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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Ensemble Model of U-Net EfficientNet-B3, U-Net EfficientNet B6, CoaT, SegFormer for Segmenting Functional Tissue Units in Various Human Organs 14

Ensemble Model of U-Net EfficientNet-B3, U-Net EfficientNet ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Bharadwaj, Guda Venkat Sree, Yaramala Ramya Varshita, Jammigumpula Lakshmi Chebrolu, Srilatha National Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350335095

Accurately identifying and segmenting functional tissue units is critical for comprehending the structure and function of human organs. This work presents a pioneering method for identifying and semantically segmenting functional tissue units in diverse human organs using an ensemble model. The proposed approach initially extracts features from medical images using a pre-trained U-Net with a backbone as EfficientNet-B3, U-Net with a backbone as EfficientNet-B6, SegFormer MiT-B3, CoaT lite small, and CoaT lite medium, which is then ensembled and utilized to forecast the pixel-wise segmentation mask. Additionally, attention mechanisms were integrated to capture the contextual dependencies of tissue units within the images. To assess the performance of the proposed ensemble model, images from data sources Human Bio-Molecular Atlas Program (HuBMAP) and Human Protein Atlas (HPA) consisting of biopsy slides from various organs were considered. Experiments indicate that the proposed ensemble model outperforms individual models when evaluated using the Dice Co-efficient metric. This innovative approach has the potential to be utilized in other medical imaging tasks that involve functional tissue units for disease diagnosis and treatment planning. © 2023 IEEE.

关键词： Biopsy

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Machine Learning Based Approach to Disinformation Detection Using Twitter Data 2

Machine Learning Based Approach to Disinformation Detection ...

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Yadav, Sanjeev Kumar, C.R.S. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

Fake news detection on social media has assumed enhanced importance and is a trending research topics for data analysts around the world. While most of fake news may be aimed at commercial or personal gains, some of it may cause deliberate harm to a nation's progress. Twitter, as part of social media platforms (SMPs) is a leading portal being used by everyone for gaining access and giving out information. Leading the spread of disinformation propagation via fake news, twitter has emerged as one of the leading SMPs. The sheer quantum of tweets makes it very challenging to sort the news and remove anomalies. Various machine learning techniques have been devised to identify fake tweets. This report aims at studying prominent machine learning techniques for detection of disinformation propagation on Twitter. Generally, researchers have identified detection of fake tweets as a classification problem but some unsupervised and unconventional learning models are also available. Major effort for any supervised learning technique is creation of data corpus. The paper highlights some techniques for the same. Consequently, inclusion of context as part of feature extraction has been achieved to align the intent behind the tweet with the text. As a first step towards the solution, a sentiment analysis of tweets from a set of users has been recommended details have been illustrated in subsequent sections. Various context based technique have been studied and implemented on data corpus and results produced have been compared. The report also studies various NLP techniques for data pre-processing and feature extraction before using classifiers to draw the outcome. © 2023 IEEE.

关键词： Feature extraction

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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An Efficient Deep Learning Technique for Diabetes Classification and Prediction Based on Indian Diabetes Dataset 3

An Efficient Deep Learning Technique for Diabetes Classifica...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Prateeksha Silakari, Sanjay Agrawal, Shikha University Institute of Technology Rgpv Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350342338

Diabetes is a chronic metabolic disorder that affects millions of people worldwide, and early diagnosis and effective prediction of this disease are crucial for its management and prevention of associated complications. Recently, deep_learning shows effective results in various healthcare applications, including disease classification and prediction. This paper presents diabetes classification and prediction using machine learning approach. Proposed system is increased the accuracy of the results by diabetes prediction framework using artificial neural network (ANN) deep learning algorithms. The performance of the overall classification results is improved as a consequence of this. The use of machine learning (ML) algorithms is currently effective for sickness detection. © 2023 IEEE.

关键词： ANN Diabetes Classification Disease Machine Learning

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Fish Species Classifier for Allergic People using CNN Algorithm 7

Fish Species Classifier for Allergic People using CNN Algori...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Reddy, Puchalapalli Surya Vradhan Krishna, Mannepalli Vamsi Aishwarya, R. Yogitha, R. Kumar, K. Ashok Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9781665464086

Some people may be allergic to a variety of crustaceans, including prawns, crab, and lobster, or they may be sensitive to some types of fish. Cross-reactivity is the term for this kind of condition. This approach is useful because it is challenging to predict which fish will cause an allergic reaction in you. It is challenging to determine which fish may cause an allergic reaction. Deep learning has been a huge help in recent years in solving numerous issues. Real-time datasets are not implemented in the current systems, and they additionally the current systems are not very accurate. The proposed system utilizes CNN algorithm for detecting and classifying fish that cause allergies. The implemented system aims to help people with seafood allergies, clinical immunology, or allergies. specialist in image analysis of caught fish, detection, and species classification of fish from images. © 2023 IEEE.

