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Detection of Compatibility, Proximity and Expectancy of Bengali Sentences using Long Short Term Memory

Detection of Compatibility, Proximity and Expectancy of Beng...

引用

international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Ashraful Haider Chowdhury Nasirul Mumenin Muhammad Taus Mohammad Abu Yousuf Bangladesh University of Professionals Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Text classification is known to be a supervised machine learning technique used in one or more predefined categories to classify sentences or text archives. To be a perfect phrase to convey one's feelings or to be significant, a Bengali sentence must have three properties i.e. Compatibility, Proximity and Expectancy. In this paper, we have proposed a method that is able to detect whether a Bengali sentence has compatibility, proximity and expectancy using Long Short Term Memory network. Our model is trained with word embedding layer and LSTM layer for the detection of Compatibility of a sentence but POS tagging is included for assuring the syntactic structure in case of Proximity and Expectancy detection. The model is tested on around 75000 Bengali simple sentences. The proposed framework achieves an accuracy of 97.5 percent, 85.5 percent and 97 percent for Compatibility, Proximity and Expectancy respectively. The result analysis proves that our model gives better performance.

关键词： Text categorization machine learning Tagging Syntactics signal processing Long short term memory Robots

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A machine learning Approach to Identify the Students at the Risk of Dropping Out of Secondary Education in India 2nd

A Machine Learning Approach to Identify the Students at the ...

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2nd international conference on Soft Computing and signal processing, ICSCSP 2019

作者： Nangia, Sagarika Anurag, Jhilmil Gambhir, Ishani Oracle India Pvt. Ltd. Bengaluru India TATA Motors Pune India TATA Motors Lucknow India

ISBN: (纸本)9789811524745

Having a significant number of students dropping studies at higher education limits their possibilities of having better employment opportunities. According to a UNESCO report, India is fifty years behind in achieving the goal of universal secondary education. There is almost a 30% drop in gross enrolment ratio (GER) from secondary education to higher education. In this paper, we use machine learning models to identify the students who are likely to drop out in the ongoing session. We demonstrate, to the best of our knowledge, the first results on a secondary-level education in a developing nation like India to classify students as potential dropouts. We collect data from secondary-level institutions for the academic session 2016–17, which captures the variation in ethnic, social and financial background of students as well as their academic performances. We do feature analysis to find out the most dominant attributes in the dataset and use them in different machine learning algorithms on the task of binary classification for potential dropout. Our best algorithm is able to classify correctly with least number of false negatives. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Students

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Component Recognition Method Based on Deep learning and machine Vision 19

Component Recognition Method Based on Deep Learning and Mach...

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2nd international conference on Image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Tang, Hao Chen, Jie Zhen, Xuesong Chongqing CEPREI Ind Technol Res Inst Chongqing Peoples R China

ISBN: (纸本)9781450360920

Traditional component coding recognition adopts manual recognition or primitive machine vision technology in the electronic component testing and screening industry, which has the issues of low testing efficiency and high recognition error rate. Therefore, we proposed a novel method of component coding recognition based on machine vision combining with deep learning. The machine vision imaging system have been developed to obtain the images of component, and the processing operators such as grayscale conversion, mean filter, slant correction and other techniques are used for preprocessing. The component coding of different types and materials were recognized by deep learning model of deep convolution neural network. Extensive experiments in the component testing center and comparisons with traditional recognition demonstrate that this method has high recognition accuracy and wide range of components recognition.

关键词： Deep learning machine Vision Image processing Electronic Components Recognition

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Human facial expression recognition using deep learning technique 2

Human facial expression recognition using deep learning tech...

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2nd international conference on signal processing and Communication, ICSPC 2019

作者： Harshitha, S. Sangeetha, N. Shirly, Asenath P. Abraham, Chandy D. Karunya Institute of Technology and Sciences Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9781728118499

The emotion is recognized from facial expression by using static images. It is one of the categories in signal processing which is applied in various fields, similarly for human and computer interaction. Some sources are proposed to automatic emotion recognition, which uses machine learning approach. Many real-time problems have been solved by Deep learning technique. In this work we have defined Convolutional Neural Network (CNN) is used to identify 6 elementary emotions this technique has been implemented in MATLAB. © 2019 IEEE.

