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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Raman Singh Sean Sturley Bhisham Sharma Imed Ben Dhaou School of Computing Engineering and Physical Sciences University of the West of Scotland United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Engineering Computing and Informatics Dar Al-Hekma University Saudi Arabia

The Internet of Things (IoT) is the network of multiple devices known as “things” which includes sensors, security cameras, smart lights, smart TV, traffic lights etc. in the smart home or industrial environment. In many applications, these IoT devices are installed in open areas for example traffic lights/ security cameras in a smart city. Strong authentication and authorisation for these devices need to be deployed to ensure trust among IoT networks. IoT devices produce and forward security-sensitive data and hence confidentiality, authentication and proper authorisation should be the primary priority of an IoT system. Implementing Certificate Authority-based digital certificate solutions is costly because of the number of devices involved in IoT networks. Blockchain is a decentralized ledger-based technology which can help to provide seamless yet cost-effective solutions for confidentiality, authentication, and authorisation for IoT environments. A blockchain-based system for device registration, authentication, authorisation, and data confidentiality is proposed. The paper shows the methodological and procedural details of the proposed security scheme.

关键词： Authorization Technological innovation Smart cities Supply chains Authentication Cameras Blockchains

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Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

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Computers, Materials & Continua 2022年第1期70卷 715-738页

作者： Shidrokh Goudarzi Seyed Ahmad Soleymani Mohammad Hossein Anisi Domenico Ciuonzo Nazri Kama Salwani Abdullah Mohammad Abdollahi Azgomi Zenon Chaczko Azri Azmi Centre for Artificial Intelligent(CAIT) Universiti Kebangsaan MalaysiaBangi43600Malaysia School of Computer Engineering Iran University of Science and TechnologyResalat Sq.16846-13114TehranIran School of Computing Faculty of Engineering Universiti Teknologi MalaysiaJohorMalaysia School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Department of Electrical Engineering and Information Technologies University of Naples“Federico II”Naples80125Italy Razak Faculty of Technology and Informatics Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

关键词： Unmanned aerial vehicles wireless sensor networks group method data handling particle swarm optimization river flow prediction

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MixLight: Borrowing the Best of both Spherical Harmonics and Gaussian Models

arXiv

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arXiv 2024年

作者： Ji, Xinlong Zhan, Fangneng Lu, Shijian Huang, Shi-Sheng Huang, Hua The School of Computing Science & Technology Beijing Institute of Technology Beijing100081 China The Max Planck Institute for Informatics Saarbrücken66123 Germany The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The School of ArtificialIntelligence Beijing Normal University Beijing100875 China

Accurately estimating scene lighting is critical for applications such as mixed reality. Existing works estimate illumination by generating illumination maps or regressing illumination parameters. However, the method of generating illumination maps has poor generalization performance and parametric models such as Spherical Harmonic (SH) and Spherical Gaussian (SG) fall short in capturing high-frequency or low-frequency components. This paper presents MixLight, a joint model that utilizes the complementary characteristics of SH and SG to achieve a more complete illumination representation, which uses SH and SG to capture low-frequency ambient and high-frequency light sources respectively. In addition, a special spherical light source sparsemax (SLSparsemax) module that refers to the position and brightness relationship between spherical light sources is designed to improve their sparsity, which is significant but omitted by prior works. Extensive experiments demonstrate that MixLight surpasses state-of-the-art (SOTA) methods on multiple metrics. In addition, experiments on Web Dataset also show that MixLight as a parametric method has better generalization performance than non-parametric methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Light sources

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Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

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Computers, Materials & Continua 2024年第11期81卷 2357-2394页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Razaz Waheeb Attar Farman Ali Ahmed Alhomoud Babar Shah School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia College of Technological Innovation Zayed UniversityDubai19282United Arab Emirates

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control *** these networks,nodes function as senders,receivers,and *** such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely *** the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power *** optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in *** paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in *** energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient *** link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of *** simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing *** evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

关键词： FANET UAV internet of UAVs relay mechanism energy consumption routing IEE-DLSA

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LiteLSTM Architecture for Deep Recurrent Neural Networks

arXiv

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arXiv 2022年

作者： Elsayed, Nelly ElSayed, Zag Maida, Anthony S. School of Information Technology School of Computing and Informatics University of Cincinnati United States University of Louisiana Lafayette United States

Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power for learning and implementing from both software and hardware aspects. This paper proposes a novel LiteLSTM architecture based on reducing the computation components of the LSTM using the weights sharing concept to reduce the overall architecture cost and maintain the architecture performance. The proposed LiteLSTM can be significant for learning big data where time-consumption is crucial such as the security of IoT devices and medical data. Moreover, it helps to reduce the CO2 footprint. The proposed model was evaluated and tested empirically on two different datasets from computer vision and cybersecurity domains. Copyright © 2022, The Authors. All rights reserved.

