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International Conference on computing and Networking Technology (ICCNT)

作者： A. Z. Khan Neeraj Panwar N K Rayaguru Priya M. Shelke Rakesh Kumar Giri P. Jaishankar Applied Physics Department Yeshwantrao Chavan College of Engineering Nagpur India Department of School of Computing University GEHU Dehradun India Department of EEE. Vel Tech Rangarajan Dr sagunthala R & D Institute of Science and Technology Department of InformationTechnology Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Sunrise University Alwar India Department of School of Civil Engineering SASTRA Deemed to be University India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Measuring the electromagnetic overall performance and configuration of networks in high-interference environments can be challenging. In those environments, the indicators of hobby can be infected with unwanted heritage indicators, i.e., noise, leading to degraded overall performance and misguided size readings. This paper investigates the most reliable techniques for measuring networks’ electromagnetic performance and configuration in these problematic environments, utilizing introducing a unique multistage framework that combines sign-processing strategies with some community parameters. In particular, the framework includes four degrees: sign reception, sensitivity analysis, pass-correlation, and community configuration optimization-an analysis of the results of varying environmental conditions on sign reception and sensitivity change into conduct. A cross-correlation approach has evolved to decide the extent of correlation among one-of-a-kind alerts and to locate the specific configurations of the particular networks. Sooner or later, an optimization set of rules was carried out to locate the network’s optimum configuration. The final result of this research is a reliable method for measuring the electromagnetic performance and configuration of networks in high-interference environments. The proposed technique is anticipated to help researchers and engineers better understand the electromagnetic behaviors of complicated networks in actual-world scenarios.

关键词： Correlation Sensitivity analysis Atmospheric measurements Noise Electromagnetic interference Reliability engineering Particle measurements Electromagnetics Optimization Monitoring

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The Terms Estimation of S Robotics using MTRS as well as FEM

The Terms Estimation of S Robotics using MTRS as well as FEM

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R. Gopal Mustafa Mudhafar Amjad Hashem Venkadeshan Ramalingam Dibyahash Bordoloi Muhammad Numan Ali Khan Myasar Mundher Adnan Zahraa N. Abdulhussain Assistant Professor Information and communication Engineering College of Engineering University of Buraimi Sultanate of Oman Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences– Shinas Sultanate of Oman Dept of CSE Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun Uttarakhand Faculty – Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences – Shinas Sultanate of Oman Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq College of Technical Engineering National University of Science and TechnologyDhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This look at addresses the critical project of reaching dependable and precise proprioception in soft robots, it really is essential for his or her effective deployment in complicated environments. By integrating multi-tap resistive sensors in the robot’s form, our technique lets in for the distinct sensing of nearby deformations at various factors throughout the robotics’ frame, moving beyond traditional techniques that anticipate steady curvature. We beautify the decision of these deformations and couple the sensory facts with a strong finite detail technique (FEM) model. This included framework allows the correct estimation of the smooth robot’s form and the significance of outside forces applied to it, attaining a mean relative inaccuracy of about 4%. Additionally, our technique proves its software program in tender manipulation structures, in which it achieves thirteen% relative mistakes in external force estimation inside diverse zero to five N. The approach gives a promising direction for enhancing robotic interactions in sensitive and dynamic responsibilities, providing massive enhancements in robotic proprioception without reliance on outdoor monitoring generation.

关键词： Deformable models Deformation Force Estimation Soft robotics Robot sensing systems Software

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Human Presence Detection with Thermal Sensor using Multilayer Perceptron Algorithm 7

Human Presence Detection with Thermal Sensor using Multilaye...

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7th IEEE World Forum on Internet of Things, WF-IoT 2021

作者： Puurunen, Lassi Chaudhary, Jatin Kanth, Rajeev Heikkonen, Jukka University of Eastern Finland Department of Computer Science Microkatu 1 Kuopio70201 Finland Univeristy of Turku Department of Computing Vesilinnantie 5 Turku20520 Finland Savonia University of Applied Sciences School of Engineering and Technology Microkatu 1 Kuopio70201 Finland Univeristy of Turku Department of Computing Vesilinnantie 5 Turku20520 Finland

ISBN: (纸本)9781665444316

The thermal sensor employing MEMS technology has enabled high accuracy, smaller footprint, low-noise, compact size, and contactless measurement in many embedded computing applications. In the Industrial Internet of Things and home automation system, the automation of different devices requires detecting human presence to initiate proper functioning. We have presented a low-profile IoT platform for realizing the human presence by using the Omron D6T-44L06 thermal sensor. The entire prototype has been built over Aurdino Nano. The experimentation has been conducted mainly in two phases - first, implementing the hardware for the dataset preparation and second, creating a model for Artificial Neural Network(ANN). The prototype built was first used for logging data in the presence and absence of humans using the thermal sensor, and then the model was trained using Keras library in Python. Finally, the realized and trained model has an efficiency of 99.6% and was further validated by experimental outlook. © 2021 IEEE.

