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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Chavhan, Ganesh K. Singh, Sumeshwar Sahebrao Doke, Ankush Pilli, Daniel Kailasavalli, S. Vyankatesh Ghamande, Manasi K. J. Somaiya College of Arts Commerce and Science Department of Geography Ahmednagar Maharashtra Kopargaon India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Mahatma Phule Nutan Maha Vidyalaya Department of Geography Maharashtra Mirajgaon India Klbs Koneru Lakshmaiah Educational Foundation Mba Department Andhra Pradesh Vijayawada India Psna College of Engineering and Technology Mathematics Tamilnadu Dindigul India Vishwakarma Institute of Information Technology Engineering and Applied Science Maharashtra Pune India

ISBN: (纸本)9798350396638

Recently, numerous disaster-related studies have integrated Remote-Sensing (RS) technology alongside picture processing and conventional methods. One example is identifying earthquake damage to buildings in orthophotos. Common approaches to creating damage maps from RS pictures include automatic and visual methods. However, because of the need for manual sampling, the visual method is laborious. By extracting the defect features, the automatic technique can identify the damaged structure. Challenges to the widespread use of automatic methods are presented by the great variety of design approaches and the dynamic nature of real-world situations, such as variations in shadow and light. To better train neural networks with less interference from measurement noise, principal component analysis (PCA) is used. After PCA is applied to the DI values, an ensemble of neural networks is utilized to provide predictions about the location and severity of damage. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two widely used methods in machine learning, and the CRNN approach combines the two (RNNs). As compared to the CNN and RNN models, the proposed technique performs admirably. © 2023 IEEE.

关键词： Principal component analysis

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Utilizing probabilistic entanglement between sensors in quantum networks

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Physical Review applied 2024年第6期22卷 064085-064085页

作者： Emily A. Van Milligen Christos N. Gagatsos Eneet Kaur Don Towsley Saikat Guha Department of Physics The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Program in Applied Mathematics The University of Arizona Tucson Arizona 85721 USA Cisco Quantum Lab Los Angeles USA College of Computer Science University of Massachusetts Amherst Massachusetts USA Department of Electrical and Computer Engineering The University of Maryland College Park Maryland 20742 USA

One of the most promising applications of quantum networks is entanglement-assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete-variable case and quantum squeezing in the continuous-variable case, distributed amongst all of the sensors in a given network. However, it can be difficult to ensure that all sensors preshare entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.

关键词： Quantum entanglement

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Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI

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computers in Biology and Medicine 2024年 182卷 109183-109183页

作者： Ullah, Muhammad Sami Khan, Muhammad Attique Albarakati, Hussain Mubarak Damaševičius, Robertas Alsenan, Shrooq Department of Computer Science HITEC University Taxila47080 Pakistan Department of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah24382 Saudi Arabia Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that enable people to comprehend, properly trust, and create more explainable models. In medical imaging, XAI has been adopted to interpret deep learning black box models to demonstrate the trustworthiness of machine decisions and predictions. In this work, we proposed a deep learning and explainable AI-based framework for segmenting and classifying brain tumors. The proposed framework consists of two parts. The first part, encoder-decoder-based DeepLabv3+ architecture, is implemented with Bayesian Optimization (BO) based hyperparameter initialization. The different scales are performed, and features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique. The extracted features are passed to the output layer for tumor segmentation. In the second part of the proposed framework, two customized models have been proposed named Inverted Residual Bottleneck 96 layers (IRB-96) and Inverted Residual Bottleneck Self-Attention (IRB-Self). Both models are trained on the selected brain tumor datasets and extracted features from the global average pooling and self-attention layers. Features are fused using a serial approach, and classification is performed. The BO-based hyperparameters optimization of the neural network classifiers is performed and the classification results have been optimized. An XAI method named LIME is implemented to check the interpretability of the proposed models. The experimental process of the proposed framework was performed on the Figshare dataset, and an average segmentation accuracy of 92.68 % and classification accuracy of 95.42 % were obtained, respectively. Compared with state-of-the-art techniques, the proposed framework shows improved accuracy. © 2024 Elsevier Ltd

关键词： Data fusion

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Some fractional-order modeling and analysis of the transmission dynamics together with prevention controls

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The European Physical Journal Special Topics 2024年 1-25页

作者： Guirao, Juan L. G. Jan, Rashid Baleanu, Dumitru Mohammed, Pshtiwan Othman Abdullah, Farah Aini Chorfi, Nejmeddine Department of Applied Mathematics and Statistics Technical University of Cartagena Hospital de Marina Cartagena Spain Department of Civil Engineering Institute of Energy Infrastructure (IEI) College of Engineering Universiti Tenaga Nasional (UNITEN) Kajang Malaysia Mathematics Research Center Near East University TRNC Nicosia Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Institute of Space Science-Subsidiary of INFLPR Magurele-Bucharest Romania Department of Mathematics College of Education University of Sulaimani Sulaimani Iraq Research and Development Center University of Sulaimani Sulaymaniyah Iraq School of Mathematical Sciences Universiti Sains Malaysia USM Penang Malaysia Department of Mathematics College of Science King Saud University Riyadh Saudi Arabia

