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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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A Multi-Carrier Deep Learning CSK Communication System

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IEEE Communications Letters 2025年第6期29卷 1191-1195页

作者： Mobini, Majid Kaddoum, Georges Herceg, Marijan Babol Noshirvani University of Technology Department of ECE Babol Iran University of Québec École de technologie supérieure Département de génie électrique MontréalQC Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon University of Osijek Faculty of Electrical EngineeringComputer Science and Information Technology Department of Communications Croatia

A new Multi-Carrier Deep Learning Chaos Shift Keying (MC-DLCSK) modulation is designed in this paper. The proposed receiver includes a Neural Network (NN)-based classifier that recovers MC-DLCSK signals. The results demonstrate that the MC-DLCSK system achieves a considerable Bit Error Rate (BER) gain compared to all existing similar systems under both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. © 1997-2012 IEEE.

关键词： White noise

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Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

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computers, Materials & Continua 2022年第10期73卷 899-915页

作者： Ashit Kumar Dutta Yasser Albagory Abdul Rahaman Wahab Sait Ismail Mohamed Keshta Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,*** the interesting applications of autonomous systems,their applicability in agricultural sector becomes *** unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop *** spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the *** recently developed deep learning(DL)models have exhibited effective performance in several data classification *** this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)*** proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target ***,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing ***,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of *** the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is *** design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

关键词： Autonomous systems object detection precision agriculture unmanned aerial vehicles deep learning parameter tuning

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Implementation of Integration of Advanced Optimized Models With Block Chain Models 3

Implementation of Integration of Advanced Optimized Models W...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jayalakshmi, M. Singh, Prabhdeep Tanwar, Varun Kumar Rahim Alatba, Sura Mohanty, Swaroop Shingadiya, Chetan J Scholar of Mysore University Commerce & Management New Horizon College of Commerce and Management India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India G. L. Bajaj Institute of Technology and Management Applied Science Greater Noida India Al-Turath Universiy College Computer Science Department Baghdad Iraq Management Cv Raman Global University Bhubaneswar India Computer Engineering Rk University India

ISBN: (纸本)9798350399264

There are several unanimity techniques for block chain technology, each of which has particular advantages and disadvantages. In real use, POA falls into a novel class of Byzantium Adaptable consensus mechanism as compared to conventional byzantine fault - tolerant methods. Several companies are switching to a more traditional POA coin with Byzantine Automatic Failover. It is exceedingly difficult to stop assaults inside nodes without a similarly strong consensus method. Our goal is to do research to determine the best way to develop contracts with enhanced techniques to lower the danger of cryptocurrency assaults. We construct blocks, add relating to an identified' voter profiles, modify them based on present in the study' voting habits, and gather chain rewards by using the POA and POV procedures. The bitcoin has been very popular over the past few years, and the underpinning blockchains have also received a lot of interest in the scholarly institution. In order to improve the functionality of Cryptocurrency and create an uniform and adaptable to operate on most systems, this project sought to examine various chain settlement techniques.A ground-breaking variation of blockchain technology is hybrid blockchain. In this manual, we will examine the fundamental components of a hybrid blockchain and discover its *** world is changing in a way that only the blockchain can. Corporations, government, and other organizations are able to oversee workflows more successfully and improve present system and better alternatives because to it. It is currently changing how we keep, retrieve, and utilize data in an effort to improve a never loop of growth in technology. Other aspects of it are also impacted, including how connections are *** different applications of blockchain exist: private, public, consortium, and hybrid. You may be familiar with how private and public blockchain operate if you've read about it before. © 2023 IEEE.

关键词： Blockchain

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Research on the Relationship between Mobile Network Data and COVID-19 Cases 14

Research on the Relationship between Mobile Network Data and...

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14th IEEE International Conference on computer Research and Development, ICCRD 2022

作者： Tian, Jianmeng Zhao, Haoyuan Information Science and Technology University Computer School of Beijing Beijing China Fraser University Faculty of Applied Sciences of Simon VancouverBC Canada

ISBN: (纸本)9781728177212

During this nearly two-years-long pandemic period, the COVID-19 impacts people's lives dramatically, many people were forced to stay at home by the government's lockdown policy, and they also need to work and study at home. Therefore, there is an equivalent impact on networks as people are more dependent on them. But there are only a limited number of research has been done in this intersection area between the pandemic and networks. So, we want to fill this gap. In this paper, we will study the mobile network data from U.S. Federal Communications Commission (FCC) and COVID-19 cases data from the U.S. centers for disease control and prevention (CDC), then use machine learning to investigate the relationship between mobile network data and COVID-19 cases. We will discuss other related works, which used other methods or investigated this topic in other regions, then we will introduce our machine learning methods, experiments and give the conclusion. © 2022 IEEE.

