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2021 International Conference on Information technology, ICIT 2021

作者： Mizher, Manar Abduljabbar Ang, Mei Choo Abdullah, Siti Norul Huda Sheikh Ng, Kok Weng Mazhar, Ahmad A. Mizher, Manal Abd-Aljabbar Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Engineering University of Nottingham Malaysia Semenyih Malaysia SAE Institute Media Production Department Amman Jordan

ISBN: (纸本)9781665428705

Determining video authenticity has become a challenging task in video forgery detection and localization systems. One of the core difficulties is to extract a suitable feature descriptor. The recent studies on passive object-based video authentication are limited to localize the forged frames in copy-paste/delete object forgery. That is done without considering the object motion interpolation. It performs video authentication on simple videos with a static camera without using dynamic texture. This research work proposes an Action Passive Object-based Video Authentication Algorithm (APOVA). The APOVA consists a new statistical texture descriptor and a forged keyframes localization algorithm to validate video data. The APOVA algorithm has been tested on the Shih-Tang dataset. The experimental results shows that APOVA can obtain the best results in accuracy using the proposed descriptor. In addition, it is able to achieve the best performance in forged keyframes localization with F0.5_score = 0.93 and video authentication with F0.5_score = 0.99. As a result, APOVA based on APOKL is considered suitable algorithm for complex color video authentication, especially for passive object-based video forgery systems. © 2021 IEEE.

关键词： Textures

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DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

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Medical Image Analysis 2021年 70卷 101995页

作者： Lee, Gyuhyun Oh, Jeong-Woo Her, Nam-Gu Jeong, Won-Ki Korea Republic of Department of Health Sciences and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University Korea Republic of Institute for Refractory Cancer Research Samsung Medical Center Korea Republic of Department of Computer Science and Engineering College of Informatics Korea University Korea Republic of

In this paper, we propose a novel microscopy image translation method for transforming a bright-field microscopy image into three different fluorescence images to observe the apoptosis, nuclei, and cytoplasm of cells, which visualize dead cells, nuclei of cells, and cytoplasm of cells, respectively. These biomarkers are commonly used in high-content drug screening to analyze drug response. The main contribution of the proposed work is the automatic generation of three fluorescence images from a conventional bright-field image;this can greatly reduce the time-consuming and laborious tissue preparation process and improve throughput of the screening process. Our proposed method uses only a single bright-field image and the corresponding fluorescence images as a set of image pairs for training an end-to-end deep convolutional neural network. By leveraging deep convolutional neural networks with a set of image pairs of bright-field and corresponding fluorescence images, our proposed method can produce synthetic fluorescence images comparable to real fluorescence microscopy images with high accuracy. Our proposed model uses multi-task learning with adversarial losses to generate more accurate and realistic microscopy images. We assess the efficacy of the proposed method using real bright-field and fluorescence microscopy image datasets from patient-driven samples of a glioblastoma, and validate the method's accuracy with various quality metrics including cell number correlation (CNC), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), cell viability correlation (CVC), error maps, and R2 correlation. © 2021 Elsevier B.V.

关键词： Fluorescence microscopy

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Industrial informatics in optimizing software-defined vehicles for logistics 2

Industrial informatics in optimizing software-defined vehicl...

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2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024

作者： Samrat, Bhavuk Thomas, P.K. Kumar, Santosh Benila, A. Bhardwaj, Rishabh Vigenesh, M. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India

ISBN: (纸本)9798350368949

Integration of industrial informatics and software-defined vehicle optimization is revolutionizing logistics. This study discusses how this integration may improve logistics safety, sustainability, and efficiency. Logistics are the backbone of international companies;thus, they must run smoothly. Industrial informatics and optimization studies yielded new findings. This powerful technology combines advanced machine learning algorithms, data analytics, and real-time monitoring to change logistics. Data-driven decisions are the foundation of industrial informatics. This research determines the best data analytics methods for gas and labor savings. Real-time monitoring keeps logistical operations adaptive to changing circumstances, while predictive maintenance and energy-efficient driving boost system efficiency. Nearest Neighbor, Branch and Bound, Particle Swarm Optimization, Monte Carlo Simulation, and Genetic Algorithms reveal that the suggested technique is superior. This study shows the indicated approach's considerable effect. Its cheaper pricing, faster delivery, and lesser carbon emissions make it better than conventional solutions. Logistics operations are safer with the proposed method, which has a higher safety score and fewer accidents and incidents. The suggested method benefits from its flexibility, scalability, and adaptability, which are crucial in a fast-changing firm. Expense savings: Save $250,000 using the proposed technique. The new method results in a time savings of 15.79%. The suggested approach reduces carbon emissions by 3,000 kg (26.09%). The suggested method improves safety ratings by 10 - 11.36%. © 2024 IEEE.