关键词： Deep learning

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Distributed Denial of Service Attacks Detection System by machine learning Based on Dimensionality Reduction 2

Distributed Denial of Service Attacks Detection System by Ma...

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2nd international conference of Modern Applications on Information and Communication Technology, ICMAICT 2020

作者： Abbas, Sara Abdalelah Almhanna, Mahdi S. College of Information Technology University of Babylon Babil Iraq

Data mining algorithms have essential methods and rules that can contribute in detecting and preventing various types of network attacks. These methods are utilized with the intrusion detection systems that can be designed and developed preserve the information in organizations from damage. Specifically, the data mining technique allows users to effectively distinguish between normal and malicious traffic with good accuracy. In this paper, a methodology for revealing and detecting (DDOS) network attack was suggested using DM algorithms. The utilized methodology is divided especially into four parts, each part has its own rules, as the following: First one is the pre-processing which consists of three sub-steps: (i) encoding, (ii) log2, and (iii) PCA. Encoding is used by converting the original nominal packets into numeric features. Standardization of data was performed using logarithmic algorithm. Finally the PCA technique is applied eight times for several different features to reduce the dimensions of the dataset. The second stage is an anomaly detection model, (RF) algorithm is implemented for the extraction of data patterns while classification the types of the given features in training step, (NB) algorithm was also used in classifying the data to compare the results of its classification with the results of using the classifier (RF). In the third stage, the outcomes were tested by implementing the already trained datasets. In the fourth stage, the proposed system performance evaluation metrics were collected such as the rates of accuracy, false alarm, detection, precision, and ***. MIX dataset were utilized to train and test the proposed model which resulted from merging two datasets (PORTMAP+LDAP), which are used from the CICDDOS2019 datasets, each consisting of several types of attack packets, and benign packets. Several metrics were utilized in the evaluation of the proposed system. The best outcomes were obtained for detection by using the log2 algor

关键词： Classification (of information)

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Comparative analysis of various machine learning Techniques for Intrusion Detection System 2

Comparative analysis of various Machine Learning Techniques ...

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2nd international conference on signal processing and Communication, ICSPC 2019

作者： Ajitha, Deborah D. Basil, Xavier S. David, Shibin Kathrine, JaspherWillsie Karunya Institute of Technology and Sciences Department of CSE Coimbatore India

ISBN: (纸本)9781728118499

Surfing internet becomes common now-a-days that gave a chance for intruders to steal information. Therefore security is very important to detect any unwanted activities by using intrusion detection system. Intrusion detection system is one of the vast new technologies in this decade which makes the system to learn by itself and predict the values using machine learning techniques. To analyze intrusion detection system for detecting network attacks using various machine learning techniques has been proposed in this paper. machine learning algorithms such as J48, Naive Bayes, Random Forest and REP tree are compared using Kddcup99 dataset. When comparing these machine learning algorithms in which random forest gives high detection rate. © 2019 IEEE.

关键词： Intrusion detection

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Sentence Sentiment Classification Using Convolutional Neural Network in Myanmar Texts 20

Sentence Sentiment Classification Using Convolutional Neural...

