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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： T Karthikayini N.K. Srinath New Horizon College of Engineering Bangalore India RV college of Engineering Bangalore India

ISBN: (纸本)9781538620458

Opinion mining has emerged as an active domain among the research fraternity because an enormous amount of heterogeneous user data is continuously increasing every day via www, viz., e-commerce websites, social networks, discussion forums, blogs etc. Intentions are expressed in a different way with different vocabulary, short forms, and jargon making the data massive and disorganized. It has turned out an exciting new trend in social media with a scope of practical applications like analyzing marketing strategy, political analysis, social media analysis, financial analysis to take an effective decision based on user's feedback. With that being said, there are many unsolvable issues and challenges still existing that keeps the field more dynamic. As we know, it is very difficult to understand human language and more complex to program the machine to analyze the user's context. The proposed work aims at evaluating the sentiments of Amazon product reviews by using programming languages with the help of existing natural language processing APIs. Our aim is to categorize the sentiments of a Meta dataset, parse and protrude the comparative accuracy levels. Senti - A new algorithm is proposed to analyze the sentiments accurately which outperforms the existing APIs.

关键词： machine learning algorithms Data mining Tools Sentiment analysis Conferences Information technology

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Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using machine learning algorithms

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SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-ELECTRONIC AND ELECTRICAL SYSTEMS 2016年第1期9卷 114-122页

作者： Taie, Mostafa Anwar Moawad, Eman Magdy Diab, Mohammed ElHelw, Mohamed Nile Univ iSAQB Giza Egypt Nile Univ Giza Egypt

New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS' components needs special improvements. Since software contribution in ADAS' development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS' safety critical components;e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework's elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS' components, and different types of input attributes to these MLA are proposed for some basic ADAS' components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS' components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.

关键词： Prognosis Driver Assistance Systems Remote diagnostics machine learning algorithms Sensor fusion advanced driver assistance systems (ADAS) Braking systems multispectral linear arrays

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Survey on machine learning algorithms as cloud service for CIDPS

Survey on machine learning algorithms as cloud service for C...

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Telecommunications Forum (TELFOR)

作者： Indira Avdagic Kemal Hajdarevic Registry of Securities of FBH Faculty of Electrical Engineering University of Sarajevo

Today IT vendors and mail/web/internet providers put their cloud strategy in the first place. Challenges such as data security, privacy protection, data access, storage model, lack of standards and service interoperability were set up almost ten years ago. This paper presents a part of the research on the cloud security systems at the infrastructure layer and its sublayer - network layer. To analyze and protect cloud systems we need storage and machines with extra features. Due to these needs, we used new technologies from Microsoft to suggest framework of host and network based systems for cloud intrusion detection and prevention system (CIDPS). The purpose of this research is to recommend use of the architecture for the detection network anomalies and protection of large amounts of data and traffic generated by cloud systems.

关键词： Cloud computing Big Data Intrusion detection Algorithm design and analysis machine learning algorithms Anomaly detection

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Application of machine learning algorithms on Diabetic Retinopathy

Application of Machine Learning Algorithms on Diabetic Retin...

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Ridam Pal Jayanta Poray Mainak Sen Computer Science & Engineering Dept. Techno India University West Bengal

ISBN: (纸本)9781509037056

Diabatic Retinopathy (DR) is one of the leading cause of sight inefficiency for diabetic patients. The clinical diagnostic results and several outcome of eye testing methods reviled a set of observations that eases the decision making in the case of diabetic retinopathy for the doctor, therapist. machine learning, a branch of artificial intelligence is applied in clinical data analytic as it can detect patterns in data, and then use these uncovered patterns to predict future data or perform some kind of decision making under uncertainty. In case of DR finding the co-relation between the depth of affection and the clinical result is very much critical, as several parameters are need to be taken into consideration for optimal decision making by the therapist. In this paper we have reviewed the performance of a set of machine learning algorithms and verify their performance for a particular DR data set.

关键词： machine learning algorithms in medical diagnostics Diabetic retinopathy Support vector machine WEKA simulator Python in machine learning algorithms Diabetic Retinopathy machine learning algorithms Support Vector Network DR decision making machine APPLICATIONS Artificial Intelligence

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An Application of machine learning algorithms in Big Data Research of Subway Systems——A Case Study of Shanghai Subway Card Data

An Application of Machine Learning Algorithms in Big Data Re...

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2017第四届国际信息技术与就业教育大会

作者： Feng Han Aijia Zhang Beijing Union University Teachers College Foundation Department Capital Normal University

With the era of big data is coming, machine learning algorithms plays a vital role in many fields of research, especially in transportation systems. This Paper introduces the processing method of Big data and illustrates the application of Big data in the research of Subway system. It also analyses the problems of the application, then give the solutions. At last, it gives the details of algorithms implementation and results.

关键词： machine learning algorithms K-means clustering Hadoop platform

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Addressing Bias in machine learning algorithms: A Pilot Study on Emotion Recognition for Intelligent Systems

Addressing Bias in Machine Learning Algorithms: A Pilot Stud...

