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Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

computer Modeling in engineering & sciences 2023年第10期137卷 939-964页

作者： M.C.Shanmukha Sokjoon Lee A.Usha K.C.Shilpa Muhammad Azeem Department of Mathematics Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Computer Engineering(Smart Security) Gachon UniversitySeongnam13120South Korea Department of Mathematics Alliance School of Applied MathematicsAlliance UniversityBangalore562106India Department of Computer Science and Engineering Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahore54660Pakistan

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical *** molecules of chemical compounds are modelled as a graph to study the properties of the *** geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular *** article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and *** topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological *** study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological ***,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

关键词： Topological descriptors weighted entropy graphenylene

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Internet of Things (IoT) in Smart Cities: A Statistical Survey 2

Internet of Things (IoT) in Smart Cities: A Statistical Surv...

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

作者： Iqbal, Muhammad Waseem Khaliq, Khowla Al-Dmour, Nidal A. Aqeel, Muhammad Ali, Nasir Hamid, K. Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Sciecnce Lahore54000 Pakistan Mutah University College of Engineering Department of Computer Engineering Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.

关键词： Internet of things

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FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal

FAQNet: Frequency-Aware Quaternion Network for Endoscopic Hi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Dingzhou Huang, Guoheng Yuan, Xiaochen Chen, Xuhang Zhong, Guo Pun, Chi-Man Deng, Jie Guangdong University of Technology School of Computer Science and Technology China Macao Polytechnic University Faculty of Applied Sciences China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Guangdong University of Foreign Studies School of Information Science and Technology China First Affiliated Hospital of Sun Yat-Sen University China

ISBN: (纸本)9798350386226

Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors but also poses a challenge to subsequent computer vision tasks. To tackle this issue, we introduce FAQNet, a network specifically designed for endoscopic image highlight removal. FAQNet seamlessly integrates multi-channel information leveraging quaternion convolution and spatial channel attention within our Quaternion Multi-Channel Fusion (QMCF) Module. This allows it to capture intricate details of color, texture, spatial information, and highlight characteristics within the imaged organ. Additionally, by employing frequency domain transformation and dilated convolution, the Contextual Information Integration (CII) Module effectively enlarges the receptive field, organizing contextual information between highlight regions and their surrounding areas. Lastly, the PixelShuffle Upsampling (PSU) Module generates the restored image. We validate our model's performance on two benchmark datasets, demonstrating its superiority over existing highlight removal methodologies. © 2024 IEEE.

关键词： Endoscopy

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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Anti-Interference Low-Power Double-Edge Triggered Flip-Flop Based on C-Elements

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Tsinghua science and Technology 2022年第1期27卷 1-12页

作者： Zhengfeng Huang Xiao Yang Tai Song Haochen Qi Yiming Ouyang Tianming Ni Qi Xu School of Electronic Science and Applied Physics Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China School of Electrical Engineering Anhui Polytechnic UniversityWuhu 241000China

When the input signal has been interfered and glitches occur,the power consumption of Double-Edge Triggered Flip-Flops(DETFFs)will significantly *** effectively reduce the power consumption,this paper presents an anti-interference low-power DETFF based on *** improved C-element is used in this DETFF,which effectively blocks the glitches in the input signal,prevents redundant transitions inside the DETFF,and reduces the charge and discharge frequencies of the *** C-element has also added pull-up and pull-down paths,reducing its *** with other existing DETFFs,the DETFF proposed in this paper only flips once on the clock edge,which greatly reduces the redundant transitions caused by glitches and effectively reduces power *** paper uses HSPICE to simulate the proposed DETFF and other 10 *** findings show that compared with the other 10 types of DETFFs,the proposed DETFF has achieved large performance indexes in the total power consumption,total power consumption with glitches,delays,and power delay product.A detailed analysis of variance indicates that the proposed DETFF features less sensitivity to process,voltage,temperature,and Negative Bias Temperature Instability(NBTI)-induced aging variations.

关键词： Double-Edge Triggered Flip-Flop(DETFF) glitch low-power C-element

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WHAT-IF SCENARIO ANALYSIS OF REGIONAL AIR MOBILITY OPERATIONS IN SOUTH KOREA 34th

WHAT-IF SCENARIO ANALYSIS OF REGIONAL AIR MOBILITY OPERATION...

