Search results for: click prediction

Authors: Afroze Ibrahim Baqapuri, Ilya Trofimov

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Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). Click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture of solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First, we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides a significant improvement over existing models.

Keywords: neural networks, sponsored search, web advertisement, click prediction, click-through rate

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Authors: Jinshou Shi, Xiaozhou Zhou, Yingwei Zhou, Tuoyang Zhou, Ning Li, Chi Zhang, Zhanshuo Zhang, Ziang Chen

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In order to select the optimal interaction method for common computer click tasks, the click experiment test adopts the ISO 9241-9 task paradigm, using four common operations: mouse, trackball, touch, and eye control under visual feedback, auditory feedback, and no feedback. Through data analysis of various parameters of movement time, throughput, and accuracy, it is found that the movement time of touch-control is the shortest, the operation accuracy and throughput are higher than others, and the overall operation performance is the best. In addition, the motion time of the click operation with auditory feedback is significantly lower than the other two feedback methods in each operation mode experiment. In terms of the size of the click target, it is found that when the target is too small (less than 14px), the click performance of all aspects is reduced, so it is proposed that the design of the interface button should not be less than 28px. In this article, we discussed in detail the advantages and disadvantages of the operation and feedback methods, and the results of the discussion of the click operation can be applied to the design of the buttons in the interactive interface.

Keywords: cursor control performance, feedback, human computer interaction, throughput

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Authors: Payal Mazumdar, Sunita Rattan, Monalisa Mukherjee

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Polymer nanocomposites are a special class of materials having unique properties and wide application in diverse areas such as EMI shielding, sensors, photovoltaic cells, membrane separation properties, drug delivery etc. Recently the nanocomposites are being investigated for their use in biomedical fields as biosensors. Though nanocomposites with carbon nanoparticles have received worldwide attention in the past few years, comparatively less work has been done on nanographite although it has in-plane electrical, thermal and mechanical properties comparable to that of carbon nanotubes. The main challenge in the fabrication of these nanocomposites lies in the establishment of homogeneous dispersion of nanographite in polymer matrix. In the present work, attempts have been made to synthesize the nanocomposites of polystyrene and nanographite using click chemistry. The polymer and the nanographite are functionalized prior to the formation of nanocomposites. The polymer, polystyrene, was functionalized with alkyne moeity and nanographite with azide moiety. The fabricating of the nanocomposites was accomplished through click chemistry using Cu (I)-catalyzed Huisgen dipolar cycloaddition. The functionalization of filler and polymer was confirmed by NMR and FTIR. The nanocomposites formed by the click chemistry exhibit better electrical properties and the sensors are evaluated for their application as biosensors.

Keywords: nanocomposites, click chemistry, nanographite, biosensor

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Authors: Judith Enriquez-Gibson

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This paper meddles with the notion of phatic communion introduced 90 years ago by Malinowski, who was a Polish-born British anthropologist. It explores the phatic in Twitter within the contents of tweets related to moocs (massive online open courses) as a topic or trend. It is not about moocs though. It is about practices that could easily be hidden or neglected if we let big or massive topics take the lead or if we simply follow the computational or secret codes behind Twitter itself and third party software analytics. It draws from media and cultural studies. Though at first it appears data-driven as I submitted data collection and analytics into the hands of a third party software, Twitonomy, the aim is to follow how phatic communion might be practised in a social media site, such as Twitter. Lurking becomes its research method to analyse mooc-related tweets. A total of 3,000 tweets were collected on 11 October 2013 (UK timezone). The emphasis of lurking is to engage with Twitter as a system of connectivity. One interesting finding is that a click is in fact a phatic practice. A click breaks the silence. A click in one of the mooc website is actually a tweet. A tweet was posted on behalf of a user who simply chose to click without formulating the text and perhaps without knowing that it contains #mooc. Surely, this mechanism is not about reciprocity. To break the silence, users did not use words. They just clicked the ‘tweet button’ on a mooc website. A click performs and maintains connectivity – and Twitter as the medium in attendance in our everyday, available when needed to be of service. In conclusion, the phatic culture of breaking silence in Twitter does not have to submit to the power of code and analytics. It is a matter of human code.

