Search results for: KaraAgroAI cocoa dataset
791 A Review on Big Data Movement with Different Approaches
Authors: Nay Myo Sandar
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With the growth of technologies and applications, a large amount of data has been producing at increasing rate from various resources such as social media networks, sensor devices, and other information serving devices. This large collection of massive, complex and exponential growth of dataset is called big data. The traditional database systems cannot store and process such data due to large and complexity. Consequently, cloud computing is a potential solution for data storage and processing since it can provide a pool of resources for servers and storage. However, moving large amount of data to and from is a challenging issue since it can encounter a high latency due to large data size. With respect to big data movement problem, this paper reviews the literature of previous works, discusses about research issues, finds out approaches for dealing with big data movement problem.Keywords: Big Data, Cloud Computing, Big Data Movement, Network Techniques
Procedia PDF Downloads 75790 Kirchhoff’s Depth Migration over Heterogeneous Velocity Models with Ray Tracing Modeling Approach
Authors: Alok Kumar Routa, Priya Ranjan Mohanty
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Complex seismic signatures are generated due to the complexity of the subsurface which is difficult to interpret. In the present study, an attempt has been made to model the complex subsurface using the Ray tracing modeling technique. Add to this, for the imaging of these geological features, Kirchhoff’s prestack depth migration is applied over the synthetic common shot gather dataset. It is found that the Kirchhoff’s migration technique in addition with the Ray tracing modeling concept has the flexibility towards the imaging of various complex geology which gives satisfactory results with proper delineation of the reflectors at their respective true depth position. The entire work has been carried out under the MATLAB environment.Keywords: Kirchhoff's migration, Prestack depth migration, Ray tracing modelling, velocity model
Procedia PDF Downloads 357789 Event Data Representation Based on Time Stamp for Pedestrian Detection
Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita
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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption
Procedia PDF Downloads 89788 Cultural Effects on the Performance of Non- Profit and For-Profit Microfinance Institutions
Authors: Patrick M. Stanton, William R. McCumber
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Using a large dataset of more than 2,400 individual microfinance institutions (MFIs) from 120 countries from 1999 to 2016, this study finds that nearly half of the international MFIs operate as for-profit institutions. Formal institutions (business regulatory environment, property rights, social protection, and a developed financial sector) impact the likelihood of MFIs being for-profit across countries. Cultural differences across countries (power distance, individualism, masculinity, and indulgence) seem to be a factor in the legal status of the MFI (non-profit or for-profit). MFIs in countries with stronger formal institutions, a greater degree of power distance, and a higher degree of collectivism experience better financial and social performance.Keywords: Hofstede cultural dimensions, international finance, microfinance institutions, non-profite
Procedia PDF Downloads 265787 Health Expenditure and its Place in Economy: The Case of Turkey
Authors: Ayşe Coban, Orhan Coban, Haldun Soydal, Sükrü Sürücü
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While health is a source of prosperity for individuals, it is also one of the most important determinants of economic growth for a country. Health, by increasing the productivity of labor, contributes to economic growth. Therefore, countries should give the necessary emphasis to health services. The primary aim of this study is to analyze the changes occurring in health services in Turkey by examining the developments in the sector. In this scope, the second aim of the study is to reveal the place of health expenditures in the Turkish economy. As a result of the analysis in the dataset, in which the 1999-2013 periods is considered, it was determined that some increase in health expenditures took place and that the increase in the share of health expenditures in GDP was too small. Furthermore, analysis of the results points out that in financing health expenditures, the public sector is prominent compared to the private sector.Keywords: health, health service, health expenditures, Turkey
Procedia PDF Downloads 357786 Artificial Intelligence Based Meme Generation Technology for Engaging Audience in Social Media
Authors: Andrew Kurochkin, Kostiantyn Bokhan
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In this study, a new meme dataset of ~650K meme instances was created, a technology of meme generation based on the state of the art deep learning technique - GPT-2 model was researched, a comparative analysis of machine-generated memes and human-created was conducted. We justified that Amazon Mechanical Turk workers can be used for the approximate estimating of users' behavior in a social network, more precisely to measure engagement. It was shown that generated memes cause the same engagement as human memes that produced low engagement in the social network (historically). Thus, generated memes are less engaging than random memes created by humans.Keywords: content generation, computational social science, memes generation, Reddit, social networks, social media interaction
