determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAd modules, including MrI, PET, and CT, struggle with feature extraction and segmentation...
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ISBN:
(数字)9798350384369
ISBN:
(纸本)9798350384376
determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAd modules, including MrI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene deep Learning Framework (MSdLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSdLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives.
This research investigates the effectiveness of employing a machine learning methodology and an Explainable Artificial Intelligence (XAI) tool for the assessment of Meibomian Glanddysfunction (MGd). Utilizing resNet3...
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ISBN:
(数字)9798350360240
ISBN:
(纸本)9798350384161
This research investigates the effectiveness of employing a machine learning methodology and an Explainable Artificial Intelligence (XAI) tool for the assessment of Meibomian Glanddysfunction (MGd). Utilizing resNet34, a convolutional neural network (CNN), trained on a diverse dataset comprising images depicting varying degrees of MGd severity, the machine learning approach endeavors to classify MGd severity into fourdistinct grades (0, 1, 2, 3) by leveraging extracted characteristic features. Findings reveal comparable accuracy between resNet34 and the XAI tool, with resNet34 achieving a slightly lower accuracy of 99.1% in contrast to the XAI tool's 99.4%. However, resNet34 demonstrates marginally higher precision and F1-score at 98.2% and 98.45%, respectively, suggesting a nuanced advantage in precision-recall equilibrium. Conversely, the XAI tool maintains a commendable precision of 97.2% and an Fl-score of 97.94%, alongside a recall of 98.7%, underscoring its effectiveness in discerning positive instances. Noteworthy is that resNet34 operates as a black-box model, providing limited interpretability, while the XAI tool emphasizes transparency by furnishing clinicians with understandable MGd grading elucidations derived from meibomian gland segmentation and atrophy analysis. This transparency enhances trust and comprehension, which are integral for clinical acceptance anddecision-making. Furthermore, the utilization of XAI heatmap generation facilitates data quality enhancement, as evidenced by instances of erroneous artifact identification, emphasizing the imperative nature of artifact elimination for the refinement of MGd grading procedures.
Cryptographic protocols are used to relax the ever-developing quantity of linked gadgets that make up the net of things (IoT). Those cryptographic protocols have been designed to make certain that IoT tool traffic sta...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Cryptographic protocols are used to relax the ever-developing quantity of linked gadgets that make up the net of things (IoT). Those cryptographic protocols have been designed to make certain that IoT tool traffic stays cozy and personal, even while nevertheless allowing tool-to-device and cloud-to-tool communications. Examples of these protocols consist of TLS/SSL, PGP/GPG, IPsec, SSL VPN, and AES encryption. Every one of these protocols enables authentication, message integrity, and confidentiality via encryption and key trade. Moreover, a lot of these protocols are carried out in the form of diverse hardware and software answers, such as smart playing cards and gateways, to make certain that IoT traffic is secured. With the appropriate implementation of those cryptographic protocols, establishments can ensure that their IoT facts are blanketed and securely transmitted.
In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter pro...
In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links forredirecting to phishing websites. researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitterdatasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics.
The novel miniaturized Wi-Fi logo slotted microstrip antenna application for 28 GHz is presented in this paper. The operating frequency is 28GHz-Ka-band application. The antenna is modeled by using substrate material ...
The novel miniaturized Wi-Fi logo slotted microstrip antenna application for 28 GHz is presented in this paper. The operating frequency is 28GHz-Ka-band application. The antenna is modeled by using substrate material of rogers rT/duroid 5880 with the relative permittivity value ԑ r =2.2. The proposed antenna design has return loss parameters S 11 <-10dB. The proposeddesign works in the frequency range: 28GHz. The radiation pattern is unidirectional and good gain is obtained. Slot configuration enhances the bandwidth of the antenna. It has a multibandresponse. The proposeddesign is simulated using HFSS (High-Frequency Simulation Software) simulator. It is a compact antenna with a size of 7x7mm a thickness of 0.8mm, a wide operational band, and good gain. The performance of the antennas in terms of return loss, bandwidth, efficiency, gain, anddirectivity are analyzed.
Lung disease remains a significant public health concern in India, with a rising prevalence attributed to various environmental and lifestyle factors. recent statistics reveal that chronic obstructive pulmonary diseas...