关键词： Automation

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Flight Delay Prediction Using Machine Learning

Flight Delay Prediction Using Machine Learning

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International Conference for Convergence of Technology (I2CT)

作者： Ravula Tarun Reddy Peeta Basa Pati K. Deepa S. Tresa Sangeetha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman

The prediction of flight delay can be considered as one of the most challenging problems to solve. Delay of an aircraft is not only a problem for an airline but also for the passengers. Flights can be delayed due to several reasons, the weather being the primary one. In this paper, our focus is to predict the delay of the flights due to bad weather. The dataset used consists of flight data from JFK airport from the sources Bureau of Transportation statistics and weather data from the National Centers for Environmental Information. The results of different machine learning algorithms like Linear Regression, SVR, Decision Tree Regressor, Random Forest Regressor, Ridge, and Lasso Regressor, for prediction of flight delay, are compared. XGboost regressor had the best performance in all the scenarios with least RMSE score of 0.81.

关键词： Machine learning algorithms Linear regression Transportation Forestry Airports Delays Regression tree analysis

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Cross-Domain Multicarrier Waveform Design for Integrated Sensing and Communication

Cross-Domain Multicarrier Waveform Design for Integrated Sen...

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IEEE Conference on Wireless Communications and Networking

作者： Fan Zhang Tianqi Mao Ruiqi Liu Zhu Han Octavia A. Dobre Sheng Chen Zhaocheng Wang Department of Electronic Engineering Tsinghua University Beijing China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing China Wireless and Computing Research Institute ZTE Corporation Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA Faculty of Engineering and Applied Science Memorial University St. John's NL Canada School of Electronics and Computer Science University of Southampton Southampton U.K.

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Integrated sensing and communication (ISAC) is expected to be a promising technology in the sixth-generation (6G) wireless networks for its ability to alleviate resources shortage and excessive hardware expenses. One typical representative for ISAC waveforms is the orthogonal frequency division multiplexing (OFDM) waveform, which divides the time-frequency resources into orthogonal resource elements (REs). In order to satisfy their diverse design requirements and mitigate mutual interference, the communication and sensing subsystems can be assigned with different REs, which necessitates effective allocation strategies of different resources across time and frequency domains. In this article, a cross-domain multicarrier waveform design method-ology is proposed, which optimizes the RE assignment and power allocation strategies for the OFDM-based ISAC system. Specifically, for sensing performance enhancement, the unit cells of the ambiguity function (AF) of the sensing components are spe-cially shaped to achieve a “locally” perfect auto-correlation (AC) property within a predefined region of interest (RoI) in the Delay-Doppler domain. Afterwards, the irrelevant cells outside the RoI, which can determine the sensing power allocation strategy, are optimized alternatively with the communication power allocation strategy to maximize the throughput for the communication purpose. Numerical results demonstrate the superiority of the cross-domain multicarrier waveform design, which also provides useful guidelines for parameter settings of the proposed OFDM-based ISAC system.

关键词： Time-frequency analysis OFDM Design methodology Wireless networks Radar Interference Throughput

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A Classifier to Detect Stream Applications Based on Network Traffic Statistical Metrics

A Classifier to Detect Stream Applications Based on Network ...

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Information Technology (ACIT)

作者： Omar Darwish Sarah Herzallah Majdi Maabreh Shouroq Al-Eidi Mahmoud Al-Maani Yahya Tashtoush Information Security and Applied Computing Eastern Michigan University Ypsilanti Michigan Electrical and Computer Engineering Eastern Michigan University Ypsilanti Michigan Department of Information Technology The Hashemite University Zarqa Jordan Computer Science Department Tafila Technical University Tafila Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

One of the most crucial considerations, when considering security vulnerabilities, is network traffic. There is still potential for more research on the inter-arrival time side, even though some studies concentrate on network traffic from the perspective of the packet fields such as packet length and packet number. Inter-arrival timings are crucial to investigate because there are numerous attacks, such as Covert Timing Channels attacks, that heavily rely on them. In this article, we conduct a statistical analysis of the TCP inter-arrival times of two major key streaming programs (Zoom and Skype), which are frequently used, particularly during and following the COVID-19 pandemic. Using two internet-connected devices and the statistical measures of TCP, a dataset of 18,371 instances is created for this use. Five machine learning algorithms are evaluated on balanced and imbalanced forms of the dataset. The results revealed that the traffic of Zoom and Skype calls can be identified by machine learning algorithms with an accuracy of up to 99% by random forest.

关键词： Measurement COVID-19 Machine learning algorithms Statistical analysis Pandemics Telecommunication traffic Timing

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Heterogeneous data analysis of hypertrophic cardiomyopathy to prioritize important genes

Heterogeneous data analysis of hypertrophic cardiomyopathy t...

