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共 783 条记录，以下是511-520 订阅

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Facial Expression Analysis using K-Nearest Neighbor Classification Method: Enhancing Emotion Detection and Stress Monitoring in an Interactive Music Player

Facial Expression Analysis using K-Nearest Neighbor Classifi...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： S Madhan S Sridharan Sugitha Deivasigamani R Rajesh R Surendran Department of Computer Science and Engineering University College of Engineering Thirukkuvalai (A Constituent College of Anna University:Chennai) Nagapattinam India E.G.S. Pillay College of Engineering Old Nagore Road Thethi Village Nagapattinam India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Since facial expressions convey important information about human feelings and emotions in a dangerous way, they are an effective means of human communication. It is a crucial part of affective computing systems, which aim to identify and understand human emotions more effectively. Decision-making, humanmachine interfaces, and emotion analysis can all benefit from automatic facial expression recognition. But it’s difficult to automatically identify different face expressions. Consequently, the study of facial expressions has gained popularity in the domains of image processing, machine learning, and human-computer interaction in addition to the subject of human recognition. In emotion detection, the HAAR CASCADES algorithm will be used to automatically detect face parts, and the Support Vector machine algorithm will be used to classify emotions and play a playlist of music that, based on the K-Nearest Neighbor classification method, are appropriate for his current mood. On the testing end, an example image whose expression it wants to identify would be provided. To play music based on recognized emotions, the tested image would be compared to facial databases; if the emotion exceeds ten percent of stress, a warning signal with suggestions would be displayed. In the end, it offers an enhanced recognition rate for emotion-based music players.

关键词： Emotion recognition Accuracy machine learning algorithms Image recognition Databases Face recognition Nearest neighbor methods Feature extraction Cameras Classification algorithms

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Online Fake News Detection using Pre-trained Embeddings

Online Fake News Detection using Pre-trained Embeddings

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Junaid Ali Reshi Rashid Ali Department of Computer Engineering Aligarh Muslim University Aligarh India Department of Computer Engineering and Interdisciplinary Centre for Artificial Intelligence Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

Fake news is a word, often heard. We often come across this term and its dangerous consequences with respect to society. To curb the menace of fake news is a big challenge in today's world. Many efforts have been made through different approaches to detect the fake news on online media. In this study, we propose a fake news detection system that utilizes pre-trained embeddings for the detection of fake news. We use a benchmark dataset of social media to carry our experiments. this study uses pre trained GloVe embedding based embedding matrix to generate the embeddings for the text as an input to our deep learning model. We also compare the performance of simple deep neural network based model with the LSTM based deep neural network on the same input embeddings. the results are indicative of good performance of the models to detect fake news.

关键词： Deep learning Social networking (online) Neural networks signal processing Benchmark testing Natural language processing Communications technology

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machine learning & deep learning algorithm reviews for diabetic retinopathy detection

Machine learning & deep learning algorithm reviews for diabe...

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Advances in Science and Technology (ICAST), international conference on

作者： Pranali Hatode Maniroja M Edinburgh Dept. of EXTC KJSIEIT Mumbai India Dept. of EXTC TSEC Mumbai India

ISBN: (纸本)9781665492645

An artificial neural network, expressed as (ANN) a very popular algorithm in machine learning, developed from artificial neurons, has less computation capability, and so is less popular amongst the researchers. Deep learning or may also be called as deep neural network (DNN) has evolved with great computation capabilities with graphic processing units and also large amount of data acquisition is possible with DNN. this paper is the pure analysis and review of the performances of the ML and DL algorithms and also compares the parameters of the fundus images as it aims the detection of Diabetic Retinopathy (DR) with better accuracies. It also speaks about the analysis of another type of images particularly Optical Coherence tomography angiography also called as OCTA used for detection of DR and shows how its accuracies of detection and grading of the disease differs from that of the fundus images. DR is an impairment of diabetes has an effect on eyes and is a consequence of damaged retinal blood vessels. If these complications are identified in the very early stage, then the patient's vision misfortune can be avoided.

关键词： Deep learning Support vector machines machine learning algorithms Sensitivity Retinopathy Angiography Artificial neural networks

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Modulation Recognition Method of Communication signal Based on CNN-LSTM Dual Channel

Modulation Recognition Method of Communication Signal Based ...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yanjie Ren Xiaogang Tang Binquan Zhang Junhao Feng Minghui Gao Institute of Aerospace Information Space Engineering University Beijing China

Considering the problem of low recognition rate and a large number of parameters in end-to-end modulation recognition methods based on deep learning, a CNN-LSTM dual channel(DCNN-LSTM) modulation recognition method based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory(LSTM) network is proposed in this paper. Firstly, the proposed method extracts the frequency domain features of the IQ signal using one-dimensional convolution in the CNN channel and the time-domain features of the transformed AP data of the IQ signal in the LSTM channel. then, fusing the features of the two channels by the Concatenate operation and convolution layer. Finally, the method uses Global Average Pooling (GAP) instead of Flatten, which integrates the feature information of the vectors without additional parameters. Experimentally, the number of parameters of the proposed method is respectively reduced by 98.4%, 77.8%, and 88.9% compared with CNN, LSTM, and SCRNN, while the recognition rate is respectively improved by 9.7%, 3.3%, and 0.4%. the method proposed in this paper has theoretical reference value for the research field of communication modulation signal recognition, and engineering reference significance for the study of the intelligent classification of spatial signals in complex environments.

