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Siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews

Soft Computing 2024年第13-14期28卷 7627-7647页

作者： Kothuri, Sri raman rajaLakshmi, N.r. Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

Sentiment analysis of online car reviews is a significant process in natural language processing. Numerous car enthusiasts and influencers cr.a.e video reviews of automobiles on platforms for example YouTube. In r.a.istic settings for sentiment analysis within this context, the task includes evaluating spoken reviews, the video content, and the emotions expressed by both the content cr.a.ors and the viewers as reflected in the comments section. The vulnerability to facing circumstances in r.a.-world scenarios where critical components are necessary for performing sentiment analysis on multimedia car reviews may unexpectedly be absent. To overcome this issue, siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews is proposed in this research work. Initially, pre-processing and feature extraction are performed on the audio, video and text infor.a.ion. The feature fusion is performed by enhancing spiking neural networks with geometric attention fusion network. The fused feature is fed as an input to siamese capsule gorilla troops network which classifies the emotion states as valence, arousal, and trustworthiness. Also, the algorithm called gorilla troop optimization (GTO) optimizes the classification error of the proposed siamese capsule network. Finally, this research work assesses high- and low-level features across modalities, introduces a novel enhancing spiking neural networks with geometric attention fusion network, enhances classification accuracy through new audio, video, and text embeddings, and boosts system robustness by integrating incomplete multimodal data. The achieved accuracy, precision, recall, and F-score values are 99%, 98.55%, 98.75%, and 98.02%, respectively, for the active multimodal sentimental analysis in car review (MuSe-Car) dataset. © The Author(s), under exclusive licence to Springer-Ver.a. GmbH Ger.a.y, part of Springer Nature 2024.

关键词： Sentiment analysis

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Enhancing Bitcoin Price Prediction Using LSTM and GrU Models 1

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12th Inter.a.ional Conference on recent Trends in Computing, ICrTC 2024

作者： Anitha rajakumari, P. Karthick, S. Gupta, Shruti Gupta, Shreya Mahapatra, r.P. Vinoth Kumar, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad201204 India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

Abstract Bitcoin, as the pioneering cryptocurrency, has witnessed significant fluctuations in its price since its inception. Predicting these fluctuations is of gr.a. interest to investors, traders, and researchers alike. This paper presents a comparative study of Long Short-Term Memory (LSTM) and Gated recurrent Unit (GrU) models for predicting Bitcoin prices. Cryptocurrency markets, particularly Bitcoin, exhibit high volatility and non-linearity, making accurate price prediction challenging. recurrent Neural Networks (rNNs), including LSTM and GrU, have shown promise in capturing temporal dependencies and patterns in sequential data. In this study, we train and evaluate LSTM and GrU models separately on historical Bitcoin price data to forecast future price movements. Through comprehensive experimentation and analysis, we assess the perfor.a.ce of both models in terms of accuracy and effectiveness in Bitcoin price prediction. The perfor.a.ce of these models is evaluated based on various metrics such as Mean Absolute Error (MAE), root Mean Square Error (rMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate the effectiveness of LSTM and GrU in predicting Bitcoin prices and offer insights into their comparative perfor.a.ce. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Long short-term memory

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Building an interoperable space for smart agriculture

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Digital Communications and Networks 2023年第1期9卷 183-193页

作者： Ioanna roussaki Kevin Doolin Antonio Skarmeta George routis Juan Antonio Lopez-Morales Ethel Claffey Manuel Mora Juan Antonio Martinez Institute of Communications and Computer Systems AthensGreece Walton Institute WaterfordIreland Department of Information and Communications Engineering University of MurciaMurciaSpain School of Business Waterford Institute of TechnologyWaterfordIreland R&D Department Odin SolutionsMurciaSpain

The digital transfor.a.ion in agriculture introduces new challenges in terms of data,knowledge and technology adoption due to critical interoperability issues,and also challenges regarding the identification of the most suitable data sources to be exploited and the infor.a.ion models that must be ***(Building an Interoperable,Data-Driven,Innovative and Sustainable European Agri-Food Sector.a.dresses these challenges by providing an overarching solution that integrates various heterogeneous hardware and software resources(e.g.,devices,networks,platforms)and enables the seamless sharing of data and knowledge throughout the agri-food *** paper introduces the main concepts of DEMETEr.a.d its reference architecture to address the data sharing and interoperability needs of farmers,which is validated via two rounds of 20 large-scale pilots along the DEMETEr *** paper elaborates on the two pilots carried out in region of Murcia in Spain,which target the arable crops sector.a.d demonstrate the benefits of the deployed DEMETEr reference architecture.