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2nd international conference on Image, Video and signal processing, IVSP 2020

作者： Oo, Saung Hnin Pwint Theeramunkong, Thanaruk Hung, Nguyen Duy Sirindhorn International Institute of Technology Thammasat University Thailand

ISBN: (纸本)9781450376952

There are still few works on application of deep learning for Myanmar language. This paper presents an approach to use a convolutional neural network (CNN) model to classify sentence sentiment in Myanmar texts. A CNN model is constructed on the top of a word embedding model (i.e., Word2Vec), which converts words into vectors. The model classifies the input sentences and labels each sentence with positive, negative, neutral, unrelated and unreadable sentiments. The model is learnt from 1,152 sentences taken from the customers' reviews of products, which is provided by a telecommunication company. Then, the model is tested on 495 unseen sentences with the result of 86.26% accuracy and 82.58% average f-measure in prediction. The model is compared with the traditional machine learning (ML) classifiers, especially support vector machine (SVM), naïve Bayes (NB), and logistic regression (LR). The model outperforms these classifiers since SVM results in 64.44% accuracy, NB obtains 60.20% accuracy and LR gets 55.15% accuracy. © 2020 ACM.

关键词： Neural network models

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Cyber Attack Detection Model (CADM) Based on machine learning Approach

Cyber Attack Detection Model (CADM) Based on Machine Learnin...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Fahima Hossain Marzana Akter Mohammed Nasir Uddin Jagannath University Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

A reliable Cyber Attack Detection Model (CADM) is a system that works as safeguard for the users of modern technological devices and assistant for the operators of networks. The research paper aims to develop a CADM for analyzing the network data patterns to classify cyber-attacks. CADM finds out attack wise detection accuracy using ensemble classification method. LASSO has been used to extract important features. It can work with large datasets and it has more visualization capability. Gradient Boosting and Random Forest algorithms have been used for classification of network traffic data to build an ensemble method. Gradient Boosting algorithm trains weak learning models and select the best decision trees to deliver more improved prediction accuracy and Random Forest algorithm trains each tree in parallel manner. In this research work, five datasets such as NSL-KDD, KDD Cup 99, UNSWNB15, URL 2016 and CICIDS 2017 are also applied to check the efficiency of the proposed model.

关键词： signal processing algorithms Prediction algorithms Feature extraction Boosting Classification algorithms Random forests Cyberattack

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Bacterial foraging optimization in non-identical parallel batch processing machines 1

引用

2nd international conference on machine Intelligence and signal processing, MISP 2019

作者： Majumder, Arindam Laha, Dipak Mechanical Engineering Department National Institute of Technology Agartala799046 India Mechanical Engineering Department Jadavpur University Kolkata700032 India

ISBN: (数字)9789811513664

ISBN: (纸本)9789811513657

The paper presents a modified bacterial foraging optimization (BFO) algorithm to solve non-identical parallel batch processing machine scheduling problems with the objective of minimizing total weighted tardiness. The proposed algorithm combines the shortest position value-based heuristic with the basic BFO algorithm to enhance the solution quality. The computational experimental results based on ninety randomly generated problem instances reveal that the proposed algorithm performs better than the existing particle swarm optimization algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Particle swarm optimization (PSO)

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An Overview of learning Algorithms and Inference Techniques on Restricted Boltzmann machines (RBMs) 2019

An Overview of Learning Algorithms and Inference Techniques ...

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2nd international conference on Software Engineering and Information Management (ICSIM) / 2nd international conference on Big Data and Smart Computing (ICBDSC)

作者： Merindasari, Esti Widyanto, M. Rahmat Basaruddin, T. Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781450366427

Restricted Boltzmann machines (RBMs) is one of machine learning's methods which within past decades, the development of RBMs has quite increase. Researches of RBMs focused on theories and applications of RBMs. The application of RBMs has proofed that RBMs good at finishing many tasks, such as feature extraction method, document modeling, representation learning, classification and others. The RBMs' theories also have great movements, such as the development of the learning algorithm and inference techniques of RBMs. The key factors making the RBM success on finishing task are the learning algorithm and inference techniques. They motivated the development of inference techniques which successfully improved the deep neural network (DNN) performance. The aim of this research is reviewing the various types of RBMs as the application side, and the development of learning algorithm and inference techniques as theoretical side. Hopefully, it could motivate more development on the RBMs in order to contribute on overcoming implementation tasks especially on image processing tasks.

关键词： Restricted Boltzmann machine learning algorithm inference

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