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international conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Ahmed Elsheikh Ahmed S. Ibrahim Mahmoud H. Ismail Department of Eng. Mathematics and Physics Faculty of Engineering Cairo University Giza Egypt Electrical and Computer Eng. Dept. Florida International University Miami FL USA Department of Electrical Engineering American University of Sharjah Sharjah UAE

ISBN: (纸本)9781665482387

Employing 1-bit analog-to-digital converters (ADCs) is necessary for large-bandwidth massive multiple-antenna systems to maintain reasonable power consumption. However, conducting channel estimation with such 1-bit ADCs and with low complexity is a challenging task. In this paper, we propose to employ an Ensemble Regression (ER) model to conduct low-complexity and high-quality channel estimation. the amount of proposed computations are less than 3% of that proposed by similar deep learning (DL) methods, and in turn requires approximately 4% of the power consumed in computations while maintaining the same level of performance.

关键词： Training Adaptation models Power demand Computational modeling Channel estimation Signal processing algorithms Signal processing

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5th international conference on Advances in Computing and Data Sciences, ICACDS 2021

5th International Conference on Advances in Computing and Da...

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5th international conference on Advances in Computing and Data Sciences, ICACDS 2021

ISBN: (纸本)9783030814618

the proceedings contain 103 papers. the special focus in this conference is on Advances in Computing and Data Sciences. the topics include: Recognition of Isolated Gestures for Indian Sign Language Using Transfer Learning;a Study of Five Models Based on Non-clinical Data for the Prediction of Diabetes Onset in Medically Under-Served Populations;representation and Visualization of Students’ Progress Data through Learning Dashboard;denoising of Computed Tomography Images for Improved Performance of Medical Devices in Biomedical Engineering;image Dehazing through Dark Channel Prior and Color Attenuation Prior;predicting the Death of Road Accidents in Bangladesh Using Machine Learning algorithms;numerical Computation of Finite Quaternion Mellin Transform Using a New Algorithm;predictive Modeling of Tandem Silicon Solar Cell for Calculating Efficiency;Text Summarization of an Article Extracted from Wikipedia Using NLTK Library;handling Class Imbalance in Electroencephalography Data Using Synthetic Minority Oversampling Technique;grapheme to Phoneme Mapping for Tamil Language;comparative Study of Physiological Signals from Empatica E4 Wristband for Stress Classification;an E-Commerce Prototype for Predicting the Product Return Phenomenon Using Optimization and Regression Techniques;crop Yield Prediction for India Using Regression algorithms;a Novel Framework for Multimodal Twitter Sentiment Analysis Using Feature Learning;an Iterative Approach Based Reversible Data Hiding with Weight Update for Dual Stego Images;lower and Upper Bounds for ‘Useful’ Renyi Information Rate;sign Language Recognition Using Convolutional Neural Network;Prediction of Stock Price for Indian Stock Market: A Comparative Study Using LSTM and GRU;early Prediction of Cardiovascular Disease Among Young Adults through Coronary Artery Calcium Score Technique.

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IoT-Driven Credit Scoring Models: Improving Loan Decision Making in Banking

IoT-Driven Credit Scoring Models: Improving Loan Decision Ma...

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Recent Trends in Computer Science and Technology (ICRTCST), international conference on

作者： P G thirumagal Tapas Das Seshanwita Das C S Vinit Sikka C S Amit Kumar T. Kusuma Department of MBA VISTAS Chennai Tamil Nadu Department of School of Business Management Christ University Ghaziabad UP Department of School of Commerce Finance & Accountancy Christ University Ghaziabad UP Department of UG Management Studies School of Leadership and Management Manav Rachna International Institute of Research and Studies Faridabad Haryana Department of Commerce Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

By the game-changing possibilities of credit scoring models driven by the Internet of things, this hopes to shed light on how the banking sector may enhance its loan decision-making procedures. Financial organisations are putting more and more faith in Internet of things technologies to improve their risk assessment and lending processes. these IoT-driven models provide a more accurate and thorough assessment of creditworthiness by including real-time and detailed data on borrowers' activities, spending habits, and asset utilisation. this research examines the practicality and accuracy of Internet of things (IoT) credit scoring by comparing it to conventional methods, looking closely at case researches, and analysing empirical data. the findings shed light on potential ways to enhance the loan approval and risk prediction procedures while also addressing concerns and considerations related to data privacy, security, and regulatory compliance. It is possible that decision-making frameworks could be altered by IoT-driven credit scoring algorithms, which could lead to a more inclusive and informed lending atmosphere. the contributes to the growing area of banking credit evaluation by showing that these models have promise.

