Human activity recognition (HAR) is vital in healthcare settings, facilitating personalized care and early intervention. Recent advancements in sensor technology and machinelearning have paved the way for more accura...
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In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional datamining methods are inadequate wh...
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ISBN:
(数字)9798331534622
ISBN:
(纸本)9798331534639
In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional datamining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. This study optimizes datamining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and patternrecognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. The experimental results demonstrate that the proposed method significantly outperforms traditional machinelearning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios.
With the evolving lifestyle, many cardiac ailments are becoming more frequent, and it has become necessary to provide detailed surveillance of the heart's functioning to ensure healthy living. ECG signals provide ...
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ISBN:
(纸本)9783030972554;9783030972547
With the evolving lifestyle, many cardiac ailments are becoming more frequent, and it has become necessary to provide detailed surveillance of the heart's functioning to ensure healthy living. ECG signals provide details regarding the various forms of arrhythmias. However, owing to the complexities and non-linearity of ECG signals, it is impossible to examine these signals manually. Conventional approaches for specialist inspection of ECGs on paper or television are inadequate for ambulatory, long-term monitoring, and sports ECGs. Automated applications that use signal processing and patternrecognition would be extremely beneficial. Identification of arrhythmias from ECGs is an essential branch of biomedical signal processing and patternrecognition. Motion-induced artifacts are well-known to be a major source of misrecognition and misdiagnosis. On the other hand, the feature extraction method has a significant effect on the reliability and performance of ECG patternrecognition. This paper proposes new approaches and algorithms for pre-processing multi-channel ECG signals and neural networks for arrhythmia classification using independent component analysis (ICA) with two distinct goals: (1) to eliminate motion-induced or associated artifacts, and (2) to better select the features and allow more effective patternrecognition. When processing noisy ECG data with the MIT dataset, cross-validation reveals a major improvement. For noisy signals, classification sensitivity of 97.9% and positive predictivity of 98.1% was achieved in this work. A tenfold neural validation rule was used to achieve 99.3% accuracy in arrhythmia classification. The lower the signal-to-noise level, the more significant is the improvement. This proposed algorithm would be a valuable method for physicians in justifying their diagnosis. With its quick reaction time, the proposed algorithm can be easily integrated into an automated healthcare management system.
The proceedings contain 54 papers. The topics discussed include: an intelligent approach for food recipe rating prediction using machinelearning;hardware implementation of IP-enabled wireless sensor network using 6Lo...
ISBN:
(纸本)9780738131771
The proceedings contain 54 papers. The topics discussed include: an intelligent approach for food recipe rating prediction using machinelearning;hardware implementation of IP-enabled wireless sensor network using 6LoWPAN;an efficient machinelearning-based approach for android v.11 ransomware detection;robotics to enhance the teaching and learning process;an efficient patternrecognition based method for drug-drug interaction diagnosis;enhancing the prediction of MERS-CoV survivability using stacking-based method;a new solution to the brain state permanency for brain-based authentication methods;towards efficient detection and crowd management for law enforcing agencies;and AI support marketing: understanding the customer journey towards the business development.
We propose a fast procedure based on neural networks (NN) to correct the typically complex background of recto-verso historical manuscripts, where the texts of the two sides often appear mixed. The purpose is to elimi...
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Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects,...
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ISBN:
(纸本)9783031133244;9783031133237
Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to distinguish objects. Both provide high confidence for drone detections, and eliminating false detections requires efficient algorithms and approaches. Our previous work, which uses YOLOv5, uses both real and synthetic data and a Kalman-based tracker to track the detections and increase their confidence using temporal information. Our current work improves on the previous approach by combining several improvements. We used a more diverse dataset combining multiple sources and combined with synthetic samples chosen from a large synthetic dataset based on the error analysis of the base model. Also, to obtain more resilient confidence scores for objects, we introduced a classification component that discriminates whether the object is a drone or not. Finally, we developed a more advanced scoring algorithm for object tracking that we use to adjust localization confidence. Furthermore, the proposed technique won 1st Place in the Drone vs. Bird Challenge (workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP 2021).
Artificial neural networks have transformed machinelearning and had a significant impact on a wide range of industries, including robotics, deep learning, healthcare, sports, and surveillance. Convolutional Neural Ne...
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ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
Artificial neural networks have transformed machinelearning and had a significant impact on a wide range of industries, including robotics, deep learning, healthcare, sports, and surveillance. Convolutional Neural Networks (CNNs) are unique among these developments because they combine cutting-edge deep learning methods with conventional Artificial Neural Networks (ANNs). CNNs are essential for tasks like data analysis, text classification, speech and face recognition, and patternrecognition in a variety of fields. In order to improve spelling correction, this article uses a dataset that was upgraded from the National Institute of standards & Technology (NIst) and includes 70,000 grayscale pictures of handwritten numbers.
We propose Nester, a method for injecting neural networks into constrained structured predictors. Nester first uses a neural network to compute an initial prediction that may or may not satisfy the constraints, and th...
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We propose Nester, a method for injecting neural networks into constrained structured predictors. Nester first uses a neural network to compute an initial prediction that may or may not satisfy the constraints, and then applies a constraint-based structured predictor to refine the raw predictions according to hard and soft constraints. Nester combines the advantages of its two components: the network can learn complex representations from low-level data while the constraint program on top reasons about the high-level properties and requirements of the prediction task. An empirical evaluation on handwritten equation recognition shows that Nester achieves better performance than both the either component in isolation, especially when training examples are scarce, while scaling to more complex problems than other neuro-programming approaches. Nester proves especially useful to reduce errors at the semantic level of the problem, which is particularly challenging for neural network architectures.
Nowadays wearable devices are part of our everyday lives. More and more devices that we carry every day are becoming smart and gain more and more features. One of them is the humble watch, which has been telling the t...
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machinelearning and artificial intelligence has recently become a prominent technology. Given its popularity and strength in patternrecognition and categorization, many corporations and institutions have begun inves...
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ISBN:
(纸本)9781607685395
machinelearning and artificial intelligence has recently become a prominent technology. Given its popularity and strength in patternrecognition and categorization, many corporations and institutions have begun investing in healthcare research to improve illness prediction accuracy. Using these strategies, however, has several drawbacks. One of the primary issues is the lack of huge data sets for medical pictures. An introduction to deep learning in medical image processing, from theoretical foundations to real-world applications. The article examines the general appeal of deep learning (DL), a collection of computer science advances. The next step was to learn the basics of neural networks. That explains the use of deep learning and CNNs. So we can see why deep learning is rapidly advancing in various application fields, including medical image processing. The goal of this research was to use innovative methodologies on cancer datasets to explore the feasibility of combining machinelearning and deep learning algorithms for cancer detection. This study used text and picture databases to classify cancer. The datasets are the Liver BUPA disorder database and brain MRI pictures. This article provides optimization methods that outperform the suggested approaches' accuracy. Using two alternative training methods, Levenberg Marquardt (lm) and Resilient back propagation (rp), two classification algorithms were evaluated with different groups of neurons to identify benign and malignant patients. Cascade correlation utilizing the train (rp) outperformed feed forward back propagation using the train (lm). The second deep neural network model presented a technique (based on CNN) for automated brain tumour identification using MRI data. The Water Cycle Algorithm is used to optimise CNN. The established approach is very accurate. The suggested framework examines innovative texture classification algorithms using Discrete Wavelet Transform (DWT) and the Gray Level Co occurrence
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