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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： T. Lalitha Jerzy Ryszard Szymański Marta Żurek-Mortka Mithileysh Sathiyanarayanan K. R. Srilekha Computer Science School of Information Science Presidency University Bengaluru Karnataka India Department of Electrical Drives and Industrial Electronics Faculty of Transport Electrical Engineering and Computer Sciences Casimir Pulaski University of Radom Poland Europe Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pułaskiego Radom Poland Europe MIT Square London I M.Tech Integrated(Software Engineering) VIT University Vellore Tamilnadu India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal’s death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation Network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis.

关键词： Temperature measurement Pandemics Snow Sea measurements Carbon dioxide Imputation Fuels Vehicle dynamics Socioeconomics Meteorology

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A Neural Collaborative Filtering Model for Adverse Drug Reaction Prediction

A Neural Collaborative Filtering Model for Adverse Drug Reac...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Zetong Xiong Zihao Du Xi Rong Yuting Yang Xiaoyi Zhang School of Information and Safety Engineering Zhongnan University of Economic and Law Wuhan China Department of Computing and Software McMaster University Hamilton Canada School of Engineering Tufts University Medford MA United States Department of Computer Science and Engineering The Ohio State University Columbus USA Department of Mathematics University of California San Diego USA

As adverse drug reactions (ADRs) can cause serious consequences to medication treatment, identifying and predicting adverse drug reactions (ADRs) play a crucial role in drug effects and drug use safety. In this paper, we reviewed existing machine learning methods in ADR prediction and proposed a neural collaborative filtering model (NCF) for the prediction of monopharmacy ADRs. NCF is based on dimension reduction by matrix factorization (MF) and combined with an MLP model to enhance prediction performance by handling complex non-linear relationships using MLN. Our NCF model optimizes the MF model by replacing dot product in the MF model with MLP, integrating drug and ADR embeddings from monopharmacy ADR benchmark data and drug-related chemical, physical and biological descriptors in a two-layer deep neural network. The model was tested by using 10-fold cross-validation. On 10-fold cross-validation, the resulting AUC, AUPR, and running time indicated a better performance and higher efficiency on drug-ADR prediction achieved by applying our NCF model.

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An innovation analysis of LZ77 and LZ78 Compression Algorithms for Data Compression & Source Coding

An innovation analysis of LZ77 and LZ78 Compression Algorith...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rakesh Kumar Giri Abhinav Mishra Aruna Sri Rongali Laxmi A. Bewoor R Raghavendra M. S. Kavitha Department of Computer Science & Engineering Sunrise University Alwar India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Information Technology Emphasis/Technology Software Engineer Ph.D University of The Cumberlands US Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA College of Computing Sciences And Information Technology Jain (deemed to be) University Bangalore Karnataka India Department of ECER. M. K. Engineering College RSM Nagar Kavaraipettai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper evaluates innovation on two of the most widely used compression algorithms — the LZ77 and the LZ78 algorithms — for information compression and supply coding programs. A detailed assessment of these algorithms presents insights approximately their design philosophy, technical traits, and performance metrics. Additionally, the studies offer a complete overview of their packages and implementations. The paper investigates the prominent considerations for implementing compression algorithms and analyzes their relative blessings and downsides. An exploration of the constraints of every algorithm is likewise undertaken. Furthermore, the research explores distinctive design elements of the algorithms, such as data structure, compression engine, and set of rules optimization. Eventually, the paper assesses the algorithms by reading the exchange-offs between their performance and complexity. This study facilitates practitioners and developers to apprehend the results of using those algorithms for diverse compression and supply coding applications. It can assist practitioners in identifying the first-class compression set of rules for their unique application needs. This paper gives an innovative evaluation of the LZ77 and LZ78 Compression Algorithms. These algorithms are algorithms designed for statistics compression and source coding. They’re used to take a given series of characters (or bits) and encode them right into an area-green representation. The innovation evaluation presents a valuable perception of how the algorithms improve compression ratios and shop area. It also discusses the effect of positive parameters such as window length, repeat cycle, and buffer size on compression efficiency. ultimately, the paper will speak to future studies’ challenges in this region and the capability packages of those algorithms.

关键词： Measurement Technological innovation Image coding Codes Philosophical considerations Source coding Green products Approximation algorithms Compression algorithms Optimization

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Performance evaluation of different algorithms for crack detection in concrete structures 2

Performance evaluation of different algorithms for crack det...