Keywords: click, Twitter, phatic communion, social media data, mooc

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Authors: Aulia F. Hadining, Atya Nur Aisha, Dimas Kurninatoro Aji

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Utilization of social media as a marketing tool is growing rapidly, including for SMEs. Social media allows the user to give product evaluation and recommendations to the public. In addition, the social media facilitate word-of-mouth marketing communication. One of the social media that can be used is Facebook, with Facebook Ads. This study aimed to evaluate the location of Facebook Ads, to obtain an appropriate advertising design. There are three alternatives location consist of desktop, right-hand column and mobile. The effectiveness and efficiency of advertising will be measured based on advertising metrics such as reach, click, Cost per Click (CUC) and Unique Click-Through-Rate (UCTR). Facebook's Ads Manager was used for seven days, targeted by age (18-24), location (Bandung), language (Indonesia) and keywords. The result was 13,999 total reach, as well as 342 clicks. Based on the results of comparison using ANOVA, there was a significant difference for each placement location based on advertising metrics. Mobile location was chosen to be successful ads, because it produces the lowest CUC, amounting to Rp 691,- per click and 14% UCTR. Results of this study showed Facebook Ads was useful and cost-effective media to promote the product of SME, because it could be view by many people in the same time.

Keywords: marketing communication, social media, Facebook Ads, mobile location

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Authors: Chrislaura Carmo, Luca Deiana, Mafalda Laranjo, Abilio Sobral, Armando Cordova

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Photodynamic therapy (PDT) is currently used for the treatment of malignancies and premalignant tumors. It is based on the capture of a photosensitizing molecule (PS) which, when excited by light at a certain wavelength, reacts with oxygen and generates oxidizing species (radicals, singlet oxygen, triplet species) in target tissues, leading to cell death. BODIPY (4,4-difluoro-4-bora-3a,4a-diaza-s-indaceno) derivatives are emerging as important candidates for photosensitizer in photodynamic therapy of cancer cells due to their high triplet quantum yield. Today these dyes are relevant molecules in photovoltaic materials and fluorescent sensors. In this study, it will be demonstrated the possibility that BODIPY can be covalently linked to thioglycolic acid through the click reaction. Thiol−ene click chemistry has become a powerful synthesis method in materials science and surface modification. The design of biobased allyl-terminated precursors with high renewable carbon content for the construction of the thiol-ene polymer networks is essential for sustainable development and green chemistry. The work aims to synthesize the BODIPY (10-(4-(allyloxy) phenyl)-2,8-diethyl-5,5-difluoro-1,3,7,9-tetramethyl-5H-dipyrrolo[1,2-c:2',1'-f] [1,3,2] diazaborinin-4-ium-5-uide) and to click reaction with Thioglycolic acid. BODIPY was synthesized by the condensation reaction between aldehyde and pyrrole in dichloromethane, followed by in situ complexation with BF3·OEt2 in the presence of the base. Then it was functionalized with allyl bromide to achieve the double bond and thus be able to carry out the click reaction. The thiol−ene click was performed using DMPA (2,2-Dimethoxy-2-phenylacetophenone) as a photo-initiator in the presence of UV light (320–500 nm) in DMF at room temperature for 24 hours. Compounds were characterized by standard analytical techniques, including UV-Vis Spectroscopy, 1H, 13C, 19F NMR and mass spectroscopy. The results of this study will be important to link BODIPY to polymers through the thiol group offering a diversity of applications and functionalization. This new molecule can be tested as third-generation photosensitizers, in which the dye is targeted by antibodies or nanocarriers by cells, mainly in cancer cells, PDT and Photodynamic Antimicrobial Chemotherapy (PACT). According to our studies, it was possible to visualize a click reaction between allyl BODIPY and thioglycolic acid. Our team will also test the reaction with other thiol groups for comparison. Further, we will do the click reaction of BODIPY with a natural polymer linked with a thiol group. The results of the above compounds will be tested in PDT assays on various lung cancer cell lines.

Keywords: bodipy, click reaction, thioglycolic acid, allyl, thiol-ene click

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Authors: Martin L. Zurita García, Erick O. Martínez Luque, Jessica Morales Perdomo, Yivy Martínez Duarte, Jorge Ari Noriega

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Colombia’s click beetles (Family Elateridae) are poorly documented in their diversity and taxonomy. There are listed 43 genera for Colombia but without a specific number of their species. No species of the genus Agriotes Eschscholtz (Elaterinae: Agriotini) have been recorded in Colombia. Currently, the subtribe Agriotina in the New World includes three genera: Agriotes, Bohartina Arias and Dalopius Eschscholtz. Historically, the genera Agriotes and Dalopius have had significant taxonomic problems due to their morphological resemblance. These similarities motivated several specialists to generate taxonomic movements such as synonyms and segregations of species in these genera on several occasions. To trying to solve these taxonomic problems, an author proposes some morphological characters present in the larvae, but mainly some genital characters of the females, such as the sclerotized plates of the bursa copulatrix. After examining material newly collected in Colombia, we found a click beetle having a unique combination of diagnostic characters that clearly separate it from any other genera of Agriotina. Hence, we describe and illustrate this click beetle under the name Raphaellus deusminutosi Zurita-García, Morales-Perdomo & Martínez-Luque n. gen. & n. sp., and we also compare this genus with the other New World genera of the subtribes Agriotina.

