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machine learning Based Methods for Arabic Duplicate Question Detection 5

Machine Learning Based Methods for Arabic Duplicate Question...

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5th international conference on Intelligent Systems and Computer Vision, ISCV 2022

作者： Zouitni, Mohamed Hamza, Alami Lafkiar, Said Burmani, Nabil Taleb, Mohammed En-Nahnahi, Noureddine Sidi Mohamed Ben Abdellah University LISAC Laboratory Faculty of Sciences Dhar El Mahraz Fez Morocco Innov-Tech Laboratory Departement of Engineering High Technology School Rabat Morocco

ISBN: (纸本)9781665495585

Incorporating a duplicate question detection system can be beneficial for various systems such as community forums or question answering systems. Detecting question that have already an answer improves the user experience by reducing the search time and returning the correct answer. In this paper, we construct several methods for Arabic duplicate question detection based on machine learning. First, the pre-processing step is applied to clean and normalize questions. Next, we use Term Frequency Inverse Document Frequency (TF-IDF), Word2Vec, and FastText methods to map questions from their textual format into a vector space. then, we trained various shallow learning methods (SVM, XGBoost, Random Forest, Logistic Regression) and deep learning methods (CNN, RNN, LSTM, GRU) with the objective of detecting if a pair of questions is duplicate or not. Various experiments were conducted to evaluate the performances of our models. the results obtained show that the deep learning model based on GRU with FastText representation performed better compared to the other models. © 2022 IEEE.

关键词： Vector spaces

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Aspect-Based Sentiment Analysis Using SemEval and Amazon Datasets 5

Aspect-Based Sentiment Analysis Using SemEval and Amazon Dat...

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5th IEEE international conference of Women in Data Science (WiDS-PSU)

作者： Rahin, Saima A. Hasib, Tamanna Hassan, Mohibul BRAC Univ Dept CSE Dhaka Bangladesh

ISBN: (纸本)9781665408127

Sentiment analysis has become one of the most important tools in natural language processing, since it opens many possibilities to understand people's opinions on different topics. Aspect-based sentiment analysis aims to take this a step further and find out what exactly someone is talking about, and if he likes or dislikes it. Real world examples of perfect areas for this topic are the millions of available customer reviews in online shops. there have been multiple approaches to tackle this problem, using machine learning, deep learning and neural networks. However, currently the number of labeled reviews for training classifiers is very small. therefore, we undertook multiple steps to research ways of improving ABSA performance on small datasets, by comparing recurrent and feed-forward neural networks and incorporating additional input data that was generated using different readily available NLP tools.

关键词： Opinion Milling Natural Language processing Recurrent Neural Network Feed- Forward Neural Network Part-of-Speech Tagging Dependency Parsing Word Vectors

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Design of Optimized low-power GPS-Yagi Antenna using machine learning techniques 5

Design of Optimized low-power GPS-Yagi Antenna using Machine...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Jessintha, D. Sobirovich, Kenjaev Sanjar Ananth, Christo Kumar, T. Ananth Electronics and Communication Engineering Easwari Engineering College Chennai India Samarkand State University Faculty of International Education Programs Uzbekistan Samarkand State University Department of Natural and Exact Sciences Uzbekistan Computer Science and Engineering IFET College of Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350398267

the time-consuming process of developing analytical models can be accelerated with the help of machine learning, which is a technique for data processing. Using machine learning, antenna designers can quickly and intelligently optimize their physical antenna designs. this is achieved by developing trained models of the designers' designs. Consequently, antenna designers can create more efficient antennas. Due to this, antenna designers can continue developing new designs despite the increasing complexity of antennas. When discussing this specific type of antenna, 'Yagi' is frequently used as an abbreviation for 'Yagi-Uda' the full name of the antenna. the length of the 'driven dipole' may be equal to or shorter than the length of the 'directors.' the ability to rapidly execute a diverse set of optimization algorithms and objectives, made possible by the trained models, makes it easier to conduct rapid comparisons and a diverse set of studies (including stochastic analysis for tolerance studies, etc.). While the device was operating at a frequency of ten gigahertz, the concept of two parasitic directors was conceived, and these parasitic directors were designed and optimized to improve the device's directivity further. this antenna offers numerous advantages, one of which is its simplicity of manufacture. this antenna can be manufactured relatively easily due to its diminutive size and uncomplicated overall layout. the stochastic global search and optimization method known as simulated annealing (SA) is extraordinarily effective. this method is implemented instead of more conventional ones to achieve optimal element spacing. © 2022 IEEE.

