In many fields, such as medical imaging, electronic games, and security monitoring, it is very necessary to perform target image recognition and processing on computers. Through image processing and visual communicati...
详细信息
EEG signals are very useful in giving an insightful view of what39;s going on inside the brain. However, such readings are still tricky and challenging to interpret because of the inherent non-linearity and dynamic ...
详细信息
In recent years, notable advancements have been made in medical imaging technology, with Magnetic Resonance Imaging (MRI) assuming a pivotal role in the diagnosis of brain tumors. Despite these advancements, medical i...
详细信息
ISBN:
(纸本)9798350372977;9798350372984
In recent years, notable advancements have been made in medical imaging technology, with Magnetic Resonance Imaging (MRI) assuming a pivotal role in the diagnosis of brain tumors. Despite these advancements, medical image segmentation continues to pose a formidable challenge, as highlighted by various factors documented in existing literature. This study delves into the cutting-edge developments in Deep Learning for semantic segmentation, specifically concentrating on the precise identification of brain tumor pixels in 2D images. Employing U-Net and DeepLabV3+ architectures, the research provides experimental evidence that underscores the unparalleled performance of DeepLabV3+ with the Binary Cross Entropy loss function, offering valuable insights for enhancing the accuracy of brain tumor segmentation in medical imaging.
Due to planarians39; remarkable regenerative capabilities, they have been a popular biological model for researching the effects of drugs on tissue regeneration. These unique abilities provide insights into potentia...
详细信息
ISBN:
(纸本)9798350372977;9798350372984
Due to planarians' remarkable regenerative capabilities, they have been a popular biological model for researching the effects of drugs on tissue regeneration. These unique abilities provide insights into potential applications of regenerative medicine. Despite extensive research, machine-learning models have yet to be integrated into this field of study. This paper proposes the first artificial intelligencemachine-learning models used to predict planarian morphology when exposed to different drugs and manipulations, leveraging datasets derived from 1716 experiments documented across 119 publications spanning from 1898 to 2018. Two machine learning models are explored in this paper: a random forest classifier model, and a deep learning neural network multi-output regression model. The initial results indicate that the random forest model achieved over 96% accuracy for predicting lethal, tail, and trunk morphologies, and 64%, 76%, and 80% accuracy for predicting pharynx, head, and eye morphologies. For the neural network regression model, the loss is at 0.18. Additionally, in order to understand drugs' effects on planarian morphology, learning, and memory, experimentation is conducted with different alcohol concentrations. This paper aims to facilitate the analysis of the effects of drugs on planarians using artificial intelligence technologies and to aid future experiments.
作者:
Gumma, Yalamandeswara RaoPeram, SubbaraoVignan Foundation for Science
Technology and Research Department of Computer Science and Engineering Guntur - Tenali Rd Andhra Pradesh Vadlamudi522213 India
Department of Information Technology Guntur -Tenali Rd Andhra Pradesh Vadlamudi522213 India
This research explores the use of deep learning and machine Learning techniques in cybersecurity to detect and mitigate cyber threats. Specifically, this study focuses on Long Short-Term Memory (LSTM) and Graph Neural...
详细信息
Based on the shortcomings of existing metal surface damages identification methods such as multiple limitations, and requiring a lot of manpower and resources, this paper intends to introduce deep learning algorithms ...
详细信息
ISBN:
(纸本)9798350352634;9798350352627
Based on the shortcomings of existing metal surface damages identification methods such as multiple limitations, and requiring a lot of manpower and resources, this paper intends to introduce deep learning algorithms into the field of steel surface damage identification, in order to find an efficient, simple, low-threshold and high-precision identification method for surface damages. After comparing existing convolutional neural network models such as VGG16, Resnet50, InceptionV3, DenseNet121, we optimized Resnet50 from the aspects of batchsize and optimizers. Based on the best settings and drawing on the structure of residual blocks in Resnet50, a new convolutional neural network model "Cbam Resnet" specifically designed for surface damage problem recognition was constructed. Finally, Cbam Resnet achieved an accuracy of 99.43% on the test set.