关键词： Accuracy Atmospheric modeling Computational modeling Decision making Banking Data models Real-time systems

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Precision Medicine in Oncology - Leveraging Machine Learning for Accurate Neoplasm Diagnosis

Precision Medicine in Oncology - Leveraging Machine Learning...

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Emerging Technology (INCET), international conference for

作者： B. Ashreetha Upputuru Sumanth Lebaka Naveen Kumar Reddy KDV Prasad Kale Naga Venkata Srinivas Dankan Gowda V 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 Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Aditya College of Engineering Surampalem India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

the precision medicine in oncology provided a novel era where the patients have better chances of being cured and improved outcomes if they adopted the personalized treatment strategies. the core part of this change is the utilization of the ML (machine learning) algorithms which showcases good specificity as compared to humans in making neoplasm diagnoses. this research paper will be exploring an application of ML approaches within precision medicine area for oncology patients and will be trying to fill-up the gap between big genome data and decision making processes in the clinical area. this work proposes a complete study which investigates the usefulness of several strategies of ML in diagnosis under cancer with the help of the detailed dataset that consists of genomic and clinicological profile of patients. In our study we investigated several types of ML algorithms covering decision trees, neural networks as well as support vector machines. Every algorithm underwent strict test and validation for its diagnostic power to be identified. Our approach inaugurated the use of ROC curves, precision-recall curves, as well as survival analysis to objectively evaluate model performance, along with feature importance rankings which allowed for identification of biomarkers as well as genetic signatures known to be predictive of specific cancer subtypes and treatment responses. ML work is seen to show a substantially higher sensitivity, specificity and accuracy compared to conventional diagnostic methods. Having ML integrated with precision medicine, makes the screening of cancer in early stages and the accurate diagnosis possible. Additionally, it provides the opportunity to devise personal treatment plans depending on the various genetic tumor types. Besides that, the results from survival analysis illustrated that these ML-based prognostic models can be used for accurately forecast patient outcomes. this has an implication of not only better management of existing treat

关键词： Analytical models Accuracy Machine learning algorithms Precision medicine Machine learning Predictive models Oncology

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Inverse modelling of PV power prediction based on GA method 5

Inverse modelling of PV power prediction based on GA method

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5th international conference on Energy Engineering and Environmental Protection, EEEP 2020

作者： Tang, Cecilia Zhou, Hang Canadian International School of Hong Kong Hong Kong Hong Kong New International Education Service Inc. Dongcheng Dist. Beijing China

Traditional modelling methods are generally forward modelling, that is, the basic parameters of photovoltaic cells provided by manufacturers need to be known before in order to calculate the power output of photovoltaic cells. However, the traditional modelling method is no longer applicable to complex situations, such as if manufacturer parameters are not available, or if the original manufacturer parameters are inaccurate due to prolonged usage. In this study, a new inverse modelling method is proposed, which uses genetic algorithm (GA) to identify the internal parameters of photovoltaic cell equation and solve the equation by Gauss-Seidel iterative method. the experimental test was conducted in Wuhan, China. the results show the root mean square error is only 1.34V and 2.66V for two-day tests, which demonstrated the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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5th international conference on algorithms and Discrete Applied Mathematics, CALDAM 2019

5th International Conference on Algorithms and Discrete Appl...

