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检索条件"丛书名=Advances in Intelligent Systems and Computing"

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Secured Congestion Control in VANET Using Greedy Perimeter Stateless Routing (GPSR) 4th

Secured Congestion Control in VANET Using Greedy Perimeter S...

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4th International Conference on Artificial Intelligence and Evolutionary Computations in Engineering systems (ICAIECES)

作者： Narayan, S. Arvind Reddy, R. Rajashekar Josephin, J. S. Femilda SRM Inst Sci & Technol Dept Software Engn Chennai Tamil Nadu India

ISBN: (纸本)9789811501999;9789811501982

Vehicular Adhoc networks (VANET), a subset of mobile adhoc networks (MANET), is revolutionizing the intelligent transport system. With this advancement, the environment around a car can be more effective with transmitting information for emergency messages and also periodical messages like distance, speed, etc. In today's world, due to the increasing vehicles on road, there is a need for traffic congestion control due to instances like road construction and management, accidents and weather conditions. This can be achieved by intimating the driver on board about the traffic ahead. In a dense traffic road, information comes from all the vehicles on the segment, hence this information needs to be processed and shared between all the vehicles in the segment. Therefore, there is need for a protocol that all the adhoc vehicles must adhere and detect if the traffic is greater than the threshold, consequently to avoid growing into severe congestion. Many protocols have been proposed for VANET such as AODV, DSDV and AOMDV. The aim of the research is to analyze different existing protocols over different conditions so as to find the optimal protocol for VANET by validating the metrics of network effectiveness such as throughput, delay, etc., and implement traffic congestion detection and rerouting using GPSR protocol in a secured way using RSA algorithm. The VANET system can be simulated over the software such as SUMO and NS2.

关键词： VANET NS2 SUMO AODV AOMDV DSDV GPSR RSA

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Mining High Utility Itemset for Online Ad Placement Using Particle Swarm Optimization Algorithm 3rd

Mining High Utility Itemset for Online Ad Placement Using Pa...

引用

International Conference on Computational Vision and Bio-Inspired computing (ICCVBIC)

作者： Keerthi, M. Anitha, J. Agrawal, Shakti Varghese, Tanya DSATM Dept Comp Sci & Engn Bangalore 560082 Karnataka India

ISBN: (纸本)9783030372187;9783030372170

The process of obtaining relevant content from the raw datasets, present in huge databases is called data mining. It consists of four stages: Data Sources, Data Gathering, Modeling, and Deployment. These databases are searched thoroughly using various data mining techniques. Association rule mining, also known as ARM is the significant method of mining the data that tells us about the relationships of the items that are available in the proceedings. Traditionally, the mining of data focuses on the association between the Frequent Itemset that are present in large numbers in the databases. Mining High Utility Itemset (HUI) is an advancement of the problem of Frequent Itemset Mining (FIM). In the mining of HUI, the aim is to find itemsets that have more utility use than that of the threshold utility value. In this paper, Particle Swarm Optimization (PSO) algorithm is used to mine high utility itemsets.

关键词： Data mining High utility itemset Particle Swarm Optimization Ad placement Ad clicks and impressions

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Fraudulence Detection and Recommendation of Trusted Websites 2nd

Fraudulence Detection and Recommendation of Trusted Websites

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2nd International Conference on computing, Communications and Data Engineering (CCODE)

作者： Senthilkumar, N. C. Gitanjali, J. Monika, A. Monisha, R. Vellore Inst Technol Sch Informat Technol Vellore Tamil Nadu India

ISBN: (纸本)9789811501357;9789811501340

We live in the world of science where everything could be done possibly through online facilities. As the online services increase, the fraudulent sites also increase, however. Therefore, it enhances the difficulty of people to identify a fake or fraudulent or scam website. Fraudsters are highly intelligent in making or persuading fake products which seem exactly same as the original. Some scam websites use low prices to attract people. Most of the fraudulent sites use domain names that reference a popular brand or product name and design with mere change. Some websites will get your card details before you intend to buy a product. Fraudsters try to attract people's attention by advertising the fake products in many social websites such as Facebook, Instagram and Twitter, and when people wishes to view about the product and its details, they are forced to install an app of that site. So, here we have a solution to find those fraudulent products and recommend some trusted products for people to buy with good quality and at a fair price. We use the reviews or feedback or comments and the rating from the users who already purchased the product and shared their experience for identifying fraudulent products and giving a caution to the remaining people. This paper concentrates on analyzing the reviews and extracting the useful information to guide the customers using text mining. As a result of this, people will get a better idea of making a proper decision in buying the product through online.

关键词： Decision making Fraudulent Feedback Rating Recommendation Reviews Text mining

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Effective Model Integration Algorithm for Improving Prediction Accuracy of Healthcare Ontology 3rd

Effective Model Integration Algorithm for Improving Predicti...

