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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Yusuf Olayinka Imam-Fulani Nasir Faruk Aliyu D. Usman Abubakar Abdulkarim Abdoulie M.S Tekanyi Abdulmalik S. Yaro Emmanuel Alozie Salisu Garba Kayode S. Adewole Abdulkarim A. Oloyede Olugbenga A. Sowande Bashir Abdullahi Baba Abdulwaheed Musa Agbotiname Lucky Imoize Yinusa A. Adediran Hawau I. Olagunju Lawan S. Taura Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Directorate of Information and Communication Technology Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunication Engineering Kampala International University Kampala Uganda Department of Informatics and Qualitative Methods Faculty of Informatics and Management University of Hradec Králové Hradec Králové Czech Republic Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Electronics Engineering University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer’s Disease

arXiv

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arXiv 2023年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China The Department of Systems and Enterprises Stevens Institute of Technology United States School of Science and Technology Hong Kong Metropolitan University Hong Kong College of Engineering and Computer Science VinUni-Illinois Smart Health Center VinUniversity Viet Nam Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Game Design Faculty of Arts Uppsala University Sweden The School of Software Tiangong University Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer’s disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Green and rapid and instrumental one-pot method for the synthesis of imidazolines having potential anti-SARS-CoV-2 main protease activity

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Sustainable Chemistry and Pharmacy 2023年 34卷 101136页

作者： Azimi, Sabikeh Merza, Muna S. Ghasemi, Fatemeh Dhahi, Hasan Ali Baradarbarjastehbaf, Farid Moosavi, Mehdi Kargar, Pouya Ghamari Len, Christophe Department of Chemistry Faculty of Sciences University of Birjand Birjand 97175-615 Iran Prosthetic Dental Techniques Department Al-Mustaqbal University College Babylon 51001 Iraq MSC in Software Engineering Kowsar The Institute of Higher Education Ministry of Science Research and Technology Computer Engineering Department Qazvin Iran National University of Science and Technology Dhi Qar Iraq Faculty of Pharmacy Department of Pharmaceutical Technology and Biopharmacy University of Pécs Pécs Hungary Department of Chemistry Faculty of Chemistry Mazandaran University Babolsar Iran Chimie ParisTech PSL University CNRS Institute of Chemistry for Life and Health Sciences 11 Rue Pierre et Marie Curie Paris F-75005 France Sorbonne Universités Universite de Technologie de Compiegne Compiegne F- 60200 France

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is responsible for ongoing epidemics in humans and some other mammals and has been declared a public health emergency of international concern. In this project, several small non-peptide molecules were synthesized to inhibit the major proteinase (M^pro) of SARS-CoV-2 using rational strategies of drug design and medicinal chemistry. M^pro is a key enzyme of coronaviruses and plays an essential role in mediating viral replication and transcription in human lung epithelial and stem cells, making it an attractive drug target for SARS-CoV. The antiviral potential of imidazoline derivatives as inhibitors of (SARS-CoV-2) M^pro was evaluated using in-silico techniques such as molecular docking simulation, molecular dynamics (MD), and ADMET prediction. The docking scores of these imidazoline derivatives were compared to that of the N3 crystal inhibitor and showed that most of these compounds, particularly compound E07, interacted satisfactorily in the active site of the coronavirus and strongly interacted with the residues (Met 165, Gln 166, Met 165, His 41, and Gln 189). Furthermore, the results were confirmed by MD simulations after exposure to long-term MD simulations and ADMET predictions. © 2023 Elsevier B.V.

关键词： Imidazolines In-silico studies N-Bromosaccharin (NBSac) SARS-CoV-2) Mpro

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

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PeerJ computer Science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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A Novel Fault Diagnosis Method Using a Two-Stage Deep Framework

A Novel Fault Diagnosis Method Using a Two-Stage Deep Framew...

引用

International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Qiu Junlin Zhu Bin Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China College of Computer and Information Hohai University Nanjing China

ISBN: (数字)9781728123486

ISBN: (纸本)9781728123493

Intelligent fault diagnosis is an interesting topic in current period. In practice, fault diagnosis can ensure the safety of the process of the industry production, which can prevent unnecessary loss caused by the fault. Rotating elements are the key component of industrial production equipment, which is also vulnerable during the running process. Therefore, most researches focus on the faults that are found on rotating elements, such as the running bearings. However, fault diagnosis is challenging since original signal collected from the rotating elements is commonly non-stationary in nature. In this time, traditional signal processing methods can hardly extract representative features for fault diagnosis. Aiming to solve this problem, this paper proposes a two-stage deep framework to learn the representation of the original signals, which underlies the following fault classification of rolling bearings. Experimental evaluations on the open database from Case Western Reserve University demonstrate that our proposed method can obtain higher accuracy and better adaptability in contrast to existing fault diagnosis methods.