关键词： machine learning

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UWB-IMU Pose Estimation for Roadheader Based on machine learning

UWB-IMU Pose Estimation for Roadheader Based on Machine Lear...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Hongbo Lv Xinyue Zheng Yuhao Qi Yixu Song College of Electromechanical Engineering North China University of Technology Beijing China Yankuang Energy Group Company Limited Shandong China Department of Computer and Technology Tsinghua University Beijing china

Abastact- the roadheader is an important equipment for mining coal underground, and precise positioning is the key technology for the intelligentization of the roadheader. this paper proposes a machine learning-based ultra wide band-inertial measurement unit (UWB-IMU) positioning method for the roadheader. By fitting the nonlinear transformation relationship between the 16 ranging values of UWB, 2 attitude angles of IMU, and the position and yaw angle of the roadheader using machine learning, the three-axis position average absolute error of the method is less than 0.10m, and the average absolute error of the three-axis attitude angle is less than 1°, which meets the positioning requirements of the roadway roadheader according to experimental verification.

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Clustering of Photoplethysmography Data signals for Developing Noise Filters 5

Clustering of Photoplethysmography Data Signals for Developi...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Abdillah, Rifqi Sarno, Riyanarto Amri, Taufiq Choirul Haq, Faris Atoil Sungkono, Kelly Rossa Sunaryono, Dwi Inst Teknologi Sepuluh Nopember Fac Intelligent Elect & Informat Technol Dept Informat Surabaya Indonesia

ISBN: (纸本)9781665456456

this paper aims to evaluate Photoplethysmography signals taken using fingertip pulse waves on human fingers which are generally not all in good condition. In common devices, the data obtained does not only contain photoplethysmography signals, but some noise also contaminates it. Noise is an unwanted ripple-shaped signal existing in signal transmission. Noise will interfere the desired quality of the received signal and ultimately change the information contained in the signal. this situation requires improvements to the photoplethysmography signal to make the signals are in the best condition so machine learning produces a more optimal output. Noise filters cannot be done with the same treatment because noise level in each data is different and must have different filter weights. this paper proposes a method to filter noise based on the level of noise in the signal. the approach taken in this study uses two stages, clustering and noise filtering. the first approach is clustering using the K-means clustering method by utilizing the coefficient of variation and slope features to group signals based on their noise level. the second approach uses exponential filtering, which performs by weighting the filter based on the cluster so that the data have different adjustments ratio of the level of smoothing. the result of the signal-to-noise ratio on Non-filtered Data is 181.49. signal to noise ratio on the Constant Weighted Filter is 183.79 and increases to 187.48 after using the Clustered and Weighted Filter method.

关键词： photoplethysmography signal processing clustering noise filtering k-means exponential filter

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Accelerated Scaled Gradient Descent Algorithm For Low-Rank Matrix Factorization

Accelerated Scaled Gradient Descent Algorithm For Low-Rank M...

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Computer Engineering and Intelligent Control (ICCEIC) international conference on

作者： Xiaojing Jia Xiangchu Feng Hua Wu School of mathematics and statistics Xidian University Xi'an China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

Low-rank matrix factorization is a particularly effective method which finds numerous applications in signal processing, machine learning and imaging science. Recent literature has proposed that scaled gradient descent (ScaledGD) algorithm. It achieves linear convergence without depending on the condition number, while maintaining the low per-iteration cost. the task of this paper is to propose an accelerated ScaledGD algorithmic approach called scaled momentum algorithm, which using a method similar to momentum to accelerate ScaledGD. Momentum is a commonly used optimization algorithm in deep learning that helps accelerate gradient descent in the relevant direction and dampens oscillations. the new scaled momentum algorithm has a benign convergence guarantee similar to the ScaledGD that is independent of the number of conditions, while prominently improve the rate of convergence than ScaledGD.

关键词： Deep learning machine learning algorithms signal processing algorithms Imaging signal processing Sensors Optimization Oscillators Convergence Intelligent control

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Exploring Non-convex Optimization in Sparse signal Recovery: A Comparative Study of Non-convex Dantzig Selector and LASSO 1

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Devadas, Raghavendra M. Hiremani, Vani Sharma, Aditi Venugopal, Anita Ichangi, Raghavendra M. Kulkarni, Naveen Pavithra, N. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Pune India Department of IT Unit Dhofar University Muscat Oman Sphoorthy Engineering College Nadargul Hyderabad India School of Engineering Dayananda Sagar University Kanakapura Road Bangalore India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (数字)9789819761036

ISBN: (纸本)9789819761029

Sparse signal recovery, lying at the core of signal processing and machine learning, has been traditionally attacked with convex optimization approaches such as LASSO and the Dantzig Selector. this paper investigates the field of non-convex optimization for sparse signal recovery, particularly comparing non-convex variants of Dantzig Selector and LASSO. A motivating choice of non-convex penalties may thus capture more complicated structures in sparse signals, offering a flexible framework for modeling complex sparsity patterns. Evaluate the performance of a non-convex Dantzig Selector in comparison with a non-convex variant of LASSO and investigate the sensitivity of the parameters in both methods. the non-convex Dantzig Selector introduces Huber loss, whereas the non-convex LASSO introduces a square root penalty. the performance metrics include sparsity and accuracy of recovered signals. the comparison between those methods in different scenarios gives the relative merits, adding up to the discussion on what methods to use for the reconstruction of signals. the study finds that the non-convex Dantzig Selector is of the final sparsity of the order of 10%, basically selecting only 10% of the variables. Contrarily, the non-convex LASSO is of about 90% sparsity, selecting about 90% of the variables. Finally, the non-convex LASSO has final accuracy closer to 1 with low prediction error, while the non-convex Dantzig Selector has final accuracy closer to 0.6, pointing to the reality of higher prediction error. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convex optimization

