What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- Operating system
Ali Kashif Bashir mainly investigates Resource allocation, Computer network, Wireless sensor network, Intrusion detection system and Efficient energy use.
His research in Resource allocation intersects with topics in Structural equation modeling, Usability, Technology acceptance model and Process.
His study in the field of Base station is also linked to topics like sFlow.
His Wireless sensor network research integrates issues from Cluster analysis, Energy, Data mining and Imbalanced data.
His Intrusion detection system research includes themes of Time division multiple access, Overhead, Networking hardware, Network packet and Wireless ad hoc network.
His studies deal with areas such as Operating system and Process management as well as Efficient energy use.
His most cited work include:
- A Survey on Resource Management in IoT Operating Systems (83 citations)
- Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT (55 citations)
- Performance Analysis of FD-NOMA-Based Decentralized V2X Systems (54 citations)
What are the main themes of his work throughout his whole career to date?
Ali Kashif Bashir mostly deals with Computer network, Artificial intelligence, Computer security, The Internet and Distributed computing.
His research on Computer network focuses in particular on Wireless sensor network.
Ali Kashif Bashir combines subjects such as Video tracking, Real-time computing, Efficient energy use and Energy consumption with his study of Wireless sensor network.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Machine learning, that overlap with his field of interest in Robustness.
His Distributed computing study combines topics from a wide range of disciplines, such as Enhanced Data Rates for GSM Evolution, Resource allocation and Big data.
In his study, Quality of service is inextricably linked to Communication channel, which falls within the broad field of Throughput.
He most often published in these fields:
- Computer network (29.02%)
- Artificial intelligence (21.76%)
- Computer security (15.54%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (21.76%)
- Machine learning (13.47%)
- The Internet (13.99%)
In recent papers he was focusing on the following fields of study:
Ali Kashif Bashir mainly focuses on Artificial intelligence, Machine learning, The Internet, Computer security and Computer network.
While the research belongs to areas of Artificial intelligence, Ali Kashif Bashir spends his time largely on the problem of Identification, intersecting his research to questions surrounding Multi sensor, Filter, Soft set and Recurrent neural network.
Ali Kashif Bashir interconnects Reliability and Robustness in the investigation of issues within Machine learning.
His The Internet research is multidisciplinary, incorporating elements of Service quality and Usability, Technology acceptance model.
His research in the fields of Blockchain, Access control, Encryption and Smart contract overlaps with other disciplines such as Drone.
His study in Computer network is interdisciplinary in nature, drawing from both Semantics and Energy.
Between 2020 and 2021, his most popular works were:
- CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques (22 citations)
- Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications (18 citations)
- Efficient and Secure Data Sharing for 5G Flying Drones: A Blockchain-Enabled Approach (12 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- Operating system
Artificial intelligence, Machine learning, Big data, Algorithm and Edge computing are his primary areas of study.
Ali Kashif Bashir frequently studies issues relating to Identification and Artificial intelligence.
The study incorporates disciplines such as Semantic data model and Reliability in addition to Machine learning.
His Big data research is multidisciplinary, incorporating elements of Unavailability, Distributed computing, Distributed Computing Environment and Authorization.
His research integrates issues of Energy consumption, Quality of service, Scheduling, Job shop scheduling and Server in his study of Edge computing.
His studies examine the connections between Job shop scheduling and genetics, as well as such issues in Cloud computing, with regards to Event, Interoperability, Resource allocation and Scalability.
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