关键词： Signaling

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Fostering Synergy: Exploring the Intersection of Operations and Human Resources Management through an RF-SVM Approach

Fostering Synergy: Exploring the Intersection of Operations ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Keyurkumar M Nayak Renjith Krishnan K Surekha Dehu Khetree Manu Vasudevan Unni A. Vignesh S. Ramya University of Mumbai's Garware Institute of Career Education and Development Mumbai India Department of Business Administration University of Technology and Applied Sciences Shinas Oman Department of Mechanical Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Management St. Claret College Bangalore Karnataka India Department of Business Administration Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Master of Computer Applications M.Kumarasamy College of Engineering Karur Tamilnadu India

Since its inception a century ago, HRM has seen significant changes in both theory and practice, with the most notable changes occurring during the past two decades. As a result of a number of factors, both internal and external to the organization, human resource management is no longer seen as merely a maintenance function with minimal financial impact; instead, it is seen by many in the academic and professional communities as the source of sustained competitive advantage in today's global economy. The first stages of the proposed method include preprocessing, feature selection, and model training. It is crucial to apply pre-processing and representation to text input before feeding it to classifiers in order to optimize the performance of machine learning models. Data mining and knowledge -based authentication rely heavily on feature selection. LDA-BERT models are then trained after feature selection has taken place. The proposed method outperforms LDA and BERT, two widely used alternatives. Using the offered method, the system was able to obtain a maximum accuracy of 96.31%.

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A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records

arXiv

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arXiv 2023年

作者： Zhou, Sicheng Wang, Nan Wang, Liwei Sun, Ju Blaes, Anne Liu, Hongfang Zhang, Rui MS Institute for Health Informatics University of Minnesota MinneapolisMN United States School of Statistics University of Minnesota MinneapolisMN United States Department of AI and Informatics Research Mayo Clinic RochesterMN United States Department of Computer Science & Engineering University of Minnesota MinneapolisMN United States Department of Medicine University of Minnesota MinneapolisMN United States Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States

Objective: The generalizability of clinical large language models is usually ignored during the model development process. This study evaluated the generalizability of BERT-based clinical NLP models across different clinical settings through a breast cancer phenotype extraction task. Materials and Methods: Two clinical corpora of breast cancer patients were collected from the electronic health records from the University of Minnesota and the Mayo Clinic, and annotated following the same guideline. We developed three types of NLP models (i.e., conditional random field, bi-directional long short-term memory and CancerBERT) to extract cancer phenotypes from clinical texts. The models were evaluated for their generalizability on different test sets with different learning strategies (model transfer vs. locally trained). The entity coverage score was assessed with their association with the model performances. Results: We manually annotated 200 and 161 clinical documents at UMN and MC, respectively. The corpora of the two institutes were found to have higher similarity between the target entities than the overall corpora. The CancerBERT models obtained the best performances among the independent test sets from two clinical institutes and the permutation test set. The CancerBERT model developed in one institute and further fine-tuned in another institute achieved reasonable performance compared to the model developed on local data (micro-F1: 0.925 vs 0.932). Conclusions: The results indicate the CancerBERT model has the best learning ability and generalizability among the three types of clinical NLP models. The generalizability of the models was found to be correlated with the similarity of the target entities between the corpora. © 2023, CC BY.

关键词： Natural language processing systems

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Middle East and North African Health informatics Association (MENAHIA): Technological initiatives for 'One Health'

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Yearbook of Medical informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health informatics Association) is the International Medical informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health informatics Health Information Technologies International Medical informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

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Hierarchical Blockchain Radio Access Networks: Architecture, Modelling, and Performance Assessment

arXiv

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arXiv 2024年

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Liu, Hongwu Khalid, Waqas Tsiftsis, Theodoros Dobre, Octavia A. Department of Research and Development InnoCube P.C. 17is Noemvriou 79 Thessaloniki55534 Greece Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China Institute of Industrial Technology Korea University Sejong30019 Korea Republic of Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X9 Canada

Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (RANs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in RANs, while enabling dynamic coverage expansion through the use of intermediate commercial or private wireless nodes. To assess the efficiency and limitations of the architecture, we employ Markov chain theory in order to extract a theoretical model with increased engineering insights. Building upon this model, we quantify the latency as well as the security capabilities in terms of probability of successful attack, for three scenarios, namely fixed topology fronthaul network, advanced coverage expansion and advanced mobile node connectivity, which reveal the scalability of the blockchain-RAN architecture. © 2024, CC BY.

关键词： Markov chains

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Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis

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Journal of Dermatological Science 2025年

作者： Sakai, Takashi Sawada, Ryusuke Ichinose, Otoha Terabayashi, Takeshi Hatano, Yutaka Yamanishi, Yoshihiro Ishizaki, Toshimasa Department of Dermatology Faculty of Medicine Oita University Oita Japan Department of Pharmacology Okayama University Graduate School of Medicine Dentistry and Pharmaceutical Sciences Okayama Japan Department of Bioscience and Bioinformatics Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of Pharmacology Faculty of Medicine Oita University Oita Japan Department of Complex Systems Science Graduate School of Informatics Nagoya University Aichi Japan

