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检索条件"主题词=software algorithms"

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Improving Gene Expression Prediction of Cancer Data Using Nature Inspired Optimization algorithms

Improving Gene Expression Prediction of Cancer Data Using Na...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Payal Patel Kalpdrum Passi Chakresh Kumar Jain Department of Mathematics and Computer Science Laurentian University Sudbury Ontario Canada Department of Biotechnology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781665458429

Cancer being one of the most vital diseases in the medical history needs adequate focus on its causes, symptoms and detection. Various algorithms and software have been designed so far to predict the cancer at cellular level. The most crucial aspect for sorting the cancerous tissues is the classification of such tissues based on the gene expression data. Gene expression data consists of high amount of genetic data as compared to the number of data samples. Thus, sample size and dimensions are a major challenge for researchers. In this work, four different types of cancer microarray datasets are analyzed viz., breast cancer, lung cancer, leukemia and colon cancer. The analysis of the cancer microarray datasets was done using various nature-inspired algorithms like Grasshopper Optimization (GOA), Particle Swarm Optimization (PSO), and Interval Value-based Particle Swarm Optimization (IVPSO). To study the accuracy of the prediction, five different classifiers were used: Random Forest, K-Nearest Neighborhood (KNN), Neural Network, Naïve Bayes and Support Vector Machine (SVM). The Grasshopper Optimization (GOA) outperforms in accuracy compared to the other two optimization algorithms with SVM classifier on leukemia, lung and breast cancer datasets selecting the best genes/attributes to correctly classify the dataset.

关键词： Support vector machines software algorithms Prediction algorithms Breast cancer Classification algorithms Gene expression Particle swarm optimization

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Implementation of Fuzzy Logic on Microcontroller for Quails Coop Temperature Control

Implementation of Fuzzy Logic on Microcontroller for Quails ...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Nisa Rizqiya Fadhliana Sayekti Harits Suryawan Ariyadi Program Studi Informatika Institut Teknologi Kalimantan Balikpapan Indonesia Program Studi Teknik Informatika Fakultas Sains dan Teknologi Universitas Muhammadiyah Kalimantan Timur Samarinda Indonesia

ISBN: (纸本)9781665401524

Quail (cortunix cortunix japonica) is a type of poultry that is quite popular for its eggs and meat as an alternative protein source. However, for tropical climates such as Indonesia, there are challenges faced by quail farmers, namely heat stress. One way that can be a solution to deal with these challenges is to regulate the room temperature in the quail cage. This study aims to design an automatic temperature control system for quail cages using a microcontroller embedded with a fuzzy algorithm to determine the action to be taken to adjust the temperature and humidity of the air in the quail cage. The prototype developed in this research is tested by comparing the output simulation of the microcontroller with the simulation performed using Matlab software.

关键词： Heating systems Temperature dependence Microcontrollers Atmospheric modeling Simulation software algorithms Humidity

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Reinforcement Learning Based Penetration Testing of a Microgrid Control Algorithm

Reinforcement Learning Based Penetration Testing of a Microg...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Christopher Neal Hanane Dagdougui Andrea Lodi José M. Fernandez Polytechnique Montreal Montreal Canada

ISBN: (数字)9781665414906

ISBN: (纸本)9781665430586

Microgrids (MGs) are small-scale power systems which interconnect distributed energy resources and loads within clearly defined regions. However, the digital infrastructure used in an MG to relay sensory information and perform control commands can potentially be compromised due to a cyberattack from a capable adversary. An MG operator is interested in knowing the inherent vulnerabilities in their system and should regularly perform Penetration Testing (PT) activities to prepare for such an event. PT generally involves looking for defensive coverage blind spots in software and hardware infrastructure, however the logic in control algorithms which act upon sensory information should also be considered in PT activities. This paper demonstrates a case study of PT for an MG control algorithm by using Reinforcement Learning (RL) to uncover malicious input which compromises the effectiveness of the controller. Through trial-and-error episodic interactions with a simulated MG, we train an RL agent to find malicious input which reduces the effectiveness of the MG controller.

关键词： Conferences software algorithms Reinforcement learning Microgrids Batteries State of charge Penetration testing

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Evaluating SZZ Implementations Through a Developer-Informed Oracle

Evaluating SZZ Implementations Through a Developer-Informed ...