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5th international conference on algorithms and Discrete Applied Mathematics, CALDAM 2019

ISBN: (纸本)9783030115081

the proceedings contain 23 papers. the special focus in this conference is on algorithms and Discrete Applied Mathematics. the topics include: On rectangle intersection graphs with stab number at most two;dominating induced matching in some subclasses of bipartite graphs;localized query: Color spanning variations;a lower bound for the radio number of graphs;improved descriptional complexity results on generalized forbidding grammars;on selecting leaves with disjoint neighborhoods in embedded trees;the balanced connected subgraph problem;covering and packing of triangles intersecting a straight line;h-free coloring on graphs with bounded tree-width;the induced star partition of graphs;the relative signed clique number of planar graphs is 8;bumblebee visitation problem;on graphs with minimal eternal vertex cover number;on chordal and perfect plane triangulations;fault-tolerant additive weighted geometric spanners;maintaining the visibility graph of a dynamic simple polygon;on the bitprobe complexity of two probe adaptive schemes storing two elements;on m-Bonacci-sum graphs;linear time algorithm to check the singularity of block graphs;b-coloring of the mycielskian of regular graphs;drawing bipartite graphs in two layers with specified crossings.

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Power Quality Enhancement in AC Microgrids Using Integrated Genetic Algorithm in ANN 5

Power Quality Enhancement in AC Microgrids Using Integrated ...

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5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Karthik, M. Vijay Chaudhary, Priyanka Noida International University Department of EEE Greater Noida India

ISBN: (纸本)9781665435246

this article presents the detailed analysis of power quality improvement in microgrids by using the multiple schemes. the microgrid contains the various renewable energy sources, and they are interconnected with the power electronic converters. these converters are faced the many issues with respect to the reactive inrush currents and harmonic voltages. these currents also resulted in voltage drop across the line impedances. the voltage harmonics and fluctuations can generate the malfunctioning, overheating and equipment tripping issues. the microgrid stability mainly depends upon the optimization method and compensation of various micro grid units. this article mainly focused on development of smart grid system by combining the two individual microgrids using electronically coupled distributed energy resources (EC-DER) inverter and smart impedance (SI) converter. thus, the optimal power flow was achieved based on the each individual microgrid available power. the two 'parallel optimization' methods are used to optimize various power quality challenges such as operation mode transfer transients, harmonic load sharing and parallel control of current and voltage quality. thus, artificial neural network (ANN) approach is used for improving EC-DER performance and genetic algorithm (GA) is used for improving SI performance. the stability of the system is achieved by operating the ANN-GA in parallel processing manner. thus, two microgrids develops the individual powers and optimal power flow is generated in smart grid. the simulation results shows that the proposed ANN-GA controller results the reduced total harmonic distortion (thD) and fault setting time in microgrids as compared to the conventional model predictive control (MPC) approach. © 2021 IEEE.

关键词： Genetic algorithms

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Join the Fold: Video Games, Science Fiction, and the Refolding of Citizen Science

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DESIGN ISSUES 2023年第1期39卷 70-87页

作者： Milburn, Colin Buse, Katherine Dhaliwal, Ranjodh Singh Wills, Melissa Aldosari, Raida Camarador, Patrick Miller, Josh Aaron Siegel, Justin Colin Milburn is Gary Snyder Chair in Science and the Humanities and a professor of Science and Technology Studies English and Cinema and Digital Media at the University of California Davis. His research focuses on science literature and media technologies. His books include Nanovision: Engineering the Future (2008) Mondo Nano: Fun and Games in the World of Digital Matter (2015) and Respawn: Gamers Hackers and Technogenic Life (2015). Katherine Buse is an assistant professor of Media Studies at the University of Chicago. Her research focuses on digital media the environmental humanities and the cultures of science and technology. Her current book project Speculative Planetology: Science Culture and the Building of Model Worlds tells the story of how the sciences and the arts co-constructed a toolkit for thinking about planets as whole systems. Ranjodh Singh Dhaliwal is Ruth and Paul Idzik College Chair in Digital Scholarship and an assistant professor of English and Film Television and Theatre at the University of Notre Dame. His work spans media theory science and technology studies and literary criticism. His current book project Rendering: A Political Diagrammatology of Computation shows how our cultural politico-economic and epistemic formulations crystallize into hardware and software architectures. Melissa Wills is a visiting scholar in the Department of Science and Technology Studies at the University of California Davis. She completed her PhD in English at UC Davis in 2021. Her research addresses the relationship between the life sciences and science fiction. Her current book project Ruptures: Life without Germs in the Microbiome Era examines microbiology's debt to the imagination of germ-free worlds. Raida Aldosari is the lead artist on the Foldit: First Contact development team. She received her BS in Nutrition Science from the University of California Davis in 2018. She has experience as a freelance graphic designer and as a project manager for nutrition training and courses.

