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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Tripathi, Padmaja Chandra, Harish Keval, Ram Kesarvani, Atul Singh, Kundan Madan Mohan Malviya University of Technology Department of Mathematics and Scientific Computing Gorakhpur India MJP Rohilkhnad University Department of Applied Mathematics Bareily India Rajkiya Engineering College Department of Electronic Engineering India Rajkiya Engineering College Department of Electronics Engineering 231206 India

ISBN: (纸本)9798350368109

There has been a major increase in research on digitizing the disease's tracking systems. In the scientific community, automated screening for malaria is one of the detection methods that is becoming very common. Although the Fourth Industrial Revolution (4IR) has gained widespread acceptance, the treatment for malaria, one of the deadliest and most enduring diseases, has not evolved. Contextualization of malaria monitoring systems must be established at the national or regional level, and this requires process integration with other protocols. This study suggests a digital malaria monitoring system for a country or region in Africa. The development of a distinctive model for forecasting disease spread, grounded in the dynamics of various types of malaria, represents a significant benefit of this digital system. We analyze the SITRR (susceptible-infected-treated resistant-recovered) epidemic model of the illness's spread to produce a mathematical forecast and to define the design of the diagnosis system. © 2024 IEEE.

关键词： Diseases

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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350352528

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models. © 2025 IEEE.

关键词： AI/ML net load forecasting visual analytics

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FusionMamba:dynamic feature enhancement for multimodal image fusion with Mamba

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Visual Intelligence 2024年第1期2卷 470-487页

作者： Xinyu Xie Yawen Cui Tao Tan Xubin Zheng Zitong Yu School of Computing and Information Technology Great Bay UniversityDongguanGuangdong523000China Faculty of Applied Sciences Macao Polytechnic UniversityMacaoChina Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong KongChina

Multimodal image fusion aims to integrate information from different imaging techniques to produce a comprehensive,detail-rich single image for downstream vision *** methods based on local convolutional neural networks(CNNs)struggle to capture global features efficiently,while Transformer-based models are computationally expensive,although they excel at global *** addresses these limitations by leveraging selective structured state space models(S4)to effectively handle long-range dependencies while maintaining linear *** this paper,we propose FusionMamba,a novel dynamic feature enhancement framework that aims to overcome the challenges faced by CNNs and Vision Transformers(ViTs)in computer vision *** framework improves the visual state-space model Mamba by integrating dynamic convolution and channel attention mechanisms,which not only retains its powerful global feature modeling capability,but also greatly reduces redundancy and enhances the expressiveness of local *** addition,we have developed a new module called the dynamic feature fusion module(DFFM).It combines the dynamic feature enhancement module(DFEM)for texture enhancement and disparity perception with the cross-modal fusion Mamba module(CMFM),which focuses on enhancing the inter-modal correlation while suppressing redundant *** show that FusionMamba achieves state-of-the-art performance in a variety of multimodal image fusion tasks as well as downstream experiments,demonstrating its broad applicability and superiority.

关键词： Multimodal Image fusion Feature enhancement Mamba

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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Atmospheric Convection Model Based Digital Confidentiality Scheme

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Computers, Materials & Continua 2022年第6期71卷 4503-4522页

作者： Noor Munir Majid Khan Mohammad Mazyad Hazzazi Amer Aljaedi Sajjad Shaukat Jamal Iqtadar Hussain Department of Applied Mathematics and Statistics Institute of Space TechnologyIslamabadPakistan Department of Mathematics College of ScienceKing Khalid UniversityAbha61413Saudi Arabia College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Department of Mathematics Statistics and PhysicsQatar UniversityDoha2713Qatar

Nonlinear dynamics is a fascinating area that is intensely affecting a wide range of different disciplines of science and technology *** combination of different innovative topics of information security and high-speed computing has added new visions into the behavior of complex nonlinear dynamical systems which uncovered amazing results even in the least difficult *** generation of complex actions froma very simple dynamical method has a strong relation with information *** protection of digital content is one of the inescapable concerns of the digitally advanced ***,information plays an important role in everyday life and affects the surroundings *** digital contents consist of text,images,audio,and videos,*** to the vast usage of digital images in the number of social and web applications,its security is one of the biggest *** this work,we have offered an innovative image encryption technique based on present criteria of confusion and *** designed scheme comprises two major nonlinear dynamical *** have employed discrete fractional chaotic iterative maps to add confusion capability in our suggested algorithm and continuous chaotic Lorenz *** have verified our offered scheme by using statistical *** investigations under the statistical tests suggested that our proposed technique is quite reasonable for the security of digital data.

