Purpose: The intricate language of eukaryotic gene articulation remains deficiently comprehended. Notwithstanding the significance recommended by numerous protein variations genuinely related to human infection, virtu...
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This innovative study applies Machine Learning (ML) techniques to construct an all-encompassing Multiple Disease Prediction System using datasets from Kaggle. The dataset holds great potential for marking 'Parkins...
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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 ...
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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
In today’s digital epoch, the notion of the Internet of Things (IoT) is widely engaged in delivering a variety of services. Internet technologies are highly used for online communication. Hence, the validity of digit...
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The accurate and early detection of abnormalities in fundus images is crucial for the timely diagnosis and treatment of various eye diseases, such as glaucoma and diabetic retinopathy. The detection of abnormalities i...
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The accurate and early detection of abnormalities in fundus images is crucial for the timely diagnosis and treatment of various eye diseases, such as glaucoma and diabetic retinopathy. The detection of abnormalities in fundus images using traditional methods is often challenging due to high computational demands, scalability issues, and the requirement of large labeled datasets for effective training. To address these limitations, a new method called triplet-based orchard search (Triplet-OS) has been proposed in this paper. In this study, a GoogleNet (Inception) is utilized for feature extraction of fundus images. Also, the residual network is employed to detect abnormalities in fundus images. The Triplet-OS utilizes the medical imaging technique fundus photography dataset to capture detailed images of the interior surface of the eye, known as the fundus and the fundus includes the retina, optic disk, macula, and blood vessels. To enhance the performance of the Triplet-OS method, the orchard optimization algorithm has been implemented with an initial search strategy for hyperparameter optimization. The performance of the Triplet-OS method has been evaluated based on different metrics such as F1-score, specificity, AUC-ROC, recall, precision, and accuracy. Additionally, the performance of the proposed method has been compared with existing methods. Few-shot learning refers to a process where models can learn from just a small number of examples. This method has been applied to reduce the dependency on deep learning [1]. The goal is for machines to become as intelligent as humans. Today, numerous computing devices, extensive datasets, and advanced methods such as CNN and LSTM have been developed. AI has achieved human-like performance and, in many fields, surpasses human abilities. AI has become part of our daily lives, but it generally relies on large-scale data. In contrast, humans can often apply past knowledge to quickly learn new tasks [2]. For example, if given
With the substantial volume of social media data generated daily and the concurrent emergence of powerful multimedia modification techniques, detecting manipulations in photographs and videos has become an important t...
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This paper proposes a paradigm shift from the current wave of Internet of Musical Things (IoMusT) research, which is mostly centered on technological development, towards the new wave of the Internet of Musical Things...
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In the current era of pervasive short video content, the exposure of passersby's data frequently raises privacy concerns. Traditional anonymization techniques for passersby, like blurring and mosaicing, are often ...
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In permissionless blockchain systems, Proof of Work (PoW) is utilized to address the issues of double-spending and transaction starvation. When an attacker acquires more than 50% of the hash power of the entire networ...
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Software-defined networking (SDN) is software-based networking technology which overcomes the complication tackled by traditional networking structures. The knowledge behind the SDN gives more flexibility to handle ne...
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