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Cyberbullying and Hate Speech Detection on Kazakh-Language Social Networks 7

Cyberbullying and Hate Speech Detection on Kazakh-Language S...

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7th IEEE international conference on Big Data Security on Cloud, 7th IEEE international conference on High Performance and Smart Computing, and 6th IEEE international conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2021

作者： Sultan, Daniyar Mussiraliyeva, Shynar Toktarova, Aigerim Nurtas, Marat Iztayev, Zhalgasbek Zhaidakbaeva, Lyazzat Shaimerdenova, Lazzat Akhmetova, Oxana Omarov, Batyrkhan Al-Farabi Kazkah National University Faculty of Information Systems Almaty Kazakhstan A. Yassawi International Kazakh-Turkish University Turkistan Kazakhstan International Information Technology University Kazakh-British Technical University Almaty Kazakhstan M.Auezov South Kazakhstan University Shymkent Kazakhstan

ISBN: (纸本)9781665439275

Currently, there is a growing number of online bullying, attacks, and propaganda of prohibited materials over the Internet. the main means of exchanging information, selecting and promoting personnel for such structures is the Internet, namely web resources, social networks and e-mail. In this regard, the task of identifying, identifying communication topics, connections, as well as monitoring behavior and predicting threats emanating from individual users, groups and network communities that generate and distribute information of a bullying network on the Internet. the article is devoted to the research and data mining related to the topic of detecting bullying on the Internet. the study examines the development of the data set aimed to train the machine learning algorithms in the Kazakh language and marking them as con-sist of bullying phrasesor not. © 2021 IEEE.

关键词： Machine learning

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Integrating Machine Learning with Binary Integer Programming for Optimal PMU Placement in Dynamic Power Networks

Integrating Machine Learning with Binary Integer Programming...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： N V Uma Reddy R Anuradha Sorabh Lakhanpal Rawya Read P. Subhashini Kanan Divetia Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Hyderabad Telangana India Lovely Professional University Phagwara Punjab India Hilla University College Babylon Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

PMUs or synchronized PMUs are rapidly becoming essential in today’s power systems acting as monitors in the advancement of mainstream grid supervisory, protective, and controlling systems. this paper presents an improvement of the existing methodology for PMU placement by incorporating Machine Learning algorithm coupled with binary integer programming in order to feed real-time grid conditions and demand forecasts into the algorithm. Addressing four primary constraints: 1) observability in fully and partially observable cases; 2) conventional measurements across various cases; 3) network robustness against single PMU or a communication line; and finally, 4) flexibility with regards to increasing or decreasing failure rates, this augmented approach includes, but does not limit to, machine learning-based predicative analysis for a dynamic power grid management. Employed on IEEE 14-bus, a 21-bus $400-\mathrm{kV}$ real power system, as well as the IEEE 30-bus system, strategy helps in creating a cost-effective, secure and smart monitoring system.

关键词： Integer programming Machine learning algorithms Heuristic algorithms Power system dynamics Machine learning Phasor measurement units complexity theory Synchronization Observability Monitoring

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Optimisation of a Workpiece Clamping Position with Reinforcement Learning for Complex Milling Applications 7th

Optimisation of a Workpiece Clamping Position with Reinforce...

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7th international conference on Machine Learning, Optimization, and Data Science (LOD) / 1st Symposium on Artificial Intelligence and Neuroscience (ACAIN)

作者： Enslin, Chrismarie Samsonov, Vladimir Koepken, Hans-Georg Baer, Schirin Luetticke, Daniel Rhein Westfal TH Aachen Inst Informat Management Mech Engn Aachen Germany Siemens AG Digital Ind Erlangen Germany

ISBN: (纸本)9783030954703;9783030954697

Fine-tuning and optimisation of production processes in manufacturing are often conducted with the help of algorithms from the field of Operations Research (OR) or directly by human experts. Machine Learning (ML) methods demonstrate outstanding results in tackling optimisation tasks within the research field referred to as Neural Combinatorial Optimisation (NCO). this opens multiple opportunities in manufacturing for learning-based optimisation solutions. In this work, we show a successful application of Reinforcement Learning (RL) to the task of workpiece (WP) clamping position and orientation optimisation for milling processes. A carefully selected clamping position and orientation of a WP are essential for minimising machine tool wear and energy consumption. With the example of 3- and 5-axis milling, we demonstrate that a trained RL agent can successfully find a near-optimal orientation and positioning for new, previously unseen WPs. the achieved solution quality is comparable to alternative optimisation solutions relying on Simulated Annealing (SA) and Genetic algorithms (GA) while requiring orders of magnitude fewer optimisation iterations.

关键词： Reinforcement Learning Supervised learning Manufacturing Process optimisation Milling optimisation Tool path

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7th international Symposium on Combinatorial Optimization, ISCO 2022

7th International Symposium on Combinatorial Optimization, I...