关键词： Smart agriculture Internet of things(IoT) DEMETEr reference architecture Interoperability Agricultural infor.a.ion model(AIM) Pilot validation

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Deep Learning-Based Networks to Detect Leaf Disease in Maize and Corn

Deep Learning-Based Networks to Detect Leaf Disease in Maize...

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2023 Inter.a.ional Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Singh, Samridhi Ver.a. Ar.a. Guler.a. Vishal Yadav, Shekhar Singh, Nagendra Pratap National Institute of Technology Department of Computer and Engineering Hamirpur India Madan Mohan Malaviya University of Technology Department of Computer and Engineering Gorakhpur India Dr B R Ambedkar National Institute of Technology Department of Electrical Engineering Jalandhar India

ISBN: (纸本)9798350302820

Corn or maize possesses an exceptionally high level of genetic diversity as well as a very high capacity for productivity. It is classified with the other grains as a food group and is onto impor.a.t crop grown for both people and animals. Corn, despite its crucial role, is particularly vulnerable to both pests and diseases due to its susceptibility to both. Both productionand quality are experiencing a decline as a result. The plant's tissue becomes infected, which causes the plant's growth to become stunted and its colour to change from green to yellow. The consequences of such issues are not just emotionally and physically taxing but also financially costly. Taking that part into consideration, we have worked with deep neural networks and performed experiments using resNet50, VGG-16, EfficientNetB0, and EfficientNet B3. The best results were obtained on resNet50 with an accuracy of 97.02 percent and a loss of 8.56 percent, as the model has been fine-tuned by utilizing a gridsearch algorithm, which searches for the best parameters to be utilized in order to achieve best possible train and test accuracy. © 2023 IEEE.

关键词： Image classification

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Smart Flood Detection and Communication in Hazardous Zones via MANET-Based IoT and Parallely Distributed Slimmable Neural Networks 4

Smart Flood Detection and Communication in Hazardous Zones v...

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4th Inter.a.ional Conference on Sustainable Expert Systems, ICSES 2024

作者： Sathya, V. Bhadula, Shuchi Srinivasan, r. Bhar.a., H.K. Panda, Bhavani Sankar Barve, Amit Department of Computer Science Vallam Tamil Nadu Thanjavur613403 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Avadi Tiruvallur Tamil Nadu Chennai600054 India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Tumakuru572106 India Raghu Engineering College Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam531162 India Parul Institute of Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering P.O.Limda Gujarat 391760 India

ISBN: (纸本)9798331540364

Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely disaster management. Hence in addition to MANET technology, deep learning algorithms and Inter of Things (IoT) can also be integrated into disaster detection and communication systems. The presented research developed a novel parallely distributed slimmable neural network with orchard algorithm for the accurate flood disaster prediction. The model processed the dataset containing various measurements such as temperature, humidity, precipitation, air, soil moisture and rainfall level that are collected by the IoT sensor deployed in the various location of the hazardous environment. The gathered data are initially pre-processed using the correlation coefficient min-max nor.a.ization appr.a.h. Further, the relevant characteristics of the flood disaster from the data are extracted through spike driven transformer process. These refined and dimensionality reduced data are then entered into the proposed framework for the disaster prediction. Here the integrated orchard algorithm incr.a.ed the disaster monitoring accuracy on the basis of better optimized parameters. The model resulted 95% accuracy, 93% precision, 94% recall and 96% f-score. Therefore, the presented method is an effective prediction mechanism for the disaster management in IoT MANET. © 2024 IEEE.

关键词： Soil moisture

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An Intelligent Appr.a.h for retinal Vessels Extraction Based on Transfer Learning

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SN computer Science 2024年第8期5卷 1072页

作者： Ver.a. Prem Kumari Kaur, Jagdeep Singh, Nagendra Pratap Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar 144008 India

An essential step in diagnosing ocular illnesses is automatically segmenting retinal blood vessels. Although current deep-learning techniques have attained excellent accuracy in artery segmentation, maintaining vascular structural connections is still tricky. The most frequent consequence of diabetes, and the main factor in adult blindness, is diabetic retinopathy. Currently, diagnosing fundus pictures is the primary method clinicians use to pinpoint the origin of diabetic retinopathy. Conducting manual screening on a large scale for retinal health is challenging. Therefore, the objective of my article is to state the segmentation of retinal blood vessels for early retinal disease detection and diagnosis. The proposed research method includes a U-net network with VGG19 and ImageNet is the backbone for extracting retinal vessels, and the extraction perfor.a.ce is found to be better with respect to another existing appr.a.h in the literature. The proposed methodology includes pre-processing, augmentation, patching, and model setup and implementation. First, pre-processing the available data to enhance the vessels of retinal image. The skip connections, the semantic gap left by a direct simple connection is filled using the nested connection method to combine the feature maps obtained from the intermediate decoder with the original features from the encoder. Data augmentation is also done on the original image to incr.a.e resilience and avoid the overfitting issue brought on by insufficient data. A mixed loss function is suggested to address the class imbalance in vascular pictures. After exhaustive experimental analysis observed, the Accuracy, Ar.a.Under Curve (AUC), and Jaccard index values are 0.9728, 0.9961, and 0.9175 for the DrIVE Date-set and) 0.9668, 0.9851, and 0.8975 for STArE Date-set respectively. Compared to the DrIVE data set, the proposed strategy performed better.a. the beginning but worse towards the end for the STArE data set. For the DrIVE and STArE