The purpose of the present research is to improve our understanding of how coronavirus infection spreads by creating an epidemic model that incorporates isolation and quarantine measures. The dynamics of this viral spread are represented using the Caputo–Fabrizio operator. By analyzing the equilibria of the model and utilizing the next-generation matrix method, we determine the endemic indicator, $${\mathcal {R}}_0$$ . The main findings demonstrate that the equilibrium without infection is locally stable when $${\mathcal {R}}_0$$ is less than one and unstable otherwise. Also, we demonstrate the existence and uniqueness of the solution of our recommended model. In addition to this, we interrogate the solution pathways of the system by varying different factors, aiming to comprehend the intricate transmission of COVID-19 and visualize the crucial factors of the system that can aid public health officials in the control of the spread of infection.

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Chaos and proportional integral derivative (PID) control on cancer dynamics with fractal fractional operator

Results in Engineering

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Results in engineering 2025年 26卷

作者： Farman, Muhammad Nisar, Kottakkaran Sooppy Jamil, Khadija Akgül, Ali Sambas, Aceng Bayram, Mustafa Habib, Mustafa Hassani, Murad Khan Faculty of Arts and Science Department of Mathematics Near East University Nicosia99138 Cyprus Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Research Center of Applied Mathematics Khazar University Baku1096 Azerbaijan Department of Mathematics College of Science and Humanities in Alkharj Prince Sattam Bin Abdulaziz University Alkharj11942 Saudi Arabia Institute of Mathematics Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapi Istanbul34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard Nicosia/Mersin 10 PC: 99138 Turkey Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya46196 Indonesia Department of Mathematics University of Engineering and Technology Lahore Pakistan Ghazni University Department of Mathematics Afghanistan Applied Science Research Center Applied Science Private University Amman11937 Jordan

This study presents a cancer dynamics model incorporating a fractal-fractional operator with a Mittag-Leffler kernel to capture complex interactions among cancer cells, tumor suppressor cells, immune cells, and oncolytic viruses. The model aims to enhance understanding of tumor-immune dynamics and improve treatment strategies. The existence and uniqueness of the solution are established using fixed point theory under the Lipschitz condition. Lyapunov stability of the system is also analyzed in the context of the fractal-fractional operator. To address chaotic behavior in cancer progression, chaos and Proportional-Integral-Derivative (PID) control techniques are implemented. These control methods effectively stabilize the system and regulate treatment administration. Numerical simulations illustrate the influence of fractional-order derivatives on tumor suppression and immune response, confirming the model's effectiveness in reflecting real-world cancer dynamics. © 2025 The Author(s)

关键词： Fractals

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Exploring household food security through institutional factors: A statistical and mathematical analysis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9179-9195页

作者： Khan, Younas Ashraf, Shahzaib Farman, Muhammad Abdallah, Suhad Ali Osman Department of Sociology Kohat University of Science and Technology Kohat Pakistan Institute of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Mathematics Faculty of Arts and Science Near East University Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Applied College Khamis Mushait King Khalid University Abha Saudi Arabia

Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2= 102.386;P = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke's R Square (R2 = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2 = 0.237 and Nagelkerke's R2 = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P = 0.004, militancy P = 0.000, social stratification P = 0.021 and poverty P = 0.000) significantly predicted FS at the household level. Mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F3 (poverty) > F2 (militancy) > F4 (social stratification) > F1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by

关键词： Food supply

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Predicting Power Output of Solar Photovoltaic Panels Using Machine Learning Techniques

Predicting Power Output of Solar Photovoltaic Panels Using M...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Karimulla Syed Mohammad Ameer Hamza Yousuf Murali Krishna Boddu Sainadh Singh K Mahaboob Shareef Syed Afaq Ahmad Engineering Department University of Technology and Applied science-Shinas Shinas Sultanate of Oman Department of EEE BVRIT Narsapur India Vignan's Lara ITS India Department of Mathematics and Computer Science Modern College of Bussiness & Science Muscat Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The prediction of solar power generation is essential for effective integration of renewable energy into power grids, aiding in grid stability, energy planning, and efficient resource allocation. Due to the inherent variability of solar energy caused by factors like weather patterns, time of day, and seasonal changes, machine learning (ML) has appeared as a powerful tool to improve forecasting accuracy. Solar panels with various tilt angle combinations are set up to collect experimental data. This paper uses supervised regression learner in machine learning application for solar power prediction. In this paper, linear regression and step wise linear regression models are giving fruitful results out of 27 models that are analyzed. A detailed study of model selection is provided, alongside an examination of evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Means Square Error (RMSE), and regression coefficient R 2 scores. This study shows the effectiveness of Machine Learning (ML) in enhancing short, and medium-term solar power forecasting, supporting more efficient energy management and promoting the scalability of renewable energy systems. For the linear regression algorithm, we obtained regression coefficient (R 2 ) of 1, mean absolute percentage error (MAPE) of 0.7% and for the stepwise linear regression algorithm, we obtained a regression coefficient (R 2 ) of 1, mean absolute percentage error (MAPE) 0.45%.

