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Protein Topology prediction algorithms Systematically Investigated in the Yeast Saccharomyces cerevisiae

BIOESSAYS 2019年第8期41卷 1800252-1800252页

作者： Weill, Uri Cohen, Nir Fadel, Amir Ben-Dor, Shifra Schuldiner, Maya Weizmann Inst Sci Dept Mol Genet IL-7610001 Rehovot Israel Weizmann Inst Sci Dept Life Sci Core Facil IL-7610001 Rehovot Israel

Membrane proteins perform a variety of functions, all crucially dependent on their orientation in the membrane. However, neither the exact number of transmembrane domains (TMDs) nor the topology of most proteins have been experimentally determined. Due to this, most scientists rely primarily on prediction algorithms to determine topology and TMD assignments. Since these can give contradictory results, single-algorithm-based predictions are unreliable. To map the extent of potential misanalysis, the predictions of nine algorithms on the yeast proteome are compared and it is found that they have little agreement when predicting TMD number and termini orientation. To view all predictions in parallel, a webpage called TopologYeast: was created. Each algorithm is compared with experimental data and a poor agreement is found. The analysis suggests that more systematic data on protein topology are required to increase the training sets for prediction algorithms and to have accurate knowledge of membrane protein topology.

关键词： membrane proteins prediction algorithms termini orientation transmembrane domains yeast

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Enhancing Energy Efficiency and Security in Wireless Sensor Networks: A Survey and Proposed Methodology Integrating prediction algorithms and Deep Neural Networks

Enhancing Energy Efficiency and Security in Wireless Sensor ...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Shaik Shafiuddin Konda Hari Krishna Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

This research study provides a complete assessment of energy- and trust-aware techniques in IoT-WSNs, emphasizing the significant problems and limits of current methodologies. Traditional methodologies frequently face challenges such as excessive processing overhead, difficulty reaching real-time optimal solutions, and increased delay, especially in large-scale or resource-constrained contexts. To solve these issues, the paper suggests a new approach that combines powerful prediction algorithms, deep neural networks, and strong security frameworks. The process entails initializing exact inputs and simulation settings, modeling advanced prediction algorithms, developing deep neural networks, and constructing resilient sensor nodes, all while implementing extensive security measures. The suggested approach seeks to considerably improve energy efficiency, network lifetime, and security by focusing on mobile sink movement prediction and network operation optimization. This study not only covers existing methodologies, but also shows the promise of the proposed methodology through extensive analysis and preliminary results, demonstrating its ability to overcome the limits of present WSN deployments.

关键词： Surveys Wireless sensor networks Artificial neural networks Predictive models prediction algorithms Real-time systems Energy efficiency Stability analysis Security Optimization

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Improving Traditional Stock Market prediction algorithms using Covid-19 Analysis

Improving Traditional Stock Market Prediction Algorithms usi...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Rajni Jindal Nikhil Bansal Nitin Chawla Sanskriti Singhal Delhi Technological University New Delhi India

ISBN: (数字)9781728185194

ISBN: (纸本)9781728185200

The stock market is an organized body where public companies offer their stocks through initial public offerings and traders buy/sell these stocks so as to obtain profits. It is dynamic and volatile in nature which makes the task of stock market trend prediction a complex problem. In recent times, the COVID-19 pandemic has made this task even harder. With the rising number of COVID-19 cases across the globe, the market has never been more volatile. This has resulted in the poor performance of various traditional trend prediction algorithms because these algorithms do not account for the impact of the pandemic on the stock market trends. The proposed work aims to enhance the stock market prediction ability of various common prediction models by taking into account the factors related to COVID-19. The forecasting techniques analysed are Decision Tree Regressor, Random Forest Regressor and Support Vector Regressor (SVR). Currently the most affected countries by COVID-19 are the United States of America, India and Russia. Therefore we have analysed the prediction performance of various approaches discussed in this paper on S&P 500 Index, Nifty50 Index and RTS Index using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results obtained showcase that all the techniques used perform better when the COVID-19 features were included.

关键词： COVID-19 Vegetation Market research prediction algorithms Indexes Stock markets Regression tree analysis

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A comparison of pitch prediction algorithms in forward and backward adaptive CELP systems

A comparison of pitch prediction algorithms in forward and b...

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International Conference on Communication Systems

作者： H. Chen W.C. Wong C.C. Ko Department of Electrical Engineering National University of Singapore Singapore

Compares several pitch prediction algorithms in forward and backward adaptive CELP system. Since a forward adaptation system uses the original speech signal to compute the filter parameters, while a backward adaptation system only uses the locally decoded speech which contains encoding noise, there are important differences between the two systems in the implementation of a pitch prediction algorithm. The authors investigated such pitch prediction strategies in these two systems in terms of the pitch prediction configuration, the pitch determination algorithms, and the pitch predictor adaptation technique.< >

关键词： prediction algorithms Adaptive systems Encoding Acoustic noise Delay Decoding Standardization Power harmonic filters Speech Statistics

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Experimental Investigation on the Analysis of Three Data Imputation Methods for Cervical Cancer Risk prediction algorithms

Experimental Investigation on the Analysis of Three Data Imp...

