Investment in the stock market has become a trend in today’s era. The primary force moving the market in a specific direction is the large buying and selling of hedge funds, pension funds, banks, etc. This paper prop...
详细信息
The increasing spread of diseases transmitted by mosquitoes, including malaria and dengue, poses a major global health challenge. Traditional mosquito detection methods, which are based on manual trapping and counting...
详细信息
Animal emotion detection, including elephant emotions, is highly possible, but what the traditional emotion detection approaches highlight is their blatant ignorance of adopting edge-enabled intelligence and serverles...
详细信息
Schizophrenia (SZ) is a complex neuropsychiatric disorder affecting approximately 1% of the global population. The early diagnosis of SZ, with electroencephalograph (EEG) signals using deep learning (DL), can help in ...
详细信息
Schizophrenia (SZ) is a complex neuropsychiatric disorder affecting approximately 1% of the global population. The early diagnosis of SZ, with electroencephalograph (EEG) signals using deep learning (DL), can help in timely interventions which may mitigate the risk of progression to clinical psychosis. This study introduces five novel machine learning (ML)/DL-based frameworks for identifying SZ using EEG signals. The first framework involves extracting complexity features using discrete wavelet transform (DWT) of the EEG signal. In the second framework, to capture the interrelatedness among the EEG channels the complexity features are computed using multivariate empirical mode decomposition (MEMD). In both of these frameworks, the complexity features extracted are transformed into their 2D representation which uses convolutional neural network (CNN) based model for classification. Various CNN models, including conventional CNN and pretrained models were used for this purpose. In the third framework, to obtain the benefit of multiple view of the EEG signal, the complexity features extracted from DWT and MEMD features in vector representation were fused using concatenation. The combined feature was integrated with a feedforward neural network (FFNN). To obtain the optimized multiview feature set principal component analysis (PCA) was used on the concatenated feature set in the fourth framework. Finally, in the fifth framework, to further optimize the fusion of DWT and MEMD feature set canonical correlation analysis (CCA) based approach was proposed. This study is one of the first to apply a 2D representation of entropy features extracted from DWT and MEMD transformed signals for diagnosis of SZ. Furthermore, this is the first study to propose an optimized multiview feature derived from the fusion of 1D-DWT and 1D-MEMD transformed complexity features using PCA and CCA for identifying SZ from healthy control (HC). The classification performance of various CNN models in
Date In response to the imperative need for mitigating criminal activities and ensuring public safety, this research proposes a novel approach leveraging deep learning techniques for real-time weapon detection. In con...
详细信息
Time series forecasting is an important field of research, especially when the series is completely random, known as a strictly non-stationary time series (NS-TS). To handle the randomness efficiently, the paper prese...
详细信息
In emergencies, Twitter has become an essential way for people to communicate. Since many people use smartphones, they can easily report events they see. Because of this, more organizations are using Twitter to monito...
详细信息
Cancer detection is critical for early diagnosis and treatment, and non-invasive imaging methods like microwave imaging have shown promise in this domain. This research presents a CNN-based machine learning model to i...
详细信息
Wheat is the most important cereal crop,and its low production incurs import pressure on the *** fulfills a significant portion of the daily energy requirements of the human *** wheat disease is one of the major facto...
详细信息
Wheat is the most important cereal crop,and its low production incurs import pressure on the *** fulfills a significant portion of the daily energy requirements of the human *** wheat disease is one of the major factors that result in low production and negatively affects the national ***,timely detection of wheat diseases is necessary for improving *** CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop ***,these models are computationally expensive and need a large amount of training *** this research,a light weighted modified CNN architecture is proposed that uses eight layers particularly,three convolutional layers,three SoftMax layers,and two flattened layers,to detect wheat diseases *** high-resolution images were collected from the fields in Azad Kashmir(Pakistan)and manually annotated by three human *** convolutional layers use 16,32,and 64 *** filter uses a 3×3 kernel *** strides for all convolutional layers are set to *** this research,three different variants of datasets are *** variants S1-70%:15%:15%,S2-75%:15%:10%,and S3-80%:10%:10%(train:validation:test)are used to evaluate the performance of the proposed *** extensive experiments revealed that the S3 performed better than S1 and S2 datasets with 93%*** experiment also concludes that a more extensive training set with high-resolution images can detect wheat diseases more accurately.
Accurate detection and classification of road faults such as cracks is critical for transportation infrastructure maintenance. Road cracks impede comfortable traveling, endanger passenger safety, and create incidents....
详细信息
暂无评论