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检索条件"主题词=software algorithms"

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Machine Learning-Enabled Healthcare Decision Support System: An Advanced Disease Prediction with Patient-Doctor Collaboration

Machine Learning-Enabled Healthcare Decision Support System:...

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Computing and Data Science (ICCDS), International Conference on

作者： V. Ragavi Panneru Kalva Preethi Reddy Vommina Lakshmi Thanmaye Eswari Umaeswari P Computer Science and Business Systems R.M.K Engineering College Chennai Tamil Nadu

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

In this research, a revolutionary healthcare software system that gathers various patient symptoms in order to anticipate and assess diseases is presented. To generate predictions, the system uses five different algorithms: Random Forest, XG Boost, Support Vector Machine, Logistic Regression, and Naive Bayes. The most accurate prediction is chosen as the final result after a comparative study of the forecasts and accuracies from each method. Healthcare practitioners can make better decisions when using a graphical representation. A noteworthy development in proactive healthcare is demonstrated by the system's introduction of a function that recommends appropriate doctors to the patient. This invention supports both individual well-being and more general public health goals.

关键词： Support vector machines Technological innovation Accuracy software algorithms software systems Prediction algorithms Bayes methods

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Sub-Optimization based Random Forest Algorithm for Accurate and Efficient Land use and Land Cover Classification using Landsat Time Series Data

Sub-Optimization based Random Forest Algorithm for Accurate ...

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software, Systems and Information Technology (SSITCON), International Conference on

作者： B. D Parameshachari Laith Jasim Shruti Shivaraj Pilli Suneetha Chinthakunta Manjunath Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University of Babylon Babylon Iraq Department of Civil Engineering HKE Society’s Sir. M Visvesvaraya College of Engineering Raichur (Affiliated to VTU) Raichur India Department of Information Technology S.R.K.R. Engineering College Bhimavaram India Department of Computer Science and Engineering Christ (Deemed to be University) Bangalore India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The land use and land cover (LULC) play an essential role to investigate the impacts of environmental factors and socio-economic development in the Earth’s surface. Extracting the hidden information from the remote sensing images in the observed earth environment is the challenging process. In this research, implemented a model that uses Landsat data to investigate the LULC changes. Utilized the Landsat 5,7 and 8 as inputs for the 1985 to 2019 by Google Earth Engine (GEE) is applied for the robust classification. This paper proposed a Sub-forest optimization based Random forest (SO-RF) classifier with faster diagnosis speed for LULC classification. Moreover, to increase the multispectral Landsat band’s resolution from 30 m to 15 m, the pan-sharpening algorithm is utilized. In addition, analyzed the various image configurations grounded numerous spectral indices and other supplementary data such as land surface temperature (LST) and digital elevation model (DEM) on final classification accuracy. The proposed SO-RF produced higher accuracy (0.97 for kappa, 96.78% Overall accuracy (OA), 0.94 for f1-score) than Copernicus Global Land Cover Layers (CGLCL) map and state of art methods like K-Nearest Neighbor (KNN), Decision Tree (DT), and Multi-class Support Vector machine (MSVM).

关键词： Earth Accuracy software algorithms Land surface Landsat Nearest neighbor methods Classification algorithms Land surface temperature Random forests Optimization

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Research on the On-line Calibration and Traceability Method for Underwater C-scan ACFM

Research on the On-line Calibration and Traceability Method ...

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Qingdi Lan Jingyu Zhang Chaoyue Li Peng Gao Ying Li Wenqiang Duan Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China Tianjin Special Equipment Supervision and Inspection Technology Research Institute Tianjin China School of Engineering and Technology Tianjin Agricultural University Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

The reliable and fast non-destructive testing equipment is required for the maintenance and upkeep of old underwater pipelines in the offshore oil exploration. The alternating current field measurement (ACFM) technology is the most promising technical method for solving oil transportation pipelines and critical structures on offshore platforms. In this paper, the theoretical model and simulation model for seawater environment ACFM are introduced to explore various influencing factors of measurement signal of ACFM. Then the error generation pathways of the three-axis scanning ACFM test platform are analyzed, and the error source analysis is carried out. The on-line calibration algorithm for ACFM is proposed. The ACFM C-scan detection experiment is conducted. The error in crack length detection is mostly around 5%. Compared to crack length, the calculation error of crack depth is larger, with a relative error of up to 31.82%. And the crack width calculation is with largest error because of its smallest size and the scan direction. The signal characteristics extraction method and C-scan route of crack width and depth should be improved and the simulation work need more in-depth research.

