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patterns recognition of Sentimental patterns from the X-dataset using Deep learning Algorithm

Patterns Recognition of Sentimental Patterns from the X-Data...

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Soft Computing for Security Applications (ICSCSA), international conference on

作者： Praveen S R Konduri Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

this research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. the study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. the results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. this research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research.

关键词： Deep learning Sentiment analysis Accuracy Social networking (online) Reviews Blogs Artificial neural networks pattern recognition Security Optimization

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Differential Privacy Protection Algorithm for data Clustering Center 4th

Differential Privacy Protection Algorithm for Data Clusteri...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Ma, Mingyang Yang, Hongyong Liu, Fei Ludong University Shandong Yantai264025 China

ISBN: (纸本)9783031200953

With the rapid development of the information era, data mining is widely used in various fields as a technique that can discover the interrelationship between data, but it also inevitably brings some privacy leakage problems. Differential privacy as an emerging privacy protection technology, because of its strict provability it has received great attention and been widely studied. By analyzing the privacy leakage problem in the traditional K-means algorithm, this paper proposes an improved differential privacy K-means algorithm to improve and protect for the selection of data clustering centers. When improvement, the degree of privacy protection and the availability of the clustering algorithm are analyzed. the initial centroids are selected by using the within-cluster sum of squared errors, and the different privacy intensity assigned to each cluster is calculated by using the silhouette coefficient. Finally, the experimental results demonstrate the feasibility of the differential privacy protection algorithm for data clustering center proposed in this paper. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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Multimodal IR and RF Based Sensor System for Real-Time Human Target Detection, Identification, and Geolocation 4th

Multimodal IR and RF Based Sensor System for Real-Time Human...

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4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

作者： Cheng, Peng Lin, Xinping Zhang, Yunqi Blasch, Erik Chen, Genshe Intelligent Fusion Technology Inc GermantownMD20874 United States Air Force Research Laboratory RomeNY United States

ISBN: (纸本)9783031526695

the Dynamic data Driven Applications System (DDDAS) paradigm incorporates forward estimation with inverse modeling, augmented with contextual information. For cooperative infrared (IR) and radio-frequency (RF) based automatic target detection and recognition (ATR) systems, advantages of multimodal sensing and machine learning (ML) enhance real-time object detection and geolocation from an unmanned aerial vehicle (UAV). Using an RF subsystem, including the linear frequency modulated continuous wave (LFMCW) ranging radar and the smart antenna, line-of-sight (LOS) and non-line-of-sight (NLOS) friendly objects are detected and located. the IR subsystem detects and locates all human objects in a LOS scenario providing safety alerts to humans entering hazardous locations. By applying a ML-based object detection algorithm, i.e., the YOLO detector, which was specifically trained with IR images, the subsystem could detect humans that are 100 m away. Additionally, the DDDAS-inspired multimodal IR and RF (MIRRF) system discriminates LOS friendly and non-friendly objects. the whole MIRRF sensor system meets the size, weight, power, and cost (SWaP-C) requirement of being installed on the UAVs. Results of ground testing integrated with an all-terrain robot, the MIRRF sensor system demonstrated the capability of fast detection of humans, discrimination of friendly and non-friendly objects, and continuously tracked and geo-located the objects of interest. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning

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DeepD_DrugC: Deep and distributed workflow to predict drug- candidates 4

DeepD_DrugC: Deep and distributed workflow to predict drug- ...

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

作者： Sid, Karima Zertal, Soumia Mezioud, Chaker University of Oum El Bouaghi Department of Mathematics & Computer Science Algeria Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

the applications of computational tools at various stages of drug discovery is one of the most active axes of research. Virtual Screening (VS) is a very common application, which aims to screen and analyze large chemical libraries using algorithms and models to extract drug-candidates that can bind to therapeutic targets. machine learning (ML) techniques are widely applied as a tool to analyze the chemical libraries in ligand-based virtual screening (LBVS). Deep learning (DL) is a novel mode of machine learning that provides several new architectures primarily based on classical Artificial Neural Network algorithms, but with many hidden layers to learn features with multiple levels of abstraction. Recently, chemical libraries are identified as Big data, due to their huge size, the variety of data, and the speed at which they are created, streamed and aggregated. In this context, we need advanced tools to handle and treat this type of data. Apache Spark is the most widely used engine for big data processing, with many improvements that make it more suitable for virtual screening analysis. In this work, we propose a novel workflow named DeepD_ DrugC based on Spark and Deep Neural Network model implemented with Deeplearning4j (DL4J) to improve the prediction results in LBVS. To evaluate the workflow, we suggest a process to create training datasets using the PubChem Bioassay database for cancer disease. the evaluation results show a good precision more than 93%, with acceptable scaling behavior. © 2022 IEEE.

