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RETRACTED ARTICLE: SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System

IETE Journal of Research 2023年第11期69卷 XLIV-LVII页

作者： M. Kavitha Margret E. Golden Julie Y. Harold Robinson D. Vijayanandh S. Vimal Seifidine Kadry Ahmed M. El-Sherbeeny Mohammed A. El-Meligy 1 Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore India 2 Department of Computer Science and Engineering Anna University Regional Campus Tirunelveli India 3 School of Information Technology and Engineering Vellore Institute of Technology Vellore India 4 Department of Electrical and Electronics Engineering Hindustan College of Engineering and Technology Coimbatore India 5 Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamilnadu India 6 Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway 7 Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:M. Kavitha Margret, E. Golden Julie, Y. Harold Robinson, D. Vijayanandh, S. Vimal, Seifidine Kadry, Ahmed M. El-Sherbeeny & Mohammed A. El-Meligy (2021) SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System, IETE Journal of Research, DOI: 10.1080/03772063.2021.1994041Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

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A Quantum Beta Distributed Multi-Objective Particle Swarm Optimization Algorithm for Twitter Fake Accounts Detection

TechRxiv

引用

TechRxiv 2022年

作者： Aboud, Ahlem Rokbani, Nizar Mirjalili, Seyedali Hussain, Amir Chabchoub, Habib Alimi, Adel M. University of Sousse ISITCom Sousse4011 Tunisia BP 1173 Sfax3038 Tunisia High Institute of Applied Science Technolgy of Sousse University of Sousse Tunisia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom College of Business Al Ain University of Science and Technology Abu Dhabi United Arab Emirates Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa University of Sousse Tunisia

Fake account detection is a topical issue when many Online Social Networks encounter several issues caused by the growing number of unethical online activities. This study presents a new Quantum Beta-behaved Multi-Objective Particle Swarm Optimization (QB-MOPSO) algorithm for machine learning based Twitter fake accounts detection. The proposed approach aims to improve the learning process of deep neural networks, random forest, through minimizing simultaneously the feature dimensionality and the classification error rate. The main contribution consists in proposing a quantum beta MOPSO to handle the training phase of neural and deep architectures. The QB-MOPSO is used to perform a multi-objective training of the random forest algorithm. The QB-MOPSO has two optimization profiles: the first one uses a quantum-behaved equation for improving the exploratory behaviour of PSO, while the second one uses a beta function to enhance PSO’s exploitation. An extensive experimental study is carried out using two open Twitter datasets with 1982 and 928 accounts. The new proposal is a random forest QB-MOPSO. Results showed that random forest QB-MOPSO accuracy is about 99.19% and 97.52% accounts on datasets 1 and 2. Comparative analysis of the prosed architecture toward the original architecture showed that the use of QB-MOPSO for learning enhances the random forest algorithm which perform then the original ones. © 2022, CC BY-NC-SA.

关键词： Social networking (online)

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COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

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Sensors 2024年第5期24卷 1557页

作者： Bougourzi, Fares Distante, Cosimo Dornaika, Fadi Taleb-Ahmed, Abdelmalik Hadid, Abdenour Chaudhary, Suman Yang, Wanting Qiang, Yan Anwar, Talha Breaban, Mihaela Elena Hsu, Chih-Chung Tai, Shen-Chieh Chen, Shao-Ning Tricarico, Davide Chaudhry, Hafiza Ayesha Hoor Fiandrotti, Attilio Grangetto, Marco Spatafora, Maria Ausilia Napoli Ortis, Alessandro Battiato, Sebastiano Institute of Applied Sciences and Intelligent Systems National Research Council of Italy Lecce73100 Italy Laboratoire LISSI University Paris-Est Creteil Paris Vitry sur Seine94400 France Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Manuel Lardizabal 1 San Sebastian20018 Spain IKERBASQUE Basque Foundation for Science Bilbao48011 Spain UMR 8520 Universite Polytechnique Hauts-de-France Université de Lille CNRS Valenciennes59313 France Sorbonne Center for Artificial Intelligence Sorbonne University of Abu Dhabi P.O. Box 38044 Abu Dhabi United Arab Emirates College of Computer Science and Technology Taiyuan University of Technology Taiyuan030024 China School of Computing National University of Computer and Emerging Sciences Islamabad44000 Pakistan Faculty of Computer Science Alexandru Ioan Cuza University Iasi700506 Romania Institute of Data Science National Cheng Kung University No. 1 University Rd. East Dist. Tainan City701 Taiwan Dipartimento di Informatica Universita degli Studi di Torino Corso Svizzera 185 Torino10149 Italy Department of Mathematics and Computer Science University of Catania Catania95125 Italy

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient’s state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. © 2024 by the authors.