Keywords: click beetles, new genus, new species, elaterinae, raphaellus deusminutosi

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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

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Authors: Edgar Aguilar-Ortíz, Nicolas Lévaray, Mireille Vonlanthen, Eric G. Morales-Espinoza, Ernesto Rivera, Xiao Xia Zhu

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Four new star compounds bearing a porphyrin core and cholic acid units, (TPPh(Zn) tetra-CA, TPPh(2H) tetra-CA, TPPh(Zn) octa-CA and TPPh(2H) octa-CA), have been synthesized using the Click Chemistry approach, which consist on azide-alkyne couplings. These novel functionalized porphyrins were characterized by 1H and 13C NMR spectroscopy and their structure was confirmed by MALDI-TOF. The optical properties of these compounds were studied by absorption and fluorescence spectroscopy. On the other hand, order to evaluate the amphiphilic properties of the cholic acid units combined with the optical response of the porphyrin core, we performed absorption and fluorescence studies in function of the polarity of the environment. It was found that as soon as we increase the polarity of the solvent, the Zn-metallated porphyrins, (TPPh(Zn) tetra-CA and TPPh(Zn) octa-CA), are able to form J aggregates, whereas the free-base porphyrins, TPPh(2H) tetra-CA and TPPh(2H) octa-CA, behaved differently.

Keywords: aggregates, amphiphilic, cholic acid, click-chemistry, porphyrin

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Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

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Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: flood, HEC-HMS, prediction, rainfall, runoff

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Authors: Yuhan Lin, Rohan Kekatpure, Cassidy Yeung

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In Digital Marketing, robust quantification of View-through attribution (VTA) is necessary for evaluating channel effectiveness. VTA occurs when a product purchase is aided by an Ad but without an explicit click (e.g. a TV ad). A lack of a tracking mechanism makes VTA estimation challenging. Most prevalent VTA estimation techniques rely on post-purchase in-product user surveys. User surveys enable the calculation of channel multipliers, which are the ratio of the view-attributed to the click-attributed purchases of each marketing channel. Channel multipliers thus provide a way to estimate the unknown VTA for a channel from its known click attribution. In this work, we use Convex Optimization to compute channel multipliers in a way that enables a mathematical encoding of the expected channel behavior. Large fluctuations in channel attributions often result from overfitting the calculations to user surveys. Casting channel attribution as a Convex Optimization problem allows an introduction of constraints that limit such fluctuations. The result of our study is a distribution of channel multipliers across the entire marketing funnel, with important implications for marketing spend optimization. Our technique can be broadly applied to estimate Ad effectiveness in a privacy-centric world that increasingly limits user tracking.

Keywords: digital marketing, survey analysis, operational research, convex optimization, channel attribution

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Authors: N. H. Adenan, M. S. M. Noorani

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River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources.

Keywords: river flow, nonlinear prediction method, phase space, local linear approximation

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Authors: Muhammet Baldan, Emel Timuçin

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Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, molecular descriptors, machine learning, random forest

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Authors: Bandar Alahmadi

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The fact that people tend to remember pictures better than texts, motivates researchers to develop graphical passwords as an alternative to textual passwords. Graphical passwords as such were introduced as a possible alternative to traditional text passwords, in which users prove their identity by clicking on pictures rather than typing alphanumerical text. In this paper, we present a scheme for graphical passwords that are resistant to shoulder surfing attacks and spyware attacks. The proposed scheme introduces a clicking technique to chosen images. First, the users choose a set of images, the images are then included in a grid where users can click in the cells around each image, the location of the click and the number of clicks are saved. As a result, the proposed scheme can be safe from shoulder surface and spyware attacks.

