The growth in penetration of renewable energy sources leads to reduction in the number of grid-tied dis-patchable synchronous generators. Since the contributions of non-dispatchable, renewable energy-based generators ...
详细信息
The protection of intellectual property rights (IPR) has taken on increased importance in today’s interconnected world, but it also faces new obstacles. As patents, copyrights, trademarks, and trade secrets are incre...
详细信息
computer vision is one of the state-of-The-Art technologies for object detection problems. Accurate detection of obstacles could assist blind and visually impaired people to n avigate safely while they walk. However, ...
详细信息
In this era, wireless networks are economically feasible and flexible to establish in healthcare, defense, surveillance, and traffic monitoring, fire detection. Effective intruder detection systems utilize advanced te...
详细信息
The proposed project uses a Raspberry Pi microcontroller to prevent crop losses caused by animals like dog, wild pigs, and monkeys. These animals pose a significant threat to farmers, leading to financial losses. This...
详细信息
In many previous work, weakly supervised video anomaly detection is formulated as a multiple instance learning (MIL) problem, which represents the video as a bag of multiple instances. However, most MIL-based framewor...
详细信息
This paper presents a base station antenna with dual-band and dual-polarization for 5G mobile communication. It consists of a pair of bowtie and bent stripline cross dipoles to obtain two linear polarizations (±4...
详细信息
Multi-view feature selection has gained popularity as a study area in the machine learning field, because it can effectively reduce the dimensionality of data. Due to the scarcity of labeled information, numerous algo...
Multi-view feature selection has gained popularity as a study area in the machine learning field, because it can effectively reduce the dimensionality of data. Due to the scarcity of labeled information, numerous algorithms for unsupervised feature selection have surfaced, while supervised or semi-supervised feature selection algorithms are relatively few. However, when there is a certain amount of labeled information, unsupervised learning algorithms fail to explore the true structure of samples due to their inherent limitations. A multi-view semi-supervised feature selection method based on adaptive graph and tensor learning is proposed to effectively reduce the dimension of data with few labels. The method completes the feature selection task by exploring high-order connections between views and discriminative label information. An efficient algorithm is designed to solve the resulted optimization problem. Compared with other mainstream methods, we have achieved a certain improvement in classification accuracy on multiple basic datasets which proves the superiority and universality of our method. We also conducted experiments such as convergence analysis to prove the effectiveness of our proposed method.
Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has be...
Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository’s epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods.
The Hadamard transform is an ancient method for efficient data compression, especially for medical image data. Thus, the proposed work consists of analyzing the filter performance using a novel Hadamard transform to a...
详细信息
暂无评论