Cervical cancer poses a significant danger to the lives of women globally and with the changing lifestyle choices leading to a rise in the number of detected cases, early detection and treatment are imperative for com...
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ISBN:
(数字)9798350367720
ISBN:
(纸本)9798350367737
Cervical cancer poses a significant danger to the lives of women globally and with the changing lifestyle choices leading to a rise in the number of detected cases, early detection and treatment are imperative for combating this disease, particularly in developing countries. Cervical cancer often presents as squamous cell carcinoma or adenocarcinoma. The development of more accurate diagnostic and therapeutic models to address this challenge has been greatly helped by the additional input of machinelearning. Literature suggests that the use of magnetic resonance imaging (MRI) in conjunction with convolutional neural networks (CNN) enhances the efficiency of cervical cancer detection and prevention. These approaches enable the identification of cancerous cells, with specialized models such as CNN. However, the computational demands of imageprocessing and preprocessing pose significant challenges. In response to these challenges, our research proposes an efficient deep-learning model tailored for cervical cancer detection in high-resolution cervical images. This model incorporates three distinct feature selection modules—low-variance feature extraction, - dimensional feature selection, and recursive feature elimination— while integrating multiple views of cervical images. We evaluated various classification models, including ResNet, DenseNet, and VGG, for cervical cancer diagnosis. Simulation results are considered to validate the proposed model's accuracy. Using AI and ML techniques, our model aims to contribute to advancing cervical cancer diagnosis and treatment, ultimately improving the efficiency in treating cervical cancer.
The conflict between computational overhead and detection accuracy affects nearly every Automated Accident Detection (AAD) system. Although the accuracy of detection and classification approaches has recently improved...
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In latest generations, there has been a significant increase in study interest in the growing applications of artificial intelligence in health and medicine. The purpose of this study is to present a worldwide and chr...
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ISBN:
(数字)9798350355338
ISBN:
(纸本)9798350355345
In latest generations, there has been a significant increase in study interest in the growing applications of artificial intelligence in health and medicine. The purpose of this study is to present a worldwide and chronological overview of AI study in the areas of medical care and health. The online science tool was used to obtain a total number of articles that were released between. The detailed study looked at the number of publications, as well as the cooperation between writers and nations. Generally, indicate that a factor, along with robotics, algorithms, neural networks, artificial cognitive computing, and natural language processing, were identified through a vast network of researchers' key words and phrases and message review of pertinent academic papers. These methods are regularly used in clinical forecasting and rehabilitation. The most articles were on cancer, followed by those on cardiovascular disease, stroke, blindness, Early onset dementia, and sadness. Additionally, the lack of study on applying AI to some illnesses with a high disease prevalence indicates potential paths for Ai development. The study proposes the creation of international and national guidelines and laws on the rationale and application of pertaining to medical products and provides a first and complete image of the global efforts made in this significant and lucrative study area.
The COVID-19 pandemic has significantly impacted the healthcare systems, other societal systems, and the global economy. The COVID19 virus of the twenty-first century has claimed millions of lives globally in less tha...
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The paper presents a summary of the 1st Competition on Script Identification in the Wild (SIW 2021) organised in conjunction with 16th internationalconference on Document Analysis and recognition (ICDAR 2021). The go...
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ISBN:
(纸本)9783030863371;9783030863364
The paper presents a summary of the 1st Competition on Script Identification in the Wild (SIW 2021) organised in conjunction with 16th internationalconference on Document Analysis and recognition (ICDAR 2021). The goal of SIW is to evaluate the limits of script identification approaches through a large scale in the wild database including 13 scripts (MDIW-13 dataset) and two different scenarios (handwritten and printed). The competition includes the evaluation over three different tasks depending of the nature of the data used for training and testing. Nineteen research groups registered for SIW 2021, out of which 6 teams from both academia and industry took part in the final round and submitted a total of 166 algorithms for scoring. Submissions included a wide variety of deep-learning solutions as well as approaches based on standard imageprocessing techniques. The performance achieved by the participants prove the elevate accuracy of deep learning methods in comparison with traditional statistical approaches. The best approach obtained classification accuracies of 99% in all three tasks with experiments over more than 50K test samples. The results suggest that there is still room for improvements, specially over handwritten samples and specific scripts.