关键词： Training Renewable energy sources Analytical models Machine learning algorithms Linear regression Machine learning Prediction algorithms Solar panels Forecasting Meteorology

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Mayfly-Algorithm Selected Features for Classification of Breast Histology Images into Benign/Malignant Class 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Srivastava, Gautam Meqdad, Maytham N. Department of Applied Data Science Noroff University College Kristiansand Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Electronics and Instrumentation St. Joseph’s College of Engineering Tamilnadu Chennai600 119 India Department of Mathematics and Computer Science Brandon University 270 18th Street BrandonR7A 6A9 Canada Al-Mustaqbal University College Babil Hillah Iraq

ISBN: (数字)9783031215179

ISBN: (纸本)9783031215162

Incidence rate of Breast Cancer (BC) is rising globally and the early detection is important to cure the disease. The detection of BC consist different phases from verification to clinical level diagnosis. Confirmation of the cancer and its stage is performed normally with breast biopsy. This research aims to develop a framework to identify Benign/Malignant class images from the Breast Histology Slide (BHS). This technique consist the following phases;(i) Cropping and resizing the image slice, (ii) Deep-feature extraction using pre-trained network, (iii) Discrete Wavelet Transform (DWT) feature mining, (iv) Optimal feature selection with Mayfly algorithm, (v) Serial feature concatenation, and (vi) Binary classification and validation. This work considered the test image with dimension 896 × 768 × 3 pixels. During the investigation, every picture is cropped into 25 slices and resized to 224 × 224 × 3 pixels. This work implements the following stages;(i) BC detection with deep-features and (ii) BC recognition with concatenated features. In both the cases, a 5-fold cross validation is employed and the experimental investigation of this research confirms that the proposed work helped to achieve an accuracy of 91.39% with deep-feature and 95.56% with concatenation features. © 2022, Springer Nature Switzerland AG.

关键词： Discrete wavelet transforms

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Exact Output Synchronization of Discrete-Time Multi-Agent Systems in the Presence of Disturbances and Measurement Noise with Known Frequencies via Scale-Free Protocol Design

Exact Output Synchronization of Discrete-Time Multi-Agent Sy...

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Chinese Control Conference (CCC)

作者： Zhenwei Liu Meirong Zhang Ali Saberi Anton A. Stoorvogel College of Information Science and Engineering Northeastern University Shenyang China School of Engineering and Applied Science Gonzaga University Spokane WA USA School of Electrical Engineering and Computer Science Washington State University Pullman WA USA Department of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands

This paper aims to achieve scalable exact output synchronization for discrete-time homogeneous multi-agent systems in the presence of disturbances and measurement noise with known frequencies. The key contribution of this paper is on the distributed protocol that only uses the information of agent models, rather than the communication network information and the agent number, so as to achieve the scalable exact synchronization under disturbances and measurement noise. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents.

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Efficient N-to-M Checkpointing Algorithm for Finite Element Simulations

arXiv

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arXiv 2024年

作者： Ham, David A. Hapla, Vaclav Knepley, Matthew G. Mitchell, Lawrence Sagiyama, Koki Department of Mathematics Imperial College London United Kingdom Department of Earth and Planetary Sciences ETH Zurich Department of Applied Mathematics FEECS at VSB-TU Ostrava Czech Republic Department of Computer Science and Engineering The University Buffalo United States NVIDIA Corporation Santa Clara United States

In this work, we introduce a new algorithm for N-to-M checkpointing in finite element simulations. This new algorithm allows efficient saving/loading of functions representing physical quantities associated with the mesh representing the physical domain. Specifically, the algorithm allows for using different numbers of parallel processes for saving and loading, allowing for restarting and post-processing on the process count appropriate to the given phase of the simulation and other conditions. For demonstration, we implemented this algorithm in PETSc, the Portable, Extensible Toolkit for Scientific Computation, and added a convenient high-level interface into Firedrake, a system for solving partial differential equations using finite element methods. We evaluated our new implementation by saving and loading data involving 8.2 billion finite element degrees of freedom using 8,192 parallel processes on ARCHER2, the UK National Supercomputing Service. © 2024, CC BY.

关键词： Degrees of freedom (mechanics)

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