Procedia PDF Downloads 127785 Automatic Threshold Search for Heat Map Based Feature Selection: A Cancer Dataset Analysis
Authors: Carlos Huertas, Reyes Juarez-Ramirez
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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.Keywords: biomarker discovery, cancer, feature selection, mass spectrometry
Procedia PDF Downloads 330784 The Effect of Finding and Development Costs and Gas Price on Basins in the Barnett Shale
Authors: Michael Kenomore, Mohamed Hassan, Amjad Shah, Hom Dhakal
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Shale gas reservoirs have been of greater importance compared to shale oil reservoirs since 2009 and with the current nature of the oil market, understanding the technical and economic performance of shale gas reservoirs is of importance. Using the Barnett shale as a case study, an economic model was developed to quantify the effect of finding and development costs and gas prices on the basins in the Barnett shale using net present value as an evaluation parameter. A rate of return of 20% and a payback period of 60 months or less was used as the investment hurdle in the model. The Barnett was split into four basins (Strawn Basin, Ouachita Folded Belt, Forth-worth Syncline and Bend-arch Basin) with analysis conducted on each of the basin to provide a holistic outlook. The dataset consisted of only horizontal wells that started production from 2008 to at most 2015 with 1835 wells coming from the strawn basin, 137 wells from the Ouachita folded belt, 55 wells from the bend-arch basin and 724 wells from the forth-worth syncline. The data was analyzed initially on Microsoft Excel to determine the estimated ultimate recoverable (EUR). The range of EUR from each basin were loaded in the Palisade Risk software and a log normal distribution typical of Barnett shale wells was fitted to the dataset. Monte Carlo simulation was then carried out over a 1000 iterations to obtain a cumulative distribution plot showing the probabilistic distribution of EUR for each basin. From the cumulative distribution plot, the P10, P50 and P90 EUR values for each basin were used in the economic model. Gas production from an individual well with a EUR similar to the calculated EUR was chosen and rescaled to fit the calculated EUR values for each basin at the respective percentiles i.e. P10, P50 and P90. The rescaled production was entered into the economic model to determine the effect of the finding and development cost and gas price on the net present value (10% discount rate/year) as well as also determine the scenario that satisfied the proposed investment hurdle. The finding and development costs used in this paper (assumed to consist only of the drilling and completion costs) were £1 million, £2 million and £4 million while the gas price was varied from $2/MCF-$13/MCF based on Henry Hub spot prices from 2008-2015. One of the major findings in this study was that wells in the bend-arch basin were least economic, higher gas prices are needed in basins containing non-core counties and 90% of the Barnet shale wells were not economic at all finding and development costs irrespective of the gas price in all the basins. This study helps to determine the percentage of wells that are economic at different range of costs and gas prices, determine the basins that are most economic and the wells that satisfy the investment hurdle.Keywords: shale gas, Barnett shale, unconventional gas, estimated ultimate recoverable
Procedia PDF Downloads 293783 Review on Rainfall Prediction Using Machine Learning Technique
Authors: Prachi Desai, Ankita Gandhi, Mitali Acharya
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Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts.Keywords: ANN, CNN, supervised learning, machine learning, deep learning
Procedia PDF Downloads 188782 On the Effect of Immigration on Destination: Country Corruption
Authors: Eugen Dimant, Tim Krieger, Margarete Redlin
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This paper analyzes the impact of migration on destination-country corruption levels. Capitalizing on a comprehensive dataset consisting of annual immigration stocks of OECD coun-tries from 207 countries of origin for the period 1984-2008, we explore different channels through which corruption might migrate. We employ different estimation methods using fixed effects and Tobit regressions in order to validate our findings. What is more, we also address the issue of endogeneity by using the Difference-Generalized Method of Moments (GMM) estimator. Independent of the econometric methodology we consistently find that while general migration has an insignificant effect on the destination country’s corruption level, immigration from corruption-ridden origin countries boosts corruption in the destination country. Our findings provide a more profound understanding of the economic implications associated with migration flows.Keywords: corruption, migration, impact of migration, destination-country corruption
Procedia PDF Downloads 317781 One Plus One is More than Two: Why Nurse Recruiters Need to Use Various Multivariate Techniques to Understand the Limitations of the Concept of Emotional Intelligence
Authors: Austyn Snowden