引用

International Conference on Computational Vision and Bio-Inspired computing (ICCVBIC)

作者： Monika, P. Raju, G. T. VTU Dayananda Sagar Coll Engn Bengaluru Karnataka India VTU RNS Inst Technol Bengaluru Karnataka India

ISBN: (纸本)9783030372187;9783030372170

In this digital era, the web is filled with enormous data in various formats & sizes resulting in decreased utilization of the data. Healthcare is one of the major domains that contributes data to web on daily basis in large numbers and numerous formats. Semantic web deals intelligently with its ontological data. The concept of Machine Learning coupled with statistical techniques for good prediction accuracy includes tree based algorithms like ID3, C4.5 etc. During the phase of resolving tree under-fitting & over-fitting issues which arises based on the dataset quality, couple of strong rules may be lost due to tree pruning which results in decreasing the prediction accuracy. An Effective Model Integration Algorithm (EMIA) has been proposed as a solution based on the concepts of Hidden Markov Model (HMM) and Stochastic Automata Model (SAM). The presented approach attempts to generate improved decision rules by integrating the rules of chosen models based on threshold in a Healthcare ontology resulting in increased prediction accuracy of 86%.

关键词： Semantic web Ontologies Ontology agents Ontologies integration Health care Diabetology Domains Decision support Decision tree SPARQL Rule based reasoning

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Genetic Algorithm Based Optimal Feature Selection Extracted by Time-Frequency Analysis for Enhanced Sleep Disorder Diagnosis Using EEG Signal

Genetic Algorithm Based Optimal Feature Selection Extracted ...

引用

intelligent systems Conference (IntelliSys)

作者： Islam, Md Rashedul Rahim, Md Abdur Islam, Md Rajibul Shin, Jungpil Univ Aizu Sch Comp Sci & Engn Fukushima Japan Univ Asia Pacific Dhaka Bangladesh

ISBN: (纸本)9783030295134;9783030295127

Sleep disorders have a significant effect on psychological depression and many other human diseases. Nowadays, technology, as well as innovation, has become an essential and analytical part of the world. Detection of sleep disorders by brain waves has become a dynamic study. From this point of view, this paper proposed a model for detecting sleep disorders using time-frequency analysis based feature extraction model based on EEG (Electroencephalogram) signal. In this proposed method, Empirical mode decomposition (EMD) and wavelet packet transform (WPT) time-frequency analysis techniques are used for extracting effective features, because those techniques are very effective for analyzing the non-stationary signal like EEG. In this research, the EMD and WPT are used to decompose the input signal. In EMD decomposition, up to 9th IMFs (Intrinsic Mode Functions) are decomposed. In WPT decomposition, the EEG signal is decomposed up to third level wavelet coefficient. After the decomposition process, different statistical features are extracted, i.e., Shannon entropy, energy, standard deviation, skewness, and kurtosis. However, identifying the optimal sub-band of time-frequency analysis is very challenging. Thus, the genetic algorithm (GA) is used to select the effective subset of the feature. In the detection process, SVM classifier is used and sleep disorders are classified based on trained knowledge. As a result, the performance of the proposed method is evaluated for various statistical features and to find the optimal features for detecting sleep disorder. According to the experimental results, the proposed model shows improved performance by 4.88% improved classification accuracy.

关键词： Sleep disorder Electroencephalogram (EEG) signal Empirical mode decomposition (EMD) Wavelet packet transform (WPT) Genetic algorithm (GA)

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A Remote Puppet Control System for Humanoid Communication Robot 11th

A Remote Puppet Control System for Humanoid Communication Ro...

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11th International Conference on intelligent Networking and Collaborative systems (INCoS) / 22nd International Conference on Network-Based Information systems (NBiS)

作者： Haramaki, Toshiyuki Nishino, Hiroaki Oita Univ Fac Sci & Technol Div Comp Sci & Intelligent Syst Oita Japan

ISBN: (纸本)9783030290351;9783030290344

When a speaker communicates a message to the listener, it can be expected to be transmitted more accurately by attaching an appropriate gesture to message. That theory is valid, whether the speaker and the listener are near or far away. However, for a speaker to deliver messages and gestures to a remote listener synchronously, it is necessary to have a real-time conversation system using video images. Therefore, in this research, we propose a mechanism for presenting gestures synchronized with gestures at minimal cost when sending messages to remote listeners. To realize that, we develop a method to move the robot near the speaker instead of conveying the gesture of the speaker on the video shot. In this proposed method, motion data of the hand of the speaker is acquired using a motion sensor, the data is processing, converting into information for moving the motor corresponding to the joint of the robot, and the information is transmitting to the remote robot, the robot moves with motion according to the intention of the speaker. To realize the proposed method, we prepared a mechanism to convert the hand gesture of the speaker into the motion data of the robot in real-time and manipulate the robot in a faraway place. In our system, we realize a function to transmit information of extreme urgency such as disaster information all at once to robots installed in each household. Based on these requirements, we developed a prototype of an information presentation system. By utilizing this system, we expect the speaker's message to communicate with the multiple listeners effectively.

关键词： Robots

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Design Principles for Increasing the Comprehensibility of Icons in HMIs Through Motion Design

Design Principles for Increasing the Comprehensibility of Ic...