关键词： Accuracy Current measurement Oils Pipelines software algorithms Transportation Calibration Systems simulation software measurement Testing

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An Average-Case Efficient Two-Stage Algorithm for Enumerating All Longest Common Substrings of Minimum Length $k$ Between Genome Pairs

An Average-Case Efficient Two-Stage Algorithm for Enumeratin...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Mattia Prosperi Simone Marini Christina Boucher Dept. of Epidemiology University of Florida Gainesville FL USA Dept. of CISE University of Florida Gainesville FL USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

A problem extension of the longest common sub-string (LCS) between two texts is the enumeration of all LCSs given a minimum length $k$ (ALCS-k), along with their positions in each text. In bioinformatics, an efficient solution to the ALCS- $k$ for very long texts -genomes or metagenomes- can provide useful insights to discover genetic signatures responsible for biological mechanisms. The ALCS-k problem has two additional requirements compared to the LCS problem: one is the minimum length $k$ , and the other is that all common strings longer than $k$ must be reported. We present an efficient, two-stage ALCS-k algorithm exploiting the spectrum of text substrings of length $k$ (k-mers). Our approach yields a worst-case time complexity loglinear in the number of k-mers for the first stage, and an average-case loglinear in the number of common k-mers for the second stage (several orders of magnitudes smaller than the total k-mer spectrum). The space complexity is linear in the first phase (disk-based), and on average linear in the second phase (disk- and memory-based). Tests performed on genomes for different organisms (including viruses, bacteria and animal chromosomes) show that run times are consistent with our theoretical estimates; further, comparisons with MUMmer4 show an asymptotic advantage with divergent genomes.

关键词： Codes software algorithms Genomics Random access memory Switches Data structures software

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Addressing Practical Limitations in DC Motor Speed Control

Addressing Practical Limitations in DC Motor Speed Control

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Cybernetics & Informatics (K&I)

作者： Stefan Chamraz Richard Balogh Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

This paper focuses on the precise control of DC motor speed in its whole range. Complexity of such task is demonstrated on the first order system control. We find that simplified models from textbooks often fail to explain the system’s real-world behaviour. Our approach involves a detailed analysis of all components of the control loop, from speed measurement to motor control. This approach allows us to design a more robust controller that better accounts for practical limitations. A key aspect of our work is a thorough analysis of the dimensions of physical quantities and their impact on controller design.

关键词： Microcontrollers Velocity control software algorithms Decentralized control Process control Pulse width modulation DC motors Hardware Delays Velocity measurement

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Implementation of a Somatosensory Interactive Game with Sensor and Neural Network Integration

Implementation of a Somatosensory Interactive Game with Sens...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yue-Ru Chuang Hung-Hua Liang Pei-Hxiu Weng Wen-Hui Chen Yu-Chieh Lin Department of Electrical Engineering Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

This paper presents an interactive motion-sensing game designed for ocean protection, aligned with the United Nations' 14th Sustainable Development Goal. The game uses wearable gloves with accelerometer and gyroscope sensors for hand posture recognition, processed via sensor fusion and a feedforward neural network (FNN). Bluetooth Low Energy (BLE) with a 4-to-1 Central and Peripheral architecture facilitates communication, while the neural network function runs on the BLE microcontroller. The system development includes hardware, algorithms, and Unity game software implementation.

关键词： Accelerometers Somatosensory Microcontrollers software algorithms Games Sensor fusion Hardware Gyroscopes Sustainable development Wearable sensors

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AI-Driven Approach for Generating Automatic GUI

AI-Driven Approach for Generating Automatic GUI

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Jamal Zraqou Wissam Alkhadour Faris Abuhashish Nancy Al–Ramahi Waleed Maqableh Jawad AlKhateeb Dept of Computer Science/Virtual & Augmented Reality University of Petra Amman Jordan Dept of E-Business & Commerce University of Petra Amman Jordan Dept of Computer Science/Virtual & Augmented Reality Amman Jordan Dept of Basic Sciences Al-Zaytoonah University of Jordan Amman Jordan Dept of Extended Reality and Game Design Applied Science University Amman Jordan Dept of Computer Engineering & Science Prince Mohammad Bin Fahd University Al Khobar KSA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The user interface is considered one of the most important analysis processes in software engineering. Creating controls for the forms can resolve the problem of being an experience of coding and reduce the time required for implementation. The main aim of the proposed tool is to create high-quality software and handle the user interface to interact with the user. The proposed case tool algorithm uses machine learning patterns to recognize hand drawings and create graphical user interfaces. The software engineer or the end user can convert the strikes of his/her hand drawing to make the intended graphical user interface efficient. The system received high marks from 100 users, as 85% found it easy to operate, while 80% reported shorter design times than drag-and-drop interfaces. The system acknowledged positive feedback about its user-friendly learning process, meaningful speed improvements, and adjustable user interface design.