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ISBN:
(数字)9798331516284
ISBN:
(纸本)9798331516291
Lung disease remains a significant public health concern in India, with a rising prevalence attributed to various environmental and lifestyle factors. recent statistics reveal that chronic obstructive pulmonary disease (COPd) affects approximately 57.8 million people in India, while pulmonary fibrosis accounts for a substantial portion of respiratory-related morbidity and mortality nationwide. despite the growing prevalence, accurate lung function diagnosis and monitoring remain challenging, particularly in resource-constrained settings. This study presents a novel approach utilizing hierarchical Bayesian modelling in conjunction with low-cost IoT-based spirometry technology for predicting forced vital capacity (FVC) in pulmonary fibrosis patients. Leveraging real-time data from the Indian population,[7] [8] where respiratory diseases are prevalent, ourresearch underscores the critical need forreliable and accessible spirometry measurements for effective disease management. We begin by elucidating the epidemiological landscape of lung disease in India, highlighting the alarming rates of COPd and pulmonary fibrosis. recent studies suggest that when integrated into clinical practice, spirometry data can significantly improve diagnostic accuracy and treatment outcomes forrespiratory conditions. Leveraging data from the Indian population, where respiratory diseases are prevalent, ourresearch underscores the critical need forreliable and accessible spirometry measurements for effective disease management. Our proposed methodology integrates the YFS201 flow sensor with an ESP8266 microcontroller, providing a cost-effective solution forreal-time measurement of airflow. Through hierarchical Bayesian modeling, we demonstrate the capability of our approach to accurately predict FVC in pulmonary fibrosis patients, thereby facilitating early detection and personalized treatment strategies. By incorporating spirometry data into clinical practice, ourresearch aims to empow
Oral Cancer of the mouth kills millions of people, Oral cancer is better treated and more often survived when caught early. Convolutional neural networks (CNNs) have recently shown remarkable promise for cancerdiagno...
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ISBN:
(数字)9798350384598
ISBN:
(纸本)9798350384604
Oral Cancer of the mouth kills millions of people, Oral cancer is better treated and more often survived when caught early. Convolutional neural networks (CNNs) have recently shown remarkable promise for cancerdiagnosis and other medical image processing applications. In this study, we train deep convolutional neural networks (CNNs) to detect oral cancer early. The suggested approach makes use of a large database of pictures of the mouth and its cavities. develop andrefine a convolutional neural network (CNN) model using the use of radiographs, histology slides, and intraoral images. Oral carcinogenic tumours, both precancerous and malignant, may be identified and classified automatically using this approach. The process begins with picture pre-processing, continues with dataset supplementation for better model generalizability, and culminates in the use of complex neural networks for feature extraction and learning. Transfer learning methods modify previously taught models for this medical imaging task. The system’s performance, sensitivity, and accuracy are evaluated using a set of images that have pre-existing diagnoses. deep learning CNNs were able to identify oral cancer with an efficiency and accuracy of 93.62%, according to the research. The trained model is a valuable tool for early cancer screening because of its high sensitivity and specificity. By automating the process, oral cancer may be detected and treated more quickly, which improves patient outcomes anddecreases the disease burden.
Countries and authors in the academic periphery occasionally have been criticized for contributing to the expansion of questionable publishing because they share a major fraction of papers in questionable journals. On...
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This study examines the constraints of current methodologies, specifically in diverse acoustic environments, to surmount the obstacles associated with speech recognition for individuals with hearing impairments. Conve...
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ISBN:
(数字)9798350372816
ISBN:
(纸本)9798350372823
This study examines the constraints of current methodologies, specifically in diverse acoustic environments, to surmount the obstacles associated with speech recognition for individuals with hearing impairments. Conventional processes frequently experience reduced accuracy and utility as a result of their incapability to dynamically adjust to evolving circumstances. This study presents an innovative approach that surpasses these constraints through the utilization of a transformer-based model. Through the implementation of sophisticateddata augmentation techniques, the proposed model demonstrates exceptional performance across various acoustic environments, notwithstanding obstacles such as speaker adjustments and background noise. One notable characteristic of this methodology is its transformer architecture, which aptly captures subtle speech inflections by employing self-attention processes. Additionally, through independent learning, the model improves its ability to address the unique challenges associated with hearing-impaired speech. Integrating these components, the model achieves superior accuracy and adaptability compared to current technologies.
Weeds compete for natural resources both in forest areas, harming the development of native vegetation, and in agricultural areas, affecting crop quality. The need then arises to classify these species, so that mechan...
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