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10th International Conference on Applied Human Factors and Ergonomics / AHFE International Conferences on Usability and User Experience, and Human Factors and Assistive Technology

作者： Blank, David Kosuru, Ravi Kanth Fraunhofer IAO Inst Ind Engn Nobelstr 12 D-70569 Stuttgart Germany Univ Stuttgart Inst Human Factors & Technol Management IAT Stuttgart Germany

ISBN: (纸本)9783030191351;9783030191344

Animation is a significant game changer for designing icons that can communicate their action or status without any additional elements in any design language. This can increase the comprehensibility of Human-Machine-Interfaces (HMI). In this paper, we introduce design principles on how to animate icons for HMIs to increase the comprehensibility and to draw the users' attention. This initial paper builds the foundation for further research on this topic. The results of an expert review on animated icons are presented. The results show that there is a highly expected positive effect of animated icons on comprehensibility and reduction of error rate.

关键词： Human-Machine-Interface Icons Usability User experience Animation Motion design Design principles Design

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Integration of Interpolation and Inference with Multi-antecedent Rules 19th

Integration of Interpolation and Inference with Multi-antece...

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19th Annual UK Workshop on Computational Intelligence (UKCI)

作者： Naik, Nitin Shen, Qiang Minist Def Def Sch Commun & Informat Syst Blandford Forum England Aberystwyth Univ Dept Comp Sci Aberystwyth Dyfed Wales

ISBN: (纸本)9783030299330;9783030299323

The efficacious fuzzy rule based systems perform their tasks with either a dense rule base or a sparse rule base. The nature of the rule base decides on whether compositional rule of inference (CRI) or fuzzy rule interpolation (FRI) should be applied. Given a dense rule base where at least one rule exists for every observation, CRI can be effectively and sufficiently employed. For a sparse rule base where rules do not cover all possible observations, FRI is required. Nonetheless, certain observations may be matched partly or completely with any of the existing rules in the sparse rule-base. Such observations can be directly dealt with using CRI and the conclusion can be inferred via firing the matched rule, thereby avoiding extra overheads of interpolation. If no such matching can be found then correct rules should be selected to ensure the accuracy while performing FRI. This paper proposes a generalised approach for the integration of FRI and CRI. It utilises the notion of alpha-cut overlapping to determine the matching degree between rule antecedents and a given observation in order to determine if CRI is to be applied. In the event of no matching rules, the nearest rules will be chosen to derive conclusion using FRI based on the best suitable distance metric among possible alternatives such as the Centre of Gravity, Hausdorff Distance and Earth Mover's Distance. Comparative results are presented to demonstrate the effectiveness of this integrated approach.

关键词： Computational rule of inference Rule interpolation Rule extrapolation Integration of interpolation and inference Multi-antecedent rules

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An SNN-DBSCAN Based Clustering Algorithm for Big Data 1

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3rd International Conference on Advanced computing and intelligent Engineering (ICACIE)

作者： Pandey, Sriniwas Samal, Mamata Mohanty, Sraban Kumar PDPM Indian Inst Informat Technol Design & Mfg Comp Sci & Engn Jabalpur 482005 Madhya Pradesh India

ISBN: (数字)9789811510816

ISBN: (纸本)9789811510816;9789811510809

Clustering is a technique to partition data into different groups in such a way that data items in a group are more similar to each other than the data points in any other group. The assumption of infinite main memory is very usual while designing most of the clustering algorithms but this assumption fails when the size of data set starts increasing. In this scenario, data needs to be stored in the secondary memory and time spent in the input/outputs (I/O) dominates the actual computational time. Therefore by reducing the I/O, the efficiency of the clustering techniques can be improved. In this paper, one shared near neighbor based algorithm is devised by minimizing its I/O complexity to make it suitable for the Big Data in external memory model proposed by Aggarwal and Vitter. There is no change in the computational steps, hence cluster quality remains the same. We implement the algorithm in the STXXL library to show its efficacy for Big Data sets.

关键词： Clustering SNN clustering External memory algorithms Big data clustering

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Fault Prognosis Method of Industrial Process Based on PSO-SVR 19th

Fault Prognosis Method of Industrial Process Based on PSO-SV...

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19th Annual UK Workshop on Computational Intelligence (UKCI)

作者： Yao, Yu Cheng, Dongliang Peng, Gang Huang, Xuejuan Wuhan Univ Wuhan 430072 Peoples R China Huazhong Univ Sci & Technol Wuhan 430079 Peoples R China Wuhan Sports Univ Wuhan 430079 Peoples R China

ISBN: (纸本)9783030299330;9783030299323

For latent faults or situations where the pre-failure characteristics are not obvious, fault prognosis techniques are needed. This work proposes a fault prognosis method based on support vector regression (SVR), in which particle swarm optimization (PSO) algorithm is utilized to optimize the parameters to improve the prediction accuracy. The SVR algorithm and grey prediction are tested on benchmark data taken from Tennessee-Eastman process and the "NASA prognosis data repository", and the experiments compare the prediction accuracy difference between the two algorithms.

关键词： Particle Swarm Optimization PSO-SVR Fault prognosis

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