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Aim: To examine the construct validity of the Trait Emotional Intelligence Questionnaire Short form. Background: Emotional intelligence involves the identification and regulation of our own emotions and the emotions of others. It is therefore a potentially useful construct in the investigation of recruitment and retention in nursing and many questionnaires have been constructed to measure it. Design: Secondary analysis of existing dataset of responses to TEIQue-SF using concurrent application of Rasch analysis and confirmatory factor analysis. Method: First year undergraduate nursing and computing students completed Trait Emotional Intelligence Questionnaire-Short Form. Responses were analysed by synthesising results of Rasch analysis and confirmatory factor analysis.Keywords: emotional intelligence, rasch analysis, factor analysis, nurse recruiters
Procedia PDF Downloads 454780 Emotion-Convolutional Neural Network for Perceiving Stress from Audio Signals: A Brain Chemistry Approach
Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier
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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube
Procedia PDF Downloads 143779 High Speed Rail vs. Other Factors Affecting the Tourism Market in Italy
Authors: F. Pagliara, F. Mauriello
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The objective of this paper is to investigate the relationship between the increase of accessibility brought by high speed rail (HSR) systems and the tourism market in Italy. The impacts of HSR projects on tourism can be quantified in different ways. In this manuscript, an empirical analysis has been carried out with the aid of a dataset containing information both on tourism and transport for 99 Italian provinces during the 2006-2016 period. Panel data regression models have been considered, since they allow modelling a wide variety of correlation patterns. Results show that HSR has an impact on the choice of a given destination for Italian tourists while the presence of a second level hub mainly affects foreign tourists. Attraction variables are also significant for both categories and the variables concerning security, such as number of crimes registered in a given destination, have a negative impact on the choice of a destination.Keywords: tourists, overnights, high speed rail, attractions, security
Procedia PDF Downloads 151778 Software Defect Analysis- Eclipse Dataset
Authors: Amrane Meriem, Oukid Salyha
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The presence of defects or bugs in software can lead to costly setbacks, operational inefficiencies, and compromised user experiences. The integration of Machine Learning(ML) techniques has emerged to predict and preemptively address software defects. ML represents a proactive strategy aimed at identifying potential anomalies, errors, or vulnerabilities within code before they manifest as operational issues. By analyzing historical data, such as code changes, feature im- plementations, and defect occurrences. This en- ables development teams to anticipate and mitigate these issues, thus enhancing software quality, reducing maintenance costs, and ensuring smoother user interactions. In this work, we used a recommendation system to improve the performance of ML models in terms of predicting the code severity and effort estimation.Keywords: software engineering, machine learning, bugs detection, effort estimation
Procedia PDF Downloads 78777 Slice Bispectrogram Analysis-Based Classification of Environmental Sounds Using Convolutional Neural Network
Authors: Katsumi Hirata
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Certain systems can function well only if they recognize the sound environment as humans do. In this research, we focus on sound classification by adopting a convolutional neural network and aim to develop a method that automatically classifies various environmental sounds. Although the neural network is a powerful technique, the performance depends on the type of input data. Therefore, we propose an approach via a slice bispectrogram, which is a third-order spectrogram and is a slice version of the amplitude for the short-time bispectrum. This paper explains the slice bispectrogram and discusses the effectiveness of the derived method by evaluating the experimental results using the ESC‑50 sound dataset. As a result, the proposed scheme gives high accuracy and stability. Furthermore, some relationship between the accuracy and non-Gaussianity of sound signals was confirmed.Keywords: environmental sound, bispectrum, spectrogram, slice bispectrogram, convolutional neural network
Procedia PDF Downloads 120776 Wikipedia World: A Computerized Process for Cultural Heritage Data Dissemination
Authors: L. Rajaonarivo, M. N. Bessagnet, C. Sallaberry, A. Le Parc Lacayrelle, L. Leveque
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TCVPYR is a European FEDER (European Regional Development Fund) project which aims to promote tourism in the French Pyrenees region by leveraging its cultural heritage. It involves scientists from various domains (geographers, historians, anthropologists, computer scientists...). This paper presents a fully automated process to publish any dataset as Wikipedia articles as well as the corresponding linked information on Wikidata and Wikimedia Commons. We validate this process on a sample of geo-referenced cultural heritage data collected by TCVPYR researchers in different regions of the Pyrenees. The main result concerns the technological prerequisites, which are now in place. Moreover, we demonstrated that we can automatically publish cultural heritage data on Wikimedia.Keywords: cultural heritage dissemination, digital humanities, open data, Wikimedia automated publishing
Procedia PDF Downloads 122775 Analyzing Large Scale Recurrent Event Data with a Divide-And-Conquer Approach
