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2024 IEEE International Conference on computing, ICOCO 2024

作者： Chan, Vei Siang Mohamed, Farhan Bin Salleh, Faezah Binti Mohd Iglesias, Andres Jamaludin, Najwa Ayuni Binti Mustafa Albakri, Ikmal Faiq Albakri Media and Game Innovation Center of Excellence Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Applied Mathematics and Computational Sciences Universidad de Cantabria Santander Spain Pervasive Computing & Educational Technology Department of Media Interactive Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (纸本)9798331530303

Understanding the needs of genomic data visualization techniques is important to help bioinformaticians to perform their comparative analytical tasks effectively. DNA sequencing datasets consist of a long sequential string of nucleotide base pairs combination and are often involved in multiple sequences alignment for comparative analysis. Therefore, existing challenges include designing suitable visualization layout and the need of user-centered approach to ensure optimal user experience. Existing works also featured heavily on the qualitative evaluation of comparative visualization tools, such as use cases and case studies, which are difficult to quantify and conduct follow up evaluation for tool improvement. Hence, this paper aims to understand the needs of visualization tools in comparative genomic analysis to identify visualization tasks and visual design requirements. The methodology includes the creation of semi-structured interview based on personas template as guiding questions and the synthesis of thematic analysis results. These results presented the requirements of the visualization tasks for the biodiversity domain based on comparative targets. For future work, this research will design, develop, and validate the comparative visualization tool based on the discovered requirements. ©2024 IEEE.

关键词： Genome

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A tumor-immune model of chronic myeloid leukemia with optimal immunotherapeutic protocols

arXiv

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arXiv 2025年

作者： Zhang, Haifeng Zhuge, Changjing Lei, Jinzhi School of Mathematical Sciences Jiangsu University Zhenjiang212013 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China School of Mathematical Sciences Center for Applied Mathematics Tiangong University Tianjin300387 China

The interactions between tumor cells and the immune system play a crucial role in cancer evolution. In this study, we explore how these interactions influence cancer progression by modeling the relationships among naive T cells, effector T cells, and chronic myeloid leukemia cells. We examine the existence of equilibria, the asymptotic stability of the positive steady state, and the global stability of the tumor-free equilibrium. Additionally, we develop a partial differential equation to describe the conditions under which the concentration of cancer cells reaches a level that allows for effective control of cancer evolution. Finally, we apply our proposed model to investigate optimal treatment strategies that aim to minimize both the concentration of cancer cells at the end of treatment and the accumulation of tumor burden, as well as the cost associated with treatment during the intervention period. Our study reveals an optimal therapeutic protocol using optimal control theory. We perform numerical simulations to illustrate our theoretical results and to explore the dynamic behavior of the system and optimal therapeutic protocols. The simulations indicate that the optimal treatment strategy can be more effective than a constant treatment approach, even when applying the same treatment interval and total drug input. Copyright © 2025, The Authors. All rights reserved.

关键词： T-cells

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Forecasting of Groundwater Quality Utilizing an Efficient K-Means with Principal Component Analysis Approach

Forecasting of Groundwater Quality Utilizing an Efficient K-...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Myasar Mundher Adnan Manjula B M Antim Dev Mishra Swathi Ganti Bala Dhandayuthapani V The Islamic University of Babylon Babylon Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Engineering and Technology Sushant College Gurgaon India Department of Nano Technology Andhra University Visakhapatnam India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Groundwater stands as a pivotal freshwater resource in numerous regions worldwide, but the burgeoning population and diverse economic activities have adversely impacted its quality. However, the K-means algorithm is sensitive to the initial placement of centroids, where outliers can significantly influence the position of centroids resulting in suboptimal clusters. To address this, it is recommended to remove or clip outliers before applying K-means scales well to large datasets making it suitable for handling substantial amounts of data. In this proposed method, attributes are first calculated using Principal Component Analysis (PCA) determines the weight of each attribute based on eigenvalues. The PCA, by determining attribute weights and Rapid Centroid Estimation (RCE), by establishing the initial cluster centre, contribute to the overall effectiveness of the method. The classification step utilizes a Linear Support Vector Machine (SVM), suitable for linear hyper line data when the dataset can be classified into two classes using a single straight line. The results obtained showcase the superiority of the proposed method, achieving a Root Mean Squared Error (RMSE) of 1.23, R-squared (R2) of 0.90, Mean Absolute Deviation (MAD) of 0.28 and E of 0.97 on the Indian groundwater dataset. These outcomes affirm better detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN).

