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4th International Conference on Machine Learning and Machine Intelligence, MLMI 2021

作者： Ayman Mounir, Shehab Eldeen Hussein, Walid H. Karam, Omar Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Egypt Computer Science Faculty of Informatics and Computer Science The British University in Egypt Egypt Information Systems Faculty of Informatics and Computer Science The British University in Egypt Egypt

ISBN: (纸本)9781450384247

Automating the monitoring of the roads would mean safer roads for both car drivers and pedestrians. The objectives of the system were to build a real time surveillance system for intelligent roads of the future. The system should be able to detect lane lines, cars, pedestrians and other moving vehicles on the road. In case a car goes off its course, the system should report it as an accident. Relying on machine learning technology, a real time detection based neural network was constructed to be trained on picking up objects in any given scene like cars, buses, pedestrians, bikes and motor bikes. Also, an algorithm was designed to detect lane lines and highlight them on the screen using image processing techniques and edge detection. The system achieved its main required target which is being able to detect objects in almost real time. © 2021 ACM.

关键词： Accidents

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Automatic detection of Covid-19 based on lung CT images using Deep Convolutional Neural Networks (CNN) 1

Automatic detection of Covid-19 based on lung CT images usin...

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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Mahachi, Shawn Zvarevashe, Kudakwashe Nyandoro, Leslie Kudzai University of Zimbabwe Faculty of Computer Engineering Informatics and Communications Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

In recent years, the COVID-19 pandemic has spread all over the world. Due to its rapid transmission, techniques that automatically detect COVID-19 infections and distinguish it from other forms of pneumonia are crucial. The scientific community has embarked on finding solutions to quick detection of COVID-19 through implementation of deep learning(DL) techniques that can diagnose COVID-19 using computed tomography (CT) lung scans. The use of CT images has been widely accepted in medical imaging and it is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. Also, most developed DL models developed have been end-to-end from feature extraction to categorization of the COVID19 infected images. The proposed model results showed high accuracy rates on both training and testing of the model in COVID-19 classification. A customised ResNet-50 architecture has the best results in classifying the images and achieved state of art accuracy of 97% on training and testing using the COVID dataset with 200 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of normal and infected individuals. The model can help in effective early screening of COVID-19 cases hence reducing the burden on healthcare systems. © 2022 IEEE.

关键词： COVID-19

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Evaluation of Indoor Air Condition of University Building in Tropics using Building Thermal Modeling 2

Evaluation of Indoor Air Condition of University Building in...

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2nd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2021

作者： Andarini, Rahmi Widjaja, Moeljono Halim, Dareen Kusuma Universitas Multimedia Nusantara Faculty of Engineering and Informatics Deparment of Engineering Physics Tangerang Indonesia Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Informatics Tangerang Indonesia Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Computer Engineering Tangerang Indonesia

ISBN: (纸本)9781665449922

The Heating, Ventilation and Air Conditioning (HVAC) System is a significant energy user of a building. The main factor that affects the energy consumption of the HVAC system is indoor air temperature, which also will affect the relative humidity. Therefore, monitoring indoor air temperature and relative humidity is not only useful for controlling the energy consumption, but also important to maintain the comfort condition of the occupant. This research aims to model a thermal building condition in classrooms and then validate the model by the data from a monitoring system. Firstly, thermal building simulation was developed in order to find the profile of indoor air temperature and relative humidity of a classroom. Afterward, the results of thermal building modeling were compared to the reading of the monitoring system. It is found that the results of thermal building simulation and the reading of the monitoring system show a similar trend. © 2021 IEEE.

关键词： Energy utilization

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Insurance Fraud Detection using Machine Learning 1

Insurance Fraud Detection using Machine Learning

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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Tongesai, MacHinya Mbizo, Godfrey Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Many insurance companies today deal with the issue of fraudulent insurance claims, which results in significant yearly financial loss. Since the losses are covered by raising policyholders' premium costs, these frauds have a negative impact on society. The traditional claim investigation procedure has also been blamed for producing unreliable conclusions because it is time-consuming and laborious. Therefore, using machine learning and the XGBoost method, we construct an automated fraud detection application framework in this study. Accurately identifying fraud claims in a shorter amount of time is the goal. Data analysis is utilized throughout the process to validate, sanitize, and extract the pertinent data. As a result, the insurance firm can retain its reputation outside by employing this structure and has a reliable relationship with clients that they can share. © 2022 IEEE.

关键词： Insurance

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Driver drowsiness detection using Convolutional Neural Networks-inspired features and Principal component analysis with K-Nearest Neighbors 1

Driver drowsiness detection using Convolutional Neural Netwo...