Autonomous Vehicles (AVs) are among the leading technologies enhancing our world with their advanced autonomous driving capabilities and functional awareness. These AVs rely on sophisticated technologies such as weigh...
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ISBN:
(数字)9798331542108
ISBN:
(纸本)9798331542115
Autonomous Vehicles (AVs) are among the leading technologies enhancing our world with their advanced autonomous driving capabilities and functional awareness. These AVs rely on sophisticated technologies such as weight sensors, speed sensors, tire pressure sensors, 3D imaging cameras, satellite-based positioning systems, and advanced radars like light detection and ranging (LiDAR) and 3D radar. AVs utilize these components, along with highly complex control algorithms as their central processing units, to perform autonomous driving, making them vulnerable to cybersecurity attacks. These security attacks raise significant safety concerns for AVs. This article provides a comprehensive review of various cybersecurity threats associated with AVs and presents potential solutions. Additionally, it examines the vulnerabilities of the sophisticated equipment used in AVs. This article aims to enhance the understanding of the safety and security concerns associated with AVs and offers insights into the importance of cybersecurity in this evolving technological field. Furthermore, this article addresses the current state of cybersecurity measures in AVs, evaluating the effectiveness of existing protocols and identifying areas that require improvement. It also explores emerging technologies and strategies that can enhance the security of AVs, such as blockchain for secure data transactions, machinelearning for threat detection, and robust encryption methods.
With the continuous development of artificial information technology, visible light positioning (VLP) based on deep learning has emerged as a hotspot for research on indoor localization technology. To improve the accu...
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The proceedings contain 56 papers. The topics discussed include: exploring the gradient for video quality assessment;an efficient approach for using expectation maximization algorithm in capsule networks;image waterma...
ISBN:
(纸本)9781728168326
The proceedings contain 56 papers. The topics discussed include: exploring the gradient for video quality assessment;an efficient approach for using expectation maximization algorithm in capsule networks;image watermarking by Q learning and matrix factorization;attention-based face antispoofing of RGB camera using a minimal end-2-end neural network;DeepFaceAR: deep face recognition and displaying personal information via augmented reality;a classified and comparative study of 2-D convolvers;class attention map distillation for efficient semantic segmentation;and monitoring wrist and fingers range of motion using leap motion camera for physical rehabilitation.
The Plant diseases are the primary cause of decreased agricultural farming productivity. Most farmers struggle to recognize and control plant diseases. Early forecasting of these diseases will therefore help farmers a...
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ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
The Plant diseases are the primary cause of decreased agricultural farming productivity. Most farmers struggle to recognize and control plant diseases. Early forecasting of these diseases will therefore help farmers avoid further losses. Deep learning techniques are widely used in tasks involving recognition and classification these days. Even though, many researchers use DL models for plant disease detection, they still have issues like the necessity for substantial data sets, low accuracy, the significant processing overhead, and the risk of overfitting. In order to solve such issues, a unique DL model is introduced. Here, an IoT-based smart farming method is developed using deep Bidirectional LstM and artificial coyote optimization for analyzing and detecting plant disease using the IoT-collected data and soil information. Here, the input will be gathered from Plant Village and Soil Fertility Prediction datasets. IoT sensors will be used on farms during the data collecting phase to gather information about plant diseases, which will then be saved in the cloud. The control center will send a request message to obtain data from the cloud when the experts need the sensor data on the fields. Following the acquisition of data, pre-processing will be used to extract features from the pre-processed image. Following that, Deep BiLstM gets the extracted features and refines them via Artificial Coyote optimization. In order to enhance the deep BILstM model’s ability to accurately detect plant diseases, artificial coyote optimization was created by combining the qualities of artificial rabbit optimization and coyote optimization. The study is conducted using PYTHON, and performance is assessed in terms of accuracy, sensitivity, and specificity to demonstrate the efficacy of the method in identifying plant diseases.
In the last few decades, the constant growth of digital images, as the main source of information representation for scientific applications, has made image classification a challenging task. To achieve high classific...
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