What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Artificial intelligence
His primary areas of investigation include Data mining, Artificial intelligence, Identification, Computer security and Wearable computer.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
His study on Identification also encompasses disciplines like
- Biometrics which is related to area like Authentication and Keystroke logging,
- Anomaly detection most often made with reference to Cluster analysis.
His work on Encryption, Data security and Information privacy as part of general Computer security study is frequently linked to Digital watermarking, therefore connecting diverse disciplines of science.
His research on Encryption also deals with topics like
- Service, which have a strong connection to Server, Distributed computing, Recommender system and Purchasing,
- Wavelet transform which connect with Real-time computing.
The study incorporates disciplines such as Context management, Middleware, Feature vector and Big data in addition to Wearable computer.
His most cited work include:
- A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis (537 citations)
- Wavelet-Based ECG Steganography for Protecting Patient Confidential Information in Point-of-Care Systems (124 citations)
- CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living (114 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Computer network, Computer security, Data mining, Artificial intelligence and Distributed computing.
His study looks at the intersection of Computer network and topics like The Internet with XML, SOAP, Web service and Private network.
In general Computer security, his work in Encryption, Information privacy, Authentication and Anonymity is often linked to Service provider linking many areas of study.
He has included themes like Hidden Markov model and Cluster analysis in his Data mining study.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Computer vision and Pattern recognition.
The various areas that Ibrahim Khalil examines in his Distributed computing study include Overlay network, Scalability and Service.
He most often published in these fields:
- Computer network (29.90%)
- Computer security (20.62%)
- Data mining (22.16%)
What were the highlights of his more recent work (between 2018-2021)?
- Computer security (20.62%)
- Computer network (29.90%)
- Authentication (8.76%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Computer security, Computer network, Authentication, Blockchain and Scalability.
His studies in Computer network integrate themes in fields like Enhanced Data Rates for GSM Evolution and Vehicular ad hoc network.
His Scalability study combines topics in areas such as Network performance, Node and Big data.
His Big data study deals with the bigger picture of Data mining.
His Service research is multidisciplinary, incorporating perspectives in Distributed computing, Differential privacy and Artificial intelligence.
His research in Distributed computing intersects with topics in Homomorphic encryption and Server.
Between 2018 and 2021, his most popular works were:
- Local Differential Privacy for Deep Learning (27 citations)
- Efficient privacy preservation of big data for accurate data mining (26 citations)
- An Integrated Framework for Privacy-Preserving Based Anomaly Detection for Cyber-Physical Systems (22 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Artificial intelligence
Ibrahim Khalil mostly deals with Data mining, Scalability, Big data, Distributed computing and Differential privacy.
He interconnects Security analysis, Lossless compression and Information privacy in the investigation of issues within Data mining.
Ibrahim Khalil combines subjects such as Kalman filter, Statistical classification, Mixture model and Data classification with his study of Information privacy.
His work deals with themes such as Cryptosystem, Algorithm, Analytics and Data stream mining, which intersect with Scalability.
His work carried out in the field of Big data brings together such families of science as Homomorphic encryption and Encryption.
The concepts of his Distributed computing study are interwoven with issues in Feature, Deep learning, Convolutional neural network, Artificial intelligence and Service.
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