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34th Congress of the International Council of the Aeronautical sciences, ICAS 2024

作者： Lee, Juchan Kim, Seulki Ahn, Sumin Kim, Junghyun School of Applied Artificial Intelligence Handong Global University Korea Republic of School of Aerospace Engineering Georgia Institute of Technology United States School of Computer Science and Electrical Engineering Handong Global University Korea Republic of School of Aerospace Engineering Sejong University Korea Republic of

With the global emergence of Regional Air Mobility (RAM) as a new transportation concept, the Korean government has recently recognized the growing interest in employing RAM services to enhance regional connectivity within South Korea. This research starts by formulating the following research question: "What are market dynamics associated with the adoption of RAM services in South Korea?" To answer the question, this research proposes a methodology that leverages a data-driven approach incorporating four modules: 1) potential vertiport location identification, 2) trip data preparation, 3) RAM fare and travel time estimations, and 4) market share estimation. Utilizing the modules, this research conducts what-if scenario analysis on five transportation scenarios across potential origin-destination pairs and estimates market shares for different transportation modes to assess feasibility and competitiveness related to the RAM services as a new transportation system. The main contribution of this research is to establish a basis for uncovering market dynamics of the upcoming RAM operations in South Korea and to provide insights for stakeholders, policymakers, and researchers prior to the actual implementation of the RAM services in South Korea. © 2024, International Council of the Aeronautical sciences. All rights reserved.

关键词： Market Research

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PCA Lab's solution for RePSS 2024 by vision-based self-supervised remote heart rate sensing 3

PCA Lab's solution for RePSS 2024 by vision-based self-super...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Shao, Hang Luo, Lei Qian, Jianjun Zhuo, Wei Hu, Chuanfei Yang, Jian PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Automation Southeast University Nanjing211189 China Nanjing Center for Applied Mathematics Nanjing211135 China

Vision-based remote non-contact physiological measurements are crucial for detecting indicators (such as heart rate and blood pressure) that reflect important vital signs. This paper introduces the approach proposed by PCA Lab in the third challenge on Vision-based Remote Physiological Signal Sensing (RePSS) organized within IJCAI 2024. Specifically, we design an end-to-end self-supervised contrastive learning network for remote heart rate detection, which can generalize light reflection changes caused by cardiac activity in subcutaneous capillaries based on unlabeled facial videos. At the same time, we optimize the facial skin region of interest extraction method and remove most of the irrelevant content and redundant information. In addition, we construct the hybrid pipeline temporal attention module and spatiotemporal reconstruction pretraining paradigm to improve the network's ability to model long-distance sequence features. Our network is tested on 1000 samples from 200 participants provided by the Track 1 of this challenge, and it achieve the root mean squared error of 8.96941. The codes and model are publicly available at https://***/Sachiel0916/repss-track1-top3/. © 2024 Copyright for this paper by its authors.

关键词： contrastive learning remote heart rate sensing RePSS self-supervision framework spatiotemporal reconstruction

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FPGA Implementation of High-Capacity SD Card using VHDL Language 21

FPGA Implementation of High-Capacity SD Card using VHDL Lang...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Taha, Taha Basheer Ngadiran, Ruzelita Ehkan, Phaklen College of Applied Science Tishk International University Information Technology Department Erbil Iraq School of Computer and Communication Engineering Universiti Malaysia Perlis Perlis Malaysia

ISBN: (纸本)9798350374131

Limitations in capacity of internal memory chips on embedded systems lead to the usage of external memories especially when processing large amount of data as images or videos. FPGAs as embedded system devices used in different signal processing applications require extra memory for data storage. Current attempts in programming Secure Digital (SD) cards as external memories attached to FPGAs came with utilization of aiding application or of shelf tools which consumes large amount of chip clock cycles and reduce the overall performance. In this paper, a hardware implementation of SD card programming is presented by using pure and standalone VHDL code with high programming flexibility. In addition, High-Capacity SD cards (SDHC) are implemented for maximum storage capacity to handle large amount of data. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Load Balancing in Cloud Computing based on Ant Colony Optimization and Crow Search Algorithm 27

Load Balancing in Cloud Computing based on Ant Colony Optimi...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Alsheavi, Amar N. Wang, Xingfu Zhao, Wei Hawbani, Ammar Alhusaini, Naji Ahmed, Ibrahim Abdulrab Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China Computer Department Applied College Najran University Najran66462 Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen518060 China Faculty of Science Zagazig University Zagazig44519 Egypt

ISBN: (纸本)9791188428137

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Ant colony optimization

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