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共 3437 条记录，以下是711-720 订阅

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7th international Workshop on Mental Health and Well-being: Sensing and Intervention

7th International Workshop on Mental Health and Well-being: ...

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2022 ACM international Joint conference on Pervasive and Ubiquitous computing and the 2022 ACM international Symposium on Wearable Computers, UbiComp/ISWC 2022

作者： Mishra, Varun Sano, Akane Kunchay, Sahiti Abdullah, Saeed Bardram, Jakob E. Murnane, Elizabeth Choudhury, Tanzeem Musolesi, Mirco Nunes Vilaza, Giovanna Nandakumar, Rajalakshmi Rahman, Tauhidur Xu, Xuhai Zach, King Kalanadhabhatta, Manasa Adler, Daniel A. Krell, Rony Northeastern University United States Rice University United States Pennsylvania State University United States Technical University of Denmark Denmark Dartmouth College United States Cornell University United States University College London United Kingdom Cornell Tech United States University of California San Diego United States University of Washington United States University of Massachusetts Amherst United States Optum Labs United States

ISBN: (纸本)9781450394239

Mental health issues affect a significant portion of the world's population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. Toward this, ubiquitous systems can play a central role in revealing and tracking clinically relevant behaviors, contexts, and symptoms. Further, such systems can passively detect relapse onset and enable the opportune delivery of effective intervention strategies. However, despite their clear potential, the uptake of ubiquitous technologies into clinical mental healthcare is slow, and a number of challenges still face the overall efficacy of such technology-based solutions. the goal of this workshop is to bring together researchers interested in identifying, articulating, and addressing such issues and opportunities. Following the success of this workshop for the last five years, we aim to continue facilitating the UbiComp community in developing a holistic approach for sensing and intervention in the context of mental health. © 2022 Owner/Author.

关键词： mHealth

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Metaverse’s Virtual Reality and its impact on the buying behavior – an Empirical Study 7

Metaverse’s Virtual Reality and its impact on the buying be...

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7th international conference on Computational Linguistics and Intelligent systems. Volume II: Computational Linguistics Workshop, CoLInS 2023

作者： Busse, Valerie Strauss, Christine Munich University of Applied Science Department of International Management Am Stadtpark 20 Munich Germany University of Vienna Department of Marketing and International Business Oskar Morgenstern Platz 1 Vienna1090 Austria

Metaverse represents a novel iteration of the future internet which currently features various technologies. this makes the scope of applications extremely broad. An important B2C application refers to online shopping. A survey is conducted to analyze the possible influence of the attitude-behavior relationship of buyer behavior. Based on the classic behavioral perspective model of purchase and consumption (BPM model) eight hypotheses have been developed. the survey (i) investigates to what extent a selected part of the Metaverse, namely virtual reality (VR), affects the buyer behavior based on the BPM according to Foxall, and (ii) provide insights into the changed buyer behavior of digital natives aged between 20 and 25 years. © 2023 Copyright for this paper by its authors.

关键词： Sales

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A Study on the Effect of Antenna Array Parameters in Optimizing the Radiation Pattern

A Study on the Effect of Antenna Array Parameters in Optimiz...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Archisman Banerjee Sankarshan Pradhan Ayan Dutta Bitan Misra School of Electronics Engineering KIIT-Deemed University Bhubaneswar India HCL Technologies Kolkata India Department of Computer Science and Engineering Techno International New Town Kolkata India

In this work, aconcentric elliptical array of antennas is optimized with the help of three relatively new and efficient meta-heuristic algorithms; namely, Teaching Learning Based Optimization (TLBO), Multi-Verse Optimization (MVO), and Grey Wolf Optimization(GWO). this work demonstrates a detailed study of the effect of various antenna array contro lvariables for the improvemen to normalized power patterns generated by elliptical array *** objective of this study is to reduce Side Lobe Level (SLL) value to the possible amount and simultaneously reach the desired First Null BeamWidth (FNBW) value. A number of cases are studied and the outcomes are exhibited in a desegregated way with the best optimal values of eccentricities, semi-major axis, and inter-element spacing. An investigation is performed to explore theperformance of the algorithms based on the common antenna parameters such as the number of elements, obtained SLL, and obtained FNBW. Additionally, the statistical specifications of all the algorithms are examined in terms of mean, standard deviation, best objective function, and time complexity. T-test iscarried out at the end of this study, separately on each acquired data set to authenticate the result.

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Real-time Analytics in Financial Market Forecasting: A Big Data Approach 7

Real-time Analytics in Financial Market Forecasting: A Big D...

