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Minimum Annotation identification of facial affects for Video Advertisement 2

Minimum Annotation identification of facial affects for Vide...

引用

2nd International Conference on Intelligent Circuits and Systems (ICICS)

作者： Goyal, Gaurav Singh, Jaiteg Chitkara Univ Dept Comp Sci & Engn Chandigarh Punjab India

ISBN: (纸本)9781538664834

Consumer's market has witnessed a radical change. It has shifted from being a seller's market to buyer's market. Reviews and feedback play an important role deciding the marketing strategy. These reviews express opinions or sentiments of a particular individual for a particular product, which contains information that is useful for others who are interested in buying that product. The paper proposes a model to automate the product review for an advertisement by making use of affective computing. The computerized model for Facial emotion recognition using wearable biometric sensors is used analyze the consumer behavior on a video advertisement. The model detects five basic emotions of happy, sad, surprise, anger and neutral. The logistic regression classification algorithm was used to classify the various sentiments given by various users against the advertisement on the given test data. The result of the classification algorithm has been compared with training data using WEKA. The accuracy of 82% is obtained through classification algorithm.

关键词： Affective computing Neuromarketing classification algorithm Support Vector Machine Galvanic Skin Response

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Trends Identification in Medical Care 10th

Trends Identification in Medical Care

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10th International Conference on Operations Research and Enterprise Systems (ICORES)

作者： Sena, Ines Pereira, Ana Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal

ISBN: (纸本)9789897584855

Daily, health professionals are sought out by patients, motivated by the will to stay healthy, making numerous diagnoses that can be wrong for several reasons. In order to reduce diagnostic errors, an application was developed to support health professionals, assisting them in the diagnosis, assigning a second diagnostic opinion. The application, called ProSmartHealth, is based on intelligent algorithms to identify clusters and patterns in human symptoms. ProSmartHealth uses the Support Vector Machine ranking algorithm to train and test diagnostic suggestions. This work aims to study the application's reliability, using two strategies. First, study the influence of pre-processing data analysing the impact in the accuracy method when data is previously processed. The second strategy aims to study the influence of the number of training data on the method precision. This study concludes the use of pre-processing data and the number of training data influence the precision of the model, improving the precision on 8%.

关键词： Artificial Intelligence Machine Learning Support Vector Machine classification algorithm Reliability

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Detection and Prevention of Code Injection Attacks on HTML5-based Apps 3

Detection and Prevention of Code Injection Attacks on HTML5-...

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3rd International Conference on Advanced Cloud and Big Data (CBD)

作者： Xiao, Xi Yan, Ruibo Ye, Runguo Li, Qing Peng, Sancheng Jiang, Yong Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China China Elect Standardizat Inst Beijing Peoples R China Zhaoqing Univ Sch Comp Sci Zhaoqing Peoples R China

ISBN: (纸本)9781467385374

Security on mobile devices is becoming increasingly important. HTML5 are widely used to develop mobile applications due to its portability on multi platforms. However it is allowed to mix data and code together in web technology. HTML5-based applications are prone to suffer from code injection attacks that are similar to XSS. In this paper, at first, we introduce a more hidden type of code injection attacks, coding-based attacks. In the new type of code injection attacks, JavaScript code is encoded in a human-unreadable form. Then we use classification algorithms of machine learning to determine whether an app suffers from the code injection attack or not. The experimental result shows that the Precision of our detection method reaches 95.3%. Compare with the other method, our approach improves a lot in detection speed with the precision nearly unchanged. Furthermore, an improved access control model is proposed to mitigate the attack damage. In addition, filters are adopted to remove JavaScript code from data to prevent the attacks. The effectiveness and rationality have been validated through extensive simulations.

关键词： code injection classification algorithm machine learning access control model filter

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Study of biofilm evolution using a monitoring water system 25

Study of biofilm evolution using a monitoring water system

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IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME)

作者： Hnatiuc, M. Ghita, S. Inge, L. Chetehouna, K. Maritime Univ Constanta Constanta Romania INSA Ctr Val Loire Blois Loir & Cher France

ISBN: (纸本)9781728133300

Simulation of waterfall in nature can be done using a fluid level control system in a pool. This system is made of two mini-pools at a height of 50 cm from each other. The water monitoring system consists of a control and control board that reads the temperature and the flow rate of the water and pumps the water from the lower container to the top. In the pools are introduced different types of substrate (e.g. naval steel, liquid wood) that simulate the resistance of materials in contact with sea water and the evolution of biofouling formation on these materials. The aim of the project is to detect the appearance of biofilm layers on the materials that are integrated into a ship's body, during the time. A comparative analysis is presented in this paper for different type of material and different environmental condition.