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2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Ali, Luqman Harous, Saad Zaki, Nazar Khan, Wasif Alnajjar, Fady Jassmi, Hamad Al College of Information Technology United Arab Emirates University Al Ain Department of Computer Science and Software Engineering Abu Dhabi United Arab Emirates College of Engineering United Arab Emirates University Al Ain Department of Civil Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9781728194912

Detection of cracks at the earliest stage is crucial, as these are the primary indicators of infrastructure's health. Manual inspection is often carried out for infrastructure inspection which requires in-depth knowledge of domain, which is time-consuming, labor intensive. The in-accessibility of infrastructure in manual inspection make it more challenging and complex. Therefore, various efficient and fast image-based automatic techniques have been introduced in the literature for concrete crack detection task. This paper aims to evaluate the performance six hand-crafted features based traditional approaches in comparison with deep Convolutional Neural Networks (CNN's) for concrete crack detection using different performance metrics. The dataset is obtained by combing data from two publicly available datasets and consists of 40000 crack and non-crack images. Extensive experiments are conducted demonstrating that Random Forest and KNN classifier performs better with 98% accuracy with Area Under the Curve 0.99 as compared to the other classifiers using handcrafted features as well it is faster than deep convolutional neural networks. The computational time for the DCNN is larger than all other classifier but it has the capability to extract feature from images automatically. © 2021 IEEE.

关键词： computer vision

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Incremental feature selection for efficient classification of dynamic graph bags

Incremental feature selection for efficient classification o...

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作者： Chae, Dong-Kyu Kim, Bo-Kyum Kim, Seung-Ho Kim, Sang-Wook Department of Computer Science and Engineering Hanyang University Seoul Korea Republic of Department of Computer and Software Hanyang University Seoul Korea Republic of

Learning and analyzing graph data is one of the most fundamental research areas in machine learning and data mining. Among numerous graph-based data structures, this paper focuses on a graph bag (simply, bag), which corresponds to a training object containing one or more graphs, and a label is available only for a bag. This type of a bag can represent various real-world objects such as drugs, web pages, XML documents, and images, among many others, and there have been many researches on models for learning this type of bag data. Within this research context, we define a novel problem of dynamic graph bag classification, and propose an algorithm to solve this problem. Dynamic bag classification aims to build a classification model for bags, which are presented in a streaming fashion, ie, frequent emerging of new bags or graphs over time. Given such changes made to the bag dataset, our proposed algorithm aims to update incrementally the top-m most discriminative features instead of searching for them from scratch. Incremental gSpan and incremental gScore are proposed as core parts of our algorithm to deal with a stream of bags efficiently. We evaluate our algorithm on two real-world datasets in terms of both feature selection time and classification accuracy. The experimental results demonstrate that our algorithm derives an informative feature set much faster than the existing one originally designed for targeting static bag data, with little accuracy loss. © 2019 John Wiley & Sons, Ltd.

关键词： Classification (of information)

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Attribute-Specific Manipulation Based on Layer-Wise Channels

arXiv

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arXiv 2023年

作者： Yan, Yuanjie Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Electronic Science and Engineering Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China

Image manipulation on the latent space of the pre-trained StyleGAN can control the semantic attributes of the generated images. Recently, some studies have focused on detecting channels with specific properties to directly manipulate the latent code, which is limited by the entanglement of the latent space. To detect the attribute-specific channels, we propose a novel detection method in the context of pre-trained classifiers. We analyse the gradients layer by layer on the style space. The intensities of the gradients indicate the channel’s responses to specific attributes. The latent style codes of channels control separate attributes in the layers. We choose channels with the top-k gradients to control specific attributes in the maximum response layer. We implement single-channel and multi-channel manipulations with a certain attribute. Our methods can accurately detect relevant channels for a large number of face attributes. Extensive qualitative and quantitative results demonstrate that the proposed methods outperform state-of-the-art methods in generalization and scalability. © 2023, CC BY.

关键词： Semantics

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G-Good-Neighbour Diagnosability Under the Modified Comparison Model for Multiprocessor Systems

SSRN

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SSRN 2022年

作者： Wang, Mujiangshan Xiang, Dong Hsieh, Sun-Yuan School of Software Tsinghua University Beijing100084 China Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan

How to diagnose the faults in a multiprocessor system has been a hot issue for years not only since it is a great deal of impact imposed on the performance if fault occurs, but is of relatively high theoretical value combining the concepts of Graph Theory and computer science as well. In 2012, called the g-good-neighbour diagnosability, a measure for fault tolerance of the system was proposed by Peng et al, which restrains every fault-free node to contain at least g fault-free neighbours. Having become increasingly attractive in decades, the g-good-neighbour connectivity and diagnosability of many well-known multiprocessor systems have been widely investigated. In this paper, we innovatively propose a new modified comparison model, the MC model for multiprocessor systems, which combines the advantages of both the PMC and MM∗ model so that the involved computing power and time resource are saved. Based on this, not only do we then give an algorithm which describes how the MC model operates, but also lay out the sufficient and necessary conditions of multiprocessor systems G’s to be g-good-neighbour t-diagnosable under the MC model. Then, we give a general method to derive the g-good-neighbour diagnosability of a G under the MC model. Furthermore, we give evidences to explain the advantages of the MC model against the PMC and MM (the MM∗ included) one. Finally, we apply our new MC model to Hypercube and have its corresponding g-good-neighbour diagnosability. © 2022, The Authors. All rights reserved.