关键词： COVID-19

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SBM: A Time and Resource Efficient Single Backward Match Exact String Searching Algorithm 2

SBM: A Time and Resource Efficient Single Backward Match Exa...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Nassoura, Ayman Bassam Ibrahim, Muhammad Ali, Atif Safdar, Sania Ur Rehman, Shujah Ibrahim, Amer Alrababah, Hamza University Ajman Management Information Systems Department City United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan PMAS Arid Agriculture University Rawalpindi Pakistan National University of Science & Technology Pakistan Abasyn University Islamabad Pakistan American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates Skyline University College School of Information Technology University City Sharjah Sharjah1797 United Arab Emirates

ISBN: (纸本)9798350335644

As in the current era, data is expanding daily on the web, where tremendous data banks with big data need to be searched by different users according to their data needs. This is when normal users cannot afford more waiting time searching the required data and indulge themselves in uncomfortable, lengthy, and complex computational processes. Moreover, search results will also play a vital role in extracting the desired output from the existing data bank. By keeping this common and consistent need of the normal user, the effort has been made to formulate a solution in the exact string searching algorithm. The algorithm's simplicity will add efficiency and speed up the processing time compared to the existing exact string-searching algorithms in terms of worst-case complexity. The proposed algorithm is compared with Boyer Moore Hoorspool, Knuth Morris Pratt (KMP), and Back and Forth Matching Algorithms to understand the results better. © 2023 IEEE.

关键词： String searching algorithms

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Powerpoint Slide Presentation Control Based on Hand Gesture 3rd

Powerpoint Slide Presentation Control Based on Hand Gesture

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Kumar, Ankit Singh, Kamred Udham Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Uttarakhand Dehradun248007 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819773558

HCI, which stands for "human–computer interaction," has been increasingly concerned with natural interaction methods in recent years. Various ways we interact with computers have benefited from the development of real-time hand gesture recognition programs. The detection of hand motions calls for the use of a camera. The primary form of participation is the employment of a web camera as a virtual human–computer interaction device. In this body of work, we look into how vision-based HCI methods of the present day make use of hand gestures. In the event that consumers are unable to utilise any input device or touch it, this project becomes incredibly useful. Gesture recognition makes it possible to complete an activity without physically accessing the usual input devices (mouse, keyboard, etc.). The user may doodle with his index finger and use his index and middle fingers together to control the pointer's movement on the screen. The user may erase their artwork using the tips of their index, middle, and ring fingers. Using their little finger, the user may go on to the next file, and using their thumb finger and pointing to the left, they can return to the previous file. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gesture recognition

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Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and Machine Learning 1

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22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Nallakaruppan, M.K. Srivastava, Gautam Gadekallu, Thippa Reddy Reddy, Praveen Kumar Krishnan, Sivarama Polap, Dawid School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Mathematics and Computer Science Brandon University Brandon Canada School of Information Technology and Engineering Vellore Institute of Technology Department of Electrical and Computer Engineering Lebanese American University Lebanon Zhongda Group Zhejiang Jiaxing314312 China Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network

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Journal of Mobile Multimedia 2022年第2期18卷 325-348页

作者： Vinoth Kumar, V. Karthick Raghunath, K.M. Rajesh, N. Venkatesan, Muthukumaran Joseph, Rose Bindu Thillaiarasu, N. School of Computer Science and Engineering Jain (Deemed to be University) Bangalore India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India University of Technology and Applied Science Shinas Oman Department of mathematics School of Applied Sciences REVA University Karnataka Bengaluru India Department of Mathematics Christ Academy Institute for Advanced Studies Bangalore India School of Computing and Information Technology REVA University Bengaluru India

A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model. © 2021 River Publishers

关键词： Accuracy Blast Bronzing Convolution neuro-fuzzy Crown rot Disease classification Head blight Paddy

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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms 4

Towards Swarm Intelligence Ontology for Formal Description o...

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4th International Conference on Computing, Networking, Telecommunications and Engineering sciences Applications, CoNTESA 2023

作者： Tulicic, Domagoj Horvat, Marko Lovrencic, Sandra University of Applied Science in Information Technology Department of Computer System and Networks Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Foundations of Information Sciences Department of Theoretical and Applied Varaždin Croatia

ISBN: (纸本)9798350382754

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches. © 2023 IEEE.

关键词： Swarm intelligence

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