this article explores the value of science fiction narratives in games for citizen science. Focusing on the protein-folding game Foldit, it describes the process of modifying and redesigning the game to feature a framing narrative and other alterations to the main tutorial campaign. the campaign narrative, Foldit: First Contact, situates the practices of citizen science in an expanded context of meanings and ethical implications, promoting critical self-reflection on the relations of science and civic values. A study of player responses to Foldit: First Contact suggests the significance of science fiction and critical game design for attuning citizen scientists to the collective responsibilities of experimentation and innovation, drawing attention to the intersecting social, technical, and environmental domains in which gamers may contribute to scientific research.

关键词： citizen science science fiction video games narratives

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Research on kiwifruit grading detection system based on depth limit learning machine 22

Research on kiwifruit grading detection system based on dept...

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Proceedings of the 2022 5th international conference on algorithms, Computing and Artificial Intelligence

作者： Liang Wang Department of Engineering Management International College Krirk University Thailand

ISBN: (纸本)9781450398336

Aiming at the low recognition rate and low accuracy of traditional kiwifruit grading detection, a feature extraction method based on machine vision kiwifruit detection system is proposed. In order to improve the recognition accuracy, the R component map is used as the input map, and the median filtering method is used to denoise and extract the surface defect features of kiwifruit. On this basis, the least square method is used to establish the ellipse fitting curve to extract the size features, and the mean and standard deviation of H, I, S are used to extract the color features; At the same time, 30 models were selected to establish the deep extreme learning machine (DELM) model to identify kiwifruit defects. Finally, 326 kiwi images were constructed, and the results showed that the correct recognition rate of the depth limit learning machine model was 97.5%, which could achieve accurate grading.

关键词： DELM

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5th international conference on Stochastic Methods, ICSM-5 2020

5th International Conference on Stochastic Methods, ICSM-5 2...

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5th international conference on Stochastic Methods, ICSM-5 2020

ISBN: (纸本)9783030832650

the proceedings contain 27 papers. the special focus in this conference is on Stochastic Methods. the topics include: Survival Probabilities in Compound Poisson Model with Negative Claims and Investments as Viscosity Solutions of Integro-Differential Equations;on the Information Content of Some Stochastic algorithms;on Modification of the Law of Large Numbers and Linear Regression of Fuzzy Random Variables;stochastic Approach to the Vanishing Viscosity Method;generalization Bound for Imbalanced Classification;resonance in Large Finite Particle Systems;preface;on threshold Selection Problem for Extremal Index Estimation;cycles in Spaces of Finitely Additive Measures of General Markov Chains;branching Walks with a Finite Set of Branching Sources and Pseudo-sources;efficient Improved Estimation Method for Non-Gaussian Regression from Discrete Data;Two–Sided Bounds for PDF’s Maximum of a Sum of Weighted Chi-square Variables;on the Chromatic Number of a Random 3-Uniform Hypergraph;on Asymptotic Power of the New Test for Equality of Two Distributions;random Dimension Low Sample Size Asymptotics;limit of the Smallest Eigenvalue of a Sample Covariance Matrix for Spherical and Related Distributions;on Positive Recurrence of One-Dimensional Diffusions with Independent Switching: In Memory of Svetlana Anulova (19.10.1952–21.11.2020);on Two-Type Branching Random Walks and their Applications for Genetic Modelling;Mean-Square Approximation of Iterated Stochastic Integrals from Strong Exponential Milstein and Wagner-Platen Methods for Non-commutative Semilinear SPDEs Based on Multiple Fourier-Legendre Series;reconstruction of Multivariable functions Under Uncertainty by Means of the Scheme of Metric Analysis;residual Life Time of the Gnedenko Extreme - Value Distributions, Asymptotic Behavior and Applications.

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