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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Adil Ali Raja Muhammad Ali Jamshed Haris Pervaiz Syed Ali Hassan School of Electrical Engineering & Computer Science (SEECS) National University of Sciences & Technology (NUST) Pakistan Institute of Communication Systems (ICS) Home of 5G Innovation Centre (5GIC) University of Surrey UK School of Computing & Communications Lancaster University UK

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

Heterogeneous networks (HetNets) comprising of various communication technologies with wireless backhaul capabilities are becoming essential ingredients to satisfy the high data rate requirements set for future wireless systems. The use of unmanned aerial vehicles (UAVs) and the addition of Terahertz (THz)-based communication is considered a key pillar in providing such capabilities. UAVs are used to provide infrastructure less, rapid and on-demand deployment, while THz technology is a new wireless frontier and a potential enabler for providing immense data rates. Wireless backhaul capabilities are necessary to avoid cumbersome wired layouts. In this paper, we consider a multi-tier HetNet comprising of UAVs operating at sub-6GHz, small base stations (BSs) operating at THz frequencies and a macro-BS (MBS) operating at sub-6GHz. Wireless backhauling is provided by the MBS, some UAVs, and by a few THz cells. Network coverage and data rates are taken as quality of service (QoS) performance metrics. We analyze the effectiveness of the HetNet configuration on network coverage and data rates by varying different parameters. Extensive simulation has been performed, which validates the significance of deploying such a HetNet with wireless backhaul capabilities and to provide elevated data rates to the end users.

关键词： Wireless communication Fading channels Nakagami distribution Interference Absorption Performance analysis Unmanned aerial vehicles

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Development of a near-infrared (NIR) forearm subcutaneous vein extraction using deep residual U-Net

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AIP Conference Proceedings 2023年 020004页

作者： A. K. Nuraini Huda Goh Chuan Meng C. H. Lim Sayed Ahmad Zikri Bin Sayed Aluwee Mohd Nazri Bajuri Nur Haliza Abdul Wahab Department of Industrial Engineering Universiti Tunku Abdul Rahman Kampar Perak Malaysia. Department of Computer Science Universiti Tunku Abdul Rahman Kampar Perak Malaysia. Citra Centre for Liberal Study Universiti Kebangsaan Malaysia Bangi Selangor Malaysia. School of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia.

Impotence to locate the forearm subcutaneous vein leads to multiple intravenous (IV) attempts causing pain and injuries to patients such as bruise or vein damages. Various technologies and techniques were proposed and developed to overcome the multiple IV access problems. The standard techniques used in research and hospitals are Transillumination, Ultrasound Imaging, and Near-Infrared (NIR). Among those techniques, NIR is the most optimal way of locating the subcutaneous vein because of its non-invasive properties, low-cost implementation. The device can be assembled in a small size product. Nevertheless, the NIR forearm images contain noises that cause difficulties in extracting the vein features. Hence, the performance of NIR vein extraction is having the bottleneck of detecting the vein pixel accurately. Many research studies have been conducted to work on the NIR forearm subcutaneous vein detection due to such a limitation. Artificial intelligence is one of the powerful technologies that would benefit this study. However, a limited number of articles were found on the patentability search, and thus we propose an automatic vein extraction algorithm using Deep Residual U-Net architecture. Our algorithm shows 75 percent of the accuracy in extracting the NIR vein from the experiments that tested. These results show the evidence that the Deep Residual U-Net can be applied to extract the NIR vein.

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Correction: VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images

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BMC medical imaging 2024年第1期24卷 128页

作者： Anindita Saha Shahid Mohammad Ganie Pijush Kanti Dutta Pramanik Rakesh Kumar Yadav Saurav Mallik Zhongming Zhao Department of Computing Science and Engineering IFTM University Moradabad Uttar Pradesh India. AI Research Centre Department of Analytics School of Business Woxsen University Hyderabad Telangana 502345 India. School of Computer Applications and Technology Galgotias University Greater Noida Uttar Pradesh 203201 India. pijushjld@yahoo.co.in. Department of Computer Science & Engineering MSOET Maharishi University of Information Technology Lucknow Uttar Pradesh India. Department of Environmental Health Harvard T. H. Chan School of Public Health Boston MA USA. Center for Precision Health McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX 77030 USA. zhongming.zhao@uth.tmc.edu.

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery

arXiv

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arXiv 2024年

作者： Das, Adrito Khan, Danyal Z. Psychogyios, Dimitrios Zhang, Yitong Hanrahan, John G. Vasconcelos, Francisco Pang, You Chen, Zhen Wu, Jinlin Zou, Xiaoyang Zheng, Guoyan Qayyum, Abdul Mazher, Moona Razzak, Imran Li, Tianbin Ye, Jin He, Junjun Plotka, Szymon Kaleta, Joanna Yamlahi, Amine Jund, Antoine Godau, Patrick Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Rivoir, Dominik Pérez, Alejandra Rodriguez, Santiago Arbeláez, Pablo Stoyanov, Danail Marcus, Hani J. Bano, Sophia Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department of Neurosurgery National Hospital for Neurology and Neurosurgery London United Kingdom HKISI CAS China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Heart and Lung Institute Faculty of Medicine Imperial College London United Kingdom Centre for Medical Image Computing University College London London United Kingdom University of New South Wales Sydney Australia Shanghai AI Lab Shanghai China Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center University of Amsterdam Amsterdam Netherlands Sano Center for Computational Medicine Krakow Poland Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg a partnership between DKFZ University Hospital Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Muroran Institute of Technology Hokkaido Japan Niigata University of Health and Welfare Niigata Japan Konica Minolta Inc. Osaka Japan National Center for Tumor Diseases Dresden Germany Centre for Tactile Internet TUD Dresden Germany Universidad de los Andes Bogota Colombia DKFZ UKDD TUD Germany

