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Outlier Detection Based on Local Density of Vector Dot Product in data Stream 2nd

Outlier Detection Based on Local Density of Vector Dot Produ...

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2nd international conference on Security with Intelligent computing and Big-data Services (SICBS)

作者： Shou, Zhaoyu Zou, Fengbo Tian, Hao Li, Simin Guilin Univ Elect Technol Sch Informat & Commun Engn Guilin 541004 Peoples R China Minist Educ Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China

ISBN: (纸本)9783030169466;9783030169459

Outlier detection in data stream is an increasingly important research in many fields. To deal with the data stream with the properties of high dimension, rapid arrival in order, high cost of storing all data in memory and so on, an outlier detection algorithm based on local density of vector dot product in data stream (LDVP-OD) is proposed. LDVP-OD uses the model based on sliding window and multiple validations to decrease the false alarm rate, which divides the data stream into uniform-sized blocks. Local density of vector dot product (LDVP) is described in order to precisely evaluate the outlierness of data in data stream. Furthermore, an outlier judgment criterion based on supreme slope is introduced, which can determine the exact outliers without requiring the number of outliers or other parameters beforehand. Comparison experiments with existing algorithms on synthetic and real datasets prove the high detection rate, good stability, strong adaptability of LDVP-OD.

关键词： Outlier detection data stream Local density of vector dot product Multiple validations

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Comparison of Machine Learning Methods for Effective Autism Diagnosis 2nd

Comparison of Machine Learning Methods for Effective Autism ...

引用

2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Pavithra, D. Jayanthi, A.N. Nidhya, R. Department of Electronics and Communication Engineering Sri Ramakrishna Institute of Technology CoimbatoreTamil Nadu India Department of Computer Science and Engineering Madanapalle Institute of Technology and Science MadanapalleAndhra Pradesh India

ISBN: (纸本)9789811524745

Autism spectrum disorder is a neurological problem that will have challenges in social, emotional and behaviour skills. ASD can be diagnosed only by the age of three. The children with ASD will have developmental delay at every stage of their growth. The ASD is categorized as mild, moderate and severe. Proper diagnosis with treatment can cure ASD at the earliest. There are a number of tools for autism diagnosis such as the Autism Spectrum Quotient, Modified Checklist for Autism in Toddlers. Accuracy of the method mostly relies on the knowledge of the person who reviews the child. Accuracy can be improved by using AI technology like machine learning. Machine learning is a part of Artificial Intelligence where the system gets the ability to learn and improve itself. ML provides classifiers to diagnose autism at a high rate of accuracy. This paper focuses on comparing the existing machine learning classifiers (Naïve Bayes, SVM, k-NN and random forest) on ISAA data set for effective autism diagnosis. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Halftone Image Steganography with Flippability Measurement Based on Pixel Pairs 2nd

Halftone Image Steganography with Flippability Measurement B...

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2nd international conference on Security with Intelligent computing and Big-data Services (SICBS)

作者： Liu, Wanteng Lu, Wei Yin, Xiaolin Zhang, Junhong Yeung, Yuileong Sun Yat Sen Univ Sch Data & Comp Sci Guangdong Key Lab Informat Secur Technol Minist EducKey Lab Machine Intelligence & Adv Co Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783030169466;9783030169459

In recent years, many state-of-the-art steganographic schemes for halftone images have been proposed. Most of them only focus on the visual quality of the marked image but ignore the statistical security. In this paper, a novel steganographic scheme for halftone images is proposed, which focuses on both visual imperceptibility and statistical security. For halftone steganography, embedding messages can only be achieved by flipping pixels. The significant distortion will occur if the pixels are flipped arbitrarily in a halftone image. To select the optimal flipping pixels, we define the flippability measurement based on pixel pairs, which can provide a flippability score for each pixel in halftone images. The higher flippability score of a pixel means that the visual distortion caused by flipping the pixel will be smaller, so the pixels with high flippability scores can be selected as carriers. To achieve high embedding capacity and minimize the embedding distortions, syndrome-trellis code (STC) is employed in the embedding process. The experimental results demonstrate that the proposed scheme can achieve high embedding capacity and realize acceptable statistical security with high visual imperceptibility.