关键词： retinal blood vessels Segmentation UNet

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Steganography Methods for GIF Images: A review 4th

Steganography Methods for GIF Images: A Review

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4th Inter.a.ional Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Gupta, Anjali Awasthi, Lalit K. Singh, Samayveer Department of Computer Science and Engineering Dr. B R Ambedkar National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar India

ISBN: (纸本)9789819914784

Steganography is the art of hiding secret messages within a non-secret object. It helps hide infor.a.ion in images such as graphics interchange for.a. (GIF) images, text, audio, and video. In GIFs, animated GIFs are a common form of media. GIF images are mainly classified into two types: GIF87a and GIF89a. GIF87a is a type of palette-based image, and GIF89a is the improved version of GIF87a because the GIF89a image has more than one frame. Furthermore, it allows the GIF image to play simple animations. In addition, message hiding in palette-based graphics can be accomplished in two significant ways: firstly, by embedding messages into the frame, and secondly, by embedding messages into the palette. The primary goal of this paper is to review and describe numerous methodologies for hiding data within the GIF images and make a detailed comparison of these methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Software Cost Estimation: A Literature review and Current Trends 3

Software Cost Estimation: A Literature Review and Current Tr...

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3rd Inter.a.ional Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Singh, Suneeta Kumar, Kuldeep Dr B R Ambedkar National Institute of Technology Jalandhar Department of Computer Science and Engineering Punjab Jalandhar India National Institute of Technology Kurukshetra Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350300710

Software cost estimation is a challenging and complex task during software development. It directs project managers and developers to analyze and predict costs at the beginning of the software development life cycle. The most impor.a.t job for developing software projects is correctly estimating cost, time duration, and needed effort. This paper.a.ms to review different models used for software cost estimation, including algorithmic, non-algorithmic, and learning-oriented models, which have been published over the last ten years (i.e., from 2011 to 2022). The comparison is done based on the methods, selected datasets, and metrics used in different techniques. We observed that machine learning-based models perform better compared to other existing methods. However, to the best of our knowledge, analysis, and literature survey, we found that no technique in the literature provides a fit-all solution. © 2023 IEEE.

关键词： Cost estimating

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Code word Based Bi-Server.a.thentication and Key Exchange Encryption Technique 1

Code word Based Bi-Server Authentication and Key Exchange En...

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1st Inter.a.ional Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Thangarasan, T. Karthikeyan, V. Arunkumar, G. Arul Prakash, A. Keerthana, r. Gouthami, Kamsala Madanapalle Institute of Technology & Science Department of Computer Science & Engineering AndhraPradesh Madanapalle India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Department of Computer Science & Engineering Tamilnadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilandu Chennai India K.S.R.College of Engineering Department of Computer Science and Engineering Tamilnadu Tiruchengode India

ISBN: (纸本)9798350328042

The data transfer.a.d communication happening between the client and server or the source to destination should be made in some secure way. It must be protected and authenticated. the passwords related to that all will be stored in a centralized database. The majority of codeword-based user.a.thentication systems assume complete reliance in the authentication server, this refers to the location where centralized databases store passwords in plain text or easily inferred code verification infor.a.ion. System defenses against server-side offline dictionary attacks are nonexistent. In the event that insiders or outsiders hack the authentication server, all user code words become public. which could have severe legal and financial ramifications for.a.business. A codeword-based client authentication and confidential key exchange system using cutting-edge bi-server.a.chitecture serves as the answer to the aforementioned problems. The sever providing the front-end service communicates directly with users in this setup;the control server remains in the background. The proposed system provides security against the offline dictionary attacks that can happen from any of the servers. The proposed work has been implemented by using java and netbeans IDE 6.1. © 2023 IEEE.

关键词： Data transfer

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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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computers, Mater.a.s & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be gr.a.er than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the incr.a.ed success rate in clustering,the ability to determine the optimal cluster number.a.d the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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