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intelligent-IoT Based Digital Racking System for Retail Shops 3

Intelligent-IoT Based Digital Racking System for Retail Shop...

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3rd IEEE International Virtual conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： Shiva, M.C. Ramachandran, Prakash Vellore Institute of Technology Internet of Things and Sensor Systems Vellore India Vellore Institute of Technology Department of Sensor and Biomedical Engineering Vellore India

ISBN: (纸本)9781665426916

Nowadays, intelligent Internet of things (IoT) have essential influence on human life. Generally, the normal issue among retail stores is that the supervisor doesn't have data of which rack is going to get unfilled and should be refilled. This issue leads to substantial loss. The objective of this paper is to utilize computer vision procedures and IoT in racking system to perform object detection. In the proposed IoT based smart shelf intelligent IoT the changes about rack and products are distinguished in real-time utilizing OpenCV. This task is complex, because of the high number of possible variations in which similar scene of images can happen into the scene. The simple solution proposed here is both best in class locators dependent on deep learning and traditional methods for object detection. The IoT empowers connectivity of physical object with the real-time communication by ESP8266 and data logged into cloud. The smart shelf rack and products details are logged into Database Management system are some desirable characteristics that result. © 2021 IEEE.

关键词： Object detection

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Deep learning with elastic averaging SGD 3

Deep learning with elastic averaging SGD

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3rd International conference on learning Representations, ICLR 2015

作者： Zhang, Sixin Choromanska, Anna LeCun, Yann Courant Institute NYU United States Center for Data Science NYU United States

We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master). The algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master. We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance. We propose synchronous and asynchronous variants of the new algorithm. We provide the stability analysis of the asynchronous variant in the round-robin scheme and compare it with the more common parallelized method ADMM. We show that the stability of EASGD is guaranteed when a simple stability condition is satisfied, which is not the case for ADMM. We additionally propose the momentum-based version of our algorithm that can be applied in both synchronous and asynchronous settings. Asynchronous variant of the algorithm is applied to train convolutional neural networks for image classification on the CIFAR and ImageNet datasets. Experiments demonstrate that the new algorithm accelerates the training of deep architectures compared to DOWNPOUR and other common baseline approaches and furthermore is very communication efficient. © 2015 International conference on learning Representations, ICLR. All rights reserved.

关键词： optimization

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Multi-objective design optimization of main body of an unpowered underwater vehicle based on surrogate models 3

Multi-objective design optimization of main body of an unpow...

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3rd International conference on Unmanned Systems, ICUS 2020

作者： Wei, Gao Haitao, Gu Yuan, Sun Mengmeng, Feng Lingshuai, Meng Ziqing, Wang Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728180250

To improve the hydrodynamic performance and payloads assembly space of unmanned underwater vehicles, considering an unpowered underwater vehicle's main body as the research object, the minimum drag coefficient and maximal volume utilization rate as the objective functions was analyzed in this paper. An automatic computation and analysis process based on surrogate models was built in Isight. Two main body shape design schemes was compared in geometry and hydrodynamic performance. Taking the functional requirements of unpowered underwater vehicle into account, the rectangular streamline body is adopted. Samples were selected using the Optimal Latin Hypercube method and fitted by different approximation models, the model that meeting optimization need was obtained. The NSGA-II algorithm was adopted to solve multi-objective optimization and the Pareto solution set are given. Finally, an optimized design plan was selected after weighing, compared with the initial design, although the volume and volume utilization rate is reduced by 1.21%, 7.38%, respectively, but the hydrodynamic performance has been greatly improved, the pressure drag coefficient, friction drag coefficient and total drag coefficient is reduced by 65.5%, 3.22%, 23.05%, respectively. The optimization result is obvious, which provides a basis for the main body design of the unpowered underwater vehicle. © 2020 IEEE.

关键词： Drag coefficient

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A Review on framework to Detect and Diagnose Body System based Cancer using deep learning Techniques 3

A Review on framework to Detect and Diagnose Body System bas...