关键词： Halftone image steganography Flippability measurement Pixel pairs Syndrome-trellis code (STC)

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Human Pose Estimation and Activity Classification Using Machine Learning Approach 1

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Arunnehru, J. Nandhana Davi, A.K. Sharan, R. Raghul Nambiar, Poornima G. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus ChennaiTamil Nadu India

ISBN: (数字)9789811524752

ISBN: (纸本)9789811524745

Human pose estimation is mainly used for the purpose of training the robots to incorporate in a way which the actions are performed in reality. The human pose estimation is the highly exploring topic in the field of computer vision research. The objective of the dynamic pose estimation is to estimate the human pose in all the available datasets. It begins with mapping the skeletal coordinates and also visualizing the coordinates. The features within the coordinates are being obtained with the help of proportionate distance features and the divergence between the coordinates. The feature extraction has taken place separately for RGB and depth dataset. The computed data is being present in the same format as that of the vector space that is being allocated for the feature extractor. The classifiers such as support vector machines (SVMs), k-nearest neighbor (KNN), and the decision tree are being used. The major use of this learning process is to help the robot to train it similar to that of the human functionalities. This functionality can be used in any learning and training procedures to build the human-robotic system. The proper understanding of how every action takes place is learned in this process and thereby training any system with similar measures is simplified with this procedure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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An Applied Time Series Forecasting Model for Yield Prediction of Agricultural Crop 1

引用

2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Reddy, P. Chandra Shaker Sureshbabu, A. Research Scholar JNTUA AnanathapuramuAP India Department of Computer Science & Engineering CMR College of Engineering & Technology HyderabadTS India Professor of CSE Department JNTUA College of Engineering AnanathapuramuAP India

ISBN: (数字)9789811524752

ISBN: (纸本)9789811524745

Rice is an essential yield amongst the most essential food crops of India and it is grown everywhere throughout the nation. Rice is a major yield in the semi-arid district as Ananthapur is the part of Andhra Pradesh (AP) state in India. Precise and early forecasting of rice yield can give valuable information to inside season alteration of yield management. Time series data has been of incredible significance to investigate the area of forecasting strategies and times series models are used for rice yield forecasting from various investigators across the world, yet the forecast has not been precise. In this investigation, we proposed an effective approach to predict seasonal rice production of coming four years based on existing data. The model was build based on rice production data, which it is collected from agricultural department Andhra Pradesh. Rice production data of two seasons (Kharif and Rabi) was gathered in the period of 2008–2014 from Ananthapur district. In this article, we introduced Seasonal Adaptive Auto-Regressive Integrated Moving Average (ARIMA) time series model for prediction for rice crop production for next four years seasonalwise and give more precise outcomes than the previous existing models. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Parking recommender system using q-learning and cloud computing 2nd

Parking recommender system using q-learning and cloud comput...

引用

2nd international conference on Computer science and Cyber Security, ICONCS 2020

作者： Hasan, Md. Omar Ahmed, Khandakar Razoan Islam, Md. Motaharul BRAC University Dhaka Bangladesh United International University Dhaka Bangladesh

ISBN: (纸本)9783030528553

Artificial Intelligence (AI) based recommender systems help to make our life easy and comfortable. From simple chatbot to YouTube recommendation, AI is used to recommend news, videos, etc. which provide us more information and saves our time. In big cities, parking seems to be a major problem where commuters need to find a suitable parking space among many parking areas which cause wastage of time and fuel. Our paper proposes a parking recommender system where commuters will be suggested a parking area to a nearby place for helping them to save time, parking cost and ensure high security. To collect data of parking spaces, we propose a Cloud architecture where we use the concept of Edge and Cloud computing to collect and process data smoothly and reduce latency. To deal with bigger amounts of data we use data Streaming Pipelining to process and analyze those data. We use Amazon Web Services (AWS) to implement our proposed Cloud architecture. For creating the AI based recommender system, we propose the Q-learning algorithm with Ε-soft policy to suggest nearby parking areas. Our novel approach will be helpful for both local and global citizens to find an ideal parking area close to their working place, home, etc. Our proposed Cloud architecture is able to reduce latency and make data transferring system faster. Also the Q-learning algorithm can outperform in terms of both certain and uncertain situations. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2020.