Background: The development of medical treatments has traditionally relied on researchers leveraging scientific knowledge to hypothesize disease mechanisms and identify therapeutic agents. However, the depletion of novel therapeutic targets has become a significant challenge, resulting in stagnation within pharmaceutical research. Objective: To address the scarcity of therapeutic targets, we developed a machine learning (ML)-based system capable of predicting therapeutic target molecules for diseases. To validate its utility, we applied this system to psoriasis, aiming to identify novel treatment strategies. Methods: Our approach utilized a large clinical database to calculate reporting odds ratios for all drugs associated with the prevention of diseases of interest. We identified target proteins by analyzing large chemical structure databases to discover proteins commonly associated with preventive drug candidates. Experimental validation was conducted by administering a predicted therapeutic candidate in an imiquimod-induced psoriasis mouse model. Results: The ML-based predictions identified drugs for Parkinson's disease as potential preventive candidates for psoriasis. Further analysis highlighted dopamine receptor D2 (DRD2) as a therapeutic target. Administration of a DRD2 agonist alleviated psoriasis symptoms in mice, evidenced by the downregulation of mRNA expression in the IL-17 pathway and reduced serum tumor necrosis factor-α levels. Conclusion: This study demonstrates the utility of a novel ML-based system for identifying therapeutic targets, as shown by its successful application in uncovering the role of DRD2 in psoriasis. Beyond psoriasis, this system offers significant potential for exploring pathological mechanisms and discovering therapeutic targets across various diseases. © 2025 The Authors

关键词： artificial intelligence big data dopamine receptor D2 machine learning psoriasis

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Microwave signal processing using an analog quantum reservoir computer

arXiv

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arXiv 2023年

作者： Senanian, Alen Prabhu, Sridhar Kremenetski, Vladimir Roy, Saswata Cao, Yingkang Kline, Jeremy Onodera, Tatsuhiro Wright, Logan G. Wu, Xiaodi Fatemi, Valla McMahon, Peter L. Department of Physics Cornell University NY United States School of Applied and Engineering Physics Cornell University NY United States Department of Computer Science University of Maryland MD United States Joint Center for Quantum Information and Computer Science University of Maryland MD United States NTT Physics and Informatics Laboratories NTT Research Inc. CA United States Kavli Institute Cornell for Nanoscale Science Cornell University NY United States

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training is efficient in the number of required runs of the quantum processor and takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on superconducting circuits to classify microwave signals that are analog—continuous in time. However, while theoretical proposals of analog QRC exist, to date QRC has been implemented using circuit-model quantum systems—imposing a discretization of the incoming signal in time, with each time point input by executing a gate operation. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our quantum system was operated without artificially discretizing the input data, directly taking in microwave signals. Our work does not attempt to address the question of whether QRCs could provide a quantum computational advantage in classifying pre-recorded classical signals. However, beyond illustrating that sophisticated tasks can be performed with a modest-size quantum system and inexpensive training, our work opens up the possibility of achieving a different kind of advantage than a purely computational advantage: superconducting circuits can act as extremely sensitive detectors of microwave photons;our work demonstrates processing of ultra-low-power microwave signals in our superconducting circuit, and by combining sensitive detection with QRC processing within the same system, one could achieve a quantum sensing-computational advantage, i.e., an advantage in the overall analysis of microwave signals comprising just a few photons. © 2023, CC BY.

关键词： Photons

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Creating Resilient Digital Asset Management Frameworks in Financial Operations Using Blockchain technology

Creating Resilient Digital Asset Management Frameworks in Fi...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： B. T. Geetha Kafila Kafila S Tulasi Ram Dr. Amar Prabhakar Narkhede Ahmad Y. A. Bani Ahmad Mohit Tiwari Department of ECE Saveetha School of Engineering SIMATS Tamil Nadu India School of Business SR University Warangal Telangana School of Management Studies Chaitanya Bharathi Institute of Technology Hyderabad ISMS Sankalp Business School Vadgon Pune Department of Accounting and Finance Faculty of Business Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Jordan Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Using blockchain technology to build strong digital asset management tools for financial operations is a revolutionary way to bring financial systems up to date. Blockchain technology is changing the way money works by making transfers decentralised, open, and safe. Digital Asset Management (DAM) systems help businesses easily store and share digital assets like cryptocurrencies and blockchain-based assets. Using blockchain tools like smart contracts and tokenization makes financial deals safer and more trustworthy by using cryptographic encryption and ledger systems that can't be changed. Streamlining processes and payment makes financial operations more efficient. But problems like scaling and following the rules must be dealt with. Blockchain technology has many uses in finance, such as cross-border payments, tokenizing assets, and smart contracts. It is also changing how financial services are regulated. This study uses secondary data from academic research papers, peer-reviewed journals, and industry reports to look the use of blockchain technology to financial transactions, learn about DAM basics, look into blockchain tools in financial services, and judge how blockchain is used in financial transactions. This study helps us understand and use strong digital asset management systems in financial transactions that use blockchain technology by doing a thorough analysis.

关键词： Quantum computing Dams Smart contracts Transportation Tokenization Blockchains Asset management

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