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7th international conference on Future Networks and Distributed systems, ICFNDS 2023

作者： Balbaa, Muhammad Eid Astanakulov, Olim Ismailova, Nilufar Batirova, Nilufar Tashkent State University Of Economics Uzbekistan International Islamic Academy Of Uzbekistan Tashkent Uzbekistan

ISBN: (纸本)9798400709036

the dynamism and complexity inherent in today's financial markets necessitate sophisticated predictive analytics tools that can facilitate decision-making in real-time. However, traditional forecasting methodologies often fall short in harnessing the vast volumes of real-time data generated by these markets. this study introduces a transformative approach, known as the Real-time Big Data Financial Forecasting Model (), which amalgamates real-time analytics, big data technologies, and machine learning algorithms to predict financial market trends. through a robust computational architecture that efficiently processes and analyzes high-frequency trading data, the RBDF^2M model aims to provide accurate and timely forecasts of asset price movements. Employing time-series analysis techniques, particularly Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, this model is tested across multiple financial instruments to evaluate its predictive efficacy. Our results indicate a substantial improvement in forecast accuracy compared to conventional methodologies, along with significant reductions in computational latency. the research further explores the implications of these findings, notably the potential for augmented trading strategies and risk management protocols. Finally, the paper discusses the ethical considerations associated with the deployment of such advanced predictive systems in financial markets, arguing for the necessity of regulatory frameworks to mitigate the risks of market manipulation and systemic failure. © 2023 ACM.

关键词： Financial markets

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Examining the Risk Factors of Liver Disease: A Machine Learning Approach 7

Examining The Risk Factors of Liver Disease: A Machine Learn...

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7th international conference on Communication and Electronics systems, ICCES 2022

作者： Hossen, Md. Sagar Haque, Imdadul Sarkar, Puza Rani Islam, Md. Ashiqul Fahim, Wasik Ahmmed Khatun, Tania Institut Teknologi Sepuluh Nopember Dept. of Informatics Engineering Surabaya Indonesia Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Nowadays, Liver Disease (LD) is a very common clinical problem for human health and is related to morbidity and mortality. Nevertheless, an earlier prognosis of LD patients gets a scope to avoid, prior diagnosis and subsequent treatment. this research work attempts to implement a high qualified performer machine learning design to predict LD, the most wanted and unwanted risk factor of LD which could help physicians in classifying risky patients and create an analysis to restrict and control LD. the proposed research study has included all patients, who were identified as having liver diseases. Totally, 6 (six) machine learning algorithms such as Decision Tree(DT), Logistic Regression(LR), Multilayer Perceptron(MLP), Artificial Neural Network(ANN), Random Forest(RF), K Nearest Neighbor classifier(KNN) are selected to predict LD. the location underneath had been utilized to evaluate the accuracy among the six applied models. An overall total of 583 instances had been included in this scholarly research;of the 416 patients are affected by liver illness. the location which defines the receiver operating characteristic (AU ROC) of Logistic Regression, Decision Tree, Multilayer Perceptron, Random Forest, Artificial Neural Network, and K-Nearest Neighbor classifier with 10-fold-cross validation was performed. Furthermore, the reliability of LR, DT, MLP, RF, ANN and KNN with accuracy 72.89%, 81.32%, 60.24%, 86.14%, 75.61%, and 65.52%. the utilization of woodland which is certainly arbitrary within the medical setting may help doctors to detect and classify liver patients for major avoidance, surveillance, quick treatment, and management. LR, DT, MLP, RF, ANN, and KNN formulas are acclimatized to forecast and after analyzing the data set, an increased price of accuracy is achieved. © 2022 IEEE.

关键词： Multilayer neural networks

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proceedings of 2020 5th international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020

Proceedings of 2020 5th International Conference on Cloud Co...

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5th international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020

ISBN: (纸本)9781728161754

the proceedings contain 53 papers. the topics discussed include: block sizes control for an efficient real time record linkage;run time optimization using a novel implementation of parallel-PSO for real-world applications;deployment of containerized deep learning applications in the cloud;big data storage and analysis for smart farming;a cross-layered interference in multichannel MAC of VANET;an overall statistical analysis of AI tools deployed in cloud computing and networking systems;a distributed large graph coloring algorithm on giraph;a new recurrent neural network fuzzy mean square clustering method;a recommendation approach based on correlation and co-occurrence within social learning network;a re-engineering approach for extension of the tourist guide knowledge base;and bloom filter and its variants for the optimization of MapReduce’s algorithms: a review.

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Benchmarking Classifiers for Loan Default Prediction using Archetypal Analysis 23

Benchmarking Classifiers for Loan Default Prediction using A...

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proceedings of the 7th international conference on algorithms, computing and systems

作者： Anastasios Antoniadis Konstantinos Georgiou Konstantinos Charmanas Nikolaos Mittas Lefteris Angelis School of Informatics Aristotle University Greece Department of Chemistry International Hellenic University Greece

ISBN: (纸本)9798400709098

the prediction of loan default is a critical process for the successful development of financial institutions. To effectively manage credit risk, numerous machine learning models have been employed to distinguish creditworthy from high-risk applicants. However, determining an optimal model remains a challenge. To address this, in the current study, we explore an alternative approach for model benchmarking. the main concept involves the usage of a pipeline that constructs different classifiers for loan prediction and compares their performance across several evaluation metrics. To achieve this goal, we deploy an approach based on a multivariate statistical method, known as Archetypal Analysis (AA). the proposed methodology is applied to four datasets with diverse structural characteristics. the findings demonstrate that advanced classifiers like Random Forests (RF) and Artificial Neural Networks (ANN), with oversampling, simple parameter tuning, and feature selection consistently outperform traditional classifiers across most evaluation criteria. In conclusion, the results showcase the ability of AA to intuitively identify the best and worst models for each unique scenario.

