This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining var...
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Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Mag...
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ISBN:
(纸本)9798350306231
Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped Dataset with features extracted from the CNN model we provided an
In a green environment, air quality is among the prominent aspects to be considered to prevent pollution and in turn maintain safe air. Sick Building Syndrome (SBS) and Building Related Illness (BRI) are the problems ...
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This paper explores the environmental risks in supply chains and provides recommendations for subsequent studies. Typically, the environmental responsibility is a core part of sustainability which focuses attention di...
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At the moment, most of the hospitals are more interested in patient satisfaction because this has been identified as a main issue of quality of service indexes. In most of the Asian countries' the type of registra...
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The operational research (OR) become one of emerging areas and significance and its relevance to be used in the simulation and modelling. To simulating and modelling the crowd evacuation, the most important elements t...
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The paper analyses the efficiency of the Information Technology (IT) for Domain-Specific Mathematical Modelling (DSMM). IT DSMM was developed to meet the shortcomings of the IT for Domain-Specific Modelling (DSM). IT ...
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Various methods, models and standards for software process improvement have been adopted by organizations to improve their software processes. However, despite these efforts they still encounter difficulties in their ...
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Wireless technology is advancing rapidly with time. People are doing research nowadays mostly in the field of telecommunication. VANET is the most growing research area in wireless communication. With the advancement ...
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With dramatically increase of emerging applications such as social media, sensors, videos and digital pictures data is become generating from everywhere. Due to characteristic of big data it becomes very difficult to ...
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