Strawberry leaf scorch presents major risks to fruit yields through its harmful effects on plant health so early detection methods are vital for growers. This research analyzes how different Convolutional Neural Netwo...
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ISBN:
(数字)9798331527549
ISBN:
(纸本)9798331527556
Strawberry leaf scorch presents major risks to fruit yields through its harmful effects on plant health so early detection methods are vital for growers. This research analyzes how different Convolutional Neural Networks classify strawberry leaf diseases while fixing problems found in earlier studies about speed and diagnosis precision. The proposed research evaluated five CNN models namely AlexNet, DenseNet, ResNet, MobileNet, and EfficientNet through implementation on a total of 6146 images that showcased both healthy and scorch-affected strawberry leaves. The proposed research optimized feature extraction techniques and added model comparison to find the most effective configuration. EfficientNet achieved higher accuracy and precision than all test models when detecting diseases in samples. The strong disease identification abilities of DenseNet and MobileNet show promise for developing better ways to manage agricultural health problems. This study expands precision agriculture methods while helping farmers sustain their operations through modern machine learning tools.
The rapid expansion of autonomous technologies, the rise of computer vision, and edge computing present exciting opportunities in healthcare monitoring systems. Fall prevention is especially important for the elderly ...
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ISBN:
(数字)9798331515911
ISBN:
(纸本)9798331515928
The rapid expansion of autonomous technologies, the rise of computer vision, and edge computing present exciting opportunities in healthcare monitoring systems. Fall prevention is especially important for the elderly because falls from this age group often result in fatalities and serious injuries. Fall detection devices that can quickly recognize falls and alert emergency services have become more and more popular as a result. The primary goal of the project is to increase elderly home safety by implementing an ambient intelligence-based automated emergency recognition system. We present a unique method for fall posture detection utilizing an intelligence surveillance camera and a class of efficient models called MobileNets for mobile and embedded vision applications. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art Object detection: single shot detector (SSD) Mobile Net V2 and Internet of things embedded GPU platform NVIDIA’s Jetson Nano.
In today’s era medical information and disease detection as well as health care depends greatly on machine learning. Modern machine learning algorithm’s make an advantage in detecting the big health hazards like Bra...
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The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network *** Detection Systems(IDS)are essential for safeguardin...
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The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network *** Detection Systems(IDS)are essential for safeguarding network *** address the low accuracy of existing intrusion detection models in identifying network attacks,this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network(SA-ResNet).Utilizing residual connections can effectively capture local features in the data;by introducing a spatial attention mechanism,the global dependency relationships of intrusion features can be extracted,enhancing the intrusion recognition model’s focus on the global features of intrusions,and effectively improving the accuracy of intrusion *** proposed model in this paper was experimentally verified on theNSL-KDD *** experimental results showthat the intrusion recognition accuracy of the intrusion detection method based on SA-ResNet has reached 99.86%,and its overall accuracy is 0.41% higher than that of traditional Convolutional Neural Network(CNN)models.
Bone fracture is a very common medical issue faced by medical practitioners, and fracture detection is quite crucial for treatment. Till now, the most popular diagnosing method for bone fracture is X-ray imaging. Howe...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Bone fracture is a very common medical issue faced by medical practitioners, and fracture detection is quite crucial for treatment. Till now, the most popular diagnosing method for bone fracture is X-ray imaging. However, in many cases, an X-ray image of a fractured bone is prone to human error. Advancements in artificial intelligence (AI) have demonstrated the potential to achieve high accuracy in tasks like X-ray image classification of fractured bone. In this work, we have incorporated a deep learning-based approach to facilitate the diagnosis of bone fracture. We have utilized different pre-trained state-of-the-art (SOTA) convolutional neural networks (CNN), namely, DenseNet, VGG, ResNet, Xception, and EfficientNet as feature extractors, to extract higher-level feature space representations of X-ray images. We have incorporated multiple classifiers, namely Logistic Regression, Random Forest, Linear Regression, XGBoost, and a custom feed-forward network (FFN) to discriminate among the higher-level representations extracted by the aforementioned pre-trained feature extractors. Among the several feature extractor and classifier combinations we have experimented with, DenseNet169 combined with the custom FFN produced the best results, reporting an overall accuracy of 99.48%, Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 99.99%, precision of 99.48%, recall of 99.48%, and F1 score of 99.48%. The results demonstrate that combining an appropriate pretrained SOTA CNN model and a classifier can achieve high classification accuracy in the bone-fracture X-ray image classification task. Such a method previews a promising avenue in helping medical practitioners to achieve better diagnostic performance with patient benefit.
Sentiment analysis is a key element for expert reviews but is at a loss in terms of detection of sarcasm and irony, which distort the actual sentiments. This paper proposes an approach that will fuse syntactic and con...
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ISBN:
(数字)9798331523893
ISBN:
(纸本)9798331523909
Sentiment analysis is a key element for expert reviews but is at a loss in terms of detection of sarcasm and irony, which distort the actual sentiments. This paper proposes an approach that will fuse syntactic and contextual knowledge to respond to these requirements. Steps for preprocessing include tokenization, stop word removal, lemmatization, followed by feature extraction using BERT embedding. In addressing high-dimensional output, the Relief algorithm is then used to extract the relevant features. The classification model utilizes a combination of Deep Learning for sequence LSTM networks and Maximum entropy methods, i.e., Conditional Random Fields (CRF), and enables sequential dependency learning to give better accuracy. This application of data pertaining to time and context can effectively address subtle nuances such as sarcasm and irony and classify the text as positive, negative, or neutral. It brings several advancements with respect to traditional models, thus improving the understanding of emotion in text.
This paper presents a secure framework for deploying ML models client-side, addressing challenges like unauthorized access, reverse engineering, and spoofing. The proposed solution combines encryption, obfuscation, an...
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ISBN:
(数字)9798331515911
ISBN:
(纸本)9798331515928
This paper presents a secure framework for deploying ML models client-side, addressing challenges like unauthorized access, reverse engineering, and spoofing. The proposed solution combines encryption, obfuscation, and integrity verification, using JIT encryption to protect models by decrypting only as needed, minimizing exposure. Cryptographic signatures and MediaPipe ensure secure transmission and model integrity, while Hoffmann compression reduces the model size to 5-7 MB, optimizing load times without sacrificing functionality. Blockchain-based IPFS secures data storage, utilizing content-addressable storage to prevent reverse engineering and ensure data integrity. Efficient caching strategies, like HTTP caching and browser requests, enhance accessibility and reduce latency. Liveness detection verifies the user’s face by capturing 100-200 frames in 2 seconds for spoofing protection. Dynamic key encryption adapts with each transaction, countering replay attacks and reinforcing model security. The framework leverages Kubernetes for scalable, reliable deployment in large-scale applications. Edge AI enables sensitive biometric data processing on-device, maintaining high accuracy and low latency, which is ideal for real-time web and mobile uses. This comprehensive, scalable approach combines encryption, blockchain, and AI to secure client-side ML models, addressing evolving security needs in modern applications.
Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. Data-driven deep neural model-based RME showed promising performance,...
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The capstone project serves as a culmination of a computerscience student’s academic journey, encapsulating their learning across the curriculum and culminating in the creation of robust software systems relevant to...
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作者:
Tarbă, NicolaeIrimescu, Ionela N.Pleavă, Ana M.Scarlat, Eugen N.Mihăilescu, MonaDoctoral School
Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School
National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center
National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept
National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept
Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania
We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by ...
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