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16th international Symposium on Advanced Vehicle Control, AVEC 2024

16th International Symposium on Advanced Vehicle Control, AV...

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16th international Symposium on Advanced Vehicle Control, AVEC 2024

ISBN: (纸本)9783031703911

the proceedings contain 138 papers. the special focus in this conference is on Advanced Vehicle Control. the topics include: Study on Linear Actuators for Intake and Exhaust systems of Internal Combustion Engines (Analytical Consideration of thrust Characteristics by Permanent Magnet Array);drifting Maneuver Investigation via Phase Plane Analysis of Experimental Data;effects of External Tire Heating on Rolling Resistance Energy Consumption;bifurcation Analysis of a Nonlinear Vehicle Model on Banked Road;regenerative Brake Blending in Electric Hypercars: Benchmarking and Implementation;a Method for Obtaining Reference Friction Values for Validation of Road Friction Estimation algorithms;computing the Lateral Compliance of the Racing Line Using Trajectory Optimization;state Estimation and Sensorimotor Noise in a Driver Steering Model with a Gaussian Process Internal Model;energy Efficiency Oriented Robust Model Predictive Stability Control for Autonomous Electric Vehicles;profile Generation of Cooperative Driving in Urban Intersections for Energetic Evaluation;energetic Impact of Urban Cooperative Driving on the Example of Intersections;speed Control in the Presence of Road Obstacles: A Comparison of Model Predictive Control and Reinforcement Learning;a Geometric Electric Motor Model for Optimal Vehicle Family Design;road Friction Adaptive Lateral Control of Automated Vehicles with Differential Braking;model-Free Automated Reversing of Articulated Heavy Goods Vehicles;nonlinear Dynamics of a Controlled Two-Wheeled Trailer;yaw Stability Control of Vehicles Using a Slip Polytope Validated with Real Tests;Study on Accident-Avoidance Mechanism in Driver-Vehicle System When Activating Level-2 ADAS;motion Control of a 6 × 4 Heavy Vehicle: Autonomous Collision Avoidance Using Integrated Chassis Control.

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Dynamic Load Balancing for Enhanced Resource Utilization in IaaS Cloud computing

Dynamic Load Balancing for Enhanced Resource Utilization in ...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Anshul Kumar Sandeep Mathur Adarsh Malviya Department of Computer Science in School of Science Noida International University Noida Uttar Pradesh India Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India Department of Computer Science Dronacharya Group of Institutions Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Efficient load balancing is vital in cloud computing to optimize resource use, enhance performance, and maintain application availability. Traditional methods often underutilize the scalability of IaaS models. Hence, a dynamic load balancing system is crucial for real-time traffic distribution among IaaS servers based on demand and resource availability. High traffic challenges in web hosting can degrade load times. Dynamic server load balancing is pivotal here. Our approach integrates ASA genetic algorithms with process scheduling. It queues and assigns requests randomly to virtual servers, verifying connections. A genetic algorithm optimizes schedules via selection, crossover, and mutation, iterating for the best schedule. this strategy markedly reduces load times and maximizes system performance.

关键词： Cloud computing Schedules Processor scheduling System performance Scalability Heuristic algorithms Load management Servers Genetic algorithms Load modeling

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Intelligent system of labor market regulation based on the evolutionary modeling of employment 4

Intelligent system of labor market regulation based on the e...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Rustamovich, Akhatov Akmal Mekhriddin, Nurmamatov Fayzullo, Nazarov Sabharwal, Munish Samarkand State University Vice-Rector for International Relations Samarkand Uzbekistan Samarkand State University Qahramonovich Faculty of Digital Technologies Samarkand Uzbekistan Samarkand State University Mahmadiyorovich Faculty of Digital Technologies Samarkand Uzbekistan School of Computing Science & Engineering Galgotias University India

ISBN: (纸本)9781665474368

Using individual systems to solve an existing problem will allow you to quickly solve the problem for a while. Gap studies conducted on the basis of methods of predictive analysis of the labor market have proposed an improved approach to labor market coordination based on existing approaches as a result of the analysis. the methods of regulating relations in the labor market and the modeling process are investigated. As a result, a coordinated model of labor market regulation was developed. Evolutionary models of solving the problem of coordinating employment in the labor market based on digital technologies and individual models and algorithms are presented. A stochastic mathematical model of self-organization is proposed in the labor market. the degree of stagnation of the stochastic mathematical model of labor market relations is investigated. © 2022 IEEE.

关键词： Employment

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