A Light Weight Deep Learning Model for Real World Plant Identification

Arshed, Muhammad Asad; Ghassan, Hadia; Hussain, Mubashar; Hassan, Muhammad; Kanwal, Ayesha; Fayyaz, Rimsha

doi:10.1109/dchpc55044.2022.9731841

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…The task's structure and the variety of participants made the waterfall technique the obvious choice for this project. The waterfall technique follows a series of processes in a certain sequence, starting with planning and analysis, moving on through implementation, testing, and ultimately assessing the finished product [7]. Before any development was started, qualitative research interviews with parents were done to better understand the needs and expectations of the project.…”

Section: Proposed Workmentioning

confidence: 99%

“…[2,3]. Currently, machine learning also plays an important role in the field of IoT, especially in smart homes, bioinformatics, computer vision, and agriculture [4][5][6][7]. By considering the progress of IoT and machine learning (ML) in this context, an IoT-based smart real-time baby monitoring system using machine learning has been proposed to overcome the issues faced by working parents.…”

Section: Introductionmentioning

confidence: 99%

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IoT Based Smart Baby Monitoring System with Emotion Recognition Using Machine Learning

Alam

Burhan

Gillani

et al. 2023

Wireless Communications and Mobile Computing

Self Cite

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Child care is necessary for parents, but nowadays taking care of a child has become a lot more challenging, especially for working mothers. It has become increasingly difficult for parents to continuously monitor their baby’s condition. Thus, a smart baby monitoring system based on IoT and machine learning is implemented to overcome the monitoring issues and provide intimation to parents in real-time. In the proposed system, the necessary monitoring features like room temperature and humidity, cry detection, and face detection were monitored by exploiting different sensors. The sensor data is transferred to the Blynk server via controllers with an Internet connection. The system is also capable of detecting the facial emotions of the registered babies by using a machine learning model. Parents can monitor the live activities and emotions of their child through the external web camera and can swing the baby cradle remotely upon cry detection using their mobile application. They can also check the real-time room temperature and humidity level. In case an abnormal action is detected, a notification is sent to the parent’s mobile application to take action thus, making the baby monitoring system a relief for all working parents to manage their time efficiently while taking care of their babies simultaneously.

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Section: Proposed Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IoT Based Smart Baby Monitoring System with Emotion Recognition Using Machine Learning

Alam

Burhan

Gillani

et al. 2023

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…We have selected the deep learning approach and proposed a network as the importance of the Artificial Intelligence, Machine Learning and Deep Learning cannot be neglect in this era. Machine Learning and Deep learning are being used in different area like Medical, Computer Vision, Sentiment, Natural Language Processing and Internet of Things [35]- [40].…”

Section: Vgg-16 Architecture Using Transfer Learningmentioning

confidence: 99%

Interpretation of Expressions through Hand Signs Using Deep Learning Techniques

Javaid

Rizvi

Ubaid

et al. 2022

IJIST

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It is a challenging task to interpret sign language automatically, as it comprises high-level vision features to accurately understand and interpret the meaning of the signer or vice versa. In the current study, we automatically distinguish hand signs and classify seven basic gestures representing symbolic emotions or expressions like happy, sad, neutral, disgust, scared, anger, and surprise. Convolutional Neural Network is a famous method for classifications using vision-based deep learning; here in the current study, proposed transfer learning using a well-known architecture of VGG16 to speed up the convergence and improve accuracy by using pre-trained weights. We obtained a high accuracy of 99.98% of the proposed architecture with a minimal and low-quality data set of 455 images collected by 65 individuals for seven hand gesture classes. Further, compared the performance of VGG16 architecture with two different optimizers, SGD, and Adam, along with some more architectures of Alex Net, LeNet05, and ResNet50.

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“…Machine Learning is playing an important role in different fields of life as well as in medical. Due to this importance, we have planned to do this work in future with the help of machine learning & deep learning in terms of medical domain [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64].…”

Section: Simulations and Analysismentioning

confidence: 99%

Formal Modeling and Analysis of Integrated Healthcare System using Colored Petri Nets

Jabbar,

Hussain,

Farooqi

et al. 2022

VFAST trans. softw. eng.

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Given today's situation, when a disease like COVID-19 has engulfed the world, the importance of the Integrated Healthcare System (HCS) increases a lot. Pakistan's healthcare system is not linked. Due to which the patients have trouble in the flow required for treatment in the existing healthcare system. This research understands and integrates the healthcare system in Pakistan. For this purpose, a formal modeling technique is used which not only helps us to integrate the system but also validate and verify the system. First of all, this research reviews existing Healthcare Systems. A comprehensive survey leads to the limitations of existing architectures. To address the limitations, this research proposes an improved architecture for the healthcare system that alleviates the issues of existing architectures. To verify the proposed architecture at the design level, a formal approach is adopted. Therefore, this architecture is modeled using a well-known technique, called Colored Petri Net (CPN). State Space Analysis after simulating the model ensures its consistency and authenticity. The properties indicate that any patient who enters the system exits without any hindrance after undergoing treatment.

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A Light Weight Deep Learning Model for Real World Plant Identification

Cited by 8 publications

References 7 publications

IoT Based Smart Baby Monitoring System with Emotion Recognition Using Machine Learning

IoT Based Smart Baby Monitoring System with Emotion Recognition Using Machine Learning

Interpretation of Expressions through Hand Signs Using Deep Learning Techniques

Formal Modeling and Analysis of Integrated Healthcare System using Colored Petri Nets

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