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Research on Performance Test of Chinese Character APP Software Based on OL-ADE Algorithm 5

Research on Performance Test of Chinese Character APP Softwa...

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5th Asian conference on Artificial Intelligence Technology, ACAIT 2021

作者： Wang, Chao Zhao, Chenguang Fu, Quanshun Dalian Polytechnic University Office of Academic Affairs Liaoning Province Dalian China Dalian Polytechnic University School of International Education Liaoning Province Dalian China Dalian Branch China Changchun Jingdian Technology Co. Itd Liaoning Province Dalian China

ISBN: (纸本)9781665426305

Entering the information age, Chinese character APP assumes the task of cultural dissemination in a novel form as a carrier. And, software performance testing, a means to ensure software quality, is needed to maintain its performance. In view of this, the research first designs an opposition-based learning of adaptive evolution based on reverse learning strategy (OL-ADE), and then builds a Chinese character APP software performance test model on this basis and put it into application, and finally verify its effect through experiments. the results show that when the population size is 50 or 100, the OL-ADE algorithm has the least number of iterations under different data input ranges, and the number of iterations is within [40, 150]. the branch coverage is 100%, and the average number of iterations required by OL-ADE algorithm is the smallest. the above results show that OL-ADE algorithm has the best overall performance among the four algorithms, and can complete the software performance test of Chinese character APP. © 2021 IEEE.

关键词： Software testing

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A Modularized Equalizer for Large-Scale Battery Packs Based on Model Predictive Control

A Modularized Equalizer for Large-Scale Battery Packs Based ...

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IEEE international conference on Power and Energy Applications (ICPEA)

作者： Yuheng Li Bizhong Xia Jianwen Cao Division of Advanced Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9781665487627

the inconsistency of the large-scale battery pack dramatically diminishes the performance of electric vehicles. this paper proposes a flexible modularized equalizer to facilitate the practical implementation of active equalizers in battery pack. Inspired by the integrating process of battery pack, the proposed modularized equalizer is grouped into the intra-module equalizer and the inter-module equalizer. the intra-module equalizer is designed based on flyback circuit and the inter-module equalizer is featured by a shared-inductor. they can run independently at the same time, which greatly improves the speed of equalization. In order to reduce the balance loss, an equalization strategy based on model predictive control (MPC) is presented which transforms the balance problem into a linear quadratic problem with nonlinear constraints. then the particle swarm optimization (PSO) algorithm is applied for seeking the optimal control strategy. Simulation results demonstrate that the proposed equalization topology can shorten the balance time and the proposed equalization strategy can improve the energy efficiency.

关键词： Costs Equalizers Simulation Optimal control Transforms Prediction algorithms Batteries

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From Data to Diagnosis: Applying Machine Learning Model for Reliable Heart Disease Prediction

From Data to Diagnosis: Applying Machine Learning Model for ...

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Information Engineering (ACIE), IEEE Asia conference on

作者： Ananna Rashid Kumkum Anik Sen Abdullah Yousuf Noman Prajukta Majumder Tze Hui Liew Nafiz Fahad Md. Saef Ullah Miah Riadul Islam Rabbi Md. Jakir Hossen Faculty of Science and Technology American International University-Bangladesh Dhaka Bangladesh Faculty of Information Science and Technology Multimedia University Melaka Malaysia Faculty of Engineering Technology Multimedia University Melaka Malaysia

ISBN: (数字)9798331528409

ISBN: (纸本)9798331528416

Heart disease remains a significant global health challenge, demanding precise and interpretable predictive tools to aid early intervention and improve patient outcomes. this study applies the XGBoost machine learning algorithm to develop a robust model for predicting cardiovascular disease risk. Leveraging a comprehensive dataset that includes demographic, lifestyle, and clinical features, our approach combines extensive data preprocessing, feature engineering, and Bayesian hyperparameter tuning to achieve superior predictive accuracy, with an AUC of 0.98 and an accuracy of 97.48%. Key predictive factors, identified through SHAP (SHapley Additive exPlanations) values, include cholesterol, blood pressure, and physical activity, offering actionable insights for clinicians. the model's interpretability supports its potential as a reliable, real-time diagnostic tool, enhancing clinical decision-making and risk assessment. this research advances predictive healthcare analytics by showcasing the effectiveness of sophisticated machine learning methods, including XGBoost, for precise and interpretable heart disease forecasting. Subsequent study will seek to validate the model across varied populations and incorporate it into clinical environments for adaptive, individualized cardiovascular risk management.