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IST-Africa

作者： Budu Emmanuella A.W. V. Lakshmi Narasimhan Zablon A. Mbero University of Botswana Gaborone Botswana

Missing data values in medical datasets are a recurring problem, usually attributed to input errors, lack of proper documentation and patient’s privacy concerns. This hinders the work of researchers in conducting predictive analysis on these datasets. Cervical cancer risk prediction algorithms employ machine learning, and deep learning algorithms to identify patients at the risk of cervical cancer. Currently researched algorithms for predicting the risk of cervical cancer incorporate various data imputation methods to address missing values, however the effectiveness of these techniques in improving the performance of these algorithms is often not investigated in detail. This paper details an experimental investigation on the performance of three commonly utilised data imputation techniques employed in two cervical cancer risk prediction algorithms based on a Multilayer Perceptron Network (MLPN) and a Random Forest classifier (RF). Findings reveal that for the algorithm based on an RF classifier, mean substitution will yield a high accuracy but does not improve the classification accuracy. For the algorithm based on an MLPN, either mean, mode or median substitution will improve the classification accuracy. The use of effective data imputation methods can improve the classification accuracy of cervical cancer risk prediction algorithms, and in turn improve the diagnosis of the disease.

关键词： Radio frequency Machine learning algorithms Multilayer perceptrons prediction algorithms Data models Classification algorithms Predictive analytics

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An extensible simulation framework for evaluating centralized traffic prediction algorithms

An extensible simulation framework for evaluating centralize...

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International Conference on Connected Vehicles and Expo (ICCVE)

作者： Michael Wisely Ali Hurson Sahra Sedigh Sarvestani Department of Computer Science Missouri S&T Rolla Missouri Department of Electrical and Computer Engineering Missouri S&T Rolla Missouri

Due to the increasing popularity of smartphones and in-dash navigation equipment, traffic data gathered by mobile devices has grown in popularity as a basis for predicting traffic conditions. Mobile devices submit location information to a central server, where future traffic conditions are computed. Because this type of system would be expensive to test with real drivers, algorithms for road traffic prediction are often evaluated according to the outcomes of simulations. These simulations frequently use the same types of vehicle data to model the flow of traffic. Based on the observation that these algorithm simulators share common functionality, we have developed a framework to facilitate the rapid development of simulators for traffic prediction. Using our framework, developers can quickly implement traffic prediction simulators with included data processing and visualization features. To date, we have utilized the framework in creating two separate simulators for influence-aware predictive density queries and naive density queries, respectively.

关键词： Vehicles prediction algorithms Roads Servers Generators Data models Mobile handsets

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Convergence of adaptive control and prediction algorithms with and without truncation on their state estimates and controls

Convergence of adaptive control and prediction algorithms wi...

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IEEE Conference on Decision and Control

作者： Rajendra Kumar Department of Electrical Engineering California State University Fullerton CA USA

In this paper, we prove the almost sure convergence of the proposed adaptive control and prediction algorithms. The algorithms involve a weighted least squares parameter estimator and the truncation on the state estimates and controls. For the algorithm with no truncation, we prove that the tracking error in controlling a plant with N-units of delay, converges to its optimum value in a certain average sense under the usual minimum phase and passivity assumptions of the literature. With an additional regularity assumption expressed only in terms of the desired trajectory and the "white" noise term, it is also proved that the tracking error also converges in a strong sense at an asymptotically arithmetic rate. Under this regularity condition it is also demonstrated that after almost a finite number of iterations the algorithm affords equal weightings to all the measurements. Under an additional persistency condition it is also shown that the parameter estimation error converges to zero at a rate specified by the degree of excitation. When the above algorithm involves truncation on the state estimates and controls, it is also proved that the required passivity condition is considerably weakened.

关键词： Convergence Adaptive control prediction algorithms State estimation Parameter estimation Least squares approximation Error correction Delay Trajectory White noise

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An investigation into channel prediction algorithms for a switched-beam smart antenna system

An investigation into channel prediction algorithms for a sw...

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International Conference on Microwaves, Radar and Wireless Communications

作者： J. Janapsatya M.E. Bialkowski School of Information Technology & Electrical Engineering University of Queensland Brisbane QLD Australia

In a switched-beam smart antenna system, predicting the message signal during the interval of signal fading or beam changing is required. In this paper, a channel prediction algorithm that is capable of estimating a future block of fading coefficients is studied. Jakes channel model is used to represent the wireless communication channel. It is shown that the algorithm is able to predict the fading coefficients quite well. The effects of observation interval length, number of scatterer signals in the channel model and the Doppler frequency on the estimation algorithm are investigated.

关键词： prediction algorithms Lakes Predictive models Fading Rayleigh scattering Frequency estimation Receiving antennas Communication switching Wireless communication Mobile antennas

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Review of Research on prediction algorithms of Bus Headway

Review of Research on Prediction Algorithms of Bus Headway

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Lian Lian Jiashuo Shang Xuejun Zong College of Information Engineering Shenyang University of Chemical Technology Shenyang China Graduate school Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

Public transportation plays an important role in the city's intelligent transportation system. Accurate bus headway forecast is crucial for passengers to arrange their travel time and for bus operators to provide high quality service. This article first studies the three main factors affecting bus headway prediction. Then we focus on the analysis of several bus headway prediction models. Finally, we summarize the current difference and development trends of the research on bus headway.

关键词： Computational modeling Urban areas Predictive models prediction algorithms Market research Public transportation Cybernetics

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A Comparative Analysis of Machine Learning prediction algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Happy Rita Chhikara Neeti Kashyap Dept. of Computer Science & Engineering The NorthCap University Gurugram Haryana India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms’ capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape.

关键词： Support vector machines Radio frequency Machine learning algorithms Accuracy Botnet Oral communication prediction algorithms

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