The Smart Automotive Webshop Using High End Programming Technologies

Bharathiraja, N.; Shobana, M.; Manokar, S.; Kathiravan, M.; Irumporai, A.; Kavitha, S.

doi:10.1007/978-981-19-1844-5_64

Cited by 20 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using a coupled encoder-decoder network, recommended a method for jointly detecting faces and locating critical facial points in [28,29]. It describes a robust framework for recognizing faces and tracking them in unconstrained settings based on lightweight CNNs [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Face Identification Based on Active Facial Patches Using Multi-Task Cascaded Convolutional Networks

M.,

2024

JAIT

View full text Add to dashboard Cite

Face recognition technology is widely used for access control, security, identification, safeguarding, verification, timekeeping, and machine vision, etc. a new face identification algorithm referred to as Multi-Task Cascaded Convolutional Network (MTCCN) has emerged and has been widely used in high accuracy and efficiency in facial recognition, active facial patch identification framework face detection, selection of eyes, nose, lip, and eyebrow, identifying facial patches location and extraction of patches. This paper aims to discuss the recognition and identification of faces using layers of the Convolutional Neural Network (CNN). It is done to process camera frames as they appear and subsequent identification of the person. With three convolutional networks, MTCCN outperforms many face detection tests incredibly well, even though it maintains realtime performance. An active facial patch using MTCNN method is introduced for recognizing human faces in real time was developed, evaluated, and 97.62% of the time, the technique could recognize human faces correctly.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Face Identification Based on Active Facial Patches Using Multi-Task Cascaded Convolutional Networks

M.,

2024

JAIT

View full text Add to dashboard Cite

show abstract

“…Many intelligent systems based on deep learning and artificial intelligence have been reported [8][9][10], the relevant irrigation and fertilization system only have the automatic control functions. However, the intelligent level of agricultural irrigation and fertilization control system is not high enough, the deep learning model trained in the computer is based on the X86 architecture, and its instruction set is Complex Instruction Set, while the Raspberry Pi's CPU is based on the ARM architecture, and its instruction set is Reduced Instruction Set Computing, which is a method that executes fewer types of computer instructions.…”

Section: Migration and Deployment Of Deep Learning Modelsmentioning

confidence: 99%

Raspberry Pi-Embedded Intelligent Control System for Irrigation and Fertilization Based on deep learning

Jia

Wei

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

More than two-thirds of freshwater consumed worldwide are used for irrigation, and the existing control system for irrigation and fertilization is not intelligent and efficient enough. It is necessary to improve the utilization efficiency of irrigation water and fertilizer, and the intelligent level of irrigation and fertilization system. We have built tomato fertilizer deficiency and fertilization decision model based on leaf images and deep learning, this paper develops an Raspberry Pi-embedded intelligent control system for irrigation and fertilization based on previous deep learning models. The system included sensors, image acquisition, control part, control panel and remote control, the type of main hardware and sensors were selected, and the circuit of the system were designed. The C language is used to compile the source code of the deep learning framework, and the Python language is used to compress the deep learning model. The result shows that the compression rate of the deep learning model can reach 36.7% and the decision-making time can be shortened by 36% under the condition of ensuring the model accuracy. The Raspberry Pi-embedded intelligent control system can help farmers improve the utilization efficiency of irrigation and fertilizer.

show abstract

“…It is utilized in existence to boost the training experience, encourage learners, and boost the potency level of outcomes [ 11 ]. The IoT can be used in daily life in things like motion sensors, home security systems that discover motion, causing signals such as associate in nursing automatic off-switch requests, medical monitors for patients and elderly, and data sharing transmission [ [12] , [14] ]. Let us focus on a few important literature studies in terms of Smart tutoring systems with IoT features.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart education system to improve the learning system with CBR based recommendation system using IoT

M.R.M,

Dabade,

et al. 2023

Heliyon

View full text Add to dashboard Cite

The Smart Automotive Webshop Using High End Programming Technologies

Cited by 20 publications

References 16 publications

Face Identification Based on Active Facial Patches Using Multi-Task Cascaded Convolutional Networks

Face Identification Based on Active Facial Patches Using Multi-Task Cascaded Convolutional Networks

Raspberry Pi-Embedded Intelligent Control System for Irrigation and Fertilization Based on deep learning

Smart education system to improve the learning system with CBR based recommendation system using IoT

Contact Info

Product

Resources

About