Compuational Intelligence

Objectives: To propose a model which could classify in real-time if an individual is wearing a face mask or not wearing a face mask. A lightweight system that could be easily deployed and assist in surveillance. Methods/Statistical analysis: Analysis of the proposed model shows a limited number of research studies with regards to facial localizations. Several state-of-the-art methods were taken into considerations out of which the CNN architectural approach is analyzed in this study. Taking into consideration the use-case of deployments and structuring, a new Keras-based model is proposed that surpasses the achievement results of MobileNet-V2 and VGG-16 standard architectures. Effective facial localization is tackled with the MTCNN approach. Findings: The system has achieved a confidence score of 0.9914, an average weighted F1-score of 0.98, a precision value of 0.99. The proposed model has been compared with standard architectures of VGG16 and MobileNetV2 with regard to the accuracy, support values, precision, recall, and F1-score metrics. The proposed model performs better w.r.t traditional architectures. The average latency involved in prediction is 0.034 seconds making the average FPS 30 Frames per second. The compact architecture makes the model best for deployment in real-time scenarios. The system incorporates the concept of image localization with Multi-Task Cascaded Convolutional Neural Network (MTCNN) architecture. The analysis shows MTCNN is performing much better than Haar-Cascade in real-time facial prediction scenarios. Novelty/Applications: This compact architecture with minimal layers is easily deployable in edge devices. It can be used for mass screening at public places like railway stops, bus stops, streets, malls, entrances, schools, and many service-oriented business verticals requiring users to access the services as long as the mask has been worn correctly.

Objectives: To propose a model which could classify in real-time if an individual is wearing a face mask or not wearing a face mask. A lightweight system that could be easily deployed and assist in surveillance. Methods/Statistical analysis: Analysis of the proposed model shows a limited number of research studies with regards to facial localizations. Several state-of-the-art methods were taken into considerations out of which the CNN architectural approach is analyzed in this study. Taking into consideration the use-case of deployments and structuring, a new Keras-based model is proposed that surpasses the achievement results of MobileNet-V2 and VGG-16 standard architectures. Effective facial localization is tackled with the MTCNN approach. Findings: The system has achieved a confidence score of 0.9914, an average weighted F1-score of 0.98, a precision value of 0.99. The proposed model has been compared with standard architectures of VGG16 and MobileNetV2 with regard to the accuracy, support values, precision, recall, and F1-score metrics. The proposed model performs better w.r.t traditional architectures. The average latency involved in prediction is 0.034 seconds making the average FPS 30 Frames per second. The compact architecture makes the model best for deployment in real-time scenarios. The system incorporates the concept of image localization with Multi-Task Cascaded Convolutional Neural Network (MTCNN) architecture. The analysis shows MTCNN is performing much better than Haar-Cascade in real-time facial prediction scenarios. Novelty/Applications: This compact architecture with minimal layers is easily deployable in edge devices. It can be used for mass screening at public places like railway stops, bus stops, streets, malls, entrances, schools, and many service-oriented business verticals requiring users to access the services as long as the mask has been worn correctly.

Objectives: To propose a model which could classify in real-time if an individual is wearing a face mask or not wearing a face mask. A lightweight system that could be easily deployed and assist in surveillance. Methods/Statistical analysis: Analysis of the proposed model shows a limited number of research studies with regards to facial localizations. Several state-of-the-art methods were taken into considerations out of which the CNN architectural approach is analyzed in this study. Taking into consideration the use-case of deployments and structuring, a new Keras-based model is proposed that surpasses the achievement results of MobileNet-V2 and VGG-16 standard architectures. Effective facial localization is tackled with the MTCNN approach. Findings: The system has achieved a confidence score of 0.9914, an average weighted F1-score of 0.98, a precision value of 0.99. The proposed model has been compared with standard architectures of VGG16 and MobileNetV2 with regard to the accuracy, support values, precision, recall, and F1-score metrics. The proposed model performs better w.r.t traditional architectures. The average latency involved in prediction is 0.034 seconds making the average FPS 30 Frames per second. The compact architecture makes the model best for deployment in real-time scenarios. The system incorporates the concept of image localization with Multi-Task Cascaded Convolutional Neural Network (MTCNN) architecture. The analysis shows MTCNN is performing much better than Haar-Cascade in real-time facial prediction scenarios. Novelty/Applications: This compact architecture with minimal layers is easily deployable in edge devices. It can be used for mass screening at public places like railway stops, bus stops, streets, malls, entrances, schools, and many service-oriented business verticals requiring users to access the services as long as the mask has been worn correctly.