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3rd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Gandikota, Hari Prasad Abirami, S. Annamalai University Department of Computer Science & Engineering Tamilnadu Chidambaram India

ISBN: (纸本)9798350399264

Cancer imaging examination is based mainly on doctors' manual interpretation, which requires professional abilities, clinical experience, and attentiveness. However, as the volume of medical imaging data grows, radiologists face increasing challenges. Artificial Intelligence (AI) detection of Digestive System Cancer (DSC) can provide a solution for automatic picture analysis and assist doctors in achieving high-precision intelligent cancer diagnosis. The primary purpose of this study is to explain the critical research methodologies for AI-based DSC detection, as well as to give meaningful learning and reference for relevant researchers. Meanwhile, it outlines the essential issues with these methodologies and provides better direction for future research. DSC automated categorization, recognition, and segmentation can be improved by utilizing machine learning and deep learning detection techniques, which reduce internal image information that humans find difficult to uncover. Using AI to assist imaging surgeons in diagnosing DSC for multiple organs and high incidence can achieve rapid and effective cancer identification while saving doctors' diagnosis time. These can be the foundation for better clinical diagnosis, therapeutic planning, and quantitative DSC evaluation. © 2023 IEEE.

关键词： Digestive system

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An Extensive Review of intelligent Deep learning Based Anomaly Detection and Classification of 3D MRI Brain Imaging Data 3

An Extensive Review of Intelligent Deep Learning Based Anoma...

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3rd International conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Hima Bindu, J. Uma Dev, M. Vignan's Foundation for Science Dept. of Computer Science and Engineering Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350396157

MRI - Magnetic Resonance Imaging is one of the frequently employed imaging modalities for brain anomaly detection. MRI produces a massive quantity of data, which includes a consecutive set of scans taken at several time instants. Since the existence of brain anomalies that exists examined on every MR sequence, manual brain anomaly detection necessitates anatomical knowledge, costly, laborious, and inaccurate due to human error. Automatic brain anomaly segmentation from 3D Magnetic Resonance Image (MRI) is essential to perform proper diagnosis, monitoring, and treatment planning of the disease. However, owing to the structural difficulties such as hazy boundaries with uneven shapes, precise 3D brain tumor demarcation is interesting. With the recent developments of Deep learning (DL) models, this research study reviews the recent brain anomaly detection and classification models based on DL approach with 3D MRI data. The existing techniques related to brain tumor segmentation, classification, and validations are reviewed. Every reviewed technique is investigated based on the aim, underlying technique used, dataset, and evaluation parameters. Besides, a comparison table is provided by summarizing the reviewed techniques under several aspects. Finally, a quick analysis of the evaluated techniques' results is conducted to gauge their effectiveness. © 2024 IEEE.

关键词： Brain

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Design of Oral English Training System Based on Human-Computer Interaction 3

Design of Oral English Training System Based on Human-Comput...

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3rd International conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Hou, Pingying Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9798350365443

As an international common language, English is used by a large number of people and can promote international exchanges and cooperation. learning English is an upsurge, they hope to improve the oral English to achieve the communication between countries, promote economic, cultural and other aspects of the development. The method of learning English in China for a long time is only suitable for exam-oriented education, which is limited by time and space and the environment, and can not meet the needs of the majority of learners. Therefore, this paper intends to design a oral training system, which can flexibly apply human-computer interaction to provide learners with better training methods. This paper aims to explore the design and implementation of an oral English training system based on human-computer interaction. This paper mainly uses the experimental method, comparison method and investigation method, firstly analyzes the demand of oral English training system, then carries on the performance test of the designed system, compares the ability of three intelligent algorithms, and analyzes the performance of human-computer interaction. The experimental results show that the security of long short-term memor (LSTM) network is 0.9, and the security of recurrent neural network (RNN) is second (0.85). deep learning (DL) is the least secure (0.8). © 2024 IEEE.

关键词： Contrastive learning

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13th European conference on Multi-Agent Systems, EUMAS 2015 and 3rd International conference on Agreement Technologies, AT 2015

13th European Conference on Multi-Agent Systems, EUMAS 2015 ...

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13th European conference on Multi-Agent Systems, EUMAS 2015 and 3rd International conference on Agreement Technologies, AT 2015

