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Sarcasm Detection and Classification Using Deep learning Model 7th

Sarcasm Detection and Classification Using Deep Learning Mod...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Rajani, B. Saxena, Sameer Suresh Kumar, B. Narang, Geetika AMITY Institute of Information Technology Amity University Rajasthan Jaipur India School of Computer Science and Engineering Sanjay Ghodawat University Kolhapur India Trinity College of Engineering and Research Maharashtra Puna India

ISBN: (纸本)9789819720880

the Twitter and Facebook kind of web platforms have gained significant traction as prominent mediums for individuals to document and articulate their emotions, viewpoints, and evaluations. the use of appropriate extraction methodologies, such as sentiment analysis, renders this data valuable across several domains. Sarcasm detection is a methodology used to examine individuals’ cognitive and affective states, with the aim of classifying. there are several methods by which individuals might articulate their sentiments. these attitudes are occasionally attended with sarcasm, particularly when expressing strong emotions. Sarcasm has often understood as a kind of communication in which a good statement is made with an underlying negative purpose. the majority of existing research endeavors see these responsibilities as separate entities. Until far, the majority of methods for sentiment and sarcasm classification have been focused on treating them as separate and independent text categorization tasks. Nowadays, there has been notable growth in the field of study using deep learning methods, resulting in substantial improvements in the performance of standalone classifiers. One of the primary challenges encountered by these methodologies is their inability to accurately categorize sarcastic statements as negative. Given this consideration, we assert that possessing the ability to identify sarcasm will contribute to the enhancement of sentiment classification, and conversely, proficiency in sentiment classification will aid in the recognition of sarcasm. Our research has shown a positive correlation between these two activities. this study presents a system that employs a deep neural network and multi-task learning to effectively predict the connection between different tasks, with the goal of attracting the overall concert of sentiment analysis. the approach presented in this study demonstrates superior performance compared to the previous methods, with a notable margin o

关键词： Contrastive learning

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Develop Model for Recognition of Handwritten Equation Using machine learning 11th

Develop Model for Recognition of Handwritten Equation Using ...

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11th international conference on Recent Trends in computing, ICRTC 2022

作者： Kishor, Kaushal Tyagi, Rohan Bhati, Rakhi Rai, Bipin Kumar Department of Information Technology ABES Institute of Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789811988240

the problems that stand with the existing solution of handwritten equation recognizer have numerous problems such as not being purely based on ML and thus unable to occupy dominance of the large processing capacity of the machines that stand in this scenario and also not having an adequate amount of data to precisely estimate the characters, symbols, and numbers that are drawn out from the image being satiated under the software. the current handwritten equation recognizer solution is applied, and imitation utilizing different competence and performance levels is evaluated on real photos. Neural networks which give an accuracy of 95% are used for recognition of the alphabets, all the numbers, mathematical symbols, and some special symbols like π and ψ (Hossain et al. in 2018 Joint 7th international conference on Informatics, Electronics and Vision (ICIEV) and 2018 2nd international conference on Imaging, Vision and Pattern Recognition (icIVPR). IEEE, New York, pp. 250–255, 2018). the major goal of this work is to improve a software programme that detects handwritten equations, phrases, and paragraphs generated by handwritten numbers, characters, words, and mathematical symbols utilizing CNN, and some image processing expertise to achieve appropriate accuracy. the primary goal of this research work is to execute fundamental operations on the identified equation, such as number addition and subtraction. We also do text recognition, such as reading a handwritten sentence or paragraph and translating it to computer-typed format. Finally, the observational results suggest that our current software application is a huge success. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Usage of Single-Camera Video Recording to Measure Sea Surface Roughness with machine learning Methods 7th

Usage of Single-Camera Video Recording to Measure Sea Surfac...

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7th international conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Salin, Mikhail B. Vitalsky, Artem V. Nizhny Novgorod603950 Russia Nizhny Novgorod603950 Russia

ISBN: (纸本)9789819927296

Photometry is a convenient operational method for monitoring such dynamically evolving phenomena as wind waves. Nowadays machine learning allows one to avoid explicit derivation of the solution to the problem, describing all the instructions for transforming the input data into the final result. Instead, an algorithm is used to independently find solutions through the integrated use of statistical data, from which patterns are derived, on the basis of which forecasts are made. An example of a problem for which a regular solution has traditionally been applied is the prediction of wave height from the input brightness of a water surface. the task is complicated by the multitude of possible physical models and the need to apply calibration coefficients. In this paper, we solve the problem of how, basing on the obtained brightness values and the corresponding heights, to train the neural network to further predict the heights from the incoming brightness values with the greatest accuracy. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Surface roughness

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Comparison Analysis of LSTM and CNN Variants with Embedding Word Methods for Sentiment Analysis on Food Consumption Behavior 7th

Comparison Analysis of LSTM and CNN Variants with Embedding ...