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed;and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery;during live surgery;and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision;and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://***/10.5522/04/26531686. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques

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Computational intelligence and neuroscience 2023年第1期2023卷 5113417页

作者： Pradeep Kumar Tiwari Pooja Singh Navaneetha Krishnan Rajagopal K Deepa Sampada Gulavani Amit Verma Yekula Prasanna Kumar Birla Global University Gothapatna Bhubaneswar Odisha India. School of Computing Science & Engineering Department of CSE Galgotias University Greater Noida UP India. Business Studies The University of Technology and Applied Sciences Salalah Oman. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thalavapalayam Karur Tamilnadu India. Department of MCA Bharati Vidyapeeth (Deemed to Be University) Institute of Management Kolhapur Maharashtra India. University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan Mohali Punjab India. Department of Mining Engineering College of Engineering and Technology Bule Hora University Blue Hora 144 Oromia Region Ethiopia.

computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is combined with IoT. The reinforcement learning techniques created in recent years have largely focused on incorporating deep learning technology to improve the generalization skills of the algorithm while ignoring the issue of detecting and taking full advantage of the dilemma. To increase the effectiveness of exploration, a deep reinforcement algorithm based on computational intelligence is proposed in this study, using intelligent sensors and the Bayesian approach. In addition, the technique for computing the posterior distribution of parameters in Bayesian linear regression is expanded to nonlinear models such as artificial neural networks. The Bayesian Bootstrap Deep -Network (BBDQN) algorithm is created by combining the bootstrapped DQN with the recommended computing technique. Finally, tests in two scenarios demonstrate that, when faced with severe exploration problems, BBDQN outperforms DQN and bootstrapped DQN in terms of exploration efficiency.

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Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models

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computers in Biology and Medicine 2025年 194卷

作者： Hager Saleh Shaker El-Sappagh Michael McCann Saeed Hamood Alsamhi John G. Breslin Insight Research Ireland Centre for Data Analytics School of Engineering University of Galway University Road Galway H91 TK33 Ireland Atlantic Technological University Letterkenny Donegal Ireland Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha 13518 Egypt Department of Computing Atlantic Technological University Letterkenny Donegal Ireland Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea Department of Electronics Engineering IBB University Ibb Ibb Yemen

The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been designed to perform multivariate, multi-horizon time-series forecasting for vital signs, specifically for a person’s blood oxygen saturation level (SpO2) and respiratory rate (RR). The framework leverages advanced attention-based models, with a particular emphasis on the Temporal Fusion Transformer (TFT) and Temporal Convolutional Network (TCN), and we benchmark its performance against classical deep learning architectures, including LSTM, GRU, Bi-LSTM, Bi-GRU, CNN, and Sequence-to-Sequence (Seq2Seq) models with and without attention mechanisms. Both univariate and multivariate forecasting tasks are explored across multiple prediction horizons (i.e., 7, 15 and 25 min), using physiological time-series data from the MIMIC-III database. The proposed system incorporates a cascaded fine-tuning strategy, wherein the TFT model is sequentially fine-tuned on individual patients’ data, significantly enhancing the model’s generalizability to unseen patient profiles. Empirical results demonstrate that the TFT model consistently outperforms baseline models across all forecasting settings, achieving lower RMSE and MAE values, and exhibiting superior capacity for capturing long-sequence dependencies and temporal feature dynamics. To validate its applicability in real-time clinical environments, the framework integrates a simulated streaming infrastructure using Apache Kafka and Apache Flink, enabling continuous data ingestion, forecasting, and visualization of vital signs. This end-to-end deployment underscores the system’s potential for ICU monitoring, allowing clinicians to anticipate patient deterioration proactively. In summary, we introduce a comprehensive framework that uniquely integrates TFT with cascaded fine-tuning for multivar

关键词： Real-time patient monitoring Time-series data forecasting Intensive care unit Sequence-to-sequence modeling Multi-horizon forecasting Attention-based modeling Deep learning

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Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network

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Scientific reports 2023年第1期13卷 16975页

作者： Refat Khan Pathan Munmun Biswas Suraiya Yasmin Mayeen Uddin Khandaker Mohammad Salman Ahmed A F Youssef Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Bandar Sunway Selangor Malaysia. Department of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh. Department of Computer and Information Science Graduate School of Engineering Tokyo University of Agriculture and Technology Koganei Tokyo 184-0012 Japan. Centre for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University 47500 Bandar Sunway Selangor Malaysia. mayeenk@sunway.edu.my. Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka 1216 Bangladesh. mayeenk@sunway.edu.my. College of Engineering and Technology American University of the Middle East Egaila Kuwait.

Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instruments including sensors, devices, and high-end processing power. However, such drawbacks can be easily overcome by employing artificial intelligence-based techniques. Since, in this modern era of advanced mobile technology, using a camera to take video or images is much easier, this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset. Here, "Finger Spelling, A" dataset has been used, with 24 letters (except j and z as they contain motion). The main reason for using this dataset is that these images have a complex background with different environments and scene colors. Two layers of image processing have been used: in the first layer, images are processed as a whole for training, and in the second layer, the hand landmarks are extracted. A multi-headed convolutional neural network (CNN) model has been proposed and tested with 30% of the dataset to train these two layers. To avoid the overfitting problem, data augmentation and dynamic learning rate reduction have been used. With the proposed model, 98.981% test accuracy has been achieved. It is expected that this study may help to develop an efficient human-machine communication system for a deaf-mute society.

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Advances in Computational Intelligence Techniques-Based Multi-Intersection Querying Theory for Efficient QoS in the Next Generation Internet of Things

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Computational intelligence and neuroscience 2023年第1期2023卷 1388425页

作者： Ashish Kumar Kannan K Mamta Dahiya Virendra Singh Kushwah Ayesha Siddiqa Kiranjeet Kaur Saima Ahmed Rahin Department of CSE Manipal University Jaipur Jaipur Rajasthan India. Department of CSE Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh India. Department of Computer Science and Engineering SGT University Gurugram India. School of Computing Science and Engineering (SCSE) VIT Bhopal University Sehore India. Artificial Intelligence and Data Science Department Islamia Engineering College Hyderabad Telangana India. Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab 140413 India. United International University Dhaka Bangladesh.

An environment of physically linked, technologically networked things that can be found online is known as the "Internet of Things." With the use of various devices connected to a network that allows data transfer between these devices, this includes the creation of intelligent communications and computational environments, such as intelligent homes, smart transportation systems, and intelligent FinTech. A variety of learning and optimization methods form the foundation of computational intelligence. Therefore, including new learning techniques such as opposition-based learning, optimization strategies, and reinforcement learning is the key growing trend for the next generation of IoT applications. In this study, a collaborative control system based on multiagent reinforcement learning with intelligent sensors for variable-guidance sections at various junctions is proposed. In the future generation of Internet of Things (IoT) applications, this study provides a multi-intersection variable steering lane-appropriate control approach that uses intelligent sensors to reduce traffic congestion at many junctions. Since the multi-intersection scene's complicated traffic flow cannot be accommodated by the conventional variable steering lane management approach. The priority experience replay algorithm is also included to improve the efficiency of the transition sequence's use in the experience replay pool and speed up the algorithm's convergence for effective quality of service in the upcoming IoT applications. The experimental investigation demonstrates that the multi-intersection variable steering lane with intelligent sensors is an appropriate control mechanism, successfully reducing queue length and delay time. The effectiveness of waiting times and other indicators is superior to that of other control methods, which efficiently coordinate the strategy switching of variable steerable lanes and enhance the traffic capacity of the road network under multiple intersections

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Design and Implementation of Hybrid Vehicle using Control of DC Electric Motor 54

Design and Implementation of Hybrid Vehicle using Control of...

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54th International Universities Power engineering Conference, UPEC 2019

作者： Hussain, Muhammad Majid Rashid Mustafa, Al Chaudary, Muhammad Akmal Razaq, Abdul University of South Wales Faculty of Computing Engineering and Science Cardiff United Kingdom University of South Wales Centre for Automotive Power Systems Engineering Cardiff United Kingdom Ajman University Department of Electrical and Computer Engineering Ajman United Arab Emirates Abertay University School of Design and Informatics Dundee United Kingdom

ISBN: (纸本)9781728133492

The electric motors and its control technology are key components of hybrid electric vehicles (HEVs). Control of the electric motor is a fundamental issue for traction application in electric vehicles and HEVs. This paper presents the design, development and implementation of a hybrid vehicle using both an electric motor and petrol engine to increase efficiency and decrease carbon footprint. Initially, a prototype of a HEV is designed and the performance values are calculated, before a control system is developed and implemented to control the DC motor speed using a microcontroller as the vehicle's electronic control unit along with simple proportional integral derivative (PID) control using speed as a feedback mechanism. The prototype made incorporated voltage, current, speed and torque sensors for feedback resulting in a closed loop control system which successfully matched the speed input of a user-controlled pedal sensor. A user interface was developed to show the driver of the vehicle key variables such as the revolutions per minute (RPM) of the motor, the speed of the vehicle along with the current being drawn, and the voltage applied to the motor with overall power. To output a variable voltage from the Arduino, a digital output was used with pulse width modulation (PWM) capabilities in order to provide a variable DC voltage to the speed controller. © 2019 IEEE.

关键词： User interfaces

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