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2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Jelena Š uljug Snjež ana Rimac-Drlje Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495790

No-reference (NR) Video Quality Assessment (VQA) methods often use bitrate as one of the most important parameters. This usually does not take into account that the quality of the video sequence at a given bitrate depends not only on the encoder used but also on the complexity of the video content as well as its resolution. In this paper, we present a novel low complexity Content-Aware Bitrate-Based (CABB) VQA metric that takes into account the spatial resolution, bitrate, spatial and temporal information of video sequences and can be used to evaluate video quality in video streaming. The metric was developed based on the Structural Similarity Index Measure (SSIM) computed for 11 training sequences with different complexity and for 5 spatial resolutions. The metric itself does not use SSIM scoring to assess video quality but estimates the SSIM of a test video sequence. The accuracy of the proposed metric was tested on three video sequences that were not used for the metric modeling, and the Pearson Linear Correlation Coefficient (PLCC) values were calculated for the actual SSIM values and the values obtained by CABB are above 0.9. PLCC values for CABB results for 85 test video sequences and corresponding subjective Difference Mean Opinion Score values were also calculated. The PLCC results showed that the proposed metric achieved a higher correlation with the subjective quality scores compared to the published bitrate-based metric.

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Deep Learning Metaphor Detection with Emotion-Cognition Association

Deep Learning Metaphor Detection with Emotion-Cognition Asso...

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Digital Transformation and Intelligence (ICDI), International Conference on

作者： Md Saifullah Razali Alfian Abdul Halin Yang-Wai Chow Noris Mohd Norowi Shyamala Doraisamy Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Malaysia Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia

ISBN: (纸本)9798350397017

The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and performance of each feature sets are given. It is also found that a few sets performed well when they are used independently. However, even the sets that are not useful separately is proven to be very useful after the combination process.

关键词： Support vector machines Measurement Deep learning Digital transformation Classification algorithms Convolutional neural networks Task analysis

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Gym Exercise Recognition Using Deep Convolutional and LSTM Neural Network Based on IMU Sensor Data

Gym Exercise Recognition Using Deep Convolutional and LSTM N...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Sakorn Mekruksavanich Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

This study delves into the utilization of wearable devices equipped with inertial measurement units (IMUs) to gather data for human activity recognition. It explores the application of deep neural networks for automatically identifying gym workouts using IMU sensors. Our methodology involves developing a framework that integrates convolutional neural networks (CNNs) to extract features from raw sensor data, followed by using long short-term memory (LSTM) recurrent neural networks to classify sequences. IMU data from accelerometers and gyroscopes are collected from 10 individuals performing 30 standard gym routines. The CNN-LSTM pipeline is supervised on a comprehensive dataset comprising multiple sensors and subjects to distinguish between different workouts accurately. During the evaluation, the CNN-LSTM model achieved an accuracy of 93.81 % in categorizing 30 workout categories based solely on accelerometer data. Through augmentation, this accuracy is further improved to 95.75 %. This solution outperforms independently utilized CNN and LSTM models and traditional machine learning approaches. Detailed assessments offer valuable insights into the benefits of combining diverse sensor types and model architectures for robust exercise classification. This research raises a precise and dependable wearable system for identifying gym exercises using deep neural networks. The findings suggest promising avenues for future exploration in human activity recognition, mainly focusing on utilizing on-body sensors and deep learning to analyze more complex human movements.

关键词： Accelerometers Accuracy Sensor phenomena and characterization Feature extraction Sensors Convolutional neural networks Human activity recognition

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Delayed Coding Scheme for Channels with Insertion, Deletion, and Substitution Errors

Delayed Coding Scheme for Channels with Insertion, Deletion,...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Ryo Shibata Hiroyuki Yashima Dept. of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo Japan

ISBN: (纸本)9781665435413

We propose a new coding scheme, called the delayed coding (DC) scheme, for channels with insertion, deletion, and substitution (IDS) errors. The proposed scheme employs delayed encoding and non-iterative detection and decoding strategies to manage the transmission of multiple codewords in a linear code. In the DC scheme, a channel input sequence consists of subblocks of multiple codewords from the previous to current time instances. At the receiver side, the maximum a posteriori detection applies to the received sequences that contain information of the codeword at the current time instance, where priorly decoded codewords aid the detection. The channel code decoding is then performed, and extrinsic messages are exploited for the codeword estimations of the following time instances. We show that the rate achievable with the DC scheme over the IDS channel approaches the symmetric information rate of the channel. Moreover, we show the excellent asymptotic and finite-length performances of the DC scheme in conjunction with low-density parity-check codes.

