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Advanced Technology in Applications
Research Guide
What is Advanced Technology in Applications?
Advanced Technology in Applications is a cluster of research encompassing big data mining approaches that integrate machine learning, cloud computing, internet of things, deep learning, semantic analysis, data processing, image recognition, FPGA implementation, and virtual reality within information systems.
This field includes 3,764 works focused on technologies for handling large-scale data and computational tasks. Key areas span from AI-driven robotics to IoT-cloud integrations and UAV systems. Growth rate over the past 5 years is not available in the data.
Topic Hierarchy
Research Sub-Topics
Deep Learning in Advanced Robotics
This sub-topic examines the integration of deep learning techniques for perception, control, and decision-making in robotic systems. Researchers study neural network architectures tailored for robotic tasks like manipulation and navigation.
Task Scheduling Algorithms in Cloud Computing
This sub-topic focuses on optimization algorithms for allocating computational tasks across cloud resources to minimize latency and maximize efficiency. Researchers investigate heuristic, metaheuristic, and AI-based scheduling methods.
FPGA Implementation of Machine Learning Accelerators
This sub-topic covers hardware design and optimization of Field-Programmable Gate Arrays for accelerating machine learning inference and training. Researchers explore reconfigurable architectures for real-time data processing.
Big Data Mining in Cloud Systems
This sub-topic investigates scalable mining techniques like clustering and classification applied to massive datasets in cloud environments. Researchers address challenges in distributed processing and data privacy.
Artificial Intelligence for Smart Warehousing
This sub-topic explores AI-driven automation in logistics, including path planning, inventory management, and robotic coordination in warehouses. Researchers develop systems for real-time optimization and human-robot collaboration.
Why It Matters
These technologies enable efficient data handling in constrained environments, such as IIoT devices producing vast data volumes that cannot be stored locally due to energy and space limits, as noted by Mohammed et al. (2021) in "IoT and Cloud Computing Issues, Challenges and Opportunities: A Review". In robotics, Soori et al. (2023) review AI, machine learning, and deep learning applications that enhance advanced robotics capabilities with 916 citations. Practical implementations include AI for automated logistics in smart warehousing by Pandian (2019), improving order management amid rising complexity, and multi-featured medical stretchers by Kanojiya et al. (2024) that boost patient comfort and healthcare efficiency with 334 citations.
Reading Guide
Where to Start
"IoT and Cloud Computing Issues, Challenges and Opportunities: A Review" by Mohammed et al. (2021) as it provides an accessible entry into core integration challenges with 404 citations and clear discussion of IIoT data constraints.
Key Papers Explained
Soori et al. (2023) in "Artificial intelligence, machine learning and deep learning in advanced robotics, a review" (916 citations) surveys AI foundations that Mohammed et al. (2021) in "IoT and Cloud Computing Issues, Challenges and Opportunities: A Review" (404 citations) extends to IoT-cloud synergies, while Ageed et al. (2021) in "Comprehensive Survey of Big Data Mining Approaches in Cloud Systems" (146 citations) builds on these by hybridizing data mining techniques. Kanojiya et al. (2024) in "Design and Development of Multi-Featured Medical Stretcher" (334 citations) applies similar tech principles to practical medical devices, and Pandian (2019) demonstrates AI in logistics as a downstream application.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Research continues on UAV technological development and applications as surveyed by Fan et al. (2020), alongside cloud virtualization refinements in Shukur et al. (2020). Task scheduling advancements from Ibrahim et al. (2021) point to ongoing needs in resource management. No recent preprints available.
Papers at a Glance
Latest Developments
Recent developments in advanced technology research for applications include breakthroughs in AI, such as generative coding, mechanistic interpretability, and AI agents, as highlighted in MIT's 2026 list; emerging trends like AI-native development platforms, multiagent systems, and domain-specific language models from Gartner; and significant progress in AI hardware, quantum computing, and geospatial modeling from IBM Research, as of early 2026 (MIT Technology Review, Gartner, IBM Research).
Sources
Frequently Asked Questions
What does big data mining involve in cloud systems?
Big data mining in cloud systems hybridizes cloud computing, data mining, and big online data for analyzing and visualizing vast data volumes. Ageed et al. (2021) in "Comprehensive Survey of Big Data Mining Approaches in Cloud Systems" discuss how computing conventions and algorithms affect storage and data computation. This approach supports detailed data processing in distributed environments.
How does AI apply to advanced robotics?
AI, machine learning, and deep learning integrate into advanced robotics for enhanced functionality. Soori et al. (2023) in "Artificial intelligence, machine learning and deep learning in advanced robotics, a review" provide a comprehensive survey of these integrations. The paper has garnered 916 citations reflecting its influence.
What challenges exist in IoT and cloud computing?
IoT and cloud computing face issues from exponential IIoT data growth, where end devices have strict energy and storage constraints. Mohammed et al. (2021) in "IoT and Cloud Computing Issues, Challenges and Opportunities: A Review" highlight self-organization as a solution. The review has 404 citations.
What is task scheduling in cloud computing?
Task scheduling in cloud computing manages resources like storage and applications for optimal client use over the internet. Ibrahim et al. (2021) in "Task Scheduling Algorithms in Cloud Computing: A Review" explain algorithms for sharing services efficiently. This ensures requirement-based resource allocation.
How is AI used in smart warehousing?
AI applies in smart warehousing for automated logistics amid growing order complexity and management shortages. Pandian (2019) in "ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS" details these advancements. The work has 124 citations.
Open Research Questions
- ? How can virtualization optimize resource allocation in distributed cloud systems beyond current methods in Shukur et al. (2020)?
- ? What novel task scheduling algorithms improve efficiency in cloud environments as gaps remain in Ibrahim et al. (2021)?
- ? How do big data mining techniques evolve to handle increasing data volumes in cloud systems per Ageed et al. (2021)?
- ? In what ways can AI integrations in robotics address remaining challenges identified by Soori et al. (2023)?
Recent Trends
The field maintains 3,764 works with top papers like Soori et al. at 916 citations leading citations.
2023Recent high-citation work includes Kanojiya et al. with 334 citations on medical applications.
2024No growth rate, preprints, or news available in the past periods.
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