关键词： Deep learning

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Smart Waste Segregation Using IoT 4

Smart Waste Segregation Using IoT

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4th International Conference for Emerging technology, INCET 2023

作者： Bhatele, Priyanka Dalvi, Manas Kulkarni, Manas Mali, Tejas Manalwar, Manthan Manakshe, Aman Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India

ISBN: (纸本)9798350335750

Rapid expansion in the quantity and variety of solid and hazardous waste as a consequence of continued economic development, urbanization, and industrialization poses a growing challenge for national and municipal governments to ensure efficient and long-term waste management. According to estimates, the total amount of municipal solid trash produced worldwide in 2006 was 2.02 billion tonnes, a rise of 7% annually since 2003. (Global Waste Management Market Report 2007). To reduce the risk to the patient and public health and safety, as well as an environmental hazard, waste management, transportation, and disposal must be carefully handled. There is currently no system in place for households to separate dry, moist, and metallic garbage. In order to send household waste directly for processing, an automated waste segregator is suggested in this study, which is an affordable, simple-to-use alternative. It is intended to separate the garbage into metallic, wet, and dry waste. The automated waste segregator uses a moisture sensor to discriminate between wet and dry trash and an inductive proximity sensor to detect metallic objects. According to experimental findings, the automated waste segregator has been effectively used to accomplish the classification of waste into dry, wet, and metallic waste. © 2023 IEEE.

关键词： Automation

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Deep Learning Model to Detect and Classify Bone Fracture in X-Ray ImagesBOOK_conf_196895_none_none_1of1_10394986

Deep Learning Model to Detect and Classify Bone Fracture in ...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Imrankhan, M. Varalakshmi, I. Arthi, J. Pavithra, P. Reshma, J. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350315127

The skeletal system of the human body is very important and bones are vital to it. A support and facilitate body movement. To detect fractures in bones, doctors use X-ray images, but this manual technique is laborious and prone to mistakes. Therefore, an automated approach for diagnosing bone fractures is required. Power electronic device modeling is a frequent use of deep neural networks, or DNNs. The proposed system, provides a deep neural network model using ResNet 50 to distinguish between bones that are healthy and damaged. But when faced with little data, our deep learning model has a tendency to over fit. The data augmentation techniques is used to increase the data set in order to overcome this problem. This stratergy uses 5-fold cross- validation and our model is able to discriminate objects with 92.44% classification accuracy. © 2023 IEEE.

关键词： Image classification

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Forwarding Strategy Performance Using NFD Pipeline Measurements on Vehicular Named Data Networking (V-NDN) 9

Forwarding Strategy Performance Using NFD Pipeline Measureme...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ahdan, Syaiful Syambas, Nana Rachmana Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

The Named Data Networking (NDN) paradigm has been used as a promising vehicle communication model, namely the Vehicular Named Data Networking (V-NDN) model. In NDN, delivery of interest in the NDN network is done by selecting an existing interface. The problem is as to which interface is right in terms of fulfilling the request that suits the user. In this study, we compare the effectiveness of each forwarding strategy by creating new scenarios, such as variations in the number of interests and simulation time. We also measure the effectiveness of the forwarding strategy using the parameters specified in the NFD Forwarding Pipeline. The conclusion from the experiment is that superior forwarding strategy recommendations are based on content store (CS) management and data compatibility obtained based on consumer demand for producers. © 2023 IEEE.

关键词： Pipelines

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Optimal cloudlet movement and placement in a dynamic environment using cluster based technique 4

Optimal cloudlet movement and placement in a dynamic environ...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Sahkhar, Lizia Balabantaray, Bunil Kumar National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong India

ISBN: (纸本)9798350323771

User's mobility in a dynamic environment, such as a wireless metropolitan area network (WMAN), poses a challenge in finding the best position to place the cloudlet as the devices are always on the move and disrupt proper utilization of cloudlet resources. With more resource rich applications using cloudlet framework for immediate processing and computation, finding the best position to place the cloudlet in this vast and dynamic environment is an important study. This work focus on how movement of user devices defined the placement of a cloudlet at a particular position, which in turn determine the number of mobile devices coverage. To project this scenario, this paper uses a clustering method to study the movement of five hundred (500) mobile devices in a dynamic environment and how these devices are assigned to the three (3) cloudlets when they are positioned at various interval of time by considering a threshold value and capacity of the cloudlet. Through the cloudlet movement, the experiment are able to generate the initial, feasible and optimal placement position. It is also observe that mobile device coverage increases by 28.53% when cloudlets are moved from initial to the optimal position. Furthermore, through this technique, the distribution of mobile devices at various interval of time become more compact, organized and balanced with 307 to 179 to 147 for the first cloudlet, 189 to 198 to 163 for the second cloudlet and from 4 to 123 to 190 for the third cloudlet when cloudlets are moved from the initial to the feasible to the optimal position respectively. © 2023 IEEE.

关键词： K-means clustering

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