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Banana Ripeness Identification and Classification Using Hybrid Models with RESNET-50, VGG-16 and machine learning Techniques 4th

Banana Ripeness Identification and Classification Using Hybr...

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4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Mathew, Milan Bins Surya Manjunathan, G. Gokul, B. Mohana Ganesh, K. Sajidha, S.A. Nisha, V.M. School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9789819900848

For any fruit or vegetable consumers consider the ripeness of the item as one of the major indicators of its price and quality. Similarly, for bananas, the quality of the fruit is strongly associated with its ripeness and is a matter of concern for consumers, producers and traders of this fruit. this is because the ripeness of a fruit is associated with its taste, texture, and edibility which is important for any food. A manual method of classifying the bananas based on their ripeness is a tedious and impractical technique. this study aims to use modern day computer vision technologies to automatically identify and classify bananas based on their ripeness. We propose a hybrid model BanNet that aims to use a hybrid model that uses deep learning models like VGG-16 and ResNet-50 for feature extraction and a machine learning classifier to classify the banana based on its ripeness. the bananas will be classified into 4 categories—ripe, mid-ripe, overripe and unripe. the results are compared with other machine learning classifiers and another deep learning model used for feature extraction. the results reveal that the proposed system has a better overall recognition rate than the other techniques discussed. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Classification of Human Indoor Activities with Resource Constrained Network Architectures on Audio Data 5

Classification of Human Indoor Activities with Resource Cons...

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IEEE 5th international conference and Workshop Obuda on Electrical and Power Engineering (IEEE CANDO-EPE)

作者： Anand, Joseph Koch, Marcel Schlenke, Fabian Kohlmorgen, Fabian Wohrle, Hendrik Dortmund Univ Appl Sci & Arts Dept Informat Technol Dortmund Germany

ISBN: (纸本)9798350346190

Many functions and processes in Smart Homes can be improved by incorporating knowledge about the activities of the inhabitants. the inhabitant activities can be recognized by classifying typical sounds related to the activities using audio classification methods. In this paper, we present and approach for the classification of activity-based audio data, taking into account that in many cases the data processing has to be deployed on a resource-constrained device. An indoor dataset is created by recording 11 distinct human actions that are typically perfomed in a living room and kitchen setup. A Convolutional Neural Network (CNN) is used for classification that achieves an accuracy of up to 98%. In addition, network optimization methods like depthwise separable convolutions, pruning, channel scaling and quantization is used to reduce the memory footprint and computational requirements to obtain an optimized network with 93% fewer Million Floating point Operations (MFLOPs) and less than 2% drop in accuracy is achieved for use in resource-constrained devices.

关键词： Audio Classification IoT machine learning Human Activity Recognition Edge Computing

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Automated Design Rule Checker for VLSI Circuits Using machine learning 5th

Automated Design Rule Checker for VLSI Circuits Using Machi...

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5th international conference on VLSI, Communication and signal processing, VCAS 2022

作者： Rana, Mihir Malani, Nimit Gajjar, Ruchi Patel, Manish I. Panchal, Dipesh Department of Electronics and Communication Engineering Institute of Technology Nirma University Gujarat Ahmedabad India E-infochips Gujarat Ahmedabad India

ISBN: (纸本)9789819909728

Nowadays in semiconductor industries, the design rule checking (DRC) in the VLSI physical design flow is becoming more challenging. the number of DRC errors are increasing day by day with increase in complexity of the circuits. Yield reduces because of DRC violations, so making profitable integrated circuits is a difficult problem for the industry at cutting-edge technological nodes. Many industries and researchers have come up with different ideas to solve this issue and make the DRC faster and more accurate. there is an ardent need for an instantaneous DRC method that could be utilized during layout would be really useful. this paper establishes the proof of concept for a method which uses CNN model for efficient identification of DRC violations for a selected VLSI circuit D-flip flop. the dataset is prepared with the help of the microwind tool. Comparative analysis of the proposed CNN model is done with standard CNN architectures like AlexNet and VGG16 which is presented. the proposed approach is capable of detecting DRC errors with an accuracy of 98.33%. this paper is focused only on errors related to metal, polysilicon, and N-well, and it can also be expanded to other types of DRC errors related to VLSI circuits. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Errors

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