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检索条件"任意字段=3rd Conference on Learning and Intelligent Optimization"

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A primer on intelligent defense mechanism to counter cloud silent attacks 3

A primer on intelligent defense mechanism to counter cloud s...

引用

3rd International conference on Contemporary Computing and Informatics, IC3I 2018

作者： Mukoko, Fungayi Donewell Thada, Vikas Amity University Haryana India

ISBN: (纸本)9781538668948

Cloud security is a key to both Cloud clients and vendors, and this has prodded intensified massive research around this domain. The contractual continuous Cloud security research is as well fuelled by the abrupt popping up of attacks that even extend to silent or stealth. Moreover these silent or stealth attacks are characterised by low observability along with bad traceability resulting in their difficulty detection, traceability and prevention. Due to the nature of silent or stealth attacks that have difficulty detection, traceability and prevention calibre, it means traditional and/or ordinary security mechanisms won't be sufficient to compact such attacks. In turn the nearby remedy to tackle these attacks is to incorporate method(s) that can curb trends that has hidden patterns. This then calls for techniques that have capabilities for revealing hidden patterns, thus Hidden Markov Model will find its projected usage in the proposed model. As a way to offer maximum security when dealing with these sophisticated kinds of attacks, a conjuncture of cryptography will also be fused in the proposed model. A logical series of actions will be executed such that once a possible attack is sensed, then a proactive action is triggered to avoid an invasion by this attack. The intelligence is derived from the previously learning by Hidden Markov Model, although continuous active learning is still guaranteed as this algorithm carries the Machine learning algorithm standards, along with the proactive action is triggered. © 2018 IEEE.

关键词： Cryptography

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3-D object recognition system using ultrasound 3

3-D object recognition system using ultrasound

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3rd International conference on intelligent Sensing and Information Processing, ICISIP 2005

作者： Koley, C. Midya, B.L. IE Department Haldia Institute of Technology ICARE Complex HIT Campus Haldia India

ISBN: (纸本)0780395883

The patterns of ultrasonic reflected echoes from objects contain information about the geometric shape, size, orientation and the surface material properties of the reflector. Accurate estimation of the ultrasonic echo signal pattern is essential for recognition of the target object. We propose a method to classify different objects having specific geometric shape such as cylindrical, rectangular, sphere and conical of different size and material. Here Continuous Wavelet Transform (CWT) has been used for feature extraction. In the present work an attempt has been made to classify the pattern inherent in the features extracted through CWT of different echo signals with the help of two different machine learning algorithm like Self Organizing Feature Map (SOFM) and Support Vector Machine (SVM). CWT allows a time domain signal to be transformed into time frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of feature from the non-stationary signals like ultrasonic echo envelop. SOFM transforms the input of arbitrary dimension into a one or two dimensional discrete map subject to a topological (neighbourhood preserving) constraint. In the present work the SOFM algorithm with Kohonen's learning and SVM in regression mode has been used to classify the patterns inherent in the features extracted through CWT of different echo envelop. © 2005 IEEE.

关键词： Support vector machines

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optimization Algorithm for the Hybrid Pickup and Delivery Problem with Time Windows for Electric Vehicles and Automatic Delivery Robots 3

Optimization Algorithm for the Hybrid Pickup and Delivery Pr...

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3rd International conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2023

作者： Li, Yongxin Mu, Lifeng SHU-UTS Silc Business School Shanghai University Shanghai China

ISBN: (纸本)9798350372052

This paper presents the vehicle routing problem with simultaneous pickup and delivery and time windows for hybrid delivery of electric vehicles and automatic delivery robots (VRPPDTW-HEADR). Automatic delivery robots are deployed at service stations to assist electric vehicles for customer service. Electric vehicles considering charging en-route can service customers directly or handle goods at the service station to the automatic delivery robot. Simultaneous pickup and delivery services of customer-to-customer and depot-to-customer-to-depot are meticulously addressed. The problem is formulated as a mixed-integer programming model that minimizes the total cost to serve all customers. A novel hybrid metaheuristic algorithm is proposed to solve VRPPDTW-HEADR. Local search operators that fit the characteristics of the problem are customized. The performance of the proposed algorithm is tested on different scale instances. The numerical results show that it can obtain high quality solutions for all instances within a remarkably short runtime. © 2023 IEEE.