关键词： water flow/microcosm system water pump classification algorithm microbiota

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Data Analysis and Visualization for Electrical Safety Tools and Equipment 5

Data Analysis and Visualization for Electrical Safety Tools ...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Xue, Liang Yang, Lei Suzhou City Univ Comp Sci & Artificial Intelligence Coll Suzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Electric safety tool data has the characteristics of large data volume, high dimensionality, and strong effectiveness. Traditional data analysis still has problems such as insufficient accuracy, slow processing speed, and imprecise feature selection when processing this type of data. This paper proposes a method for predicting the loss of safety tools based on classification algorithms, which provides data support for procurement by predicting the loss. In addition, a supplier scoring model based on scoring indicators was constructed to explore the potential value of the data. First, the data is characterized by construction, elimination, fusion, and reconstruction, and correlation coefficients are used for feature selection and tested on different prediction models;Secondly, a data classification model based on scoring indicators is established to form a new data dimension, including after-sales service, product quality, scale indicators, and customer feedback;Finally, visualize the data set from the perspective of the overall table and sub-tables, and the changing trends of key evaluation indicators were analyzed. This method can not only provide data for predicting the loss of electric power tools but also solve the problem of supplier evaluation and realize multi-dimensional in-depth mining and comprehensive visualization of data.

关键词： component Machine learning Feature engineering classification algorithm Data analysis Data visualization

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A simple and efficient control strategy for three-phase boost rectifiers

A simple and efficient control strategy for three-phase boos...

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15th IEEE Canadian Conference on Electrical and Computer Engineering

作者： Yazdani, D Bakhshai, AR Jain, P Isfahan Univ Technol Dept ECE Esfahan Iran

ISBN: (纸本)0780377818

Performance criteria of three-phase boost rectifiers improve significantly with increasing the switching frequency, and highly depend on the control strategy used. This paper introduces a modified vector classification SVM-based control strategy for three-phase boost rectifiers. By means of the proposed technique abc-dq and dy-alphabeta transformation carried out in conventional SVM-based strategies are avoided. Therefore current regulators in d-q frame and also control difficulties associated with them are removed. In addition, the proposed technique maintains the advantages of other schemes. Simulation results provided, confirm the validity of the analytical work.

关键词： PWM techniques boost rectifier classification algorithm

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Learning Brain Computer Interface model for Amyotrophic Lateral Sclerosis (ALS) people using Random Forest - Linear Discriminant Analysis (RF-LDA) algorithm 19

Learning Brain Computer Interface model for Amyotrophic Late...

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19th IEEE-India-Council International Conference (INDICON)

作者： Kumar, Arun S. Kannur, Anil Akash, C. K. Koushik, Venkatanath P. S. Kushal, S. Anwar, C. Shaik Nagarjuna Coll Engn & Technol Informat Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781665473507

As modern technology grows and the study of the human brain makes it easier to understand its complex activity, we are moving toward the acknowledgment of imagination. It is not a matter of comfort for various paralytics. It's the potential to tweak devices with a single thought. The improvement of the Brain Computer Interface may be the genuinely unambiguous movement of the last few years. Brain Computer Interface is a prompt correspondence channel between the mind and an electrical device. It is the new advancement in HCI. There are numerous things to investigate like Asynchronous method, small signal to noise ratio (SNR) and different noise sources are amongst the greatest challenges in EEG-based BCI application. Challenges are inherent in translating any new technology to practical and useful clinical applications and BCIs are no exception. We discuss the potential uses and users of BCI systems and address some of the limitations and challenges facing the field. In this paper we propose a new algorithm which combines LDA with Random forest algorithm which sub-divides the dataset and apply LDA algorithm to each subdivision and vote for the final output. Experimental results shows that our algorithm enhance the accuracy in printing the target letter.

关键词： Brain Computer Interface Signal Processing Feature Extraction classification algorithm

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Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective 29

Where to Handle an Exception? Recommending Exception Handlin...

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29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)

作者： Jia, Xiangyang Chen, Songqiang Zhou, Xingqi Li, Xintong Yu, Run Chen, Xu Xuan, Jifeng Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China NYU Tandon Sch Engn New York NY 10003 USA

ISBN: (纸本)9781665414036

Exception handling is an effective mechanism to guarantee software reliability in modern programming languages. An exception interrupts the program execution and propagates backwards along the call chain until the exception is caught by an exception handler. In software development practices, developers may be confused in determining where to place the exception handler in the call chain. The reason is that exception handling requires a developer to take a comprehensive consideration from a global perspective of the software project. In this paper, we propose an automatic approach EHAdvisor, which recommends exception handling locations from the global perspective of the project. EHAdvisor first trains a binary classification model based on four types of features, including architectural features, project features, functional features, and exception features. Then, for a new code snippet with exceptions, EHAdvisor predicts the exception catching probability for each method in the call chain based on the classification model and recommends Top-K exception handling locations based on the probability ranking. We conducted experiments on a dataset from 29 high-quality open source projects. Experimental results show that EHAdvisor achieves an average Top-1 recommendation success rate of 70.83% for across-project location recommendation and an average Top-1 accuracy of 86.21% for intra-project recommendation. Experiments on the importance scores show that global features, such as project features and architectural features, are evidently important to the recommendation of exception handling locations.