Since the discovery of COVID-19, the wearing of a face mask has been recognized as an effective means of curbing the spread of most infectious respiratory diseases. A face mask must completely enclose the lips and nose properly for effective prevention of the disease. Some people still refuse to wear the mask, either out of annoyance or difficulty, or they are just wearing it incorrectly, which diminishes the mask's effectiveness and renders it worthless. The deep learning models described in this research provide a mechanism for assessing whether a face mask is being worn correctly or incorrectly using images. For both training and testing, the suggested method makes use of MaskedFace-Net dataset that contains annotated photos of an individual's face with proper and improper masks. Threshold optimizations are applied to produce significant results of prediction when comparing ResNet50, MobileNetV2 and DenseNet121 models. It is observed that better performance can be achieved with having accuracy as the target evaluation metric and reaching accuracy levels of 97.6%, 99.0%, and 99.8% for ResNet50, DenseNet121, and MobileNetV2, respectively after threshold optimization. As an outcome, DenseNet121 outperformed the other evaluated models when accuracy, recall, and precision metrics were used to assess the testing set. The face mask categorization can be used to automatically monitor face masks in real-time in public locations like hospitals, airports, shopping complexes and congested spaces to verify compliance with the published guidelines by the higher authorities in a country, making the results valuable for future use.

Medical instruments detection in laparoscopic video has been carried out to increase the autonomy of surgical robots, evaluate skills or index recordings. However, it has not been extended to surgical gauzes. Gauzes can provide valuable information to numerous tasks in the operating room, but the lack of an annotated dataset has hampered its research. In this article, we present a segmentation dataset with 4003 hand-labelled frames from laparoscopic video. To prove the dataset potential, we analyzed several baselines: detection using YOLOv3, coarse segmentation, and segmentation with a U-Net. Our results show that YOLOv3 can be executed in real time but provides a modest recall. Coarse segmentation presents satisfactory results but lacks inference speed. Finally, the U-Net baseline achieves a good speed-quality compromise running above 30 FPS while obtaining an IoU of 0.85. The accuracy reached by U-Net and its execution speed demonstrate that precise and real-time gauze segmentatio...

Since 2019, there has been a new virus that has changed the world order. This outbreak was named the Covid-19 pandemic and was caused by a virus called the corona virus or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus has been detected in Indonesia since early March 2020, and until now it has not been well resolved, even though a vaccine has been found (Kemenkes RI, 2020). In fact, recently the Omicron variant caused by SARS-CoV-2 B.1.1.529 was reported to have begun to be detected in Indonesia after positive cases of the corona virus began to subside (Ministry of Health of the Republic of Indonesia, 2021). Wearing a mask is one of the most effective ways to prevent the spread of the corona virus. Based on data from the Covid-19 Handling Task Force (2021) on the behavior of wearing masks in 2021, there are 76-90% of Indonesians who comply. The percentage of people who comply with wearing masks seen from the data is quite high, unfortunately the data was taken only by monitoring about 4 million Indonesians. In fact, the number of Indonesian people far exceeds that number. In addition, monitoring that cannot be carried out at any time makes this effort less effective. The above problems encourage the creation of innovative products that can help provide effective solutions, namely the Face Recognition application which can detect whether a person is wearing a mask or not in certain places. This application is one of the Deep Learning methods , namely Convolutional Neural Network (CNN) and Transfer Learning. The results of this study are CNN's modeling to classify mask users and the interface design of the application.

Emotion is an instinctive or intuitive feeling as distinguished from reasoning or knowledge. It varies over time, since it is a natural instinctive state of mind deriving from one's circumstances, mood, or relationships with others. Since emotions vary over time, it is important to understand and analyze them appropriately. Existing works have mostly focused well on recognizing basic emotions from human faces. However, the emotion recognition from cartoon images has not been extensively covered. Therefore, in this paper, we present an integrated Deep Neural Network (DNN) approach that deals with recognizing emotions from cartoon images. Since state-of-works do not have large amount of data, we collected a dataset of size 8 K from two cartoon characters: 'Tom' & 'Jerry' with four different emotions, namely happy, sad, angry, and surprise. The proposed integrated DNN approach, trained on a large dataset consisting of animations for both the characters (Tom and Jerry), correctly identifies the character, segments their face masks, and recognizes the consequent emotions with an accuracy score of 0.96. The approach utilizes Mask R-CNN for character detection and state-of-the-art deep learning models, namely ResNet-50, MobileNetV2, InceptionV3, and VGG 16 for emotion classification. In our study, to classify emotions, VGG 16 outperforms others with an accuracy of 96% and F1 score of 0.85. The proposed integrated DNN outperforms the state-of-the-art approaches.

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether...

The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks. This paper implements four pre-trained deep transfer learning models (NasNetMobile, MobileNetv2, ResNet101v2, and ResNet152v2) to classify images based on the type of face mask (KN95, N95, surgical and cloth) worn by people. Experimental results indicate that the deep residual networks (ResNet101v2 and ResNet152v2) provide the best performance with the highest accuracy and the lowest loss.

The Turing test is considered a test for human-like intelligence in a computer. Some debate exists as to whether the test it too strong, too weak, or too narrow (it tests only conversational behavior). But that it tests conversational capacity is not disputed. I argue that this view is mistaken. As performed, the test is incapable of indicating any type of machine intelligence, no matter what responses the computer contestant delivers to the judge. This failure reveals that computation provides a flawed understanding of the computer, and indicates where a more useful conception of the machine might be found.