关键词： Heart Accuracy Machine learning algorithms Machine learning Predictive models Feature extraction Data models Reliability Cardiovascular diseases Tuning

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AI-Enhanced IoT Systems for Smart Predictive Maintenance in Steel Plants

AI-Enhanced IoT Systems for Smart Predictive Maintenance in ...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： S. Lourdu Jame S. Suguna Mallika Pramod Pandey Bhavani R L. M. Merlin Livingston B Meenakshi Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University (SIMATS) Chennai Tamil Nadu India Department of Electronics and Communication Engineering DMI College of Engineering Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

this paper introduces a smart Internet of things (IoT) system that uses artificial intelligence (AI) to improve operational efficiency and reduce downtime in steel plants through predictive maintenance. It addresses equipment failures in steel plants with deployment of an AI-driven IoT system using neural networks for facilitating accurate defect prediction and minimizing maintenance expenses. the system uses neural networks for continuous operation and proactive maintenance, which are essential in steel production processes to provide productivity and safety. Early problem detection and health monitoring are made possible by integrating IoT devices for real-time data gathering and cloud computing for scalable data processing in the proposed system. Anomalies and impending breakdowns may be foreseen using neural networks trained on time-series sensor data, paving the way for predictive maintenance. the solution improves equipment dependability, optimizes maintenance schedules, and decreases operating costs using historical data and machine learning algorithms. Results from real-world applications show that the system may enhance maintenance plans while reducing downtime in steel manufacturing facilities. the results highlight how technology may revolutionize maintenance procedures, gain insight into proactive equipment management, and help the steel sector progress in smart production.

关键词： Schedules Data privacy Computational modeling Neural networks Real-time systems Maintenance Steel Internet of things Monitoring Predictive maintenance

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A CNN-inspired Reverse Search Engine for Content-based Image Retrieval

A CNN-inspired Reverse Search Engine for Content-based Image...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Shilpa Marathe Sirshendu Arosh Tamal Mondal DY Patil International University Akurdi Pune Maharashtra Asansol Engineering College (AEC) Asansol West Bengal Faculty of Computer Studies Symbiosis Center for Information Tech. Pune Maharashtra

the Reverse Search Engine enables the search and retrieval schemas of images which are visually comparable to a specific query image by utilizing the features of the images that illustrates the visual content of provided images. this contribution creates a contextual schema for recovering photographs that are indexed in distributed databases from the visual information without the requirements of linguistic annotations. We implement the generic Convolutional neural network (CNN) with deep learning strategies for retrieving feature vectors from visual information. To build the global descriptors, the pre-trained CNN architectures i.e., ResNet50, InceptionV3, and InceptionResNetV2 were recommended. these techniques outperformed in the existing methods like Metaheuristic Algorithm, Gabor Wavelet, CNN-SVM which have been compared with the proposed technique i.e., Cartoon Texture Algorithm in CBIR. We have observed that they are similar to others based on machine learning approaches, with comprising of the conventional state-of-the-art in the Content-dependent Information Retrieval.

关键词： Deep learning Visualization Machine learning algorithms Metaheuristics Image retrieval Search engines Linguistics

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Enhanced Magnetic Resonance Imaging for Accurate Classification of Benign and Malignant Brain Cells

Enhanced Magnetic Resonance Imaging for Accurate Classificat...

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Emerging Technology (INCET), international conference for

作者： Dankan Gowda V Pullela SVVSR Kumar KDV Prasad B. Ashreetha Mekala Bharath Kumar Karanam Karthikeya Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Computer Science & Engineering Aditya College of Engineering Surampalem Andhra Pradesh India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Electronics and Communication Engineering Sree Vidyanikethan Engineering College (Now it's Mohan Babu University) Tirupati Andhra Pradesh India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