关键词： Deep learning Support vector machines Convolution Frequency-domain analysis Modulation Feature extraction Convolutional neural networks

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A Hybrid Deep-learning/Fingerprinting for Indoor Positioning Based on IEEE P802.11az

A Hybrid Deep-learning/Fingerprinting for Indoor Positioning...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Nader. G. Rihan Mahmoud Abdelaziz Samy S. Soliman University of Science and Technology Zewail City of Science Technology and Innovation Giza Egypt Electronics and Electrical Communications Engineering Department Faculty of Engineering Cairo University Giza Egypt

ISBN: (纸本)9781665482387

Many different technologies were proposed in the past few years for enhancing indoor positioning: WiFi, Radio Frequency Identification (RFID), Ultra Wide Band (UWB), and Bluetooth to mention some. this study followed the recent IEEE positioning standard (P802.11 az). the standard was developed to enhance indoor navigation by minimizing the consumption power with low hardware complexity. therefore, this standard enables the usage of artificial intelligence algorithms with relatively high complexity. Also, the usage of this standard will enhance indoor localization and positioning for different commercial purposes. We proposed two methods: Time Of Arrival (TOA) and fingerprinting-deep learning, considering a simple Single Input-Single Input (SISO) system at five Gigahertz with the highest standard allowable bandwidth. the behavior of TOA had very low performance considering a realistic multi-path case. On the other hand, the deep learning algorithm achieved ultra-high indoor positioning resolution (around twelve centimeters). Although TOA is a technique that relies on a simple hardware algorithm relative to deep learning, this paper proved the failure of TOA in a simple indoor environment even using the latest IEEE positioning standard compared with the deep learning method.

关键词： Deep learning Location awareness signal processing algorithms Hardware Complexity theory Standards IEEE 802.11 Standard

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AI-Powered Smart Glasses for Blind, Deaf, and Dumb

AI-Powered Smart Glasses for Blind, Deaf, and Dumb

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Advances in Science and Technology (ICAST), international conference on

作者： Shree Lakshmi R Sneha M Swetha K A. thilagavathy Department of Computer Engineering R.M.K Engineering College Tamilnadu India

ISBN: (纸本)9781665492645

Back then, technology was far more advanced (developed) than time. We can now determine what a deaf or mute person is saying only by capturing it and comparing it to predetermined datasets, showing what the individual is striving to achieve. In this study, we propose a way of supporting impaired people, such as those who are Deaf, Dumb, or Blind, by giving them a new tech that acts as an eye, ear, and brain to them. machine learning methods are employed for object recognition with the aid of image processing to give the blind, a pair of eyes. the deaf and the dumb can both benefit from text-to-speech communication using Bluetooth or radio technology and speech-to-text translation. the convergence of all these technologies, together with AI, AR, VR, and IoT technologies, will help in finding solutions to the problems that these people who face disabilities.

关键词： Legged locomotion machine learning algorithms Image resolution Image communication Face recognition machine learning Ear

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A Study on Seizure Detection Performance in an Automated Process by Extracting Entropy Features

A Study on Seizure Detection Performance in an Automated Pro...

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international conference on signal processing and Information Security (ICSPIS)

作者： Sricheta Parui Debasis Samanta Nishant Chakravorty Wathiq Mansoor Uttam Ghosh Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India University of Dubai Dubai United Arab Emirates Meharry School of Applied Computational Sciences Nashville TN USA

ISBN: (纸本)9781665492669

Millions of people of all ages have been diagnosed with epilepsy all over the world. Electroencephalography (EEG), a quantitative component, is vital in identifying and analyzing epileptic seizures. Manual EEG detection takes a long time and has serious consequences. To prevent this circumstance, the world needs alternative detection technologies. For more than a decade, several strategies and procedures have been used to assist medical doctors in detecting technological improvement. Numerous automated seizure identification frameworks that use machine learning approaches have recently been presented to replace traditional methodologies. However, because multichannel EEG data is unpredictable, selecting optimal channels as well as characteristics and classifying them remain unsolved issues. During automatic signaling, the gadget also emits a noisy signal, making detection and prediction challenging. In this paper, we have tested various entropy features and selected the best features to classify seizure patients. First, we preprocessed the data using IMF of the raw signal, and then entropy features were extracted to classify the epileptic patients. the proposed channel selection method, SVM-GA, works well with our framework and successfully removes redundant channels from the data.