关键词： COVID-19

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Effect of Different Treatment Methods on the Purification of Bacterial Cellulose Produced from OPF Juice by Acetobacter Xylinum

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IOP Conference Series: Materials Science and Engineering 2021年第1期1092卷

作者： Nurul Athirah Raihana Nor Aziz Hashim Junaidi Zakaria Shahril Mohamad Sharifah Fathiyah Sy Mohamad Mohd Hairul Ab. Rahim Faculty of Chemical and Process Engineering Technology College of Engineering Technology Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang Malaysia Faculty of Industrial Science and Technology College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang Malaysia

Bacterial cellulose (BC) is a form of cellulose produced by Acetobacter xylinum via fermentation process. BC contains impurities such as medium broth or the cell biomass. This study aims to determine the effect of purification method on the BC produced from oil palm frond (OPF) juice. Three purification method was presented in this study; first, BC was treated with different NaOH concentration at constant temperature of 70°C. Second, the BC was treated with different temperature while the NaOH concentration was fixed at 0.1M. Lastly, the BC was further purified in distilled water at different temperature. Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscope (FESEM) analysis revealed that chemical composition of BC is similar to the natural cellulose without the present of biomass impurities as no peak at 1800 – 1500 cm−1 can be detected. Higher percentage for loss of biomass, 22.03 % can be obtained when the BC was treated with 1.0 M NaOH at 90°C and further purified in distilled water at 90°C. As conclusion, pure BC which are free from any impurities can be obtained by using proposed method.

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ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels

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IEEE Transactions on Mobile computing 2025年

作者： He, Changpeng Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated. © 2002-2012 IEEE.

关键词： Unsupervised learning

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Global convergence of a new class nonlinear conjugate gradient method with exact line search

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AIP Conference Proceedings 2021年第1期2355卷 040007页

作者： Nur Haziqah Mohd Dani Srimazzura Basri Mustafa Mamat Faculty of Applied Sciences & Technology Universiti Tun Hussein Onn Malaysia 86400 Johor Darul Takzim Malaysia. Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus of Tembila 22200 Terengganu Darul Iman Malaysia.

Unconstrained optimization is a widespread problem that can be solved by a mathematical technique known as the conjugate gradient method. This method is chosen because of its simplicity and less use of time in solving problems that can be seen when the result has less number of iteration with a faster time of the central processing unit (CPU). Motivated by this study, we are interested in researching as there are many modifications taking place in the conjugate gradient parameter. Therefore, in this study, five conjugate gradient parameters, including the preferred conjugate gradient parameter, modification of the Hestenes-Stiefel conjugate gradient parameter, will be analyzed. We focus on the problem of unconstrained optimization using the exact line search. The proof that this conjugate gradient parameter fulfilled the condition; global convergent condition under the exact line search will be shown. The performance of the conjugate gradient method with all conjugate gradient parameters was tested using 15 optimization test functions through MATLAB software to check whether the conjugate gradient method with the chosen conjugate gradient parameter could perform better and more efficiently than the conjugate gradient method with other conjugate gradient parameters based on the number of iterations and time. The conjugate gradient method’s accuracy and efficiency with each conjugate gradient parameter will be compared based on the percentage obtained in the cumulative frequency graph. The analysis shows that the conjugate gradient method’s performance with the chosen conjugate gradient parameter is more accurate and efficient than the conjugate gradient method with another conjugate gradient parameter.

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Features of Human-Centred Algorithm Design

Features of Human-Centred Algorithm Design

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Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Marianne Cherrington David Airehrour Joan Lu Qiang Xu David Cameron-Brown Ihaka Dunn School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom School of Applied Business Unitec Institute of Technology Auckland New Zealand University of Huddersfield Huddersfield United Kingdom Principal Cameron Brown Corporate Advisory Auckland New Zealand Faculty of Medicine University of Auckland Auckland New Zealand

ISBN: (数字)9781728188270

ISBN: (纸本)9781728188287

Algorithms are pervasive, unseen influencers of decisions. Algorithmic features can fluctuate widely, depending on use, user or criteria applied. This paper considers the nascent field of human-centred algorithm design (HCAD), intersecting human-centred design and algorithmic systems. Human-centred, more-than-metric feature selection approaches, create fairer and deeper meaning. More value is created. The unique impact of this paper is to integrate feature selection within a technology HCAD strategy, for a novel, innovative HCAD approach to machine learning. This flexible and evaluative approach can support data advances with human-social nuance, designed for purpose with knowledge for data-driven decisions. The design of machine learning algorithms to the uses in which they will be employed is user-centric. This is important within environments utilising automated, semi-automated or high-performance analytics.

