Browsing Data Science Institute by Author "Ali, Muhammad Intizar"
Now showing items 1-13 of 13
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Edge2Guard: Botnet attacks detecting offline models for resource-constrained IoT devices
Sudharsan, Bharath; Sundaram, Dineshkumar; Patel, Pankesh; Breslin, John G.; Ali, Muhammad Intizar (National University of Ireland Galway, 2021-03-22)In today's IoT smart environments, dozens of MCU-based connected device types exist such as HVAC controllers, smart meters, smoke detectors, etc. The security conditions for these essential IoT devices remain unsatisfactory ... -
Edge2Train: A framework to train machine learning models (SVMs) on resource-constrained IoT edge devices
Sudharsan, Bharath; Breslin, John G.; Ali, Muhammad Intizar (Association for Computing Machinery (ACM), 2020-10-06)In recent years, ML (Machine Learning) models that have been trained in data centers can often be deployed for use on edge devices. When the model deployed on these devices encounters unseen data patterns, it will either ... -
Enabling machine learning on the edge using SRAM conserving efficient neural networks execution approach
Sudharsan, Bharath; Patel, Pankesh; Breslin, John G.; Ali, Muhammad Intizar (National University of Ireland Galway, 2021-09-13)Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based analytics at the ... -
Porting and execution of anomalies detection models on embedded systems in IoT: Demo abstract
Sudharsan, Bharath; Patel, Pankesh; Wahid, Abdul; Yahya, Muhammad; Breslin, John G.; Ali, Muhammad Intizar (Association for Computing Machinery (ACM), 2021-05-18)In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs ... -
RCE-NN: a five-stage pipeline to execute neural networks (CNNs) on resource-constrained IoT edge devices
Sudharsan, Bharath; Breslin, John G.; Ali, Muhammad Intizar (Association for Computing Machinery (ACM), 2020-10-06)Microcontroller Units (MCUs) in edge devices are resource constrained due to their limited memory footprint, fewer computation cores, and low clock speeds. These limitations constrain one from deploying and executing machine ... -
Semantically Interlinked Notification System for Ubiquitous Presence Management
Mehmood, Qaiser; Ali, Muhammad Intizar; Mileo, Alessandra (Springer, 2013)Presence based notification systems play a pivotal role in any collaborative working environment by providing near real time information about the status, locality and presence of the collaborators. Instant Messaging (IM) ... -
Smart speaker design and implementation with biometric authentication and advanced voice interaction capability
Sudharsan, Bharath; Corcoran, Peter; Ali, Muhammad Intizar (CEUR-WS.org, 2019-12-05)Advancements in semiconductor technology have reduced dimensions and cost while improving the performance and capacity of chipsets. In addition, advancement in the AI frameworks and libraries brings possibilities to ... -
An SRAM optimized approach for constant memory consumption and ultra-fast execution of ML classifiers on TinyML hardware
Sudharsan, Bharath; Yadav, Piyush; Breslin, John G.; Ali, Muhammad Intizar (Institute of Electrical and Electronics Engineers, 2021-11-15)With the introduction of ultra-low-power machine learning (TinyML), IoT devices are becoming smarter as they are driven by Machine Learning (ML) models. However, any increase in the training data results in a linear ... -
SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: Demo abstract
Sudharsan, Bharath; Patel, Pankesh; Breslin, John G.; Ali, Muhammad Intizar (Association for Computing Machinery (ACM), 2021-05-19)With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine ... -
Toward distributed, global, deep learning using IoT devices
Sudharsan, Bharath; Patel, Pankesh; Breslin, John; Ali, Muhammad Intizar; Mitra, Karan; Dustdar, Schahram; Rana, Omer; Jayaraman, Prem Prakash; Ranjan, Rajiv (Institute of Electrical and Electronics Engineers (IEEE), 2021-07-20)Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed ... -
Ultra-fast machine learning classifier execution on IoT devices without SRAM consumption
Sudharsan, Bharath; Patel, Pankesh; Breslin, John G.; Ali, Muhammad Intizar (Institute of Electrical and Electronics Engineers (IEEE), 2021-05-25)With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be ... -
Update Semantics for Interoperability among XML, RDF and RDB - A Case Study of Semantic Presence in CISCO's Unified Presence Systems
Ali, Muhammad Intizar; Lopes, Nuno; Mileo, Alessandra (Springer, 2013)XSPARQL is a transformation and querying language that provides an integrated access over heterogeneous data sources on the fly. It is an extension of XQuery which supports a subset of SPARQL and SQL to provide unified ... -
XSPARQL-Viz: A Mashup-Based Visual Query Editor for XSPARQL
Gillani, Syed Zeeshan Haider; Ali, Muhammad Intizar; Mileo, Alessandra (Springer, 2013)XSPARQL is a query language which facilitates query, integration and transformation between XML and RDF data formats. Although XSPARQL supports semantic data integration by providing uniform access over XML and RDF, but ...