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A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood Raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

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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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Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm

Comparison of Biocode Based Machine Learning and Segmentatio...

引用

Image Processing, Electronics and computers (IPEC), Asia-Pacific Conference on

作者： Muhammad Wafiq Abd Zariful Najeebah Az-Zahra Tashim Tiong Hoo Lim Hanif Fakhrurroja Suriayati Chuprat Seno Adi Putra Pengcheng Liu Aida Maryam Basri Electrical and Electronic Engineering Universiti Technologi Brunei Bandar Seri Begawan Brunei Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia School of Industrial and System Engineering Telkom Universiti Bandung Indonesia Department of Computer Science University of York York United Kingdom School of Applied Science and Matematics Universiti Technologi Brunei Bandar Seri Begawan Brunei

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

The aquaculture industry has witnessed a significant surge in technological integration over the past decade, leveraging advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance production efficiency. Monitoring the growth of aquatic cultures, such as fishes and prawns, plays a pivotal role in optimizing production. However, traditional methods, notably in prawn aquaculture in Asia, still heavily rely on stress-inducing techniques like netting and manual measurements. This paper proposes a novel approach using YOLO-based detection algorithm with biocode reference point based on the ArUco design to predict prawn size based on the length and are dimension to analysed the improvement in predicting the size. The propose solution is compared extensively using statistical tests against segmentation model with and without fine-tuning. Initial results show that both YOLOv8 segmentation and YOLOv8 with Segment Anything Model (SAM) give similar results with the actual mean length of 15.7 cm and predicted mean lengths are 20.5 cm and 20.8 cm respectively. However, after fine-tuning the parameter, the accuracy of predicted results improved significantly with 15.8 cm for YOLOv8 segmentation and 16 cm for YOLOv8 with SAM. The mean absolute error of 0.93 cm for YOLOv8 segmentation and 1.10 cm for YOLOv8 with SAM, and the width of 0.48 cm and 0.78 cm respectively.

关键词： Weight measurement Legged locomotion Deep learning Image segmentation Biological system modeling Production Manuals Length measurement Predictive models Internet of Things

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Continuous Evolution of Digital Twins using the DarTwin Notation

arXiv

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arXiv 2024年

作者： Mertens, Joost Klikovits, Stefan Bordeleau, Francis Denil, Joachim Haugen, Øystein Faculty of Applied engineering: Electronics and ICT University of Antwerp Groenenborgerlaan 171 Antwerp Antwerp2020 Belgium Institute for Business Informatics - Software Engineering Johannes Kepler University Altenberger Strasse 69 Linz4040 Austria Montreal Canada Flanders Make @UAntwerpen University of Antwerp Groenenborgerlaan 171n Antwerpen Antwerp2020 Belgium Department of Computer Science and Communication Østfold University College B R A veien 4 Halden1757 Norway

Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user’s needs and their environment, demanding the adaptation of the developed DT systems. DTs are developed for a specific purpose, which generally entails the monitoring, analysis, simulation or optimization of a specific aspect of an actual system, referred to as the actual twin (AT). As such, when the twin system changes, that is either the AT itself changes, or the scope/purpose of a DT is modified, the DTs usually evolve in close synchronicity with the AT. As DTs are software systems, the best practices or methodologies for software evolution can be leveraged. This paper tackles the challenge of maintaining a (set of) DT(s) throughout the evolution of the user’s requirements and priorities and tries to understand how this evolution takes place. In doing so, we provide two contributions: (i) we develop DarTwin, a visual notation form that enables reasoning on a twin system, its purposes, properties and implementation, and (ii) we introduce a set of architectural transformations that describe the evolution of DT systems. The development of these transformations is driven and illustrated by the evolution and transformations of a family home’s DT, whose purpose is expanded, changed and re-prioritized throughout its ongoing lifecycle. Additionally, we evaluate the transformations on a lab-scale gantry crane’s DT. © 2024, CC BY.

关键词： Smart homes

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Sliding Mode and Super-Twisting Sliding Mode Control Structures for SMA Actuators*

Sliding Mode and Super-Twisting Sliding Mode Control Structu...

引用

European Control Conference (ECC)

作者： Claudia-Adina Bojan-Dragos Radu-Emil Precup Alexandra-Iulia Szedlak-Stinean Raul-Cristian Roman Elena-Lorena Hedrea Emil M. Petriu Department of Automation and Applied Informatics Politehnica University of Timisoara Bd. V. Parvan 2 Timisoara Romania Romanian Academy – Timisoara Branch Center for Fundamental and Advanced Technical Research Bd. Mihai Viteazu 24 Timisoara Romania School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Ottawa ON Canada

This paper aims to design the optimal sliding mode and super twisting sliding mode controllers for the nonlinear Shape Memory Alloy (SMA) wire actuators. The parameters of the proposed controllers are optimally tuned using the metaheuristic Grey Wolf optimizer algorithm and a comparative analysis is carried out. All control structures are validated by simulations using an accurate evolved Takagi-Sugeno-Kang fuzzy model of SMA wire actuators and their control performance is evaluated.

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Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

arXiv

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arXiv 2022年

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Applied Computer Department College of Medicals and Applied Sciences Charmo University Sulaimany Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimany Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimany Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases. © 2022, CC BY-NC-SA.

关键词： Multiobjective optimization

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Security Protection with Dynamic Assigned Address Mechanism in Industrial Control Systems 20

Security Protection with Dynamic Assigned Address Mechanism ...

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Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications

作者： Chuan-Gang Liu Chin-Jui Wu I-Hsien Liu Chi-Che Wu Jung-Shian Li Department of Applied Informatics and Multimedia Chia-Nan University of Pharmacy and Science Tainan Taiwan Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781450383042

In recent years, industrial control systems (ICS) are widely used in many occasions, which makes information security of ICS an important issue. This study discusses the communication and management on ICS to make ICS intelligent. We proposed a method to avoid attack of communication, and elaborated the means of our proposed communication in which all communication parties communicate with each other without transmitting IP information. Moreover, as an intelligent ICS, the transmission module we design can detect anomaly in the system and change the transmission rule based on our algorithm, also known as a type of moving target defense. Therefore, we proposed the scheme to improve the transmission security in ICS. The experimental results also show that our scheme can be feasible in ICS.

关键词： Intelligent system Industrial control systems Moving target defense Network security IPv6

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

引用

arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Microwave signal processing using an analog quantum reservoir computer

arXiv

引用

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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Synthetic frequency dimension state coupling in modulated LNOI ring cavity devices

Synthetic frequency dimension state coupling in modulated LN...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Armandas Balčytis Xuan Hiep Dinh Tomoki Ozawa Yasutomo Ota Toshihiko Baba Satoshi Iwamoto Arnan Mitchell Thach G. Nguyen Integrated Photonics and Applications Centre RMIT University Melbourne VIC Australia Advanced Institute for Materials Research Tohoku University Sendai Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan Department of Electrical and Computer Engineering Yokohama National University Hodogaya-ku Yokohama Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo Japan

We created a modulator-equipped thin-film lithium niobate integrated ring resonator simulating a synthetic frequency dimension tight-binding model. It produces frequency states spaced over a 600 GHz bandwidth. Reconfi... 详细信息

ISBN: (纸本)9781957171258

关键词： Electron beam lithography Frequency combs Frequency modulation Lithium niobate Ring resonators Waveguide modulators

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