Authors: Jerry Q. Cheng
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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing
Procedia PDF Downloads 158774 Texture-Based Image Forensics from Video Frame
Authors: Li Zhou, Yanmei Fang
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With current technology, images and videos can be obtained more easily than ever. It is so easy to manipulate these digital multimedia information when obtained, and that the content or source of the image and video could be easily tampered. In this paper, we propose to identify the image and video frame by the texture-based approach, e.g. Markov Transition Probability (MTP), which is in space domain, DCT domain and DWT domain, respectively. In the experiment, image and video frame database is constructed, and is used to train and test the classifier Support Vector Machine (SVM). Experiment results show that the texture-based approach has good performance. In order to verify the experiment result, and testify the universality and robustness of algorithm, we build a random testing dataset, the random testing result is in keeping with above experiment.Keywords: multimedia forensics, video frame, LBP, MTP, SVM
Procedia PDF Downloads 421773 Mask-Prompt-Rerank: An Unsupervised Method for Text Sentiment Transfer
Authors: Yufen Qin
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Text sentiment transfer is an important branch of text style transfer. The goal is to generate text with another sentiment attribute based on a text with a specific sentiment attribute while maintaining the content and semantic information unrelated to sentiment unchanged in the process. There are currently two main challenges in this field: no parallel corpus and text attribute entanglement. In response to the above problems, this paper proposed a novel solution: Mask-Prompt-Rerank. Use the method of masking the sentiment words and then using prompt regeneration to transfer the sentence sentiment. Experiments on two sentiment benchmark datasets and one formality transfer benchmark dataset show that this approach makes the performance of small pre-trained language models comparable to that of the most advanced large models, while consuming two orders of magnitude less computing and memory.Keywords: language model, natural language processing, prompt, text sentiment transfer
Procedia PDF Downloads 74772 An Automated R-Peak Detection Method Using Common Vector Approach
Authors: Ali Kirkbas
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R peaks in an electrocardiogram (ECG) are signs of cardiac activity in individuals that reveal valuable information about cardiac abnormalities, which can lead to mortalities in some cases. This paper examines the problem of detecting R-peaks in ECG signals, which is a two-class pattern classification problem in fact. To handle this problem with a reliable high accuracy, we propose to use the common vector approach which is a successful machine learning algorithm. The dataset used in the proposed method is obtained from MIT-BIH, which is publicly available. The results are compared with the other popular methods under the performance metrics. The obtained results show that the proposed method shows good performance than that of the other. methods compared in the meaning of diagnosis accuracy and simplicity which can be operated on wearable devices.Keywords: ECG, R-peak classification, common vector approach, machine learning
Procedia PDF Downloads 53771 SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area
Authors: Kamalpreet Kaur, Renu Dhir
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Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%.Keywords: climate, satellite images, prediction, classification
Procedia PDF Downloads 64770 Family Succession and Cost of Bank Loans: Evidence from China
Authors: Tzu-Ching Weng, Hsin-Yi Chi
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This study examines the effect of family succession on the cost of bank loans and non-price contractual terms. We use a unique dataset from China and find that lending banks are likely to charge high-interest rates and offer tight contractual terms, such as loan maturity and collateral requirement, for family succession firms. These findings indicate that information and default risks may arise after subsequent family successions. We also find that family succession firms can reduce the cost of bank loans by hiring top-tier auditors to enhance financial reporting credibility. This finding suggests that professional and high-quality auditors can provide extremely valuable services to family succession firms.Keywords: family succession, cost of bank loans, loan contract terms, top-tier auditor
Procedia PDF Downloads 77769 Constrained RGBD SLAM with a Prior Knowledge of the Environment
Authors: Kathia Melbouci, Sylvie Naudet Collette, Vincent Gay-Bellile, Omar Ait-Aider, Michel Dhome
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In this paper, we handle the problem of real time localization and mapping in indoor environment assisted by a partial prior 3D model, using an RGBD sensor. The proposed solution relies on a feature-based RGBD SLAM algorithm to localize the camera and update the 3D map of the scene. To improve the accuracy and the robustness of the localization, we propose to combine in a local bundle adjustment process, geometric information provided by a prior coarse 3D model of the scene (e.g. generated from the 2D floor plan of the building) along with RGBD data from a Kinect camera. The proposed approach is evaluated on a public benchmark dataset as well as on real scene acquired by a Kinect sensor.Keywords: SLAM, global localization, 3D sensor, bundle adjustment, 3D model