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7th international conference on soft computing in Data Science (SCDS)

作者： Ramzi, Nurul Izleen Yusoff, Marina Noh, Norzaidah Md Univ Teknol MARA Sch Comp Sci Coll Comp Informat & Media Shah Alam Malaysia Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IB Shah Alam Malaysia

ISBN: (纸本)9789819904044;9789819904051

Lockdowns, working from home, staying at home, and physical distance are expected to significantly impact consumer attitudes and behaviors during the COVID-19 pandemic. During the implementation of the Movement Control Order, Malaysians' food preferences are already shifting away, influencing new consumption behavior. Since it has played a significant role in many areas of natural language, mainly using social media data from Twitter, there has been increased interest in sentiment analysis in recent years. However, research on the performance of various sentiment analysis methodologies such as n-gram ranges, lexicon techniques, deep learning, word embedding, and hybrid methods within this domain-specific sentiment is limited. this study evaluates several approaches to determine the best approach for tweets on food consumption behavior in Malaysia during theCOVID-19 pandemic. this study combined unigram and bigram ranges with two lexicon-based techniques, TextBlob andVADER, and three deep learning classi-fiers, Long Short-Term Memory Network (LSTM), Convolutional Neural Networks (CNN), and their hybridization. Word2Vector and GloVe are two-word embedding approaches used by LSTM-CNN. the embedding GloVe on TextBlob approach with a combination of Unigram + Bigram [1,2] range produced the best results, with 85.79% accuracy and 85.30% F1-score. According to these findings, LSTM outperforms other classifiers because it achieves the highest scores for both performance metrics. the classification performance can be improved in future studies if the dataset is more evenly distributed across each positive and negative label.

关键词： Deep learning Lexicon-based technique Long Short-Term Memory Network Sentiment analysis

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Mechanical System Vulnerabilities: Advanced Fusion machine learning Model for Detecting Ethereum Attack Patterns in IIoT-5.0 Environments 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Sulakhe, Vishal N. Goyal, S.B. Gadekar, Amit Wasnik, Manisha Agarkar, Pankaj Rajawat, Anand Singh Mechanical Engineering Department Sandip University Nashik422213 India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Sandip Institute of Technology and Research Centre Nashik India Ajeenkya D Y Patil School of Engineering Charoli Lohegaon Maharashtra Pune412105 India School of Computer Science and Engineering Sandip University Nashik422213 India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

It is of the utmost significance to keep the reliability and confidentiality of these systems in check in light of the coming advent of Industry 5.0 as well as the growing penetration of the Industrial Internet of things (IIoT) into complicated mechanical systems. the broad adoption of blockchain technology, and in particular Ethereum, brings with it a new set of opportunities as well as hazards that have an impact on the complicated dynamic that exists between machines, data, and decentralized applications. this research investigates an advanced fusion machine learning model with the intention of locating dangerous patterns and likely attack vectors that target Ethereum transactions and contracts within IIoT-5.0 systems. the results of this investigation are intended to be used in future research. the detection of these patterns and vectors is the primary focus of the model. In an era in which there is a transition taking place in the industrial sector, this research presents a novel technique for improving cybersecurity. Examining the specific flaws that may exist in mechanical systems after they have been integrated with blockchain solutions is the strategy that will be utilized. Our model shows tremendous promise in recognizing abnormalities and potential dangers by utilizing a combination of supervised learning techniques and unsupervised learning approaches. these techniques allow for the model to develop without being directly monitored by a human. this paves the way for preventative safety precautions to be included in the design of the future generation of industrial systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ethereum

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Profiling Network Traffic by Using Classification Techniques in machine learning 1

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Chauhan, Dipti Jain, Jay Kumar Prestige Institute of Engineering Management & Research MP Indore India Sagar Institute of Research & Technology MP Bhopal India

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

In today’s scenario where our world is full of technological instances where every now and then, we are sharing our information through networks and hence we are more pruned toward the risk feature. It is not always possible that we will always access private networks or VPNs. Our network is exposed to different types of cyber-attacks in the form of information leakage every time 24 × 7. Networks are evolving to take considerable risk factors. they launch new trends and attacks. these attacks are mainly focused toward the open ports in the networking devices. Researchers are developing networking tools for this purpose like network profiling, vulnerability scanning, network mapping, etc. In order to help intrusion detection systems’ (IDS) identify fraudulent network traffic, machine learning (ML) has recently gained popularity as a technology. the essence of the ML model's cognitive function depends on the quality of the dataset for training the model. In this research paper, we are using profiling network data and training the dataset using a machine learning-based classification model to classify whether the given traffic is normal traffic or it is an anomaly. Different methods have been like Naive Bayes, Bayes Net, Naive Bayes Multinomial Text, and Naive Bayes Updateable on this dataset to build the classifier model. the data are categorized using the Weka 3.8.5 tool, and this trained dataset has been used in numerous simulations. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Deep learning-Based Algorithmic Trading Based on News and Events Strategies 7th

