–conduit interaction. However, there are no practical methods available which can... 相似文献
95.
Zaki Ahmad M. El-Nagar Ahmad M. El-Bardini Mohammad Soliman F. A. S. 《Neural computing & applications》2021,33(5):1515-1531
Neural Computing and Applications - For the current paper, the technique of feed-forward neural network deep learning controller (FFNNDLC) for the nonlinear systems is proposed. The FFNNDLC... 相似文献
96.
Meraj Talha Rauf Hafiz Tayyab Zahoor Saliha Hassan Arslan Lali M. IkramUllah Ali Liaqat Bukhari Syed Ahmad Chan Shoaib Umar 《Neural computing & applications》2021,33(17):10737-10750
Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of... 相似文献
97.
Elkenawy Ahmed El-Nagar Ahmad M. El-Bardini Mohammad El-Rabaie Nabila M. 《Neural computing & applications》2021,33(15):9221-9240
Neural Computing and Applications - This study introduces a neural network (NN) adaptive tracking controller-based reinforcement learning (RL) scheme for unknown nonlinear systems. First, an... 相似文献
98.
Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks 总被引:2,自引:0,他引:2
Muhammad Aamir Cheema Wenjie Zhang Xuemin Lin Ying Zhang Xuefei Li 《The VLDB Journal The International Journal on Very Large Data Bases》2012,21(1):69-95
In this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in Euclidean space as well as in spatial networks. Existing techniques are sensitive toward objects
and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We
present a framework for continuous reverse k nearest neighbor (RkNN) queries by assigning each object and query with a safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions.
This significantly improves the computation cost. As a byproduct, our framework also reduces the communication cost in client–server
architectures because an object does not report its location to the server unless it leaves its safe region or the server
sends a location update request. We also conduct a rigid cost analysis for our Euclidean space RkNN algorithm. We show that our techniques can also be applied to answer bichromatic RkNN queries in Euclidean space as well as in spatial networks. Furthermore, we show that our techniques can be extended for
the spatial networks that are represented by directed graphs. The extensive experiments demonstrate that our techniques outperform
the existing techniques by an order of magnitude in terms of computation cost and communication cost. 相似文献
99.
The paper proposes a new approach to find semantic meanings in visual object class structure, in line with the Gestalt laws
of proximity. Micro level semantic structures are formed by line segments (arcs also approximated into line segments based
on pixel deviation threshold) which are in close proximity. These structures are hierarchically combined till a semantic label
can be assigned. The algorithm extracts semantic groups, their inter-relations and represents these using a graph. Invariant
geometrical properties of the groups and relations are used as vertex and edge labels. A graph model captures the inter class
variability by analyzing the repetitiveness of structures and relations and uses it as a weighting factor for classification.
The algorithm has been tested on a standard benchmark database and compared with existing approaches. 相似文献
100.
Autonomous mapping of HL7 RIM and relational database schema 总被引:1,自引:0,他引:1
Shagufta Umer Muhammad Afzal Maqbool Hussain Khalid Latif Hafiz Farooq Ahmad 《Information Systems Frontiers》2012,14(1):5-18
Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients.
For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing
interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages
exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases.
In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model)
and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore
it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences
between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount
of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses
the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique
has been implemented as a working application and tested on real world healthcare systems. The application loads the target
healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using
domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the
complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once
all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information
i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application
is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily
on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the
data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental
results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme
is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility. 相似文献