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7th international Symposium on Combinatorial Optimization, ISCO 2022

ISBN: (纸本)9783031185298

the proceedings contain 24 papers. the special focus in this conference is on Combinatorial Optimization. the topics include: Mitigating Anomalies in Parallel Branch-and-Bound Based algorithms for Mixed-Integer Nonlinear Optimization;exact Price of Anarchy for Weighted Congestion Games with Two Players;nash Balanced Assignment Problem;on the thinness of Trees;generating Spanning-Tree Sequences of a Fan Graph in Lexicographic Order and Ranking/Unranking algorithms;high Multiplicity Strip Packing with three Rectangle Types;improved Bounds for Stochastic Extensible Bin Packing Under Distributional Assumptions;one Transfer per Patient Suffices: Structural Insights About Patient-to-Room Assignment;tool Switching Problems in the Context of Overlay Printing with Multiple Colours;branch-and-Cut for a 2-Commodity Flow Relocation Model with Time Constraints;optimal Vaccination Strategies for Multiple Dose Vaccinations;pervasive Domination;unified Greedy Approximability Beyond Submodular Maximization;neighborhood Persistency of the Linear Optimization Relaxation of Integer Linear Optimization;polynomial-Time Approximation Schemes for a Class of Integrated Network Design and Scheduling Problems with Parallel Identical Machines;the Constrained-Routing and Spectrum Assignment Problem: Valid Inequalities and Branch-and-Cut Algorithm;top-k List Aggregation: Mathematical Formulations and Polyhedral Comparisons;bounded Variation in Binary Sequences;on Minimally Non-firm Binary Matrices;few Induced Disjoint Paths for H-Free Graphs;on Permuting Some Coordinates of Polytopes;piecewise Linearization of Bivariate Nonlinear Functions: Minimizing the Number of Pieces Under a Bounded Approximation Error.

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Optimizing Concrete Strength Prediction: A Random Forest Approach with Hyperparameter Tuning through Random Search for Enhanced Construction Quality

Optimizing Concrete Strength Prediction: A Random Forest App...

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international conference on Informatics and Computational Sciences (ICICoS)

作者： Muhammad Rafif Puti Andam Suri Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350375886

ISBN: (纸本)9798350375893

the goal of this study is to help with the creation of concrete mixes and the building of structures, so that construction projects last longer and are more stable. As the building industry changes, it’s important to use advanced machine learning methods to get the best predictions for concrete strength and better total construction quality. During our tests, we checked how well the model worked using set parameters, Our main goal was to get a Mean Absolute Error (MAE) of less than 4 and a $R^th$ value of above 90%, which means that our forecasts were very accurate and reliable. We found that our model regularly did better than these benchmarks in a variety of testing conditions by doing a lot of testing. the mean absolute error (MAE) for our model was much less than 4 and the dependability ($\mathrm{R}^th$) was over 90%, showing that it was very good at making predictions and very reliable. Also, the RMSE values showed that the model was very good at guessing the strength of concrete with little difference from the actual values. the outcomes show that the Random Forest method works well at going above and beyond set performance goals.

关键词： Training Buildings Predictive models Solids Prediction algorithms Concrete complexity theory

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Comparison of Camera-Equipped Drones and Infrastructure Sensors for Creating Trajectory Datasets of Road Users 7

Comparison of Camera-Equipped Drones and Infrastructure Sens...

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7th international conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Kloeker, Amarin Krajewski, Robert Eckstein, Lutz Rhein Westfal TH Aachen Inst Automot Engn Steinbachstr 7 D-52074 Aachen Germany

ISBN: (纸本)9789897585135

Due to the complexity of automated vehicles, their development and validation require large amounts of naturalistic trajectory data of road users. In addition to the classical approach of using measurement vehicles to generate these data, approaches based on infrastructure sensors and drones have become increasingly popular. While advantages are postulated for each method, a practical comparison of the methods based on measurements of real traffic has so far been lacking. We present a theoretical and experimental analysis of two image-based measurement methods. For this purpose, we compare measurements of a drone-based system with a prototypical camera-based infrastructure sensor system. In addition to the detection statistics of the road users, the detection quality of both systems is also investigated using a reference vehicle equipped with an inertial navigation system. through these experiments, we can confirm each approach's advantages and disadvantages emerging from the theoretical analysis.

关键词： Automated Driving Trajectory Dataset Drone Infrastructure Sensors ITS-S Computer Vision

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A Low-complexity and High-Quality Reconstruction Algorithm for Terahertz Single-Pixel Imaging

A Low-Complexity and High-Quality Reconstruction Algorithm f...