the difficulty in differentiating between the normal and cancerous cells in the brain through the frequent magnetic resonance imaging approaches is one of the major obstacles to realization of diagnostic precision. the presented work reveals a new MRI image processing technology, which includes an original software that contains complex algorithms and trained machine learning models, as the programs that make the images much better than before. Carefully calibrated computer vision dataset which features brain scans is subjected to the well-defined novel approach, whose performance is compared with the traditional MRI methods by calculating multiple metrics such as classification accuracy, sensitivity, specificity, ROC are and so on. this paper is the climax of a deep cognition of various cell types revealed by the newest MRI method which presents a better contrast than the old techniques. Next, the quality in the detection and recognition of the tumour after, comparison of these modalities displays that the modality of higher resolution, the ability to detect the tumour earlier and better. Such technological improvements in MRI machines will enable the surgeons to identify the growth of tumors at the early stages that will lay the right groundwork for the design of personalized treatment plans and also will have positive impact on the lives of the patients. through this effect, the level of quality followed by MR imaging has been improved as well as arising new alliances between major imaging companies and machine learning technologies. It can be thought as the border - eraser diagnostics and imaging which will obey medical laws. therefore, it states that the present hypothetical world should be improved while the advanced and proven diagnostics systems should be developed. the daily clinical applications of these advanced MRIs may well be the beginning of a new era in diagnostic oncology, which will be a very important way forward in improving treatment combined wi

关键词： Accuracy Machine learning algorithms Magnetic resonance imaging Surgery Machine learning Brain cells Oncology

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Load Shifting of Residential Load for Demand Side Management Using Tunicate Swarm Algorithm

Load Shifting of Residential Load for Demand Side Management...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： P. D. Vaidya Saikat Gochhait tharmalingam Gunasekar Prabakaran Raghavendran Department of Electrical Engineering Lovely Professional University Punjab India Symbiosis Institute of Digital and Telecom Management constituent of Symbiosis International Pune India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350368710

ISBN: (纸本)9798350368727

Demand Side Management (DSM) is a critical strategy in the improvement of efficiency and reliability of the electrical system in smart grids. One of the effective DSM strategies is load shifting, which involves changing the timing of electricity consumption to off-peak hours. this paper gives a new approach to load shifting within residential areas through the Tunicate Swarm Algorithm. the TSA is an algorithm inspired by the swarming behavior of marine organisms called tunicates. these organisms are well known for their efficiency in movement and resource use. It is desired, within the scope of the proposed method, to optimize schedules for household appliances towards minimal electricity cost and minimal peak demand. TSA-based load-shifting technique: simulation result comparison with classical techniques—genetic algorithm and particle swarm optimization. It should be stated that the simulation results show a comparison of the performance of this proposed TSA-based technique about cost reduction and the mitigation of peak demand, which outperformed both GA and PSO. the results obtained in this study demonstrated that the TSA-based technique offers much potential for effective residential load management in smart grids, where quantifiable benefits could be derived in terms of energy savings and system stability. In the future, work will focus on real-world implementation and integration with other DSM strategies to enhance the overall efficiency of the electrical grid. this research adds to the ever-increasing bank of bio-inspired algorithms for energy management and acts as a springboard toward further areas of investigation in the field.

关键词： Schedules Demand side management Costs Simulation Electricity Organisms Stability analysis Smart grids Timing Reliability

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5th international conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2019

5th International Conference on Microelectronics, Electromag...

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5th international conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2019

ISBN: (纸本)9789811538278

the proceedings contain 81 papers. the special focus in this conference is on Microelectronics, Electromagnetics and Telecommunication. the topics include: A Tapered Microstrip-Fed Steering-Shaped Super-Wideband Printed Monopole Antenna;High-throughput VLSI Architectures for VLSI Signal Processing;reform Initiatives for Electrical Distribution Utilities in Jharkhand, India;FPGA Performance Evaluation of Present Cipher Using LCC Key Generation for IoT Sensor Nodes;Performance Comparison Analysis of GTTPC and AHTPC Technique for WBAN with Mobile Scenario;Estimation of FSO Link Availability for Visakhapatnam Coastal Region;CPW-Fed Monopole Antenna for RCS Reduction in X-Band Applications;Performance Improvement of Solar PV Maximum Power Point Tracking Using Sliding Mode Control Algorithm;surfactant Effect on Bandgap and Crystallite Size of ZnO–TiO2–CeO2 Nanocomposites;TEC Variation Analysis During Near Peak Solar Year Activity Over Indian Subcontinent;a Review on Schemes for Interconnecting Microgrids of Urban Buildings;performance Analysis of Multiwalled Carbon Nanotube, Composite Multiwalled Carbon Nanotube, and Copper-Based Antenna in X-Band Applications;wideband Printed Antenna Design Techniques: A Comprehensive Study;comparison of Various algorithms for Side Lobe Level Reduction in 5G Antenna Arrays;analysis of Electromagnetic Shielding Effectiveness Properties of Al6061 Metal Matrix Composites at X-Band for Aerospace Applications;a Comprehensive Study and Evaluation of Recommender Systems;design, Simulation and Experimental Validation of Patch Antenna in S-Band Satellite Communication;performance Efficient Floating-Point Multiplication Using Unified Adder–Subtractor-Based Karatsuba Algorithm;Implementation of Program Page, Read Page and Block Erase Operations in NAND Flash Memory Controller;Design of Low Standby Power 10T SRAM Cell with Improved Write Margin;preface.

