Browsing by Subject "Machine learning"
Now showing items 1-20 of 21
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Assessment and Performance Analysis of Machine Learning Techniques for Gas Sensing E-nose Systems
(2021-11)The electronic nose, commonly known as the E-nose that combines gas sensor arrays (GSAs) with machine learning, has gained a strong foothold in gas sensing technology. The E-nose, inspired from the human olfactory system, ... -
Data Embedding in Scrambled Video by Rotating Motion Vectors
(Springer, 2022-03)Data embedding in videos has several important applications including Digital Rights Management, preserving confidentiality of content, authentication and tampering detection. This paper proposes a novel data embedding ... -
Detecting Double and Triple Compression in HEVC Videos Using the Same Bit Rate
(Springer, 2021)Digital video forensics refers to the process of analysing, examining, evaluating and comparing a video for use in legal matters. In digital video forensics, the main aim is to detect and identify video forgery to ensure ... -
Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning
(2020-04)Genuineness of smiles is one aspect of the field of deception recognition, one that is prevalent in myriad social situations, and it is not easy to tell when a person’s smile is genuine or not for the average person. Machine ... -
Enhanced Water Network Leak Detection Methods
(2021-04)Water is an essential element and source of life. It is considered a scarce element that goes through several procedures for it be consumable. Once the water is treated and is deemed potable or clean, it is then distributed ... -
FPGA-Based Network Traffic Classification Using Machine Learning
(2019-11)Traffic classification is the process of associating network traffic with the application or group of applications that generated it. It is an essential part of network management at datacentres and network operators due ... -
FPGA-Based Network Traffic Classification Using Machine Learning
(IEEE Xplore, 2020)Real-time classification of internet traffic is critical for the efficient management of networks. Classification approaches based on machine learning techniques have shown promising results with high levels of accuracy. ... -
H.264/AVC Motion Vector Concealment Solutions Using Online and Offline Polynomial Regression
(Springer, 2015)This paper introduces two polynomial regression solutions for error concealment by predicting the values of motion vectors of lost macroblocks. The two solutions are online and offline polynomial regression modeling. In ... -
H.264/AVC to HEVC Video Transcoder Based on Dynamic Thresholding and Content Modeling
(IEEE, 2014)The new video coding standard, HEVC, was developed to succeed the current standard, H.264/AVC, as the state of the art in video compression. However, there is a lot of legacy content encoded with H.264/AVC. This paper ... -
HEVC Video Encryption with High Capacity Message Embedding by Altering Picture Reference Indices and Motion Vectors
(IEEE, 2022)A high capacity message embedding in encrypted HEVC video is proposed in this paper. The challenges addressed in this paper include keeping the encrypted video compliant with standardized decoders, correctly decrypting the ... -
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
(Sage Publishing, 2021)Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) ... -
MPEG-2 to HEVC Video Transcoding With Content-Based Modeling
(IEEE, 2013)This paper proposes an efficient MPEG-2 to HEVC video transcoder. The objective of the transcoder is to migrate the abundant MPEG-2 video content to the emerging HEVC video coding standard. The transcoder introduces a ... -
Predicting Compression Modes and Split Decisions for HEVC Video Coding Using Machine Learning Techniques
(2017-05)The High Efficiency Video Coding (HEVC) standard presents a substantial video compression efficiency improvement at the expense of increasing the computational complexity. This enhancement is primarily due to the introduction ... -
Predicting Hypoglycemia in Diabetic Patients using Machine Learning Techniques
(2014-06)Diabetes is a chronic disease that needs continuous blood glucose monitoring and self-management. The improper control of blood glucose levels in diabetic patients can lead to serious complications such as kidney and heart ... -
Predicting Split Decisions in MPEG-2 to HEVC Video Transcoding
(Springer, 2020)This paper proposes learning-based approaches for transcoding MPEG-2 video into HEVC. In the training mode of the transcoder, mappings between extracted features and split decisions are calculated. While in the transcoding ... -
Prediction of EV Charging Behavior Using Machine Learning
(IEEE Access, 2021)As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, ... -
A regression-based framework for estimating the objective quality of HEVC coding units and video frames
(Elsevier, 2015-05)A no-reference objective quality estimation framework is proposed. The framework is suitable for any block-based video codec. In the proposed solution, features are extracted from coding units and summarized to form features ... -
Semi-supervised Clustering of Facial Expressions
(2017-11)Automated facial expressions recognition (FER) is an important area in computer vision and machine learning due to its eminent role in human-machine interaction. FER is key in building intelligent user interfaces, particularly ... -
Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
(MDPI, 2023-06-05)This paper proposes a novel approach to activity recognition where videos are compressed using video coding to generate feature vectors based on compression variables. We propose to eliminate the temporal domain of feature ...