This study’s key contributions include:
Novel application of deep learning models (DNNs and RNNs) for in-vehicle data falsification detection.
Utilization of the new CICIoV2024 dataset, providing insights into latest IoV attack scenarios.
This paper explores the functionality of the H.265/HEVC (High Efficiency Video Coding) standard in low-bandwidth scenarios. We provide an overview of H.265’s key features and mechanisms that make it suitable for lower bandwidth environments. H.265, also known as High-Efficiency Video Coding (HEVC), is renowned for delivering superior video quality at lower bitrates. We investigate the critical features of H.265 and its application in low-bandwidth scenarios, providing insights into its efficiency, performance, and practical implementation. We present experimental results
demonstrating the performance improvements and benefits of H.265 regarding video quality and bandwidth utilisation. The paper discusses potential applications and directions for optimising video compression in constrained network conditions.
1. Environmental gases monitoring in Industry
2. Levaragimg of AI and IoT Technologies
3. Deployed IoT solutions
The main contributions of this paper are as follows.
1. First Application of BFEA in Image Thresholding: While the Bayesian Forecasting Evolutionary Algorithm (BFEA) was originally proposed in 2014, this paper marks the first time it has been employed in the field of image thresholding. By applying BFEA to image segmentation, we introduce a novel approach that leverages the advantages of this algorithm in handling complex optimization problems within the context of image processing.
2. Adaptation from Continuous to Discrete Optimization: In its original formulation, BFEA was primarily utilized for continuous function optimization. This paper simplifies and adapts BFEA to address discrete combinatorial optimization problems. By modifying the algorithm to suit multilevel thresholding tasks, we demonstrate its versatility and ability to solve a wide range of optimization problems beyond its initial scope.
3. Improved Solution Quality through Population Initialization: One of the key enhancements in this work is the integration of a population initialization strategy with BFEA. This strategy helps prevent the algorithm from becoming trapped in local optima, thereby increasing its robustness and ensuring a more thorough exploration of the solution space. As a result, the algorithm is able to achieve more accurate and reliable results, even in complex image segmentation tasks.
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The increasing world population has increased the demand for electricity and energy. This is putting pressure on the already depleting fossil fuel resources to keep up with the demand and that is why identifying alternative ways of producing energy, especially renewable energies, is critical moving forward into the future to produce the energy demand as well as tackle some of United Nations’ Sustainable Development Goals. This study aims to investigate the performance enhancement of various nanofluids on a flat plate solar collector via an experimental study using a flat plate solar collector test rig. Nanofluids, namely water-copper oxide, water-aluminium oxide, radiator coolant-copper oxide, and radiator coolant-aluminium oxide were prepared at a 0.1% nanoparticle volume concentration through magnetic stirring with the addition of 15% concentration of the Triton-X surfactant. All four nanofluids along with water and radiator coolant were investigated at 0.5 and 0.75 LMP flow rates. The data obtained were used for numerical calculation using MS Excel to calculate the thermal efficiency of the flat plate solar collector. The findings are that the water-aluminium oxide had the maximum energy efficiency at 54.7% and 53.7% at 0.5 and 0.75 LMP flow rates respectively. Overall, the higher flow rate returned a higher efficiency.
Hydrogen, an energy carrier, is deemed as a prospective substitute for fossil fuels. Data from different articles, published documents show that the consumption of hydrogen is increasing globally, and it has increased 23% in 2020 than 2015. Different sectors such as transport and power are expected to switch to greener and cleaner energy, such as hydrogen, because it produces zero or near zero emission. To achieve net zero goal by 2050, around 530Mt of hydrogen is needed and it has been forecasted that sectors such as transport and power will dominate the consumption of hydrogen in future. Many countries are adopting or enacting policy to incorporate hydrogen into the energy sector as a substitute of fossil fuel to curb emission. This paper briefly reviewed global hydrogen production scenario, and its applications and policy in different countries and continents/sub-continents. Literatures suggest that the electrolysis method of hydrogen production is above other techniques in terms of technology and commercial readiness level. Many countries have significantly invested on research and infrastructure development to incorporate hydrogen in their energy sector. The paper will provide an in depth understanding of the global hydrogen production scenario, and its application and formulated policy in different countries.
For the deaf and hard-of-hearing, sign language
is an essential mode of communication. To
some, it might be a novel way to express oneself
without using words at all. But there’s a little
thing called the language barrier that gets in the way all too often. To help address this issue, this study creates a gesture-based system to convert sign language to readable text, which can help the users a little.
Understanding and interpreting human emotions is crucial in Human-Computer Interaction (HCI), and Speech Emotion Recognition (SER) is central to this effort. Traditional methods have been used in SER for years, but recent advances in Deep Learning (DL) offer superior results. In this regard, this research introduces a novel hybrid architecture combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to enhance SER accuracy. The model is trained on a diverse dataset from four sources, covering seven emotional categories, and achieves an impressive testing accuracy of 93.40%. The study demonstrates that the proposed model consistently performs well across different emotion classes, with accuracies ranging from 88% to 99%. Notably, the model excels in recognizing “Female surprise” with a 99% accuracy, while “Male disgust” has the lowest accuracy at 88%. These results highlight the model’s robustness and ability to generalize across various emotions and demographic groups. This research not only sets a new benchmark in SER but also advances the development of emotionally intelligent systems, with applications in interactive voice response systems, mental health monitoring, and personalized digital assistants.
The main objective of this study is to investigate the thermal performance of straight microchannel heat sinks with fins on sidewalls. A mathematical model is developed and used to carry out the simulation-based study to examine the performance of the microchannel heat sink. From the CFD study, numerical results are obtained for different operational and geometrical conditions. The study shows that using a straight microchannel with pin fins on side walls is better at lower ranges of Reynolds number lower than 400; however, at Reynolds number higher than 400 the straight microchannel with smooth sidewalls shows better performance. Moreover, at a higher Reynolds number than 800, the pressure drop increases significantly. Furthermore, increasing the size of pin fins for both triangle and square fins enhances thermal resistance but also leads to higher pressure drop.