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Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

Computers, Materials & Continua 2024年第7期80卷 527-546页

作者： amna khatoon Weixing Wang Asad Ullah Limin Li Mengfei Wang School of Information Engineering Chang’an UniversityXi’an710064China School of Information Engineering Xi’an Eurasia UniversityXi’an710065China School of Electrical and Electronic Engineering Wenzhou UniversityWenzhou325035China

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader *** devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning *** modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in *** showcases its superiority over conventional detection algorithms in different road image *** constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational *** Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge *** analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

关键词： Edge computing remote sensing TinyML optimization BNNs road anomaly detection quantization model compression

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Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning

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Computers, Materials & Continua 2021年第4期67卷 595-612页

作者： Shaheen khatoon Md Maruf Hasan amna Asif Majed Alshmari Yun-Kiam Yap College of Computer Science and Information Technology King Faisal UniversityAl-Ahsa31982Saudi Arabia College of Science King Faisal UniversityAl-Ahsa31982Saudi Arabia

Tomato production is affected by various threats,including pests,pathogens,and nutritional deciencies during its growth *** control is not timely,these threats affect the plant-growth,fruit-yield,or even loss of the entire crop,which is a key danger to farmers’livelihood and food *** plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge *** and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic *** developments in Articial Intelligence(AI)and computer vision allow researchers to develop image-based automatic diagnostic tools to quickly and accurately detect *** this work,we proposed an AI-based approach to detect diseases in tomato *** goal is to develop an end-to-end system to diagnose essential crop problems in real-time,ensuring high *** paper employs various deep learning models to recognize and predict different diseases caused by pathogens,pests,and nutritional *** Convolutional Neural Networks(CNNs)are trained on a large dataset of leaves and fruits images of tomato *** compared the performance of ShallowNet(a shallow network trained from scratch)and the state-of-theart deep learning network(models are ne-tuned via transfer learning).In our experiments,DenseNet consistently achieved high performance with an accuracy score of 95.31%on the test *** results verify that deep learning models with the least number of parameters,reasonable complexity,and appropriate depth achieve the best *** experiments are implemented in Python,utilizing the Keras deep learning library backend with TensorFlow.

关键词： Tomato plant disease classication and prediction deep learning convolutional neural network RestNet VGGNet DenseNet

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Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

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Computers, Materials & Continua 2021年第9期68卷 3079-3100页

作者： Shaheen khatoon Majed AAlshamari amna Asif Md Maruf Hasan Sherif Abdou Khaled Mostafa Elsayed Mohsen Rashwan Department of Information System College of Computer Science and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Faculty of Computers and Artificial Intelligence Cairo UniversityEgypt Faculty of Computing&Information Cairo UniversityEgypt Faculty of Engineering Cairo UniversityEgypt

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made *** response authorities,law enforcement agencies,and the public can use this information to gain situational awareness and improve disaster *** case of emergencies,rapid responses are needed to address victims’requests for *** research community has developed many social media platforms and used them effectively for emergency response and coordination in the ***,most of the present deployments of platforms in crisis management are not automated,and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response *** seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become *** research project aims to develop a generalized Information Technology(IT)solution for emergency response and disaster management by integrating social media data as its core *** this paper,we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief *** specifically,we applied state-of-the-art Natural Language Processing(NLP),Machine Learning(ML),and Deep Learning(DL)techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a *** a proof of concept,a case study on the COVID-19 pandemic on the data collected from Twitter is presented,providing evidence that the scientific and operational goals have been achieved.

关键词： Crisis management social media analytics machine learning natural language processing deep learning

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