Recommender systems play an essential role in decision-making in the information age by reducing information overload via retrieving the most relevant information in various applications. They also present great oppor...
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Speech synthesis (text-to-speech, TTS) and automatic speech recognition (ASR) are opposite tasks yet they can be complementary. In our work, we try to improve the TTS by using ASR. ASR plays the role of verifying the ...
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A cooperative intelligent transport system (C-ITS) enables information sharing among ITS subsystems, such as vehicle and roadside infrastructure, with vehicle-to-everything (V2X) communications. Novel C-ITS applicatio...
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Medical image automatic classification has always been a research hotspot, but the existing methods suffer from the label noise problem, which either discards those samples with noisy labels or produces wrong label co...
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Software systems have grown significantly and in *** a result of these qualities,preventing software faults is extremely *** defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenan...
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Software systems have grown significantly and in *** a result of these qualities,preventing software faults is extremely *** defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance *** it comes to lowering software costs and assuring software quality,SDP plays a critical role in software *** a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more *** methods for detecting and addressing such flaws at a low cost have been *** approaches,on the other hand,need to be significantly improved in terms of *** in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are *** proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative *** find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository *** compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.
Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security *** are using face masks on a regular basis to protect themselves and to help reduce corona virus *** the on-...
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Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security *** are using face masks on a regular basis to protect themselves and to help reduce corona virus *** the on-going coronavirus outbreak,one of the major priorities for researchers is to discover effective *** important parts of the face are obscured,face identification and verification becomes exceedingly *** suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,to identify the problem of face masked *** the first stage,we are applying face mask detector to identify the face ***,the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10(CIFAR10),Modified National Institute of Standards and Technology Database(MNIST),Real World Masked Face Recognition Database(RMFRD),and Stimulated Masked Face Recognition Database(SMFRD).The proposed model is achieving recognition accuracy 99.82%with proposed *** article employs the four pre-programmed models VGG16,VGG19,ResNet50 and *** extract the deep features of faces with VGG16 is achieving 99.30%accuracy,VGG19 is achieving 99.54%accuracy,ResNet50 is achieving 78.70%accuracy and ResNet101 is achieving 98.64%accuracy with own *** comparative analysis shows,that our proposed model performs better result in all four previous existing *** fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.
By replacing the exponential decay function in the circular Airy beam (CAB) with a super-Gaussian function, we propose a novel abruptly autofocusing beam, the circular super-Gaussian Airy beam (CSGAB). Similar to CAB,...
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In recent years, face detection has emerged as a prominent research field within computer Vision (CV) and Deep Learning. Detecting faces in images and video sequences remains a challenging task due to various factors ...
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In recent years, face detection has emerged as a prominent research field within computer Vision (CV) and Deep Learning. Detecting faces in images and video sequences remains a challenging task due to various factors such as pose variation, varying illumination, occlusion, and scale differences. Despite the development of numerous face detection algorithms in deep learning, the Viola-Jones algorithm, with its simple yet effective approach, continues to be widely used in real-time camera applications. The conventional Viola-Jones algorithm employs AdaBoost for classifying faces in images and videos. The challenge lies in working with cluttered real-time facial images. AdaBoost needs to search through all possible thresholds for all samples to find the minimum training error when receiving features from Haar-like detectors. Therefore, this exhaustive search consumes significant time to discover the best threshold values and optimize feature selection to build an efficient classifier for face detection. In this paper, we propose enhancing the conventional Viola-Jones algorithm by incorporating Particle Swarm Optimization (PSO) to improve its predictive accuracy, particularly in complex face images. We leverage PSO in two key areas within the Viola-Jones framework. Firstly, PSO is employed to dynamically select optimal threshold values for feature selection, thereby improving computational efficiency. Secondly, we adapt the feature selection process using AdaBoost within the Viola-Jones algorithm, integrating PSO to identify the most discriminative features for constructing a robust classifier. Our approach significantly reduces the feature selection process time and search complexity compared to the traditional algorithm, particularly in challenging environments. We evaluated our proposed method on a comprehensive face detection benchmark dataset, achieving impressive results, including an average true positive rate of 98.73% and a 2.1% higher average prediction accura
In this study, we utilize a recently proposed non-parametric metaheuristic algorithm known as geometric mean optimization (GMO) to adjust the hidden layer input weights and bias of six ANN variants, namely PSNN, SPNN,...
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Ensuring strong security procedures is crucial in the rapidly advancing realm of wireless sensor networks (WSNs) in order to protect sensitive data and preserve network integrity. The resource limitations and unpredic...
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