关键词： Lorenz atmospheric convection model fractional logistic map image encryption S-box

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Availability and Demand of Jobs from the Perception of Automated Recruitment in Oman 7

Availability and Demand of Jobs from the Perception of Autom...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Karim Sadiq, Mohamed Abdul Al-Fannah, Nasser Mohammed Shanmugam, Thirumurugan Kausar, Firdous Oman Ministry of Transport Communications and Information Technology Muscat Oman College of Computing and Information Science Utas Suhar Oman College of Computing and Information Science University of Technology and Applied Sciences Suhar Oman Fisk University Department of Mathematics and Computer Science NashvilleTN United States

ISBN: (纸本)9798350342154

Natural Language Processing has been applied for automation of the recruitment process globally with the increase in volumes of profiles and jobs. This paper discusses certain challenges in the use of cutting-edge technologies for documents in the Arabic language. We analyze the availability and demand of jobs in Oman with reference to that in UAE. As a result, hurdles can be minimized in the job-resume matching process between the employers and prospective candidates. © 2023 IEEE.

关键词： Deep learning

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Efficiency Assessment of an Institute Through Parallel Network Data Envelopment Analysis 11th

Efficiency Assessment of an Institute Through Parallel Netwo...

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11th International Conference on Soft computing for Problem Solving, SocProS 2022

作者： Kumar, Atul Panwar, Ankita Pant, Millie Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9789811965241

Conventional data envelopment analysis (DEA) is a non-parametric approach to examine the efficiency of similar decision-making units (DMUs) as a whole system without considering its internal structure, i.e., the system is considered as a black box. Therefore, a network DEA (NDEA) is needed to study the internal structure of a system. By allowing for the measurement of individual components, an NDEA model can reveal inefficiencies that a traditional DEA ignores. In this study, we use the parallel network DEA to calculate the efficiency of a higher education institute with 19 decision-making units (DMUs) using two parallel processes (teaching and research) and compare it with the conventional DEA, CCR model through a numerical example. The main advantages of the parallel NDEA model are (i) to identify which DMUs are inefficient and make necessary adjustments, and (ii) the parallel DEA model has a lower efficiency score than the traditional DEA model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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A Mathematical Model to Minimize the Total Cultivation Cost of Sugarcane 11th

A Mathematical Model to Minimize the Total Cultivation Cost ...

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11th International Conference on Soft computing for Problem Solving, SocProS 2022

作者： Kumar, Sumit Pant, Millie Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand Roorkee India Mehta Family School for Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand Roorkee India

ISBN: (纸本)9789811965241

In the present study, a non-linear mathematical model is proposed to minimize the total expenditure made on sugarcane cultivation. The objective here is to optimize the costs involved in transportation, cultivation, and management of the deterioration of sugarcane. The optimization tools used are Differential Evolution (DE) and Particle Swarm Optimization (PSO). Further, the model is validated by comparing the actual expenditure of sugarcane cultivation with results obtained by PSO and DE, which indicates that the application of optimization methods may help in reducing the expenditure made on sugarcane cultivation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deterioration

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Boosting the generation rate of squeezed single-photon states by generalized photon subtraction

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Physical Review A 2024年第3期110卷 033717页

作者： Hiroko Tomoda Akihiro Machinaga Kan Takase Jun Harada Takahiro Kashiwazaki Takeshi Umeki Shigehito Miki Fumihiro China Masahiro Yabuno Hirotaka Terai Daichi Okuno Shuntaro Takeda Department of Applied Physics School of Engineering Optical Quantum Computing Research Team NTT Device Technology Labs Advanced ICT Research Institute

In optical quantum information processing with continuous variables, optical non-Gaussian quantum states are essential for universal and fault-tolerant quantum computation. Experimentally, their most typical generation method is photon subtraction (PS), where single-photon detection by an on/off detector probabilistically heralds the generation of squeezed single-photon states. In PS, however, trying to avoid unwanted multiphoton detection inevitably limits the generation rate, hindering the application of squeezed single-photon states. Here, we theoretically show that generalized photon subtraction (GPS), a simple extension of PS, can improve the generation rate while maintaining the quality of the generated states. Furthermore, we experimentally demonstrate the generation rate improvement for 2-dB- and 4-dB-squeezed single-photon states compared to PS, by more than one order of magnitude, particularly for the case of 2 dB. Our results will accelerate the application of squeezed single-photon states to more advanced quantum information protocols.

关键词： Quantum information processing Quantum optics

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