关键词： Prediction algorithms Feature extraction Measurement Artificial intelligence Human computer interaction Big Data Filtering algorithms

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A platform for high performance photon correlation measurements

arXiv

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arXiv 2020年

作者： Zadeh, Iman Esmaeil Los, Johannes W.N. Gourgues, Ronan B.M. Chang, Jin Elshaari, Ali W. Zichi, Julien van Staaden, Yuri J. Swens, Jeroen Kalhor, Nima Guardiani, Antonio Meng, Yun Zou, Kai Dobrovolskiy, Sergiy Fognini, Andreas W. Schaart, Dennis R. Dalacu, Dan Poole, Philip J. Reimer, Michael E. Hu, Xiaolong Pereira, Silvania F. Zwiller, Val Dorenbos, Sander N. Optics Research Group ImPhys Department Faculty of Applied Sciences Delft University of Technology Delft2628 CJ Netherlands Single Quantum B.V. Delft2628 CJ Netherlands Optics Research Group Imphys Department Faculty of Applied Sciences Delft University of Technology Delft2628 CJ Netherlands Stockholm106 91 Sweden School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Medical Physics & Technology Radiation Science & Technology Department Faculty of Applied Sciences Delft University of Technology National Research Council of Canada OttawaONK1A 0R6 Canada Institute for Quantum Computing Department of Electrical & Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

A broad range of scientific and industrial disciplines require precise optical measurements at very low light levels. Single-photon detectors combining high efficiency and high time resolution are pivotal in such experiments. By using relatively thick films of NbTiN (8-11 nm) and improving the pattern fidelity of the nano-structure of the superconducting nanowire single-photon detectors (SNSPD), we fabricated devices demonstrating superior performance over all previously reported detectors in the combination of efficiency and time resolution. Our findings prove that small variations in the nanowire width, in the order of a few nanometers, can lead to a significant penalty on their temporal response. Addressing these issues, we consistently achieved high time resolution (best device 7.7 ps, other devices ∼10-16 ps) simultaneously with high system detection efficiencies (80 − 90%) in the wavelength range of 780-1000 nm, as well as in the telecom bands (1310-1550 nm). The use of thicker films allowed us to fabricate large-area multi-pixel devices with homogeneous pixel performance. We first fabricated and characterized a 100 × 100 µm2 16-pixel detector and showed there was little variation among individual pixels. Additionally, to showcase the power of our platform, we fabricated and characterized 4-pixel multimode fiber-coupled detectors and carried out photon correlation experiments on a nanowire quantum dot resulting in g2(0) values lower than 0.04. The multi-pixel detectors alleviate the need for beamsplitters and can be used for higher order correlations with promising prospects not only in the field of quantum optics, but also in bio-imaging applications, such as fluorescence microscopy and positron emission tomography. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

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Neutron spin echo is a "quantum tale of two paths"

arXiv

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arXiv 2023年

作者： McKay, S. Irfan, A.A.M. Thien, Q. Le Geerits, N. Parnell, S.R. Dalgliesh, R.M. Lavrik, N.V. Kravchenko, I.I. Ortiz, G. Pynn, R. Department of Physics Indiana University BloomingtonIN47405 United States Center for Exploration of Energy and Matter Indiana University Bloomington47408 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Atominstitut TU Wien Stadionallee 2 Vienna1020 Austria Faculty of Applied Sciences Delft University of Technology Mekelweg 15 Delft2629 JB Netherlands ISIS Rutherford Appleton Laboratory Oxfordshire ChiltonOX11 0QX United Kingdom Center for Nanophase Materials Science Oak Ridge National Laboratory Oak RidgeTN37831 United States Quantum Science and Engineering Center Indiana University BloomingtonIN47408 United States Neutron Sciences Directorate Oak Ridge National Laboratory Oak RidgeTN37830 United States

We describe an experiment that strongly supports a two-path interferometric model in which the spin-up and spin-down components of each neutron propagate coherently along spatially separated parallel paths in a typical neutron spin echo small angle scattering (SESANS) experiment. Specifically, we show that the usual semi-classical, single-path treatment of Larmor precession of a polarized neutron in an external magnetic field predicts a damping as a function of the spin echo length of the SESANS signal obtained with a periodic phase grating when the transverse width of the neutron wave packet is finite. However, no such damping is observed experimentally, implying either that the Larmor model is incorrect or that the transverse extent of the wave packet is very large. In contrast, we demonstrate theoretically that a quantum-mechanical interferometric model in which the two mode-entangled (i.e. intraparticle entangled) spin states of a single neutron are separated in space when they interact with the grating accurately predicts the measured SESANS signal, which is independent of the wave packet width. Copyright © 2023, The Authors. All rights reserved.

关键词： Damping

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