关键词： Carbon dioxide

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E2PNet: event to point cloud registration with spatio-temporal representation learning 23

E2PNet: event to point cloud registration with spatio-tempor...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xiuhong Lin Changjie Qiu Zhipeng Cai Siqi Shen Yu Zang Weiquan Liu Xuesheng Bian Matthias Müller Cheng Wang Fujian Key Lab of Sensing and Computing for Smart Cities School of Informatics Xiamen University (XMU) China and Key Laboratory of Multimedia Trusted Perception and Efficient Computing XMU China Intel Labs. Fujian Key Lab of Sensing and Computing for Smart Cities School of Informatics Xiamen University (XMU) China and Yancheng Institute Of Technology China Apple Inc.

Event cameras have emerged as a promising vision sensor in recent years due to their unparalleled temporal resolution and dynamic range. While registration of 2D RGB images to 3D point clouds is a long-standing problem in computer vision, no prior work studies 2D-3D registration for event cameras. To this end, we propose E2PNet, the first learning-based method for event-to-point cloud registration. The core of E2PNet is a novel feature representation network called Event-Points-to-Tensor (EP2T), which encodes event data into a 2D grid-shaped feature tensor. This grid-shaped feature enables matured RGB-based frameworks to be easily used for event-to-point cloud registration, without changing hyper-parameters and the training procedure. EP2T treats the event input as spatio-temporal point clouds. Unlike standard 3D learning architectures that treat all dimensions of point clouds equally, the novel sampling and information aggregation modules in EP2T are designed to handle the inhomogeneity of the spatial and temporal dimensions. Experiments on the MVSEC and VECtor datasets demonstrate the superiority of E2PNet over hand-crafted and other learning-based methods. Compared to RGB-based registration, E2PNet is more robust to extreme illumination or fast motion due to the use of event data. Beyond 2D-3D registration, we also show the potential of EP2T for other vision tasks such as flow estimation, event-to-image reconstruction and object recognition. The source code can be found at: https://***/Xmu-qcj/E2PNet.

关键词：

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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A Prediction of Customer Segmentation using Random Forest in RapidMiner

A Prediction of Customer Segmentation using Random Forest in...

引用

Student Conference on Research and Development (SCOReD)

作者： Akibu Mahmoud Abdullahi Sai Ahkar Htet Nyi Nyi Nay Naing Md Shakil Hossain Shahrinaz Ismail Mohd Ariff Majmi Zaaba School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Technology Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Customer segmentation is a crucial task in marketing and business strategy, enabling organizations to better understand their customer’s behavior and needs. In today’s environment, it is critical for businesses to segment their clients and take the necessary steps to compete with other businesses. Due to the abundance, each company must satisfy client demands or run the risk of losing them to other offerings. The dataset contains information about various attributes of customers, including gender, marital status, age, education, profession, work experience, spending score, family size, and other variables. The aim of this research is to predict customer segmentation using the Random Forest algorithm in RapidMiner studio based on these attributes and to identify distinct customer groups that exhibit similar characteristics. This research attempts to offer beneficial insights for customized customer experiences, targeted marketing activities, and product development. The RapidMiner tool was used to analyze the dataset. It is an open-source program for predictive analytics that supports data mining. It offers prompt responses and lets the user build models based on their needs. To forecast fresh, uncontaminated data, the model in this classification is assessed on test data after being thoroughly trained on training data. Several decision trees are constructed using various random selections of the data and features. Each decision tree acts as a professional, offering guidance on how to categorize the data. This study can be a guide to those who are willing to use RapidMiner for prediction analysis using the Random Forest algorithm.