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International Conference on software Engineering (ICSE)

作者： Giovanni Rosa Luca Pascarella Simone Scalabrino Rosalia Tufano Gabriele Bavota Michele Lanza Rocco Oliveto University of Molise Italy Software Institute @ USI Universita della Svizzera italiana Switzerland

ISBN: (纸本)9781665402965;9781665448314

The SZZ algorithm for identifying bug-inducing changes has been widely used to evaluate defect prediction techniques and to empirically investigate when, how, and by whom bugs are introduced. Over the years, researchers have proposed several heuristics to improve the SZZ accuracy, providing various implementations of SZZ. However, fairly evaluating those implementations on a reliable oracle is an open problem: SZZ evaluations usually rely on (i) the manual analysis of the SZZ output to classify the identified bug-inducing commits as true or false positives; or (ii) a golden set linking bug-fixing and bug-inducing commits. In both cases, these manual evaluations are performed by researchers with limited knowledge of the studied subject systems. Ideally, there should be a golden set created by the original developers of the studied systems. We propose a methodology to build a "developer-informed" oracle for the evaluation of SZZ variants. We use Natural Language Processing (NLP) to identify bug-fixing commits in which developers explicitly reference the commit(s) that introduced a fixed bug. This was followed by a manual filtering step aimed at ensuring the quality and accuracy of the oracle. Once built, we used the oracle to evaluate several variants of the SZZ algorithm in terms of their accuracy. Our evaluation helped us to distill a set of lessons learned to further improve the SZZ algorithm.

关键词： software algorithms Computer bugs Manuals Prediction algorithms Natural language processing software software engineering

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Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters

Simulation Failure Robust Bayesian Optimization for Estimati...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ankush Chakrabarty Scott A. Bortoff Christopher R. Laughman Mitsubishi Electric Research Laboratories Cambridge MA USA

ISBN: (纸本)9781665442084

Advances in modeling and computation have resulted in high-fidelity digital models capable of simulating the dynamics of a wide range of industrial systems. These models often require calibration, or the estimation of an optimal set of parameters, to reflect a system’s observed behavior. While searching over the parameter space is an inevitable part of the calibration process, models are seldom designed to be valid for arbitrarily large parameter spaces. Application of existing black-box calibration methods, therefore, often require repeatedly evaluating a model over a wide range of parameters. For some parameter combinations, the simulations could be unreasonably slow or fail altogether. In general, the shape of subregions in the parameter space that could result in simulation failure is unknown and near-impossible to ascertain analytically. In this paper, we propose a novel failure robust Bayesian optimization (FR-BO) algorithm that learns these failure regions from simulation data and informs a Bayesian optimization algorithm to avoid failure regions while searching for optimal parameters. This results in acceleration of the optimizer’s convergence and prevents wastage of time trying to simulate parameters with high failure probabilities.

关键词： Analytical models Shape Computational modeling software algorithms Estimation software Calibration

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Development of Real-Time Eye Tracking Algorithm

Development of Real-Time Eye Tracking Algorithm

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Computing & Information Sciences (ICCIS), International Conference on

作者： Siti Nuradlin Syahirah Sheikh Anwar Azrina Abd Aziz Syed Hasan Adil PETRONAS Universiti Teknologi Bandar Seri Iskandar Perak Malaysia Faculty of Engineering Sciences and Technology Iqra University Karachi Pakistan

ISBN: (纸本)9781665494427

This project develops a real-time eye tracking algorithm to improve the accuracy of iris detection and gaze classification. Eye tracking has been utilized in many applications to detect driver awareness, predict human behaviour, and assist paralyzed individuals. Detecting the centre of iris is a crucial step in eye tracking development and Conventional Circular Hough Transform (CCHT) is widely used for the iris detection. However, the accuracy of the CCHT method decreases when the head of an object is not orthogonally positioned to the camera under ambient light changes. To overcome this problem, the facial landmark detector is implemented to detect the eyes as the Region of Interest (ROI), track the eyes and identify the gaze by classifying the eye positions to left, right and middle. The eye tracking algorithm has an added feature to detect eye blinking for drowsiness detection. To gain better accuracy, the classifier uses a scan method to classify the eye position based on the levels of pixel intensity. The eye tracking algorithm is implemented in OpenCV using Python software for ease of portability. The results show that the average accuracy of 100% and 90% are achieved in the iris detection and gaze position classification, respectively.

关键词： Iris Tracking software algorithms Gaze tracking Detectors Transforms Cameras

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A Ratiocinative Concept of Algorithmic Trading using MACD Indicator

A Ratiocinative Concept of Algorithmic Trading using MACD In...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Parthkumar Kanani Jenish Karasariya Narendra Zadafiya Amit Nayak Chandubhai S. Patel Institute of Technology Charotar University of Science and Technology Anand India Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Anand India

ISBN: (纸本)9781665435253

Algorithmic trading, otherwise known as black-box trading or plain machine-driven trading, can be referred as computing machine for making different trading-orders, using specific algorithmic program along with considering various aspects of the orders such as the time, buying or selling price and number of quantities to trade, or solely commencing the trade excluding human intercession. A rule-based trading approach is represented by this research by incorporating the concepts of MACD indicator and Exponential Moving Average (EMA). The study contains several things, starting from the need of algo-trading, the solution it offers and the multiple indicators using which the pure machine-based trading is possible and finally, the implementation of MACD indicator and other nomenclature of it performing which, one can enhance their trading skills.