关键词： Big data

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Ctransnet: A Modern Deep learning Model to Estimate Tropical Cyclone Intensity

Ctransnet: A Modern Deep Learning Model to Estimate Tropical...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xingchen Shan Jiagen Li Kaijun Ren College of Computer Science University of National Defense Technology ChangSha China College of Meteorology and Oceanography University of National Defense Technology ChangSha China

Tropical cyclone (TC) intensity estimation is a crucial factor in disaster protection. Fast and accurate intensity estimation can provide data support for TC forecasts. With the popularization and development of deep learning networks, massive meteorological and oceanic data, such as remote sensing satellite images and multidimensional reanalysis data, have been fully utilized, providing new methods and ideas for estimating TC intensity. However, challenges still exist in the current state of deep learning in TC estimation: the research area is mainly in the Northwest Pacific Ocean, and there is a lack of universal deep learning models for the global region. In this study, we combine the CBAM module’s adaptive attention mechanism with the Swin Transformer’s excellent global modeling ability in computer vision tasks and propose a new deep learning network named CtransNet. this network consists of two main branches: feature extraction net, fine-grained intensity classification and numerical estimation net. the first branch is used to extract high-order semantic vectors from remote sensing images, the second is to output categorical intensity probability distribution and intensity estimation. Massive experiments were carried out on the TCIR dataset, which includes multi-channel remote sensing satellite images of TC in the Northwest Pacific Ocean, the Eastern Pacific Ocean (including the Central Pacific Ocean), the Indian Ocean, the Atlantic Ocean and the Southern Hemisphere. the root mean squared error (RMSE) in the intensity estimation task reached 11.85kt. Compared with the traditional method, the performance is improved by about 31.5%.

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Hierarchical Fusion Network for Multimodal Sentiment Analysis

Hierarchical Fusion Network for Multimodal Sentiment Analysi...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yuyao Hua Ruirui Ji Sifan Yang Yi Geng Wei Gao Yun Tan School of Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In the multimodal sentiment analysis task, the role of text is superior to that of non-text modalities. Effectively learning the interaction relationships between modalities to support the text modality while preserving the specific information inherent to each modality to obtain a fusion representation that includes richer sentiment information is a challenging task. To address this issue, a multimodal sentiment analysis model based on the Hierarchical Fusion Network is proposed. the model is text-dominated and employs feature fusion to capture the interaction between modalities. Additionally, it preserves the unique information within each modality through decision fusion. the sentiment information from audio and visual modalities is processed using Gated Linear Unit, which improves the accuracy of sentiment analysis. Experimental results on the CMU-MOSI and CMU-MOSEI datasets demonstrate the effectiveness of the proposed HFN model, outperforming previous approaches in multimodal sentiment analysis.

关键词： Sentiment analysis Analytical models Visualization data preprocessing Transforms machine learning Logic gates Feature extraction Reliability data mining

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Evaluation of machine learning Performance Based on BERT data Representation with LSTM Model to Conduct Sentiment Analysis in Indonesian for Predicting Voices of Social Media Users in the 2024 Indonesia Presidential Election 5

Evaluation of Machine Learning Performance Based on BERT Dat...

引用

5th international conference on Information and Communications Technology, ICOIACT 2022

作者： Mandhasiya, Dwi Guna Murfi, Hendri Bustamam, Alhadi Anki, Prasnurzaki Universitas Indonesia Department of Mathematics Depok Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

In this era, the innovation of science and technology has changed rapidly, such as Artificial Intelligence (A.I.) which has helped a lot in human life. Deep learning (DL) as part of A.I. is the development of one of the machine learning models, namely the neural network. With many neural network layers, deep learning models can perform feature extraction and classification processes in a single architecture. this model has proven to perform state-of-the-art machine learning techniques in text classification, pattern recognition, speech, and imagery. Various text classification tasks, including sentiment analysis, have gone beyond AI-based approaches in Deep learning models. Text data can come from multiple sources, including social media, such as comments on videos on YouTube. Sentiment analysis, one of the opinion mining, is a computational study that analyzes people's opinions from texts. In this research, machine learning performance analysis is carried out on a deep learning method based on BERT data representation with the LSTM method. the implementation of the model uses youtube commentary data on political videos related to the 2024 presidential election in Indonesia;performance analysis is carried out using accuracy, precision, and recall metrics. In this study, the accuracy of the BERT-LSTM model outperformed the BERT model with an accuracy of 0.8783. © 2022 IEEE.