Invasive species are detrimental to native wildlife and pose a major threat to biodiversity. In California alone, municipalities and conservation authorities are estimated to spend approximately $50.8 million per year...
详细信息
ISBN:
(纸本)9798350372977;9798350372984
Invasive species are detrimental to native wildlife and pose a major threat to biodiversity. In California alone, municipalities and conservation authorities are estimated to spend approximately $50.8 million per year on invasive species. Effective management of bio-invasions depends on reliable identifying the invasive threats and predicting areas at risk of invasion. Yet, there is no efficient solution to address the issue. This paper proposes the first California Invasive Bird Identification and Prediction AI System (CIBIP-AI). The CIBIP-AI system utilizes a deep learning solution to automatically identify and predict two California invasive birds, the mute swan and brown-headed cowbird. The CIBIP-AI system uses a hybrid deep learning architecture based on VGG16 and ResNet50 for identification. The CIBIP-AI system can also predict the geographical areas at risk of invasion based on a set of the birds' physiology features and microclimate conditions. The CIBIP-AI database is created for storing data and continuous training. The initial experimental results show that the classification accuracy for the image input reached 96%. The audio input was more challenging to classify, with 73% accuracy. After applying knowledge-based filtering and common pattern extraction, the accuracy was improved to 77%. The video was split into multiple images and audio and input into the hybrid models. The classification accuracy of the hybrid models reached 97%. The work shows that the proposed CIBIP-AI system provides significant aid to the early detection and prevention of invasive bird species.
Nowadays, with the acceleration of the flow of information over the internet, text content such as articles, blog posts and news are now produced in digital environments. Some of this content is produced by artificial...
详细信息
ISBN:
(纸本)9798350372977;9798350372984
Nowadays, with the acceleration of the flow of information over the internet, text content such as articles, blog posts and news are now produced in digital environments. Some of this content is produced by artificial intelligence rather than traditional authorship. This situation raises important questions about the reliability of text contents as well as the ease of access to information. The aim of this study is to determine whether an academic text was written by the ChatGPT artificial intelligence content creation tool or by a human. We received our data from the Dergipark platform, which contains Turkish academic articles. The data consists of summaries of Turkish articles. The dates of the articles taken from Dergipark are before the launch of Chatgpt (November 2022). In order to make a real distinction, we had to make sure that the data was actually written by a human. Distinguishing information such as the name and keywords of each article was given to Chatgpt 3.5 and the article summary was requested (tagged as ai). Abstracts of articles with the same distinctive information are also labeled human. The accuracy rate obtained in the study using LSTM is 0.8869 for the test set. In this way, it is aimed to make it easier to determine whether a written text was written by a human or an artificial intelligence text creator.
The interaction of humans on images has been of interest in computer vision and patternrecognition. In fact, interaction recognition has become fertile ground for research in virtually all areas of research. Thus, pr...
详细信息
Protein synthesis is closely regulated in nature, and in the field of protein design, scientists have attempted to understand and replicate those processes due to the importance of proteins. Existing protein structure...
详细信息
ISBN:
(纸本)9798350372977;9798350372984
Protein synthesis is closely regulated in nature, and in the field of protein design, scientists have attempted to understand and replicate those processes due to the importance of proteins. Existing protein structure prediction methods include physics-based approaches like molecular dynamics or knowledge-based methods such as energy functions, but these methods are computationally costly and time-consuming. Artificial intelligence (AI) technology has been applied to protein structure prediction due to its ability to address current challenges more effectively. This paper will review the significant progress made recently in protein structure prediction using both classical machine learning and deep learning methods. Advancements in protein structure prediction using computational methods have been made possible due to the increase of available data, and representative examples of six databases used in protein structure prediction will be provided in this review. These public databases have diverse entries such as protein sequences, protein structures, protein interactions, enzyme nomenclature, and proteomes, which will facilitate more research in the protein design field. Despite the advancements with the incorporation of AI, several challenges remain, and the proposed solutions and research trends will be discussed in this paper.
暂无评论