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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Panganai, Marvelous Alexander Nyandoro, Leslie Kudzai Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Drowsiness and fatigue are the major reasons for triggering serious and severe road crashes in Zimbabwe and the whole world at large. The developments in technology in recent years brought support and backing to drivers using intelligent automobile systems. Several research studies used datasets of a single driver for training and testing. Some as well used mainly day time images for training and testing the models. Therefore, fatigue and drowsiness is a key possible field of study to prevent numerous number of sleep induced road crashes. In this paper, two methods of feature extraction were proposed which are the MLP (Multilayer - Perceptron), the CNN (Convolutional Neural Network). The PCA (Principal Component Analysis) method was used for dimensionality reduction. Based on these methods, five classifiers where used to detect drowsiness on the driver. The five classifiers used where the LDA, XGBoost, LR, Decision Tree and the K-Nearest neighbors. Experiments were done in order to examine the capacity and usefulness of the approaches contrasted with other techniques. Experimental outcomes demonstrate that the feature extraction technique of CNN provided high accuracy on the five classifiers. The KNN was the average best classifier with a 100% accuracy. Experimental results indicated that the PCA improved the classifiers. This study delivers important and significant answers in practice to curb motor vehicle crashes due to drowsiness. © 2022 IEEE.

关键词： Feature extraction

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders 25

Multi channel brain EEG signals based emotional arousal clas...

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25th Signal Processing and Communications Applications Conference, SIU 2017

作者： Ayata, Deger Yaslan, Yusuf Kamasak, Mustafa Faculty of Computer and Informatics Computer Engineering Department Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781509064946

The importance of learning important features in an automatic manner is growing exponentially as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEG signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99% accuracy rate with unsupervised feature learning approach for Arousal classification. Unsupervised feature learning worked better compared to handcrafted feature approach. © 2017 IEEE.

关键词： Brain computer interface

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GLCM Feature Extraction and PCA for Tuberculosis Detection with Neural Network

GLCM Feature Extraction and PCA for Tuberculosis Detection w...

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Alfa Farah, M. Nafis Hayyin Suristiyanti, Wiwiek Ibad, Sholihul Pramunendar, Ricardus Anggi Fajar Shidik, Guruh Dian Nuswantoro University Faculty of Computer Science Postgraduate Program in Informatics Engineering Indonesia Dian Nuswantoro University Faculty of Computer Science College Teacher in Informatics Engineering Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Automatic recognition system for medical images is quite a challenging job in the medical image processing field. X-rays, CT, and MRI all provide medical pictures and other modalities which are utilized for diagnostic purposes. As in medical sector, detecting tuberculosis (TB) is a very important stage before further treatment is carried out. Human interpretation of a vast array of X-ray pictures can result in detection mistakes, so an automatic recognition system is needed that can detect TB disease. In this study, we use a dataset with two classes and extract GLCM-based texture features from each class, and apply them to a two-layer feed-forward neural network, which gives a classification rate of 99%. © 2022 IEEE.

关键词： Diagnosis

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Developing phoneme-based lip-reading sentences system for silent speech recognition

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 129-138页

作者： Randa El-Bialy Daqing Chen Souheil Fenghour Walid Hussein Perry Xiao Omar HKaram Bo Li School of Engineering London South Bank UniversityLondonUK Faculty of Informatics and Computer Science British University in EgyptCairoEgypt School of Electronics and Informatics Northwestern Polytechnical UniversityXi'anChina

Lip-reading is a process of interpreting speech by visually analysing lip *** research in this area has shifted from simple word recognition to lip-reading sentences in the *** paper attempts to use phonemes as a classification schema for lip-reading sentences to explore an alternative schema and to enhance system *** classification schemas have been investigated,including characterbased and visemes-based *** visual front-end model of the system consists of a Spatial-Temporal(3D)convolution followed by a 2D *** utilise multi-headed attention for phoneme recognition *** the language model,a Recurrent Neural Network is *** performance of the proposed system has been testified with the BBC Lip Reading Sentences 2(LRS2)benchmark *** with the state-of-the-art approaches in lip-reading sentences,the proposed system has demonstrated an improved performance by a 10%lower word error rate on average under varying illumination ratios.

关键词： deep learning deep neural networks lip-reading phoneme-based lip-reading spatial-temporal convolution,transformers

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Optimal sensors and actuators placement for large-scale switched systems

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International Journal of Dynamics and Control 2019年第1期7卷 147-156页

作者： Seyed Sakha, Masoud Shaker, Hamid Reza Tahavori, Maryamsadat Department of Control Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Center for Energy Informatics University of Southern Denmark Odense Denmark

The problem of sensor and actuator placement is computationally expensive in particular in dealing with large-scale systems. This problem is even more computationally intensive in the case of switched systems. In this paper, we propose a new numerical approach for sensor and actuator placement for large-scale switched systems. We first introduce restricted genetic algorithm (RGA). RGA is an evolutionary algorithm which is developed specifically for sensors and actuator placement. We then use RGA to reduce the computational burden of the sensor and actuator placement for switched system. The proposed method uses the generalized gramians for switched systems which are called nice gramians. The nice gramians quantify the level of observability and reachability of switched systems. To the best of our knowledge, this paper presents the first results on sensor and actuator placement of switched systems. We show the effectiveness of the approach with the help of numerical examples. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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