Keywords: security, password, authentication, attack, applications

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Authors: Kefaya Qaddoum

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The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Jeonghwan Jeon

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With the advent of IPTV in the fierce competition with existing broadcasting system, it is emerged as an important issue to predict how much the adoption of IPTV service will be. This paper aims to suggest a prediction model for adopting IPTV using classification and Ranking Belief Simplex (CaRBS). A simplex plot method of representing data allows a clear visual representation to the degree of interaction of the support from the variables to the prediction of the objects. CaRBS is applied to the survey data on the IPTV adoption.

Keywords: prediction, adoption, IPTV, CaRBS

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Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

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Authors: Lei Zhang, Tingting Xu, Jun He, Zhenyu Yan

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Email marketing is one of the most important segments of online marketing. It has been proved to be the most effective way to acquire and retain customers. The email content is vital to customers. Different customers may have different familiarity with a product, so a successful marketing strategy must personalize email content based on individual customers’ product affinity. In this study, we build our personalized email marketing strategy with three types of emails: nurture, promotion, and conversion. Each type of email has a different influence on customers. We investigate this difference by analyzing customers’ open rates, click rates and opt-out rates. Feature importance from response models is also analyzed. The goal of the marketing strategy is to improve the click rate on conversion-type emails. To build the personalized strategy, we formulate the problem as a reinforcement learning problem and adopt a Q-learning algorithm with variations. The simulation results show that our model-based strategy outperforms the current marketer’s strategy.

Keywords: email marketing, email content, reinforcement learning, machine learning, Q-learning

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Authors: Nor Zila Abd Hamid, Mohd Salmi M. Noorani

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This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: chaotic approach, phase space, Cao method, local linear approximation method

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

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Authors: Supriya H. S., Chandrakala B. M.

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Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach.

Keywords: MLHN, network traffic prediction

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Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

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Authors: Paul Awolade, Parvesh Singh

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The global threat of pathogenic resistance to available therapeutic agents has become a menace to clinical practice, public health and man’s existence inconsequential. This has therefore led to an exigency in the development of new molecular scaffolds with profound activity profiles. In this vein, a versatile synthetic tool for accessing new molecules by incorporating two or more pharmacophores into a single entity with the unique ability to be recognized by multiple receptors hence leading to an improved bioactivity, known as molecular hybridization, has been explored with tremendous success. Accordingly, aware of the similarity in pharmacological activity spectrum of quinoline and 1,2,3-triazole pharmacophores such as; anti-Alzheimer, anticancer, anti-HIV, antimalarial and antimicrobial to mention but a few, the present study sets out to synthesize hybrids of quinoline and 1,2,3-triazole. The hybrids were accessed via click chemistry using copper catalysed azide-alkyne 1,3-dipolar cycloaddition reaction. All synthesized compounds were evaluated for their pharmaco-potential in an antimicrobial assay out of which the 3-OH derivative emerged as the most active with MIC value of 4 μg/mL against Cryptococcus neoformans; a value superior to standard Fluconazole and comparable to Amphotericin B. Structures of synthesized hybrids were elucidated using appropriate spectroscopic techniques (1H, 13C and 2D NMR, FT-IR and HRMS).

Keywords: bioisostere, click chemistry, molecular hybridization, quinoline, 1, 2, 3-triazole

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Keywords: Epilepsy, seizure, phase correlation, fluctuation, deviation.

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Authors: Jeena R. S., Sukesh Kumar A.

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Stroke is one of the major reasons of adult disability and morbidity in many of the developing countries like India. Early diagnosis of stroke is essential for timely prevention and cure. Various conventional statistical methods and computational intelligent models have been developed for predicting the risk and outcome of stroke. This research work focuses on a multilevel approach for predicting the occurrence of stroke based on various risk factors and invasive techniques like retinal imaging. This risk prediction model can aid in clinical decision making and help patients to have an improved and reliable risk prediction.

Keywords: prediction, retinal imaging, risk factors, stroke

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Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

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Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: accelerometer, AdaBoost, GPS, mode prediction, support vector machine

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Authors: Carl van Walraven, Meltem Tuna

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Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

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Authors: Arvinder Kaur, Deepti Chopra

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Software Entropy Metrics for bug prediction have been validated on various software systems by different researchers. In our previous research, we have validated that Software Entropy Metrics calculated for Mozilla subsystem’s predict the future bugs reasonably well. In this study, the Software Entropy metrics are calculated for a subsystem of Android and it is noticed that these metrics are not suitable for bug prediction. The results are compared with a subsystem of Mozilla and a comparison is made between the two software systems to determine the reasons why Software Entropy metrics are not applicable for Android.

Keywords: android, bug prediction, mining software repositories, software entropy

Procedia PDF Downloads 546