The focus of this study is how we can efficiently implement the neural network backpropagation algorithm on a network of computers (NOC) for concurrent execution. We assume a distributed system with heterogeneous computers and that the neural network is replicated on each computer. We propose an architecture model with efficient pattern allocation that takes into account the speed of processors and overlaps the communication with computation. The training pattern set is distributed among the heterogeneous processors with the mapping being fixed during the learning process. We provide a heuristic pattern allocation algorithm minimizing the execution time of backpropagation learning. The computations are overlapped with communications. Under the condition that each processor has to perform a task directly proportional to its speed, this allocation algorithm has polynomial-time complexity. We have implemented our model on a dedicated network of heterogeneous computers using Sejnowski's NetTalk benchmark for testing.

The focus of this study is how we can efficiently implement the neural network backpropagation algorithm on a network of computers (NOC) for concurrent execution. We assume a distributed system with heterogeneous computers and that the neural network is replicated on each computer. We propose an architecture model with efficient pattern allocation that takes into account the speed of processors and overlaps the communication with computation. The training pattern set is distributed among the heterogeneous processors with the mapping being fixed during the learning process. We provide a heuristic pattern allocation algorithm minimizing the execution time of backpropagation learning. The computations are overlapped with communications. Under the condition that each processor has to perform a task directly proportional to its speed, this allocation algorithm has polynomial-time complexity. We have implemented our model on a dedicated network of heterogeneous computers using Sejnowski's NetTalk benchmark for testing.

with the advent of computers, new technologies could finally rise up. Maybe one of the most polemic and prominent ones are the methods from artificial intelligence such as neural networks or even fuzzy logic. In this short paper we discuss a set of insights generated by the authors after studying the two-communities currently predominant in applied mathematical modeling. We conclude that applied mathematical models, here focused on biological systems, might beneath considerably by recognizing middle-way models between classical (traditional) mathematics and knowledge-based models.

with the advent of computers, new technologies could finally rise up. Maybe one of the most polemic and prominent ones are the methods from artificial intelligence such as neural networks or even fuzzy logic. In this short paper we discuss a set of insights generated by the authors after studying the two-communities currently predominant in applied mathematical modeling. We conclude that applied mathematical models, here focused on biological systems, might beneath considerably by recognizing middle-way models between classical (traditional) mathematics and knowledge-based models.

Since its "foundation," computational intelligence has been creating novel-high perspectives on modeling, on the other hand, the "old-fashioned" mathematics offers powerful methods that should not be forgotten or underestimated. On this paper, it is discussed a novel methodology proposed by the authors on the mathematical modeling, with emphasis in biomedical and biological modeling. The methodology comprises of an attempt to use simultaneously mathematics and computational intelligence in a single picture, using the concept of blindness of a model. The methodology proposed can be summarized into the name of "the middle-way-out principle." Besides the theory is presented and discussed, no real problem is treated, it is left to future works, due to the choice of the authors and current incipient state of the theory. The paper is concluded with some conclusions and final remarks, the main references used all over the work are presented in the end.

In this paper we investigate the problem of designing load balancing protocols in distributed systems involving self-interested participants. These participants have their own requirements and objectives and no a-priori motivation for cooperation. Their selfish behavior may lead to poor performance and inefficiency. To address this problem we design a load balancing mechanism with verification that provides incentives to participants to report their true parameters and follow the given algorithm. We prove that our load balancing mechanism is truthful (i.e., agents will be better off by reporting their true parameters) and satisfies the voluntary participation condition (i.e., truthful agents never incur a loss). We present a simulation study to show the performance of our load balancing mechanism.

The focus of this study is how we can efficiently implement the neural network backpropagation algorithm on a network of computers (NOC) for concurrent execution. We assume a distributed system with heterogeneous computers and that the neural network is replicated on each computer. We propose an architecture model with efficient pattern allocation that takes into account the speed of processors and overlaps the communication with computation. The training pattern set is distributed among the heterogeneous processors with the mapping being fixed during the learning process. We provide a heuristic pattern allocation algorithm minimizing the execution time of backpropagation learning. The computations are overlapped with communications. Under the condition that each processor has to perform a task directly proportional to its speed, this allocation algorithm has polynomial‐time complexity. We have implemented our model on a dedicated network of heterogeneous computers using Sejnowski's NetTalk benchmark for testing.

We present a new technique for mapping the backpropagation algorithm on hypercube and related architectures. A key component of this technique is a network partitioning scheme called checkerboarding. Checkerboarding allows us to replace the all-to-all broadcast operation performed by the commonly used vertical network partitioning scheme, with operations that are much faster on the hypercubes and related architectures. Checkerboarding can be combined with the pattern partitioning technique to form a hybrid scheme that performs better than either one of these schemes. Theoretical analysis and experimental results on nCUBE and CM5 show that our scheme performs better than the other schemes, for both uniform and nonuniform networks