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International computer science and Engineering Conference (ICSEC)

作者： Panisa Janyasupab Apichat Suratanee Kitiporn Plaimas Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics and Computer Science Faculty of Science Advance Virtual and Intelligent Computing (AVIC) Center Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781665462730

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease that is often caused by abnormal genes in the heart muscle. The identification of HCM-related genes is one of the crucial tasks to prevent and treat the patient. Gene expression analysis is a direct approach to screen for a gene with a higher or lower expression level in the HCM cell than in the normal cell. Microarray and RNA-Seq technology are used for measuring transcription levels. Both techniques have different advantages to obtain gene expression data. The integration of microarray and RNA-Seq data has already been effectively used to identify disease biomarkers. The ranking method is an interesting technique and is mostly used for ranking players or teams in sports. Each method has different strengths and can be appropriately applied to integrate various data and used to prioritize the importance genes. In this work, six ranking techniques to integrate microarray and RNA-Seq data were applied to prioritize the HCM-related genes. The performance reveals that the ranking method is also a well-suited technique in this task, and it turns out that the PageRank technique yields the best performance.

关键词： Heart computer science Data analysis Computational modeling Muscles Biomarkers Gene expression

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Social Bots: Detection and Challenges

arXiv

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arXiv 2023年

作者： Yang, Kai-Cheng Varol, Onur Nwala, Alexander C. Sayyadiharikandeh, Mohsen Ferrara, Emilio Flammini, Alessandro Menczer, Filippo Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States Network Science Institute Northeastern University United States Faculty of Engineering and Natural Sciences Sabanci University Turkey Department of Applied Science William & Mary United States Thomas Lord Department of Computer Science University of Southern California United States

While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter, we focus on malicious social bots, a prominent vehicle for such manipulation. We start by discussing recent studies about the presence and actions of social bots in various online discussions to show their real-world implications and the need for detection methods. Then we discuss the challenges of bot detection methods and use Botometer, a publicly available bot detection tool, as a case study to describe recent developments in this area. We close with a practical guide on how to handle social bots in social media research. © 2023, CC BY.

关键词： Botnet

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Securing Smart Homes Using Hybrid Stacking Ensemble Deep Learning-Enabled Framework

Securing Smart Homes Using Hybrid Stacking Ensemble Deep Lea...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Abdallah Moubayed MohammadNoor Injadat Tamer Mohamed Abdellatif Mohamed Sattam Almatarneh Malak Al-Mashagbeh Mohammad Aljaidi School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical & Computer Engineering Department Lebanese American University Byblos Lebanon Electrical & Computer Engineering Department Western University London Ontario Canada Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan School of Engineering Applied Science and Technology Canadian University Dubai Dubai UAE Cyber Security Department Zarqa University Zarqa Jordan Computer Science Department Zarqa University Zarqa Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The added deployment of Internet-of-Things (IoT) devices in various environments has led to people increasingly adding this technology to their homes to make them more comfortable and have a more convenient living environment. Despite the promise and the growth potential of the smart home market, one main concern is the security and privacy of smart home devices. This is because smart home devices equipped with IoT technology are prone to various network-based attacks. With smart home devices having limited computation and memory capabilities, many IoT manufacturers often overlook the security aspect by focusing more on the functionality. To address this issue, the concepts of machine learning (ML) and deep learning (DL) have emerged as viable and promising solutions. To that end, this paper proposes a hybrid stacking ensemble DL-enabled framework that combines both classical ML models with sophisticated DL models. The goal is to make use of the simplicity and interpretability of the classical ML models with the superior time-dependent patterns that DL models can detect. The proposed framework's performance is evaluated using multiple datasets representing different smart home devices. Experimental results show that the proposed framework achieved better detection accuracy, precision, recall, and F-score.

关键词： Performance evaluation Deep learning Privacy Accuracy Stacking Focusing Smart homes

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Padding in the mathematics of arrays 7

Padding in the mathematics of arrays

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7th ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, ARRAY 2021, held in association with PLDI 2021

作者： Chetioui, Benjamin Abusdal, Ole Haveraaen, Magne Järvi, Jaakko Mullin, Lenore University of Bergen Department of Informatics Bergen Hordaland Norway Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Hordaland Norway University of Turku Department of Computing Turku Finland College of Engineering and Applied Sciences University at Albany SUNY AlbanyNY United States

ISBN: (纸本)9781450384667

Multi-dimensional array manipulation constitutes a core component of numerous numerical methods, e.g. finite difference solvers of Partial Differential Equations (PDEs). The efficiency of such computations is tightly connected to traversing array data in a hardware-friendly way. The Mathematics of Arrays (MoA) allows reasoning about array computations at a high level and enables systematic transformations of array-based programs. We have previously shown that stencil computations reduce to MoA's Denotational Normal Form (DNF). Here we bring to light MoA's Operational Normal Forms (ONFs) that allow for adapting array computations to hardware characteristics. ONF transformations start from the DNF. Alongside the ONF transformations, we extend MoA with rewriting rules for padding. These new rules allow both a simplification of array indexing and a systematic approach to introducing halos to PDE solvers. Experiments on various architectures confirm the flexibility of the approach. © 2021 ACM.

关键词： Finite difference method

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