关键词： exception handling location recommendation machine learning global features classification algorithm

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Optical biomarkers for detection of malignant tissue using Digital Holographic Microscopy 2

Optical biomarkers for detection of malignant tissue using D...

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Conference on Advances in Microscopic Imaging II

作者： Calin, Violeta L. Mihailescu, Mona Costea, Radu V. Dumitru, Adrian Patrascu, Oana-Maria Brehar, Felix Petrescu, George Lisievici, Mihai Comanescu, Brindus Grigore, Viorela N. Butmalai, Dan Savopol, Tudor Moisescu, Mihaela G. Carol Davila Univ Med & Pharm 8 Eroii Sanitari Blvd Bucharest 050474 Romania Politehn Univ 313 Spl Independentei Bucharest 060042 Romania Univ Emergency Hosp Bucharest 169 Spl Independentei Bucharest 050098 Romania Bagdasar Arseni Clin Emergency Hosp 12 Berceni St Bucharest 041915 Romania Optoelect SA 409 Atomistilor St Magurele 077125 Romania

ISBN: (数字)9781510628465

ISBN: (纸本)9781510628465

The histopathological diagnosis in malignances requests well trained specialists and multi-step operational procedures for sample preparation. Faster and more objective evaluation protocols should be implemented to give support to the pathologists. The Quantitative Phase Imaging based methods are biological-proved to be efficient in revealing important characteristics of the living structures without any labeling. These can be further exploited for an automatic evaluation of complex tissues. Using an off-axis Digital Holographic Microscopy setup, biopsies of two histological origins: cerebral (grade II glioma and grade IV glioblastoma) and colonic malignancies (dysplastic and malignant colonic adenomatous polyps), were investigated. Various parameters of quantitative phase shift maps (QPMs) were computed (mean, variance, median, kurtosis, skewness, energy, entropy). The possibility of automatic discrimination of tumor tissues having different structural complexity and presenting various malignancy grades was evaluated using supervised machine learning algorithms. The analysis of phase shift maps has successfully discriminated between levels of malignancy with high statistical confidence in the case of gliomas. Moreover an algorithm with the ability to classify the tissue biopsies in different malignant stages using parameters based on QPMs has been implemented on glioma tissues having a high level of homogeneity. In case of colonic polyps, the heterogeneity of the multilayered tissue demanded QPMs analysis to be performed on selected area of interest even though some statistical differences were obtained for global evaluation of phase shift distributions. In case of colonic polyps, for a good accuracy of classification algorithm a larger library of QPMs is under construction.

关键词： Digital Holographic Microscopy quantitative phase maps colonic adenomatous polyps glioblastomas classification algorithm

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Motor Imagery Intention Recognition Based on Common Spatial Pattern for Manipulator Grasping 15th

Motor Imagery Intention Recognition Based on Common Spatial ...

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15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Li, Wenjie Xu, Jialu Yan, Xiaoyu Lin, Chengyu Fu, Chenglong Shenzhen Foreign Languages Sch Shenzhen Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen Peoples R China

ISBN: (纸本)9783031138225;9783031138218

With further development of brain-computer interface (BCI) technology and the BCI medicine field, people with movement disorders can be treated by using artificial limbs or some external devices such as an exoskeleton to achieve sports function rehabilitation. At present, brain-computer interfaces (BCI) based on motor imagination mainly have problems such as accuracy that need to be improved. The key to solving such problems is to extract high-quality EEG signal features and reasonably select classification algorithms. In this paper, the efficient decoding of dichotomy EEG intentions is verified by combining features such as Common Spatial Pattern (CSP) with different classifiers in the PhysioNet public data set. The results show that: CSP is used as the feature, and the support vector machine (SVM) has the best classification effect. The highest classification accuracy of five subjects reaches 87% and the average accuracy reaches 80%. At the same time, the CSP-SVM algorithm was used in the qbrobotics manipulator to conduct a grasping experiment to verify the real-time performance and effectiveness of the algorithm. This provides a new solution for the brain-computer interface to control robotic auxiliary equipment, which is expected to improve the daily life of the disabled.

关键词： Brain-computer interfaces Motor imagery Feature extraction Common spatial pattern classification algorithm

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