关键词： Sentiment analysis

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Significant CC400 Functional Brain Parcellations Based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder Detection 5th

Significant CC400 Functional Brain Parcellations Based LeNet...

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Othmani, Alice Bizet, thibaut Pellerin, Tanguy Hamdi, Badr Bock, Marc-Antoine Dev, Soumyabrata Univ Paris Est LISSI UPEC F-94400 Vitry Sur Seine France EFREI Paris Engn Sch F-94800 Villejuif France Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9783031235986;9783031235993

machine learning and computer vision have opened new pathways to investigate imaging data captured from different sensors. Numerous application areas are getting benefit from these advancements and one of these areas is medical imaging. Despite rapid advancements in machine learning based medical condition diagnosis systems (CADs), some ailments and disorders are hard to diagnose/classify due to the absence or the lack of consensus on biomarkers for specific disorders, like the Autism Spectrum Disorder (ASD). In this study, the challenging problem of classification of ASD using the magnetic resonance imaging (MRI) data is tackled. Hence, we propose an interpretable deep neural network based approach for ASD detection from MRI images. Our proposed explanation method is based on the selection of four regions of interest from the MRI images. the four significant CC400 functional brain parcellations are then concatenated and fed to a LeNet-5-based convolutional neural network to predict ASD. the performances of the proposed approach are evaluated on ABIDE dataset and promising results are achieved. three augmented datasets are considered and an accuracy of 95% is achieved by using LeNet-5 which outperforms VGG16 and ResNet-50. the achieved accuracy outperforms also the existing deep neural networks based approaches on ABIDE dataset. the use of the four significant CC400 functional brain parcellations makes our approach more interpretable and more accurate.

关键词： Deep learning Convolutional Neural Network Autism Spectrum Disorder ABIDE I MRI images

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Construction of an Intelligent Identification System for Concrete Bridge Cracks by Integrating machine learning and Neural Network Models 4

Construction of an Intelligent Identification System for Con...

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4th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2023

作者： Zhang, Zanpeng Xu, Meng Luo, Lingyu Hong, Zhong China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd. Chongqing400067 China China Merchants Chongqing Highway Engineering Testing Center Co. Ltd. Chongqing400067 China Chongqing Jiangbei District Heji Primary School Chongqing400000 China Tibet Autonomous Region Key Highway Construction Project Management Center Lhasa850000 China

ISBN: (纸本)9798350341690

With the increasing use time and frequency of bridges, they will inevitably be subjected to a large number of traffic vehicles and various human damage. In addition, the severe erosion of the natural environment will cause different situations and degrees of damage to the surface of the beam. According to relevant data, cracks are the main factor causing damage to concrete bridges. therefore, this paper combines machine learning and NN (Neural Network) model to build an intelligent identification system for concrete bridge cracks. through the system, we can clearly see the recognition of the model on the bridge crack picture. the numerical representation on the diagonal of the Confusion matrix identifies the correct type, and the numerical representation not on the diagonal represents the wrong type. And the research results show that the classification accuracy of this algorithm for the verification set is high, close to the training set, and the Loss function is close to the Loss function of the training set on the basis of the overall fluctuation, so there is no over fitting phenomenon, the uncomfortable training results are good, and the training time is very similar. the algorithm proposed in this paper can accurately identify concrete surface crack images in a short period of time, indicating that the algorithm can be used for crack localization. © 2023 IEEE.

关键词： Neural network models

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data analysis of Global Perceived Stress Scores during Covid-19 4

Data analysis of Global Perceived Stress Scores during Covid...

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4th international conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Muttaraju, Chakita Kabber, Anusha Parimala, S. Anagani, Prajwal Srinivasa, Gowri PES University Department of Computer Science and Engineering Bengaluru India PES University PES Center for Pattern Recognition Dept. of CSE Bengaluru India

ISBN: (纸本)9798350397475

the Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. this work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. machine learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 IEEE.

关键词： Decision trees

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