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international conference on Signal Processing and Communication Systems, ICSPCS

作者： Chieh-Li Wang Yuan-Hao Huang Department of Electric Engineering Natioinal Tsing Hua University Hsinchu Taiwan R.O.C

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

Single-pixel imaging is a promising technique for Terahertz (thz) imaging, because it can largely reduce the numbers of expensive thz emitter and detector. Single-pixel imaging uses compressive sensing (CS) to measure the compressed thz signal and to recover the object image. this work presents a low-complexity and high-quality total variation augmented Lagrangian method (2D-TVALM) algorithm based on the well-known total variation augmented Lagrangian alternating-direction algorithm (TVAL3). TVAL3 is a convex optimization algorithm used to solve the problem of total variation (TV) regularization. By designing the sampling matrix and some mathematic approximation, this work largely reduces the computational complexity and speed up the convergence. the simulation results show that the reconstruction quality of the proposed algorithm is about 7 dB higher than TVAL3 in PSNR and the performance is much better than that of the other CS algorithms. In the computation time, the proposed algorithm is about 31 times faster than TVAL3. Finally, the average PSNR and elapsed time of the proposed algorithm are 38.43 dB and 0.0558 s respectively with 200 iterations in the 15 test patterns.

关键词： Terahertz wave imaging TV Simulation Signal processing algorithms Signal processing Approximation algorithms Sensors Computational complexity Image reconstruction Terahertz communications

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Clutter Suppression Based on ICUR-RPCA for Random Missing Data

Clutter Suppression Based on ICUR-RPCA for Random Missing Da...

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IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Yifan Guo Xixi Chen Yingmin Wang School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Early Warning and Detection Department Air Force Engineering University Xi’an China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

the performance of clutter suppression in synthetic aperture radar (SAR) systems is significantly compromised by random missing data. Typically, traditional methods frequently encounter a balance between accuracy and computational complexity. To address the problem, a clutter suppression method based on inverse CUR-robust principal component analysis (ICUR-RPCA) is proposed for the random missing data. Taking advantage of the low-rank property, key nodes entries are completed with reorganizing the whole echo data, first. Subsequently, RPCA is combined with the recovered key nodes and ICUR decomposition, so that the clutter suppression performance with the completed whole data can be achieved by only completing a portion of the data. Finally, the superiority of our ICUR-RPCA method is shown in numerical experiments.

关键词： Accuracy Apertures Information and communication technology Computational efficiency Clutter Computational complexity Synthetic aperture radar

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Implementation of YOLOv7 for Pest Detection 1

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5th international conference on Applied Machine Learning and Data Analytics, AMLDA 2022

作者： Nayar, Pratibha Chhibber, Shivank Dubey, Ashwani Kumar Department of Electronics and Communication Amity School of Engineering and Technology Amity University Uttar Pradesh UP Noida India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Pests have been known to destroy the yield of the crops, that would soak the nutritional value of the crops. Not only this, but some of the pests can also act as carriers to various diseases that are caused due to the transmutable nature of such bacteria. the most popular pest management technique is pesticide spraying because of how quickly it works and how easily it can be scaled up. Less pesticide use is necessary now, though, as environmental and health awareness grows. Also, existing pest visual segmentation methods are bounding, less effective and time-exhausting, which originates complexity in their marketing and use. Deep learning algorithms have come to be the major techniques to deal with the technological issues linked to pest detection. In this paper, we propose a method for pest detection using a prolific deep learning technique using the newest technology YOLOv7 model. It helps detect which type of pest it is, and if it is a pest that can cause damage, thus by allowing the person to get alert and take appropriate steps. the recommended YOLOv7 model attained the peak accuracy of 93.3% for 50 epochs. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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Scalable Model Selection for Staged Trees: Mean-posterior Clustering and Binary Trees 1

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6th international conference on Bayesian Statistics in Action, BAYSM 2022

作者： Strong, Peter Smith, Jim Q. Centre for Complexity Science University of Warwick CV4 7AL Coventry United Kingdom The Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Department of Statistics University of Warwick CoventryCV4 7AL United Kingdom

ISBN: (数字)9783031424137

ISBN: (纸本)9783031424120

Several structure-learning algorithms for staged trees, asymmetric extensions of Bayesian networks, have been proposed. However, these do not scale efficiently as the number of variables considered increases, a priori restricting the set of models, or they do not find comparable models to existing methods. Here, we define an alternative algorithm based on a totally-ordered hyperstage. We demonstrate how it can be used to obtain a quadratically-scaling structural learning algorithm for staged trees that restricts the model space a posteriori. through comparative analysis, we show that through the ordering provided by the mean posterior distributions, we can outperform existing methods in computational time whilst providing comparable model scores. this method also enables us to learn more complex relationships than existing model selection techniques by expanding the model space. We illustrate how this can embellish inferences in a real study. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Binary trees

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