What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Artificial intelligence
Big data, Data science, Artificial intelligence, Distributed computing and Machine learning are his primary areas of study.
His studies examine the connections between Big data and genetics, as well as such issues in Analytics, with regards to Computer security.
The various areas that Junaid Qadir examines in his Data science study include Range, Principal component analysis and Dimensionality reduction.
His work on Deep learning and Control as part of general Artificial intelligence research is often related to InSync adaptive traffic control system and Field, thus linking different fields of science.
His Distributed computing research is multidisciplinary, incorporating perspectives in Computer network, Server, Host, Wireless network and Edge computing.
His Computer network study frequently draws connections to adjacent fields such as Wireless lan.
His most cited work include:
- Big Data in the construction industry (178 citations)
- Community detection in networks: A multidisciplinary review (99 citations)
- Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study (97 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Computer network, Artificial intelligence, The Internet, Wireless and Big data.
His Computer network study combines topics from a wide range of disciplines, such as Distributed computing, Throughput, Cognitive radio, Communication channel and Wireless network.
The study incorporates disciplines such as Broadcast radiation and Wireless mesh network in addition to Distributed computing.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Natural language processing.
His work deals with themes such as Telecommunications, Provisioning and Software-defined networking, which intersect with The Internet.
He works mostly in the field of Big data, limiting it down to topics relating to Data science and, in certain cases, Health care, as a part of the same area of interest.
He most often published in these fields:
- Computer network (26.60%)
- Artificial intelligence (23.94%)
- The Internet (14.89%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (23.94%)
- Machine learning (11.17%)
- Deep learning (9.57%)
In recent papers he was focusing on the following fields of study:
Junaid Qadir mainly investigates Artificial intelligence, Machine learning, Deep learning, Adversarial system and Robustness.
Junaid Qadir has included themes like Data modeling, Cloud computing and Integer programming in his Artificial intelligence study.
His Machine learning research includes themes of Overhead and Smart city.
His biological study deals with issues like Health care, which deal with fields such as Data science, Domain, Speech processing and Speech synthesis.
His work is dedicated to discovering how Data science, Computational intelligence are connected with Big data and other disciplines.
His research investigates the link between Computer network and topics such as Algorithm that cross with problems in Reinforcement learning.
Between 2019 and 2021, his most popular works were:
- Leveraging Data Science to Combat COVID-19: A Comprehensive Review (59 citations)
- Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward (28 citations)
- Big data analytics enhanced healthcare systems: a review (25 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Artificial intelligence
His primary areas of study are Artificial intelligence, Machine learning, Adversarial system, Robustness and Health care.
Deep learning and Leverage are among the areas of Artificial intelligence where the researcher is concentrating his efforts.
Junaid Qadir combines subjects such as Healthcare settings and Unsupervised learning with his study of Deep learning.
His studies in Adversarial system integrate themes in fields like Computer security, Intelligent transportation system and Vehicular ad hoc network.
In most of his Robustness studies, his work intersects topics such as Wireless.
The concepts of his Health care study are interwoven with issues in Variety, Software, Volume and Analytics.
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