关键词： Vehicle routing

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2024 3rd International conference on Smart Energy and Energy Internet of Things

2024 3rd International Conference on Smart Energy and Energy...

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2024 3rd International conference on Smart Energy and Energy Internet of Things, SEEIoT 2024

The proceedings contain 14 papers. The topics discussed include: virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization;analysis of green energy-saving retrofit for air-conditioning system in UHVDC converter stations;a short-term wind power forecasting model based on CUR;application and practice of industrial IoT cloud platform in oilfield intelligent transformation;multilayer decoupling control of photovoltaic integrated heat pump based on expected value;efficiency evaluation of electricity demand prediction models in lower-income countries: a case study of Panama;optimal economic operation of smart home devices based on home microgrids and time-of-use tariffs;and optimal strategies for virtual power plants to participate in medium- and long-term power trading in the context of contracts for difference (CFDs).

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ICBDR 2019 - Proceedings of the 2019 3rd International conference on Big Data Research

ICBDR 2019 - Proceedings of the 2019 3rd International Confe...

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3rd International conference on Big Data Research, ICBDR 2019

ISBN: (纸本)9781450372015

The proceedings contain 33 papers. The topics discussed include: storage design and implementation of information reconstruction system;design and implementation of information reconstruction system;design and implementation of spatiotemporal data structure and system;intelligent interactive edge detection method of image;visual application based on spatiotemporal data;parallel coordinates based visualization for high-dimensional data;performance analysis of deep learning network models of localized images in chest x-ray decision support system;improvement of big data stream mining technique for automatic bone age assessment;a sub-linear scalable MapReduce-based apriori algorithm;and anomaly detection modeling based on self-adaptive threshold voting integrating DBN-LRs.

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Using neural imaging and cognitive modeling to infer mental states while using an intelligent tutoring system

Using neural imaging and cognitive modeling to infer mental ...

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3rd International conference on Educational Data Mining, EDM 2010

作者： Fincham, Jon M. Anderson, John R. Betts, Shawn Ferris, Jennifer L. Psychology Department Carnegie Mellon University United States

ISBN: (纸本)9780615375298

Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from one day of interaction and tested with data from a later day. In terms of predicting what state a student was in during any 2 second period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. Further, the prediction accuracy using combined cognitive model and fMRI signal showed superadditivity of accuracies when using either cognitive model or fMRI signal alone.

关键词： Students

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A Critical Study of ICT Oriented Innovations based on Artificial Intelligence (AI) Applications towards BFSI Sector 3

A Critical Study of ICT Oriented Innovations based on Artifi...

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3rd IEEE International conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Talreja, Pooja Shrivastava, Amit Mishra, Ajay Gupta, Neha Indore India

ISBN: (纸本)9798350343274

The Financial Service and Insurers (BFSI) sector refers to firms who provide a wide variety of accounting products and assistance across the world. This work adds to the technical innovation of services in processing administration, a topic that has yet to be settled in the available research. AI is an interdisciplinary field of study that has distinguished itself for the technical dynamism it brings to an organization products and processes. Artificial Intelligence (AI) is one of the developing technologies driving the continued expansion of ICT-enabled applications. This study investigates the application of the use of AI and machine learning (ML), respectively, in the field of banking, as well as the policy ramifications. It grants a nontechnical foundation to understand the growth and possibilities of AI/ML systems. Artificial intelligence, or the emulation of human brain in machines, is gaining popularity and will play an important role in the new financial century. The current study aims to examine consumers' perspectives on the widespread implementation of intelligent systems with artificial intelligence in Asian countries. The banking industry also prioritizes novel AI technology to boost customer service and overall growth by making greater revenue. © 2023 IEEE.