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Framework for Studying Traffic Emission Control Relationships using Moves

Framework for Studying Traffic Emission Control Relationship...

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Global conference for Advancement in Technology (GCAT)

作者： Sarika Kaple Satpute Sunil Kumar Manoj Tarambale Department of Electrical Engineering Kalinga University Raipur CG India D. Y. Patil International University Pune MH Electrical Engineering Department PVG’s COET and GKP IOM Pune

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

this Framework for Using Optimization Strategies to Look into the Connection Between Traffic Control and Emission Control there is a system that shows the groups that are part of DD and how they share information. Researchers often do systematic literature studies to find and rate all the papers that have been written on a certain subject. this study paper talks about a way to improve microgrid power supply by using swarm intelligence and mixed energy sources together. Microgrid integration is growing quickly, and so is the number of microgrids being added to the low-voltage (LV) power system. Because there are more DG sources, an islanded microgrid would run into a number of problems during the process. the idea can help with the problems of integrating green energy sources and managing microgrids. the goal of this study is to improve the performance and economy of a microgrid by adding several voltage controls. the results of these controls will be looked at and studied. MATLAB/Simulink Sim Power Systems will also be used to test the performance of the controls. there are some problems with the safety zone and working speed of the low voltage (LV) microgrid that need to be fixed before the microgrid can be used. these problems can be fixed by making a good converter that follows the future-ride-through (FRT) rules explained in the suggested study.

关键词： Low voltage Systematics Microgrids Traffic control Real-time systems Pollution measurement Safety Particle swarm optimization Voltage control Genetic algorithms

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Design of Optimized low-power GPS-Yagi Antenna using Machine Learning techniques

Design of Optimized low-power GPS-Yagi Antenna using Machine...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： D. Jessintha Kenjaev Sanjar Sobirovich Christo Ananth T. Ananth Kumar Electronics and Communication Engineering Easwari Engineering College Chennai India Faculty of International Education Programs Samarkand State University Uzbekistan Department of Natural and Exact Sciences Samarkand State University Uzbekistan Computer Science and Engineering IFET College of Engineering Chennai Tamilnadu India

the time-consuming process of developing analytical models can be accelerated with the help of machine learning, which is a technique for data processing. Using machine learning, antenna designers can quickly and intelligently optimize their physical antenna designs. this is achieved by developing trained models of the designers’ designs. Consequently, antenna designers can create more efficient antennas. Due to this, antenna designers can continue developing new designs despite the increasing complexity of antennas. When discussing this specific type of antenna, “Yagi” is frequently used as an abbreviation for “Yagi-Uda” the full name of the antenna. the length of the “driven dipole” may be equal to or shorter than the length of the “directors.” the ability to rapidly execute a diverse set of optimization algorithms and objectives, made possible by the trained models, makes it easier to conduct rapid comparisons and a diverse set of studies (including stochastic analysis for tolerance studies, etc.). While the device was operating at a frequency of ten gigahertz, the concept of two parasitic directors was conceived, and these parasitic directors were designed and optimized to improve the device’s directivity further. this antenna offers numerous advantages, one of which is its simplicity of manufacture. this antenna can be manufactured relatively easily due to its diminutive size and uncomplicated overall layout. the stochastic global search and optimization method known as simulated annealing (SA) is extraordinarily effective. this method is implemented instead of more conventional ones to achieve optimal element spacing.

关键词： Analytical models Machine learning algorithms Dipole antennas Yagi-Uda antennas Stochastic processes Dual band Reflector antennas

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