ISBN: (纸本)9783319335087

The proceedings contain 37 papers. The special focus in this conference is on Coordination and Planning. The topics include: Reducing risk in norm-based systems;from public plans to global solutions in multiagent planning;intelligent people flow coordination in smart spaces;customized document research by a stigmergic approach using agents and artifacts;collaborative framework for monitoring reliability of distributed components of composed services;graph patterns, reinforcement learning and models of reputation for improving coalition formation in collaborative multi-agent systems;multiagent model for agile context inference based on articial immune systems and sparse distributed representations;factored MDPs for optimal prosumer decision-making in continuous state spaces;composing swarm robot formations based on their distributions using mobile agents;group-based pricing to shape demand in real-time electricity markets;human rating methods on multi-agent systems;learning in multi agent social environments with opponent models;a particle swarm optimization metaheuristic for the blocking flow shop scheduling problem;towards an agent-based negotiation scheme for scheduling electric vehicles charging;agreement technologies applied to transmission towers maintenance;a fuzzy logic adaptive reasoning agent for energy trading;a dialectical approach to enable decision making in online trading;argumentation-based hybrid recommender system for recommending learning objects;identifying malicious behavior in multi-party bipolar argumentation debates;probabilistic argumentation, a small step for uncertainty, a giant step for complexity;modeling social deviance in artificial agent societies and modeling and enforcing semantic obligations for access control.

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learning Success Prediction Model for Early Age Children Using Educational Games and Advanced Data Analytics

Learning Success Prediction Model for Early Age Children Usi...

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3rd International conference on intelligent Computing and optimization, ICO 2020

作者： Tolic, Antonio Mrsic, Leo Jerkovic, Hrvoje Algebra University College Ilica 242 Zagreb10000 Croatia

ISBN: (纸本)9783030681531

The early years of a child’s life greatly affects education potential and furthermore potential of educational achievements in adulthood. The brain develops faster during early age, and missed cognitive opportunities in that period are difficult to make up for. In this research, we have developed a machine learning model based on the data points obtained from the educational game, with aim to predict how many attempts are necessary for an individual child to complete the task or assessment as part of educational game. In-game assessments are based on the skills that the child already possess and those developed while playing the game. Training of the machine learning model is based on collected and processed data points (features), while model interconnections are related to the factors of the child’s cognitive growing up process. Model performance benchmarks are elaborated in results and conclusion section of the paper as quality measures of the forecast indicators. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data Analytics

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Improved intrusion detection accuracy based on optimization fast learning network model 3

Improved intrusion detection accuracy based on optimization ...

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3rd International conference on Engineering Technology and its Applications, IICETA 2020

作者： Ali, Mohammed Hasan Al-Jawaheri, Kerrar Adnan, Myasar Mundher Aasi, Ali Radie, A. Hussien Imam ja'Afar Al-sadiq University Computer Techniques Engineering Department Faculty of Information Technology Najaf Iraq College of Technical Engineering Computer Techniques Engineering The Islamic University Najaf Iraq School of Computing University Technology of Malaysia Faculty of Engineering Malaysia Jabir Ibn Hayyan Medical University Najaf Iraq

ISBN: (纸本)9781728182315

In current days, accuracy of Intrusion detection system still represents one of main limitation of the system. Moreover, hybrid model based on intrusion detection system achieved better results in compared with model based single algorithm. This work presents a new intrusion detection system based on hybrid model based on Fast learning Network (FLN) and Genetic Algorithm (GA) which called (GA-FLN), NSL-KDD dataset used to evaluate the proposed model. The results of new model represent with different numbers of hidden neurons to analysis the impact of neurons based on accuracy of intrusion detection system. © 2020 IEEE.

关键词： Genetic algorithms

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Schema Matching Based on Deep learning Using LSTM Model 3

Schema Matching Based on Deep Learning Using LSTM Model

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3rd IEEE International conference on Electronics, Control, optimization and Computer Science, ICECOCS 2022

作者： Merieme, El Abassi Mohamed, Amnai Ali, Choukri Fakhri, Youssef Noreddine, Gherabi Ibn Tofail University Laboratory of Computer Science Research Faculty of Sciences Kenitra Morocco National School of Applied Sciences of University Sultan Moulay Slimane Sultan Moulay Slimane University Khouribga Morocco

ISBN: (数字)9781665457231

ISBN: (纸本)9781665457231

schema matching aims to find the correspondence between two or more schemas. It is needed in many systems, such as data warehouses or data integration systems, as well as in many database applications. That is why we need to use better and more efficient methods. Different types of approaches are taken to unify the data collected and merge it into a single schema by using deep learning methods. A neural network attempts to map, and highlight the information that is needed to be expected with a function and therefore, achieve a more acceptable predicted output. In this research, a new assignment between data and schema is proposed using a popular model: The recurrent neural network (RNN) long Short term memory model (LSTM). Through a set of simulations, we choose better hyperparameters to create a model that allows us to predict the correct schema for our training data, where we established the acceptable model based on accuracy. Thus, these results are conducted using free datasets from the Udemy course platform. © 2022 IEEE.

关键词： Long short-term memory

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