关键词： Estimation Receivers Linear codes Encoding Decoding Delays Iterative methods

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Malicious URL Detection using optimized Hist Gradient Boosting Classifier based on grid search method

arXiv

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arXiv 2024年

作者： Maftoun, Mohammad Shadkam, Nima Komamardakhi, Seyedeh Somayeh Salehi Mansor, Zulkefli Joloudari, Javad Hassannataj Department of Artificial Intelligence Technical and Engineering Faculty Islamic Azad University South Tehran Branch Tehran Iran Department of Computer Engineering Islamic Azad University West Tehran Branch Tehran Iran Department of Computer Engineering Islamic Azad University Babol Branch Babol Iran Faculty of Information Science and Technology Universiti Kebangsaan Malaysia UKM Bangi 43600 Malaysia Department of Computer Engineering Faculty of Engineering University of Birjand Iran Tehran*** Iran

Trusting the accuracy of data inputted on online platforms can be difficult due to the possibility of malicious websites gathering information for unlawful reasons. Analyzing each website individually becomes challenging with the presence of such malicious sites, making it hard to efficiently list all Uniform Resource Locators (URLs) on a blacklist. This ongoing challenge emphasizes the crucial need for strong security measures to safeguard against potential threats and unauthorized data collection. To detect the risk posed by malicious websites, it is proposed to utilize Machine Learning (ML)-based techniques. To this, we used several ML techniques such as Hist Gradient Boosting Classifier (HGBC), K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM) for detection of the benign and malicious website dataset. The dataset used contains 1781 records of malicious and benign website data with 13 features. First, we investigated missing value imputation on the dataset. Then, we normalized this data by scaling to a range of zero and one. Next, we utilized the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data since the data set was unbalanced. After that, we applied ML algorithms to the balanced training set. Meanwhile, all algorithms were optimized based on grid search. Finally, the models were evaluated based on accuracy, precision, recall, F1 score, and the Area Under the Curve (AUC) metrics. The results demonstrated that the HGBC classifier has the best performance in terms of the mentioned metrics compared to the other classifiers. © 2024, CC BY-NC-SA.

关键词： Websites

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An Adaptive Deep Learning Framework for Human Activity Recognition using Video Data

An Adaptive Deep Learning Framework for Human Activity Recog...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Chejarla Raja S D. Arivazhagan S Lavanya Rajeswari C Banu S Rahmaan K Faculty of Information and Communication Engineering Anna University Chennai Tamil Nadu India AMET Business School Academy of Maritime Education and Training Deemed to be University Chennai India Department of Computer Science and Engineering KCG College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

This research presents a dual-pathway SlowFast network in order to provide a novel approach for the recognition of human actions. The method has the ability to capture low-intensity as well as high-intensity activities efficiently. The model is trained on two different datasets: one on the routine activities, such as sitting and walking, and the other on complex sports and leisure activities. The SlowFast network increases the accuracy in a wide variety of scenarios by incorporating high-level spatial context along with fast motion characteristics using a dual-path architecture. The proposed model outperforms the conventional HAR methods on different grounds such as adaptability and accuracy, and thus aptly useful for applications such as security surveillance, healthcare, and sports analysis. This approach brings forth a robust system able to identify a range of human actions in static as well as dynamic environments by removing some of the limitations from existing HAR models.

关键词： Adaptation models Analytical models Accuracy Medical services Video surveillance Real-time systems Motion capture Human activity recognition Security Sports

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Consistency Analysis of Madd rules in the Holy Quran 3

Consistency Analysis of Madd rules in the Holy Quran

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3rd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2021

作者： Mohammad, Karim Aly Kandil, Ahmed H. El-Bialy, Ahmed M. Ali Fawzi, Sahar Cairo university Systems and Biomedical Engineering Faculty of Engineering Giza Egypt Systems and Biomedical Engineering Faculty of Engineering El-Shorouk Academy Cairo Egypt Information Technology computer Science Nile university Giza Egypt

ISBN: (纸本)9781665421577

A consistency analysis is performed for one of the famous Tajweed rules in the Holy Quran - Madd rules. They are tested on records of one of the famous reference reciters - Sheikh El-Hosary-to find a consistent boundaries and to evaluate the performance of other new learners. A vowel detection algorithm is used to detect the duration of the detected Madd patterns. This algorithm was applied on a dataset of 105 minutes of Quranic records of Sheikh El-Hosary. The consistency result is found to be normally distributed with a mean of 0.379 second of one movement time of Madd and standard deviation of 0.058 second and minimum consistency of 84.6% for 68% of the detected Madd patterns. © 2021 IEEE.