关键词： Machine learning

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Research on optimization of Communication Technology Based on Deep learning 3

Research on Optimization of Communication Technology Based o...

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3rd International conference on Electronics and Information Technology, EIT 2024

作者： Zhang, Zhaoxiang China Mobile Group Device Co. Ltd BeiJing100053 China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369151

Distributed deep learning systems commonly use synchronous data parallelism to train models. However, communication overhead can be costly in distributed environments with limited communication bandwidth. To reduce communication overhead, an intuitive approach is to increase the communication interval. However, increasing the communication interval often affects the convergence speed of the model, requiring more epochs to train the model to the target accuracy, thereby reducing the statistical efficiency of the training algorithm. To address these issues in distributed synchronous data parallel training algorithms and communication interval selection methods, this paper proposes a training algorithm that combines skip communication strategies and correction techniques to ensure low communication overhead and high statistical efficiency. The training algorithm maintains a small batch size through local updates in each training process and employs correction techniques to reduce the divergence among individual local models, thereby ensuring high statistical efficiency. At the same time, the training algorithm utilizes a skip communication strategy to update the global model, meaning that the global model is not updated with every iteration, but rather updated once every few iterations. This reduces the frequency of communication, ensuring low communication overhead. Experimental results show that this training algorithm reduces total training time by 88.9%, 74.9%, 34.1%, and 82.4% compared to SSGD, SkipSSGD, LocalSGD, and SMA, respectively. © 2024 IEEE.

关键词： Training Deep learning Distributed databases Bandwidth Parallel processing Data models Communications technology Information technology optimization Convergence

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The optimization of equipment polling strategy in intelligent substation control system

The optimization of equipment polling strategy in intelligen...

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3rd International conference on Energy Materials and Material Application, EMMA 2014

作者： Hu, Chun Chao Ma, Kai Cao, Li Juan Hou, Ai Jun Cui, Hong Juan Chen, Hu Electric Power Research Institute of Guangdong Power Grid Corporation Guangzhou 510080 China Raisecom Technology Co. Ltd Beijing 100085 China

ISBN: (纸本)9783038352518

Under the condition that the increasing number of devices and invariable server hardware facilities in intelligent substation control system, an optimization algorithm of equipment polling strategy in intelligent substation control system is proposed in this paper. This paper proposes an equipment polling mechanism based on time slice scheduling, which is used to solve the problem that the entire network equipment need to detect at the same time on the hour, making the tremendous pressure to the control system. By using the proposed algorithm, it can effectively balance the load in the control system. © (2014) Trans Tech Publications, Switzerland.

关键词： Control systems

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Ordering and Transportation of Raw Materials Based on Multi objective Programming Model 3

Ordering and Transportation of Raw Materials Based on Multi ...

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3rd International conference on Applied Mathematics, Modelling, and intelligent Computing, CAMMIC 2023

作者： Liu, Chunyan Yang, Chuanqiang University of Jinan Shandong Province Jinan City China Shandong Territorial Spatial Planning Institute Shandong Province Jinan City China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

In order to formulate high-quality and reliable supplier schemes in the enterprise's raw material ordering, according to the order and supply data of 402 raw material suppliers of an enterprise in recent five years, we have determined five indicators, including average supply quantity, supply times, supply qualification rate, average satisfaction rate and maximum capacity of suppliers. On this basis, a quantitative index evaluation model based on principal component analysis and "S" curve is established, Finally, 50 most important suppliers were selected. Then, Taking 24 week ordering cost, minimum transport loss of forwarders and minimum suppliers as objective functions, a multi-objective programming model is established, we use Heuristics algorithm to solve the problem and determine the ordering and transshipment scheme. Finally, we use the method of comparative analysis to obtain that the effect of our scheme can stabilize the production capacity of the enterprise while reducing the cost of the enterprise. © 2023 SPIE.

关键词： Multiobjective optimization

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