Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre...
Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system.
Diabetic Retinopathy (DR) is an eye condition that impairs the retina's blood vessels, leading to visual loss in diabetics. Microaneurysms are the early sign for DR and it is crucial for ophthalmologists to find m...
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ISBN:
(数字)9798331516284
ISBN:
(纸本)9798331516291
Diabetic Retinopathy (DR) is an eye condition that impairs the retina's blood vessels, leading to visual loss in diabetics. Microaneurysms are the early sign for DR and it is crucial for ophthalmologists to find manually and prevent vision loss. We proposed a hybrid model by concatenating the feature detected by capsule network and vision transformer. Before training the data, SMOTE method is used to balance the data sample and green channel image is taken for accurate detection of DR classes. The hybrid model leverages the strength of capsule network by preserving the spatial information by dynamic routing and vision transformer process the global image through self-attention mechanism. A classification accuracy of 98.1 % was attained when the suggested model was trained and evaluated on a sample of DR data. This proposed model automatically detects the early sign of diabetic retinopathy. The confusion matrix is demonstrated to show how the model's effectiveness is in correctly classifying different stages of DR.
Microring-based optical switches are promising for wavelength-selective switching with the merits of compact size and low power ***,the large insertion loss,the high fabrication,and the temperature sensitivity hinder ...
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Microring-based optical switches are promising for wavelength-selective switching with the merits of compact size and low power ***,the large insertion loss,the high fabrication,and the temperature sensitivity hinder the scalability of silicon microring optical switch *** this paper,we utilize a three-dimensional(3D)microring-based optical switch element(SE)on a multi-layer Si_(3)N_(4)-on-SOI platform to realize highperformance large-scale optical switch *** 3D microring-based SE consists of a Si∕Si_(3)N_(4) waveguide overpass crossing in the bottom and the top layers,and Si_(3)N_(4) dual-coupled microring resonators(MRRs)in the middle *** switch is calibration-free and has low insertion *** the 3D microring-based SEs,we implement an 8×8 crossbar optical switch *** the resonance wavelengths of all SEs are well aligned,only one SE needs to be turned on in each routing path,which greatly reduces the complexity of the switch *** optical transmission spectra show a box-like shape,with a passband width of~69 GHz and an average on-state loss of~0.37 *** chip has a record-low on-chip insertion loss of 0.52-2.66 *** also implement a non-duplicate polarization-diversity optical switch by using the bidirectional transmission characteristics of the crossbar architecture,which is highly favorable for practical applications.100 Gb/s dual-polarization quadrature-phase-shift-keying(DP-QPSK)signal is transmitted through the switch without significant *** the best of our knowledge,this is the first time that 3D MRRs have been used to build highly scalable polarization-diversity optical switch fabrics.
Public events, such as music concerts and fireworks displays, can cause irregular surges in cross-city travel demand, leading to potential overcrowding, travel delays, and public safety concerns. To better anticipate ...
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The COVID-19-patient health tracking system is a risk factormajor public health challenge for COVID-19 patients, and it has inherited an ample supply of mindfuless recently from the medical community because of the ag...
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Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specificat...
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ISBN:
(数字)9798350364828
ISBN:
(纸本)9798350364835
Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric.
Due to the evolving lifestyle choices and the state of the environment, people nowadays suffer from a wide range of ailments. Consequently, early illness prediction becomes a critical obligation. For doctors, however,...
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ISBN:
(数字)9798331540364
ISBN:
(纸本)9798331540371
Due to the evolving lifestyle choices and the state of the environment, people nowadays suffer from a wide range of ailments. Consequently, early illness prediction becomes a critical obligation. For doctors, however, making a meaningful prognosis based only on symptoms becomes too difficult. Predicting illness correctly is the hardest problem. ML is crucial in helping to anticipate illnesses and find treatments in order to solve this issue. Using disease data, MRIs, chest X-rays, and diabetes records, machine learning (ML) assists in diagnosing and treating various conditions. Given the patient's symptoms, we utilize techniques such as SVM, Decision Trees, Random Forest Classifier, Gaussian Bayes and Multinomial Bayes, and Gradient Boost to predict illnesses. Given an X-ray image, we employ CNN models such as VGG for image classification with transfer learning to predict if a person has tuberculosis, pneumonia, or is disease-free. Brain MRIs (Magnetic Resonance Imaging) can be used to predict brain tumors. With the aid of Support Vector Machines, we are able to forecast if the patient has a pituitary tumor, meningioma, glioma, or no tumor at all (SVM). Diabetes is predicted using a number of important variables, including age and body mass index (BMI). Random forest classifiers are what we use to determine if a person has diabetes or not.
Background: Artificial intelligence (AI) applications in dental restorative procedures have significantly developed over recent years. However, there is a lack of documentation regarding the types of AI used in tooth ...
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To gain a more comprehensive understanding of the complex correlation between millets and the management of pediatric obesity, this research employs machine learning techniques. We meticulously assess the impact of a ...
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This paper presents a comprehensive Investigation of Satellite data for Monitoring Air quality through Remote sensing Technology tool (ISMART tool). Specifically, it utilizes data from the Sentinel-5 precursor (Sentin...
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