Deep Learning-Based Algorithmic Trading Based on News and Ev...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Kayathri, V. Prabakaran, P. Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai600 117 India

ISBN: (纸本)9789819720309

Successful stock market investors should consider these trends. this article defines price effect, optimal execution, and placement through the lens of algorithmic trading and strategies based on news and events. By using feedbacks controlled to include consideration of a stop-loss order, algorithmic trading allows traders and investors at large to liquidate or construct big securities positions in a fully automated manner, thus generalising the existing stock trading results. Trading decisions made by the AI agent could affect the asset price. We see this as a marketplace where people are bartering and swapping goods. Creating a new market is the driving force behind our research into generative models. this news sentiment does a good job of describing stocks, and AI systems that read the news and quantify them react to factors that affect prices far more quickly than humans do. All three of the stock market's underpinnings—price, volume, and volatility—respond strongly to news events. Sentiment analysis is a method for gauging the overall tone of a news piece and classifying it as optimistic, pessimistic, or neutral. the simulation is conducted in python to test the effectiveness of the model in trading in an automated manner for stocks. the results show that the proposed sentimental analysis for stock trading achieves higher rate of accuracy and precision than the other methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Financial markets

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Component Extraction for Deep learning through Progressive Method 7th

Component Extraction for Deep Learning Through Progressive ...

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7th international conference on Smart computing and Communication, SmartCom 2022

作者： Gao, Xiangyu Qiu, Meikang Zhao, Hui New York University New York CityNY United States Dakota State University MadisonSD57042 United States Educational Information Technology Lab. Henan University Kaifeng475000 China

ISBN: (纸本)9783031281235

machine learning has shown great impact in a lot of applications. Within all types of tools, deep learning should be one of the most important techniques thanks to its ability to capture the correlation between the input features and output results. However, the relatively long training time and high computation complexity remain a big problem in deep learning. In addition, the impossibility to explain the model makes it harder for us to look for alternatives to fix the bad fitting results. therefore, this paper aims at improving the deep learning model training result by proposing a principal component extraction algorithm. Compared with the previous Principal Component Analysis (PCA) methods, this algorithm creatively consider not only the original input components but also the computed variables in the first hidden layer in neural network so as to capture more representative components. the experiment shows that compared to previous PCA method, this can better capture the principal components from all input variables. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Principal component analysis

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Taxonomy of machine learning Techniques in Test Case Generation

Taxonomy of Machine Learning Techniques in Test Case Generat...

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international conference on ent computing and Control Systems (ICICCS)

作者： Ajmer Singh CSE Department DCRUST Murthal Sonipat India

machine learning empowers computer systems to automatically learn and improve their performance without being explicitly programmed for each specific task. It has revolutionized many industries and continues to drive advancements in artificial intelligence, enabling computers to perform complex tasks with greater efficiency and accuracy machine learning algorithms use training data to learn patterns and make predictions or decisions about new data. It involves a range of techniques such as supervised learning, unsupervised learning, reinforcement learning, deep learning, and others. machine learning (ML) can be applied to test case generation in several ways: Like Prioritization, Test case reduction, Test case minimization etc. machine learning techniques can be beneficial in test case generation in several ways. this article reviews the research work on application of machine learning algorithms in test case generation.

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Handwriting Recognition of English Digits: A Deep learning Perspective 7th

Handwriting Recognition of English Digits: A Deep Learning ...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Mahmud, Tanjim Rahman, Taohidur Aziz, Mohammad Tarek Hasan, Imran Barua, Koushick Barua, Anik Basnin, Nanziba Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Rangamati Bangladesh Leeds Beckette University Leeds United Kingdom University of Chittagong Chittagong Bangladesh Luleå University of Technology Luleå Sweden

ISBN: (纸本)9783031733239

Handwritten digit recognition is used in computer vision and pattern recognition. this research categorizes MNIST digits using multiple neural network architectures. A typical machine learning benchmark is the MNIST dataset, which contains 28×28 grayscale photos of the numbers 0–9. Our major objective is to develop and test digit recognition models. Five network models, including an FCN and an ANN, were built, deployed, and assessed in this research. CNN Sequence with Dense Layers Sequential CNN with Convolution and MaxPooling, Flatten, and Dense Layers are alternative methods. these models extract characteristics from pictures using fully connected, convolutional, and pooling layers. We discuss loading, normalizing, and rearranging the dataset. the layers, activation functions, and output requirements of each model are specified in its design. Compilation involves selecting loss functions and optimizers. the validation set evaluates model performance, whereas the training set fine-tunes them. the results suggest that our models recognize digits. Other models have accuracy ranges from 93.70% to 97.92%, whereas Sequential CNN with Convolution and MaxPooling reaches 98.96%. this research focuses on model design, layer structure, and hyperparameter tuning for categorization. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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