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IEEE Workshop on Advanced Robotics and its Social Impacts

作者： Ayanna Howard Cha Zhang Eric Horvitz School of Electrical and Computer Engineering Georgia Institute of Technology Microsoft Research

ISBN: (纸本)9781509004768

Recently, there has been an explosion of cloud-based services that enable developers to include a spectrum of recognition services, such as emotion recognition, in their applications. The recognition of emotions is a challenging problem, and research has been done on building classifiers to recognize emotion in the open world. Often, learned emotion models are trained on data sets that may not sufficiently represent a target population of interest. For example, many of these on-line services have focused on training and testing using a majority representation of adults and thus are tuned to the dynamics of mature faces. For applications designed to serve an older or younger age demographic, using the outputs from these pre-defined models may result in lower performance rates than when using a specialized classifier. Similar challenges with biases in performance arise in other situations where datasets in these large-scale on-line services have a non-representative ratio of the desired class of interest. We consider the challenge of providing application developers with the power to utilize pre-constructed cloud-based services in their applications while still ensuring satisfactory performance for their unique workload of cases. We focus on biases in emotion recognition as a representative scenario to evaluate an approach to improving recognition rates when an on-line pre-trained classifier is used for recognition of a class that may have a minority representation in the training set. We discuss a hierarchical classification approach to address this challenge and show that the average recognition rate associated with the most difficult emotion for the minority class increases by 41.5% and the overall recognition rate for all classes increases by 17.3% when using this approach.

关键词： emotion recognition Information Services Emotions Intelligent systems Recognition (Psychology) Pilot Studies Target Population Bias application developer Classifiers machine learning algorithms Dataset Artificial Intelligence

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Poster Abstract: A Beverage Intake Tracking System Based on machine learning algorithms, and Ultrasonic and Color Sensors

Poster Abstract: A Beverage Intake Tracking System Based on ...

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International Symposium on Information Processing in Sensor Networks (IPSN)

作者： Mahdi Pedram Seyed Ali Rokni Ramin Fallahzadeh Hassan Ghasemzadeh Washington State University

ISBN: (纸本)9781467391474

We present a novel approach for monitoring beverage intake. Our system is composed of an ultrasonic sensor, an RGB color sensor, and machine learning algorithms. The system not only measures beverage volume but also detects beverage types. The sensor unit is lightweight that can be mounted on the lid of any drinking bottle. Our experimental results demonstrate that the proposed approach achieves more than 97% accuracy in beverage type classification. Furthermore, our regression-based volume measurement has a nominal error of 3%.

关键词： Liquids Acoustics machine learning algorithms Volume measurement Color Monitoring Ultrasonic variables measurement

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Automation of a robotic cell using machine learning algorithms and internet of things

Automation of a robotic cell using machine learning algorith...

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IEEE Mexican Humanitarian Technology Conference (MHTC)

作者： E.H. Mayoral Arzaba O. Felix Beltran J. Cid Monjaraz F. Reyes, Cortes Benemerita Universidad Autonoma de Puebla Puebla Puebla MX

In recent years the imagen processing is an envolving issue, allows a great variety of applications. This article describe the process of automation of a conveyor belt using a machine learning algorithms that is able to immediately recognize each type different electronic board. For image processing a webcam is used. The obtained information is sent a free server of a web page by a WiFi module. Is obtained an automatic and intelligent process of low cost of robotic cell.

关键词： Robots Wireless fidelity MATLAB Automation machine learning algorithms Internet of Things

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Feature selection to detect botnets using machine learning algorithms

Feature selection to detect botnets using machine learning a...

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International Conference on Electronics, Communications and Computers (CONIELECOMP)

作者： Francisco Villegas Alejandre Nareli Cruz Cortés Eleazar Aguirre Anaya Instituto Politécnico Nacional Laboratory of Cybersecurity D.F México

ISBN: (纸本)9781509036226

In this paper, a novel method to do feature selection to detect botnets at their phase of Command and Control (C&C) is presented. A major problem is that researchers have proposed features based on their expertise, but there is no a method to evaluate these features since some of these features could get a lower detection rate than other. To this aim, we find the feature set based on connections of botnets at their phase of C&C, that maximizes the detection rate of these botnets. A Genetic Algorithm (GA) was used to select the set of features that gives the highest detection rate. We used the machine learning algorithm C4.5, this algorithm did the classification between connections belonging or not to a botnet. The datasets used in this paper were extracted from the repositories ISOT and ISCX. Some tests were done to get the best parameters in a GA and the algorithm C4.5. We also performed experiments in order to obtain the best set of features for each botnet analyzed (specific), and for each type of botnet (general) too. The results are shown at the end of the paper, in which a considerable reduction of features and a higher detection rate than the related work presented were obtained.

关键词： Feature extraction Genetic algorithms machine learning algorithms Training Testing Servers Proposals

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Predictive Analytics of Sensor Data Based on Supervised machine learning algorithms

Predictive Analytics of Sensor Data Based on Supervised Mach...

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International Conference on Next Generation Computing and Information Systems (ICNGCIS)

作者： Shreya Gupta Mohit Mittal Anupama Padha Department of CET Guru Nanak Dev University India Department of CSE MIET Jammu India

ISBN: (纸本)9781538642061

Wireless sensor network (WSN) is one of emerging technologies in today's scenario. Due to progressive advancement in micro-electro-mechanical system (MEMS) technology it can easily deployed in harsh environment. Sensor node communicates with their neighboring sensor nodes via radio frequencies and has many notable capabilities like self-configurable, self-manageable and monitoring physical phenomenon. Wireless sensor network is gaining popularity due to presence of many characteristics like cheap, cost-effective, reliable etc. along with this it has one major challenge that is limited battery life. To overcome this challenge, many solutions have found till date such as improvising routing protocols, reduction in computation of data, modification in time-stamp synchronization etc but still need more work. In this paper, our major focus is on processing of sensor dataset using various machine learning algorithms. We have managed different range of datasets from hundreds to thousands values and processed with various supervised machine learning algorithms. Simulation result shows that Gaussian Naive Bayes algorithm prominently gives better results than other algorithms in terms of accuracy parameter.

关键词： Wireless sensor networks machine learning algorithms Wireless communication Monitoring Sensors Classification algorithms Micromechanical devices

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