关键词： Support vector machines Radio frequency Groundwater Estimation Water quality Predictive models Chemicals Random forests Principal component analysis Aquifers

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Retinal Vessel Segmentation with Slime-Mould-Optimization based Multi-Scale-Matched-Filter 7

Retinal Vessel Segmentation with Slime-Mould-Optimization ba...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Damasevicius, Robertas Taniar, David Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Faculty of Information Technology ClaytonVIC3800 Australia

ISBN: (纸本)9781665441261

Even though a number of sensory organs are existing, eye plays a necessary role in the sensory system;which converts the incident light into meaningful visual information. If any abnormality arises in eye, then the whole sensory system gets stressed. The disease in eye is due to injury, infection and ageing and the untreated eye disease will lead to vision loss. The proposed research aims to propose a Computer-Aided-Procedure (CAP) to extract the blood-vessel section from Digital-Fundus-Image (DFI). In order to accomplish this task, a Multi-Scale-Matched-Filter (MSMF) is designed using the Slime-Mould-Optimization algorithm. In this work, the necessary test images are collected from the benchmark DRIVE and CHASE_DB1 dataset. After extracting the blood-vessel using the MSMF, an examination among extracted vessel and the Ground-Truth (GT) image is executed and the Image-Performance-Values are separately computed for each database. The attained result with this CAP confirms that the attained Jaccard, Dice and Accuracy of proposed approach is better compared to similar existing approaches in the literature. © 2021 IEEE.

关键词： Matched filters

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Detecting and Exploiting Unit Commitment Structures in MIPs 6

Detecting and Exploiting Unit Commitment Structures in MIPs

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6th International Conference on Power and Energy technology, ICPET 2024

作者： Su, Zhao-Gang Chen, Sheng-Jie Li, Yu-Fei Chen, Liang Kou, Caixia Cai, Zhi Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China College of Sciences Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China

ISBN: (纸本)9798350375855

The unit commitment (UC) problem has been extensively researched in the literature, which is typically formulated as a mixed integer programming (MIP) problem. However, current studies lack effective methods to identify potential UC problem structures within a general MIP problem, making it challenging to leverage well-established valid inequalities for UC problems to accelerate the solution process. In this paper, we present a novel detection method that automatically identifies UC structures within an MIP problem by analyzing the relationships between variables and constraints. Moreover, we enhance the MIP formulation by incorporating three well-studied classes of valid inequalities as cutting planes, based on the identified UC structures. Our experiments demonstrate that our proposed UC detection method greatly improves computational efficacy of a standard MIP solver compared to the default setting. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning

arXiv

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arXiv 2024年

作者： Chang, Tyler Gillette, Andrew Maulik, Romit Mathematics and Computer Science Division Argonne National Laboratory United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Information Sciences and Technology Department The Pennsylvania State University United States

Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of accuracy, but often are difficult or impractical to determine computationally. In this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently;(2) comparative performance among distinct interpolants can aid in validation goals;(3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models. We present a detailed case study of our approach for predicting lift-drag ratios from airfoil images. Code developed for this work is available in a public Github repository. © 2024, CC BY-NC-SA.

关键词： Interpolation

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qReduMIS: A Quantum-Informed Reduction Algorithm for the Maximum Independent Set Problem

arXiv

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arXiv 2025年

作者： Schuetz, Martin J.A. Yalovetzky, Romina Andrist, Ruben S. Salton, Grant Sun, Yue Raymond, Rudy Chakrabarti, Shouvanik Acharya, Atithi Shaydulin, Ruslan Pistoia, Marco Katzgraber, Helmut G. Amazon Advanced Solutions Lab SeattleWA98170 United States AWS Center for Quantum Computing PasadenaCA91125 United States Global Technology Applied Research JPMorgan Chase New YorkNY10017 United States