关键词： Support vector machines Medical services signal processing Feature extraction Entropy Electroencephalography Data mining

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A Tool for thermal Image Annotation and Automatic Temperature Extraction around Orthopedic Pin Sites

A Tool for Thermal Image Annotation and Automatic Temperatur...

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international Image processing, Applications and Systems conference (IPAS)

作者： Sowmya Annadatha Marie Fridberg Søren Kold Ole Rahbek Ming Shen Department of Electronic Systems Aalborg University Aalborg Denmark Department of Orthopedics Aalborg University Hospital Aalborg Denmark

ISBN: (纸本)9781665462204

Existing annotation tools are mainly designed for visible images to support supervised learning problems for machine learning. A few tools exist for extracting temperature information from thermal images. However, they are time and manpower consuming, require different stages of data management, and are not automated. this paper focuses on addressing the limitation of existing tools in handling big thermal datasets for annotation, temperature distribution extraction in the Region of Interest (ROI) of Orthopedic surgical wounds and provides flexibility for a researcher to integrate thermal image analysis into wound care machine learning models. We present an easy to use research tool for one click annotation of Orthopedic pin sites for extraction of thermal information, which is a preliminary step of research to estimate the reliability of thermography for home based surveillance of post-operative infection. the proposed tool maps annotations from visible registered image onto thermal and radiometric images. Mapping these annotations from visible registered images avoids manual bias in annotating thermal images. Integrating the functionality of an annotation tool by processing thermal images to acquire single-click manual annotations and extracting temperature distributions in the ROI with those acquired annotations is the novelty of the proposed work and is also crucial for research on deep learning-based investigation on surgical wound infections.

关键词： Temperature distribution Annotations Surveillance Surgery Image annotation Manuals machine learning

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Bacterial Blight and Cotton Leaf Curl Virus Detection Using Inception V4 Based CNN Model for Cotton Crops

Bacterial Blight and Cotton Leaf Curl Virus Detection Using ...

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international Image processing, Applications and Systems conference (IPAS)

作者： Sohail Anwar Abdul Rahim Kolachi Shadi Khan Baloch Shoaib R. Soomro Dept. of ElectronicEngineering Mehran University of Engineering & Technology Jamshoro Pakistan Dept. of Mechatronics Engg. Mehran University of Engineering & Technology Jamshoro Pakistan

ISBN: (纸本)9781665462204

Agriculture sector is an important pillar of the global economy. the cotton crop is considered one of the prominent agricultural resources. It is widely cultivated in India, China, Pakistan, USA, Brazil, and other countries of the world. the worldwide cotton crop production is severely affected by numerous diseases such as cotton leaf curl virus (CLCV/CLCuV), bacterial blight, and ball rot. Image processing techniques together with machine learning algorithms are successfully employed in numerous fields and have also used for crop disease detection. In this study, we present a deep learning-based method for classifying diseases of the cotton crop, including bacterial blight and cotton leaf curl virus (CLCV). the dataset of cotton leaves showing disease symptoms is collected from various locations in Sindh, Pakistan. We employ the Inception v4 architecture as a convolutional neural network to identify diseased plant leaves in particular bacterial blight and CLCV. the accuracy of the designed model is 98.26% which shows prominent improvement compared to the existing models and systems.

关键词： Training learning systems machine learning algorithms Image processing Crops Production Cotton

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Safe Reinforcement learning using Data-Driven Predictive Control

Safe Reinforcement Learning using Data-Driven Predictive Con...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Mahmoud Selim Amr Alanwar M. Watheq El-Kharashi Hazem M. Abbas Karl H. Johansson Ain Shams University Cairo Egypt Jacobs University Bremen Germany KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781665482387

Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision-making and continuous control tasks. However, applying RL algorithms on safety-critical systems still needs to be well justified due to the exploration nature of many RL algorithms, especially when the model of the robot and the environment are unknown. To address this challenge, we propose a data-driven safety layer that acts as a filter for unsafe actions. the safety layer uses a data-driven predictive controller to enforce safety guarantees for RL policies during training and after deployment. the RL agent proposes an action that is verified by computing the data-driven reachability analysis. If there is an intersection between the reachable set of the robot using the proposed action, we call the data-driven predictive controller to find the closest safe action to the proposed unsafe action. the safety layer penalizes the RL agent if the proposed action is unsafe and replaces it with the closest safe one. In the simulation, we show that our method outperforms state-of-the-art safe RL methods on the robotics navigation problem for a Turtlebot 3 in Gazebo and a quadrotor in Unreal Engine 4 (UE4).

关键词： Training Reinforcement learning signal processing Prediction algorithms Safety Task analysis Reachability analysis

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