关键词： Accuracy Training data Forestry Data science Prediction algorithms Decision trees Data mining

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Correlation Network Analysis of ASEAN Foreign Direct Investment

Correlation Network Analysis of ASEAN Foreign Direct Investm...

引用

2023 IEEE International Conference on computing, ICOCO 2023

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Yeo, Ivan Abu Bakar, Mohd Aftar Kaur, Sukhminder Razak, Fatimah Abdul School of Mathematical Sciences College of Computing Informatics & Mathematics Universiti Teknologi Mara Shah Alam Malaysia Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Eng. & Science Dept. of Mathematical & Acturial Sciences Malaysia Universiti Kebangsaan Malaysia Faculty of Science & Technology Department of Mathematical Sciences Bangi Malaysia School of Computer Science Taylor's University Faculty of Innovation & Technology Subang Jaya Malaysia

ISBN: (纸本)9798350302684

Although ASEAN aims to promote regional economic and security cooperation, among others, the empirical evidence in this study evinces that the impact of ASEAN on the economic integration between ASEAN countries remains limited. The economic integration is investigated based on the association of foreign direct investments (FDI) between Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam. The longest common period of the FDI series is 29 years beginning in 1993. We examine the yearly FDI performances using a time series plot and descriptive statistics including the coefficient of variance for performance consistency. The association, strength, and effect of ASEAN economic integration are measured and illustrated by using Pearson's correlation matrix-circle plot. There are 45 unique associations representing the economic integration between ASEAN countries that unfortunately exist only in moderate strength, at most. The highest association of 0.62 is between Cambodia and Laos, and there is negligible association between Philippines-Vietnam and Indonesia-Philippines. It is also noted that almost 60% of the associations are positively correlated to one another. For the second objective, the correlations are clustered and visualized using network plots. The correlation- based network plot reveals two subgroups linking Indonesia- Cambodia-Singapore-Laos and Malaysian-Vietnam-Brunei closer together, while Thailand, Myanmar, and the Philippines are isolated from the subgroups. The most long-lasting interconnections between economies are found in the subgroup centered on Cambodia. In addition, there are only 14 weak- medium integrated links when clustering the correlation values above magnitude 0.3. The results suggest that the vast economic cooperation opportunities have not been fully explored among the ASEAN countries. Therefore, strategies may be necessary, such as adding packages and quotas to attract FDI from individual i

关键词： Data visualization

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Neonatal Disease Prediction Using Machine Learning Techniques

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Journal of Healthcare Engineering 2023年第1期2023卷 3567194页

作者： Robi, Yohanes Gutema Sitote, Tilahun Melak Department of Information Systems College of Computing and Informatics Haromaya University P.O. Box 138 Dire Dawa Ethiopia School of Electrical Engineering and Computing Adama Science and Technology University P.O. Box 1888 Adama Ethiopia

Neonatal diseases are among the main causes of morbidity and a significant contributor to underfive mortality in the world. There is an increase in understanding of the pathophysiology of the diseases and the implementation of different strategies to minimize their burden. However, improvements in outcomes are not adequate. Limited success is due to different factors, including the similarity of symptoms, which can lead to misdiagnosis, and the inability to detect early for timely intervention. In resource-limited countries like Ethiopia, the challenge is more severe. Low access to diagnosis and treatment due to the inadequacy of neonatal health professionals is one of the shortcomings. Due to the shortage of medical facilities, many neonatal health professionals are forced to decide the type of disease only based on interviews. They may not have a complete picture of all variables that have a contributing effect on neonatal disease from the interview. This can make the diagnosis inconclusive and may lead to a misdiagnosis. Machine learning has great potential for early prediction if relevant historical data is available. We have applied a classification stacking model for the following four main neonatal diseases: sepsis, birth asphyxia, necrotizing enter colitis (NEC), and respiratory distress syndrome. These diseases account for 75% of neonatal deaths. The dataset has been obtained from the Asella Comprehensive Hospital. It has been collected between 2018 and 2021. The developed stacking model was compared to three related machine-learning models XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). The proposed stacking model outperformed the other models, with an accuracy of 97.04%. We believe that this will contribute to the early detection and accurate diagnosis of neonatal diseases, especially for resource-limited health facilities. © 2023 Yohanes Gutema Robi and Tilahun Melak Sitote.

关键词： Diseases

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