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A Secure Healthcare 5.0 System Based on Blockchain technology Entangled with Federated Learning Technique

arXiv

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arXiv 2022年

作者： Rehman, Abdur Abbas, Sagheer Khan, M.A. Ghazal, Taher M. Adnan, Khan Muhammad Mosavi, Amir School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Riphah School of Computing and Innovation Faculty of Computing Riphah International university Pakistan School of Information Technology Skyline University College University City Sharjah Sharjah1797 United Arab Emirates Selangor Bangi43600 Malaysia Department of Software Gachon University Seongnam13120 Korea Republic of Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava81107 Slovakia John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary Faculty of Civil Engineering TU-Dresden Dresden01062 Germany

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the e

关键词： Blockchain

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Embedding 3D models in offline physical environments

Embedding 3D models in offline physical environments

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作者： Ertugrul, Egemen Zhang, Han Zhu, Fang Lu, Ping Li, Ping Sheng, Bin Wu, Enhua Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space-efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user-friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low-cost autonomous robots, and 3D printing. © 2020 John Wiley & Sons, Ltd.

关键词： Digital storage

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Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions

arXiv

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arXiv 2022年

作者： Ukpabi, Dandison Karjaluoto, Heikki Bötticher, Astrid Nikiforova, Anastasija Petrescu, Dragoş Schindler, Paulina Valtenbergs, Visvaldis Lehmann, Lennard Yakaryilmaz, Abuzer University of Jyväskylä Jyväskylä University School of Business and Economics Finland Friedrich-Schiller-University Jena Department of Political Science Jena Germany University of Tartu Faculty of Science and Technology Institute of Computer Science Chair of Software Engineering Tartu Estonia University of Bucharest Faculty of Political Science Bucharest Romania Friedrich-Schiller-University Jena Project "Spheres of Freedom and Protection of Liberty in the Digital State" Jena Germany University of Latvia Faculty of Social Sciences Riga Latvia University of Latvia Faculty of Computing Riga Latvia QWorld Association Tallinn Estonia

Recently, there has been increasing awareness of the tremendous opportunities inherent in quantum computing (QC). Specifically, the speed and efficiency of QC will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased QC research funding from national and regional governments and private firms. However, critical concerns regarding legal, political, and business-related policies germane to QC adoption exist. Since this is an emerging and highly technical domain, most of the existing studies focus heavily on the technical aspects of QC, but our study highlights its practical and social uses cases, which are needed for the increased interest of governments. Thus, this study offers a multidisciplinary review of QC, drawing on the expertise of scholars from a wide range of disciplines whose insights coalesce into a framework that simplifies the understanding of QC, identifies possible areas of market disruption and offer empirically based recommendations that are critical for forecasting, planning, and strategically positioning QCs for accelerated diffusion. © 2022, CC BY.

关键词： Commerce

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Identity-Based Proxy Signing Key Insulated Signature in the Standard Model

引用

Journal of Physics: Conference Series 2020年第1期1607卷

作者： Jianhong Chen Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian 223003 China

The first signer arranges for an agent to generate a proxy private key using an identity based proxy signature (IBPS) scheme. The agent outputs the proxy signature for the first signer can generate the signature for the original signer using the proxy private key. Because the potential problem of information security must be taken into account with the development of network technology, the proxy private key needed to be protected. We propose an identity-based proxy signing key-insulated proxy signature (IBPSKIPS) scheme to prevent the proxy private key from being exposed.

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A novel approach to reducing information leakage for quantum key distribution

arXiv

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arXiv 2021年

作者： Mao, Hao-Kun Zhao, Qiang Qiao, Yu-Cheng Yan, Bing-Ze Xu, Bing-Jie Abd El-Latif, Ahmed A. Li, Qiong Department of Computer Science and Technology Harbin Institute of Technology Harbin150080 China College of Software Engineering ZhengZhou University of Light Industry ZhengZhou450053 China Guangxi Key Lab Cryptography & Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Quantum key distribution (QKD) is an important branch of quantum information science as it holds promise for unconditionally secure communication. For QKD research, a central issue is to improve the final secure key rate (SKR) and the maximal transmission distance. To address this issue, most works focused on reducing the information leakage of QKD. In this paper, we propose a novel approach to further reduce the information leakage by specially considering the overlap between the information leakage of quantum part and post-processing part. The overlap means that the information leakage of post-processing part caused solely by multi-photon pulses is considered twice in previous studies, i.e., both in quantum part and post-processing part. Since the information carried by multi-photon pulses has been considered as completely known by Eve through the photon-number-splitting attack in quantum part, there is no need to consider it in post-processing part repetitively during the SKR calculation. Therefore, our approach can theoretically reduce the information leakage of a QKD protocol. Based on this idea, we derive the formulas to calculate the amount of information leakage for decoy-BB84 and sending-or-not-sending twin-field protocols. Simulation results for these two typical protocols also demonstrate that our approach evidently improves the SKR as well as the maximal transmission distance under practical experimental parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum cryptography

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