Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

Ali, Syed Ahmad; Aslam, Ahmed Sohail; Awan, Mazhar Javed; Yasin, Awais; Damaševičius, Robertas

doi:10.3390/app112411600

Cited by 15 publications

(10 citation statements)

References 72 publications

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“…Therefore, metaheuristics-optimized techniques, specifically the Harris Hawk (HH) heuristic optimization algorithm [43], are incorporated with the traditional machine learning techniques to improve the overall recognition accuracy. HH has been successfully used before for various other applications such as feature selection [44], big data-based techniques using spark [45][46][47][48][49], pronunciation technology [50,51] and image chain based optimizers thresholding [52,53], and deep learning [54,55]. However, traditional systems have computational complexity due to a noisy environment.…”

Section: Related Workmentioning

confidence: 99%

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Ali¹,

Jaber

Khalil

et al. 2022

Applied Sciences

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Automatic speech recognition (ASR) is an effective technique that can convert human speech into text format or computer actions. ASR systems are widely used in smart appliances, smart homes, and biometric systems. Signal processing and machine learning techniques are incorporated to recognize speech. However, traditional systems have low performance due to a noisy environment. In addition to this, accents and local differences negatively affect the ASR system’s performance while analyzing speech signals. A precise speech recognition system was developed to improve the system performance to overcome these issues. This paper uses speech information from jim-schwoebel voice datasets processed by Mel-frequency cepstral coefficients (MFCCs). The MFCC algorithm extracts the valuable features that are used to recognize speech. Here, a sparse auto-encoder (SAE) neural network is used to classify the model, and the hidden Markov model (HMM) is used to decide on the speech recognition. The network performance is optimized by applying the Harris Hawks optimization (HHO) algorithm to fine-tune the network parameter. The fine-tuned network can effectively recognize speech in a noisy environment.

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Section: Related Workmentioning

confidence: 99%

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Ali¹,

Jaber

Khalil

et al. 2022

Applied Sciences

Self Cite

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“…Deep learning has tackled the COVID-19 pandemic and is guided by future developments in COVID-19. Recent work inspired by deep learning models such as convolutional neural networks [107][108][109][110][111][112] could be applied to COVID-19 X Rays, CT Scan and MR images.…”

Section: Future Directions and Recommendationsmentioning

confidence: 99%

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

et al. 2021

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The COVID-19 pandemic has frightened people worldwide, and coronavirus has become the most commonly used phrase in recent years. Therefore, there is a need for a systematic literature review (SLR) related to Big Data applications in the COVID-19 pandemic crisis. The objective is to highlight recent technological advancements. Many studies emphasize the area of the COVID-19 pandemic crisis. Our study categorizes the many applications used to manage and control the pandemic. There is a very limited SLR prospective of COVID-19 with Big Data. Our SLR study picked five databases: Science direct, IEEE Xplore, Springer, ACM, and MDPI. Before the screening, following the recommendation, Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) were reported for 893 studies from 2019, 2020 and until September 2021. After screening, 60 studies met the inclusion criteria through COVID-19 data statistics, and Big Data analysis was used as the search string. Our research’s findings successfully dealt with COVID-19 healthcare with risk diagnosis, estimation or prevention, decision making, and drug Big Data applications problems. We believe that this review study will motivate the research community to perform expandable and transparent research against the pandemic crisis of COVID-19.

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“…The answers are to minimize the potential of arbitrary claims on IoT data performance and to encourage supply chain behaviors that increase the likelihood of desirable future states being realized through mechanism design [48][49][50][51][52][53][54].…”

Section: Literature Reviewmentioning

confidence: 99%

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

et al. 2021

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Through recent progress, the forms of modern supply chains have evolved into complex networks. The supply chain management systems face a variety of challenges. These include lack of visibility of the upstream party (Provider) to the downstream party (Client); lack of flexibility in the face of sudden variations in demand and control of operating costs; lack of reliance on safety stakeholders; ineffective management of supply chain risks. Blockchain (BC) is used in the supply chain to overcome the growing demands for items. The Internet of Things (IoT) is a profoundly encouraging innovation that can help companies observe, track, and monitor products, activities, and processes within their respective value chain networks. Research establishments and logical gatherings are ceaselessly attempting to answer IoT gadgets in supply chain management. This paper presents orderly writing on and reviewing of Blockchain-based IoT advances and their current usage. We discuss the smart devices used in this system and which device is the most appropriate in the supply chain. This paper also looks at future examination themes in blockchain-based IoT, referred to as the executive’s framework production network. The essential deliberate writing audit has been consolidated by surveying research articles circulated in highly reputable publications between 2016 and 2021. Lastly, current issues and challenges are present to provide researchers with promising future directions in IoT supply chain management systems.

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Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

Cited by 15 publications

References 72 publications

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

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