Procedia PDF Downloads 405768 Corpus Linguistic Methods in a Theoretical Study of Quran Verb Tense and Aspect in Translations from Arabic to English
Authors: Jawharah Alasmari
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In inflectional morphology of verb, tense and aspect indicate action’s time either past/present or future and their period whether completed or not. The usage and meaning of tense and aspect differ in Arabic and English, therefore is no simple one -to- one mapping from an Arabic verb inflected form an appropriate English translation depends on a range of features, including immediate and wider context of use. The Quranic Arabic Corpus includes seven alternative expertly crafted English translations of each Arabic verses, which provides a test dataset for the study of appropriate Arabic to English translations of verb tense and aspect. We applied Corpus Linguistics Methods in a theoretical study of exemplary verbs, to elicit candidate verbal contexts which influence the choice of English inflection for each verse.Keywords: Corpus linguistics methods, Arabic verb, tense and aspect, English translations
Procedia PDF Downloads 384767 Learning to Recommend with Negative Ratings Based on Factorization Machine
Authors: Caihong Sun, Xizi Zhang
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Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.Keywords: factorization machines, feature engineering, negative ratings, recommendation systems
Procedia PDF Downloads 239766 A Survey on Genetic Algorithm for Intrusion Detection System
Authors: Prikhil Agrawal, N. Priyanka
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With the increase of millions of users on Internet day by day, it is very essential to maintain highly reliable and secured data communication between various corporations. Although there are various traditional security imparting techniques such as antivirus software, password protection, data encryption, biometrics and firewall etc. But still network security has become the main issue in various leading companies. So IDSs have become an essential component in terms of security, as it can detect various network attacks and respond quickly to such occurrences. IDSs are used to detect unauthorized access to a computer system. This paper describes various intrusion detection techniques using GA approach. The intrusion detection problem has become a challenging task due to the conception of miscellaneous computer networks under various vulnerabilities. Thus the damage caused to various organizations by malicious intrusions can be mitigated and even be deterred by using this powerful tool.Keywords: genetic algorithm (GA), intrusion detection system (IDS), dataset, network security
Procedia PDF Downloads 291765 Use of Segmentation and Color Adjustment for Skin Tone Classification in Dermatological Images
Authors: Fernando Duarte
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The work aims to evaluate the use of classical image processing methodologies towards skin tone classification in dermatological images. The skin tone is an important attribute when considering several factor for skin cancer diagnosis. Currently, there is a lack of clear methodologies to classify the skin tone based only on the dermatological image. In this work, a recent released dataset with the label for skin tone was used as reference for the evaluation of classical methodologies for segmentation and adjustment of color space for classification of skin tone in dermatological images. It was noticed that even though the classical methodologies can work fine for segmentation and color adjustment, classifying the skin tone without proper control of the aquisition of the sample images ended being very unreliable.Keywords: segmentation, classification, color space, skin tone, Fitzpatrick
Procedia PDF Downloads 18764 Context-Aware Recommender System Using Collaborative Filtering, Content-Based Algorithm and Fuzzy Rules
Authors: Xochilt Ramirez-Garcia, Mario Garcia-Valdez
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Contextual recommendations are implemented in Recommender Systems to improve user satisfaction, recommender system makes accurate and suitable recommendations for a particular situation reaching personalized recommendations. The context provides information relevant to the Recommender System and is used as a filter for selection of relevant items for the user. This paper presents a Context-aware Recommender System, which uses techniques based on Collaborative Filtering and Content-Based, as well as fuzzy rules, to recommend items inside the context. The dataset used to test the system is Trip Advisor. The accuracy in the recommendations was evaluated with the Mean Absolute Error.Keywords: algorithms, collaborative filtering, intelligent systems, fuzzy logic, recommender systems
Procedia PDF Downloads 416763 Using Historical Data for Stock Prediction
Authors: Sofia Stoica
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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.Keywords: finance, machine learning, opening price, stock market
Procedia PDF Downloads 181762 Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus
Authors: J. K. Alhassan, B. Attah, S. Misra
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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.Keywords: artificial neural network, classification, decision tree algorithms, diabetes mellitus
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