We propose and implement a quantum-informed reduction algorithm for the maximum independent set problem that integrates classical kernelization techniques with information extracted from quantum devices. Our larger framework consists of dedicated application, algorithm, and hardware layers, and easily generalizes to the maximum weight independent set problem. In this hybrid quantum-classical framework, which we call qReduMIS, the quantum computer is used as a coprocessor to inform classical reduction logic about frozen vertices that are likely (or unlikely) to be in large independent sets, thereby opening up the reduction space after removal of targeted subgraphs. We systematically assess the performance of qReduMIS based on experiments with up to 231 qubits run on Rydberg quantum hardware available through Amazon Braket. Our experiments show that qReduMIS can help address fundamental performance limitations faced by a broad set of (quantum) solvers including Rydberg quantum devices. We outline implementations of qReduMIS with alternative platforms, such as superconducting qubits or trapped ions, and we discuss potential future extensions. Copyright © 2025, The Authors. All rights reserved.

关键词： Trapped ions

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Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

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Computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

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Classification & Detection of Epilepsy Using IEEG Application 4

Classification & Detection of Epilepsy Using IEEG Applicatio...

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4th IEEE International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Kanna, R. Kishore Joshi, Kapil Singh, Kamreed Udham Pacholi, Ambali Thangam, Mathar Mohideen Nagoor Nimasajai, X.S. Blessing Department of Biomedical Engineering Chennai India Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India School of Computing Graphic Era Hill University Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India Gwalior India Faculty of Applied Medical Sciences University of Tabuk Department of Nursing Saudi Arabia King Khalid University College of Nursing Khamis Mushayt Saudi Arabia

ISBN: (纸本)9798350360165

More than only cognitive loss and impairment, epileptic seizures may seriously harm one's intellect. Early seizure detection is essential to minimize the harm caused by their possessions. Presently, medical professionals use optical testing of electroencephalographic (EEG) data for project labeling, which may be a labor-intensive and delayed procedure. By resolving EEG data using the best spatial convolutional interconnected system-long temporary thought (1D CNNLSTM) model, this study offers a revolutionary pattern for always identifying seizures. Before the raw EEG dossier is augmented into a 1D CNN that pulls appearance data from the dossier, it is preprocessed and standardized. Hence, worldly faces are extracted using Long Short-Term Memory (LSTM) layers, which are then combined into various suitably linked coatings for capture recognition. The projected model's representation was evaluated using the UCI epileptic capture acknowledgment dataset. For the twofold and five-class taking acknowledgment tasks, respectively, the outcomes show an exceptional acknowledgment veracity of 99.39 % and 82.00 %. The predicted model achieved better act when compared to existing machine intelligence setups and additional deep education models. These rulings highlight the promise of the proposed method for accurately diagnosing seizures in patients who are experiencing muscular spasms and acknowledge the potential for improved impartial outcomes. © 2024 IEEE.

关键词： Electroencephalography

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Sliding Mode Control Strategy for State-of-Charge (SoC) Balancing in Lithium-ion Battery Packs*

Sliding Mode Control Strategy for State-of-Charge (SoC) Bala...

引用

International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Yong Feng Fengling Han Rebecca Yang Yanmin Wang Fan Bai Nguyen-Vu Truong School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Science RMIT University Melbourne Australia School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Institute of Applied Mechanics and Informatics Vietnam Academy of Science and Technology Ho Chi Minh City Vietnam

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

This paper proposes a sliding mode control strategy for balancing the State of Charge (SoC) in lithium-ion battery packs. By thoroughly analyzing the battery balancing circuit topology, a bidirectional Cuk circuit balancing topology for a series-connected battery pack is established, and its mathematical model is developed. Utilizing sliding mode control technology, a balancing strategy is designed to equalize the SoC of all cells in a battery pack composed of multiple series-connected battery cells. The effectiveness of the proposed method is verified through experiments, demonstrating that the developed battery balancing system can successfully balance the SoC of each cell in the battery pack.

关键词： Lithium-ion batteries Circuit topology Automation Mathematical models Topology State of charge Sliding mode control

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