关键词： Normal distribution

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Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

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computers, Materials & Continua 2022年第1期70卷 361-374页

作者： Mamdouh El Haj Assad Ibrahim Mahariq Raymond Ghandour Mohammad Alhuyi Nazari Thabet Abdeljawad Sustainable and Renewable Energy Engineering Department University of SharjahP.O.Box 27272SharjahUAE College of Engineering and Technology American University of theMiddle EastKuwait Faculty of New Sciences and Technologies University of TehranTehranIran Department of Mathematics and General Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Medical Research China Medical UniversityTaichung40402Taiwan Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwan

Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their *** heat capacity of nanofluids,as one of the thermophysical properties,performs principal role in heat transfer of thermal mediums utilizing *** this regard,different studies have been carried out to investigate the influential factors on nanofluids specific ***,several regression models based on correlations or artificial intelligence have been developed for forecasting this property of *** the current review paper,influential parameters on the specific heat capacity of nanofluids are ***,the proposed models for their forecasting and modeling are *** to the reviewed works,concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the ***,by using other effective factors,the accuracy and comprehensive of the models can be ***,some suggestions are offered for the upcoming works in the relevant topics.

关键词： Specific heat capacity nanofluid artificial neural network concentration

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API Call-Based Malware Classification Using Recurrent Neural Networks

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Journal of Cyber Security and Mobility 2021年第3期10卷 617-640页

作者： Li, Chen Zheng, Junjun Department of Bioscience and Bioinformatics Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Department of Information Science and Engineering Ritsumeikan University Kusatsu 525-8577 Japan

Malicious software, called malware, can perform harmful actions on computer systems, which may cause economic damage and information leakage. Therefore, malware classification is meaningful and required to prevent malware attacks. Application programming interface (API) call sequences are easily observed and are good choices as features for malware classification. However, one of the main issues is how to generate a suitable feature for the algorithms of classification to achieve a high classification accuracy. Different malware sample brings API call sequence with different lengths, and these lengths may reach millions, which may cause computation cost and time complexities. Recurrent neural networks (RNNs) is one of the most versatile approaches to process time series data, which can be used to API call-based Malware calssification. In this paper, we propose a malware classification model with RNN, especially the long short-term memory (LSTM) and the gated recurrent unit (GRU), to classify variants of malware by using long-sequences of API calls. In numerical experiments, a benchmark dataset is used to illustrate the proposed approach and validate its accuracy. The numerical results show that the proposed RNN model works well on the malware classification. © 2021. River Publishers. All Rights Reserved.

关键词： API call sequence Gated recurrent unit (GRU) Long short-term memory Malware classification Recurrent neural network

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Stroke Prediction Based on Improved Machine Learning Algorithm

Stroke Prediction Based on Improved Machine Learning Algorit...

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2022 International Symposium on Robotics, Artificial Intelligence, and information engineering, RAIIE 2022

作者： Ren, Yiheng Wang, Chenfei Wang, Haoze Xia, Yu Faculty of Computer and Information Engineering Changzhou Institute of Technology Jiangsu China Faculty of Science Nanjing Agricultural University Jiangsu China College Nine University of California Santa CruzCA United States College of Art and Science Boston University MA United States

ISBN: (纸本)9781510660137

With the development of society, people's life pressure is increasing, and the number of stroke patients is also increasing year by year. At present, stroke has become the second leading cause of death in the world. Therefore, it is necessary to predict whether a person is more likely to have a stroke. At present, studies around the world have established a variety of stroke prediction models based on genetic or pathological indicators. However, the accuracy of the prediction models derived from these studies is still insufficient. This study designed a new method to predict stroke. After the datasets are collected and processed, the SMOTE method is used to balance the datasets in order to obtain better results in the future prediction. In the stage of constructing new patient features, eight complex new features with different correlations were created. After completing the construction of new features, this study uses the machine learning model algorithm built in the Sklearn function library in Python to train and predict the processed datasets. Experimental results show that the prediction accuracy of most algorithms has reached more than 95%, which has been greatly improved compared with previous studies. Finally, this study uses PyQt5 and QtDesigner to build a simple GUI, which can be easily operated by users who have the need to predict stroke. © 2022 SPIE.

关键词： Python

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