关键词： Hands Machine learning algorithms Tracking software algorithms XML Machine learning Pressing Feature extraction software Graphical user interfaces

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Resource and Performance Improvements of Optimized Convolutional Neural Networks for FPGA Implementations of Automatic Modulation Recognition

Resource and Performance Improvements of Optimized Convoluti...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Joshua A Rothe Haya Shajaiah Whiting School of Engineering Johns Hopkins University Baltimore MD United States

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Automatic Modulation Recognition (AMR), commonly found in software defined radios, relies on speed and accuracy to effectively interpret the modulation type of incoming signals. Convolutional Neural Networks (CNNs) are growing in popularity over traditional algorithms due to their excellent performance with classification-type problems – but these are typically resource intensive, and Radio Frequency (RF) receivers are typically part of a larger system that can benefit from less resources being tied to this classification task. Field Programmable Gate Array (FPGA) implementations provide better performance than CPU and GPU implementations in most cases, and the added benefit of these functions being off the processor allows the entire system to perform better. Since resource utilization is such a critical component of effective CNN implementation, and it is often inversely tied to performance, the effectiveness of various hardware optimization techniques as well as their effects on performance should be considered by the designer. This work evaluates the tradeoffs of various model optimizations and how they affect both implementation and performance, synthesizing the models using Xilinx’s quantization-aware FINN library. In this work, a methodology is presented for the generation of I and Q signals for CNN model training and evaluation, and the performance and hardware utilization benchmarks are evaluated to determine both design considerations and effective approaches for optimizing these models for real-world implementation.

关键词： Training software algorithms Modulation Logic gates Hardware Convolutional neural networks Resource management Field programmable gate arrays Optimization software radio

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Research on Brake Drum Surface Defect Detection System Based on Machine Vision Technology

Research on Brake Drum Surface Defect Detection System Based...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Xuefeng Han Bin Zheng School of Intelligent Manufacturing Panzhihua University Panzhihua

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This paper focuses on the development of an efficient and accurate brake drum surface defect detection system to meet the growing demands for brake safety performance in the manufacturing industry. The system analyzes common defect types on brake drum surfaces, such as chips and cracks, and designs a comprehensive detection scheme. In terms of hardware architecture, it integrates high-resolution image acquisition equipment and a customized lighting system to ensure clear and comprehensive images of the brake drum surface under different lighting conditions. At the software level, the system implements image preprocessing steps, including noise filtering and contrast enhancement, to optimize image quality. Subsequently, advanced feature extraction algorithms are used to precisely locate and analyze the characteristic information of potential defects. Ultimately, the system achieves the identification of brake drum surface defects. Experimental results show that the system not only effectively distinguishes between different types of surface defects but also exhibits significant advantages in detection speed and accuracy, significantly enhancing the efficiency of quality control on brake drum production lines. Compared with traditional manual detection methods, this system significantly reduces the rates of missed and false detections while alleviating the human workload, providing strong support for the intelligent upgrade of the brake manufacturer industry.

关键词： Accuracy Machine vision software algorithms Lighting Hardware Safety Brakes Surface cracks Surface treatment Defect detection

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Development of robotic arm for fruits and vegetables sorting

Development of robotic arm for fruits and vegetables sorting

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： C. Likithkumar Thrisha K Anbarasi S Navarasu S Electronics and Instrumentation Engineering Faculty SRM Institute Of Science and Technology Chennai India Electronics and Instrumentation Engineering Student SRM Institute Of Science and Technology Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Among various automation approaches, the pick and-place robotic arm stands out due to its versatility and potential for wide application in sorting, picking, and placing tasks. The robotic arm is equipped with advanced computer vision capabilities for automated agricultural tasks. The system incorporates object detection algorithms using THONY software to accurately identify and localize fruits or vegetables within the arm's workspace. The integration of object detection and classification enhances the system's versatility and adaptability, making it suitable for agricultural applications which includes sorting. By automating repetitive tasks and utilizing real-time object detection, the robotic arm enhances operational efficiency, reduces labor requirements, and improves precision in handling and sorting.

关键词： Deep learning Computer science Computer vision software algorithms Object detection Manipulators software Real-time systems Object recognition Sorting

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