关键词： Graph theory

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A bioinformatics and system biology technique to identify candidate biomarkers and functional pathways among stress and depression

A bioinformatics and system biology technique to identify ca...

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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： Md. Abul Basar Md. Faruk Hosen Md. Al Amin Nasima Islam Bithi Bikash Kumar Paul Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University (DIU) Dhaka Bangladesh

Stress means a lot of mental or physical strain. It can result from anything that provokes negative emotions, such as an idea or an experience. The World Health Organization cites depression as the leading nonfatal cause of disability worldwide. Major depressive disorder is one of the most common mental health illnesses that results in long-term damage (MDD). A major obstacle to the correct identification of stress and depression is the lack of good diagnostic biomarkers. By analyzing gene networks, we hoped to identify potential biomarkers of this disease. Samples of stress and depression were used to create the microarray and RNA-seq datasets GSE183648 and GSE101521. Through the use of R, it is possible to search for frequently occurring DEGs and filter preprocessed datasets. A Venn diagram illustrating the overlap between different sets of DEGs rules. When a group of DEGs is established, the PPIs network is built from them, with hub nodes selected according to topological features. In the PPI network, we found that ITGB3, ANGPTL4, ITGB1, PPARA, and RXRA serve as important hub genes. This research includes KEGG pathways as well. The development of the Gene Ontology, studies of gene-miRNA interactions, TF-gene interactions, and module analysis networks are all seen as important next steps in this field of study. Last but not least, various therapeutic possibilities have been found with identical DEGs as their primary base.

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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

arXiv

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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Canadarm, Canadarm2, and Canadarm3: The evolution of Canada's iconic robotic system and its impacts from space down to Earth 58

Canadarm, Canadarm2, and Canadarm3: The evolution of Canada'...

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58th IAA History of Astronautics Symposium at the 75th International Astronautical Congress, IAC 2024

作者： Hudon-Castillo, Yianni Lamanque, Jean-Christophe Thénault, Marion Zamudio-Turcotte, Katherine Musunuri, Sri Venkata Vathsala Thilloy, Auriane Leclair, Olivier Roberge, Julien Lavergne Elforaici, Mohamed El Amine Daigneault, Rafael Chazbek, Rachad Beltrame, Giovanni Department of Electrical Engineering Polytechnique Montréal 2500 Chemin de Polytechnique MontréalQCH3T 1J4 Canada Department of Electrical and Computer Engineering McGill University McConnell Engineering Building MontréalQCH3A 0E9 Canada Department of Aerospace Engineering Gina Cody School of Engineering and Computer Science Concordia University 1455 De Maisonneuve Blvd. W. MontréalQCH3G 1M8 Canada Department of Aerospace Engineering Polytechnique Montréal 2500 Chemin de Polytechnique MontréalQCH3T 1J4 Canada Department of Physics Université de Montréal 1375 Ave Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Department of Mechanical Engineering Université Laval QuébecQCG1V 0A6 Canada Department of Biomedical Engineering Polytechnique Montréal 2500 Chemin de Polytechnique MontréalQCH3T 1J4 Canada Department of Engineering Physics Polytechnique Montréal 2500 Chemin de Polytechnique MontréalQCH3T 1J4 Canada Department of Computer and Software Engineering Polytechnique Montréal 2500 Chemin de Polytechnique MontréalQCH3T 1J4 Canada

ISBN: (纸本)9798331312329

Considered to be Canada's groundbreaking technological achievement in the field of robotics and its greatest contribution to space, Canadarm and Canadarm2 have made their mark on both the Canadian and international scene. As the upcoming Canadarm3 reaches the culmination of its design phase for the Lunar Gateway, an opportunity to review the evolution of this robotic technology over its combined 53 years of service arises. Furthermore, the practical impacts of each Canadarm generation will be explored, regarding the safe and sustainable use of space, the commercialization of the space industry, and technological progress in the medical field, in nuclear power plant robotics and in remote explosives disarming robots. This paper will embark on a comprehensive literature review, complemented by interviews conducted with key contributors to the Canadarms' missions as well as Canadian astronauts. This methodological approach aims to provide an accurate outline of the technical, operational, and collaborative complexities that have underpinned the development and deployment of this technology. By comparing the technical specifications of each Canadarm generation in the context of their respective space program, namely Space Shuttle, ISS and Gateway, technological advancements will be highlighted from an engineering and operations standpoint. The conducted interviews with the Canadian Space Agency (CSA) and MacDonald Dettwiler And Associates (MDA) will also help illustrate the obstacles faced throughout the implementation of these programs. Foreseeing the next step in Canada's role in space exploration, this study will also examine Canadarm3's upcoming crucial contribution in missions to the Moon, Mars, and beyond. This outlook includes a mission concept study of the next generation Canadarm : Canadarm4, which will emphasize the ongoing significance of innovation and international collaboration, affirming Canada's leadership in space robotics technology. © 2024 Internation

关键词： Nuclear power plants

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