关键词： software algorithms Companies Aerospace electronics Market research software Encoding Consumer electronics

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On the Optimality of Backward Regression: Sparse Recovery and Subset Selection

On the Optimality of Backward Regression: Sparse Recovery an...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sebastian Ament Carla Gomes Cornell University Ithaca NY

Sparse recovery and subset selection are fundamental problems in varied communities, including signal processing, statistics and machine learning. Herein, we focus on an important greedy algorithm for these problems: Backward Stepwise Regression. We present novel guarantees for the algorithm, propose an efficient, numerically stable implementation, and put forth Stepwise Regression with Replacement (SRR), a new family of two-stage algorithms that employs both forward and backward steps for compressed sensing problems. Prior work on the backward algorithm has proven its optimality for the subset selection problem, provided the residual associated with the optimal solution is small enough. However, the existing bounds on the residual magnitude are NP-hard to compute. In contrast, our main theoretical result includes a bound that can be computed in polynomial time, depends chiefly on the smallest singular value of the matrix, and also extends to the method of magnitude pruning. In addition, we report numerical experiments highlighting crucial differences between forward and backward greedy algorithms and compare SRR against popular two-stage algorithms for compressed sensing. Remarkably, SRR algorithms generally maintain good sparse recovery performance on coherent dictionaries. Further, a particular SRR algorithm has an edge over Subspace Pursuit.

关键词： Greedy algorithms Machine learning algorithms Dictionaries software algorithms Signal processing algorithms Signal processing Sparse matrices

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A Learning-to-Rank Based Approach for Improving Regression Test Case Prioritization

A Learning-to-Rank Based Approach for Improving Regression T...

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Asia-Pacific Conference on software Engineering

作者： Chu-Ti Lin Sheng-Hsiang Yuan Jutarporn Intasara National Chiayi University Chiayi Taiwan Faculty of Science and Technology Suratthani Rajabhat University Surat Thani Thailand

ISBN: (纸本)9781665437851

Many prior studies with attempt to improve regression testing adopt test case prioritization (TCP). TCP generally arranges the execution of regression test cases according to specific rules with the goal of revealing faults as early as possible. It is noted that different TCP algorithms adopt different metrics to evaluate test cases' priority so that they may be effect at revealing faults early in different faulty programs. Adopting a single metric may not generally work well. In this decade, learning-to-rank (LTR) strategies have been adopted to address some software engineering problems. This study also uses a pairwise LTR strategy XGBoost to combine several existing metrics so as to improve TCP effectiveness. More specifically, we regard the metrics adopted by TCP techniques to evaluate test cases' priority as the features of the training data and adopt XGBoost to learn the weights of the combined metrics. Additionally, in order to avoid overfitting, we use a fuzzy inference system to generate additional features for data augmentation. The experimental results show that our approach achieves more excellent effectiveness than the existing TCP techniques with respect to the selected subject programs.

关键词： Measurement Fuzzy logic software algorithms Training data software engineering Testing

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software Construction Oriented Multi-agent Collaborative Modeling and Simulation

Software Construction Oriented Multi-agent Collaborative Mod...

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IEEE World Congress on Services (SERVICES)

作者： Yan Zhang Rong Xie Yangfan He Bing Li School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9781728182032

ISBN: (纸本)9781728182049

Improper collaboration frequency causes low efficiency in software development. To improve collaboration efficiency in software construction, we propose a method of multi-agent collaborative modeling and simulation. First, we propose collective action model and individual action model to describe the whole construction environment. Then, we design agents in our simulation, establish a mapping relationship between artificial bee colonies and developers group, and propose buzz factor as a parameter to describe the collaboration frequency. In particular, we introduce Q-learning algorithm to obtain the dynamic buzz factor that maximizes construction efficiency. Finally, we perform experiments on groups of agents based on our model to observe the effect of buzz factor. Comparing to the classic feature-oriented stigmergy-based model, our experiment results show a better construction efficiency and provide a reference for controlling the collaboration. Through our work, we can find a suitable collaboration frequency through the simulation, to guide the practice of software development.

关键词： Heuristic algorithms Simulation software algorithms Collaboration Switches software Data models

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