关键词： Edge computing

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Efficient Binary Static Code data Flow Analysis Using Unsupervised Learning 4

Efficient Binary Static Code Data Flow Analysis Using Unsupe...

引用

4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Obert, James Loffredo, Tim Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9781665434102

The ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. © 2021 IEEE

关键词： data flow analysis

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Information Processing Capability of soft Continuum Arms

Information Processing Capability of Soft Continuum Arms

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2nd IEEE international conference on soft Robotics (Robosoft)

作者： Torres, Estefany Nakajima, Kohei Godage, Isuru S. Depaul Univ Sch Computing Chicago IL 60604 USA Univ Tokyo Grad Sch Informat Sci & Technol Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan JST PRESTO Kawaguchi Saitama 3320012 Japan

ISBN: (纸本)9781538692608

soft Continuum arms, such as trunk and tentacle robots, can be considered as the "dual" of traditional rigid-bodied robots in terms of manipulability, degrees of freedom, and compliance. Introduced two decades ago, continuum arms have not yet realized their full potential, and largely remain as laboratory curiosities. The reasons for this lag rest upon their inherent physical features such as high compliance which contribute to their complex control problems that no research has yet managed to surmount. Recently, reservoir computing has been suggested as a way to employ the body dynamics as a computational resource toward implementing compliant body control. In this paper, as a first step, we investigate the information processing capability of soft continuum arms. We apply input signals of varying amplitude and bandwidth to a soft continuum arm and generate the dynamic response for a large number of trials. These data is aggregated and used to train the readout weights to implement a reservoir computing scheme. Results demonstrate that the information processing capability varies across input signal bandwidth and amplitude. These preliminary results demonstrate that soft continuum arms have optimal bandwidth and amplitude where one can implement reservoir computing.

关键词： Reservoirs Robot kinematics Mathematical model Information processing Bandwidth Prototypes

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Big data analytics using soft computing techniques: A study 2nd

Big data analytics using soft computing techniques: A study

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2nd international conference on Communication and computing systems, ICCCS 2018

作者： Sreekantha, D.K. Department of Computer Science and Engineering NMAM Institute of Technology NitteKarnataka India

ISBN: (纸本)9780367001476

Big data comprises large volume data having structure, partially structure and no structure gathered through scientific, social, business and industrial operations continuously. This big data needs to be analyzed for extracting the hidden knowledge patterns for taking timely, intelligent and cost effective decisions. Research on the applications of soft computing techniques for big data processing to arrive at intelligent decision making has been drawing significant attention in the recent years. Authors studied the applications of soft computing techniques for analyzing the big data. soft computing techniques differs from traditional hard computing techniques by effectively dealing with uncertainty, vagueness, partial truth and imprecision. soft computing is an hybrid technique having Fuzzy logic, Neural Networks, Genetic Algorithms, Machine learning, Support Vector Machines (SVM), Evolutionary Computation (EC) and AI as its constituents. Authors have carried out an exhaustive survey of applications in various business and industry domains. © 2019 Taylor & Francis Group, London.

关键词： Big data

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Third Eye: Assistance for Reading Disability 2nd

Third Eye: Assistance for Reading Disability

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2nd international conference on soft computing and Signal Processing, ICSCSP 2019

作者： Navya, Y. SriDevi, S. Akhila, P. Amudha, J. Jyotsna, C. Department of Computer Science & Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789811524745

Around 5–20% of students across the world have learning difficulties which occur due to several reasons. Dyslexia is the most common form of learning difficulty associated with difficulties in reading, writing, spelling and organization. Researchers show that the eye movements can reflect the difficulties faced by an individual while reading. Third eye uses eye gaze information from the eye tracker and analyzes it to extract features which can identify the reading skills of the subject and further broadly categorizes them based on their reading skills. An automated report is generated which depicts the areas of difficulty faced by the reader through various visualization techniques. A correlation study done between normal readers and dyslexic readers showcases significant differences in their eye movements in terms of fixation duration, fixation count and regressions. This platform can be used to identify the reading skills of the student which improves their self-confidence and motivates them toward further augmentation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Eye movements

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