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Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

Multimedia Tools and Applications 2024年 1-17页

作者： Mohan, G. Bharathi Kumar, R. Prasanna Elakkiya, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Birla Institute of Technology and Science Dubai345055 United Arab Emirates

Pre-trained language models have significantly advanced text summarization by leveraging extensive pre-training data to enhance performance. Many cutting-edge models undergo an initial pre-training phase on a large corpus before being fine-tuned specifically for text summarization tasks. The challenge arises when the limited data available for these summarization tasks and the high complexity of the models lead to aggressive fine-tuning, which can cause the models to over fit the training data and fail to generalize well to new, unseen data. To address these challenges, the study proposes a multi-objective based Genetic Algorithm (MOGA) approach for optimizing pre-trained models for summarization The proposed approach first compares the performance of various pre-trained models by fine-tuning them with the CNN/Daily Mail dataset. Secondly, the proposed Multi-Objective Genetic Algorithm (MOGA) is applied to all validated pre-trained models and compared using a statistical approach. Experimental results reveal that Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence (PEGASUS) model performs best among the pre-trained models. Further optimization with the proposed MOGA method yields even better results, with the optimized model achieving a Recall-Oriented Understudy for Gisting Evaluation) ROUGE-1 score of 0.514, a ROUGE-2 score of 0.4, and a ROUGE-L score of 0.5. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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computer Systems science & Engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia Department of Information Technology MLR Institute of TechnologyHyderabad500043TelanganaIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various *** same can be adapted to the problem of brain tumor ***,several deep learning models are presented earlier,but they need better classification *** efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this *** method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple *** noise-removed image has been equalized for its quality by using histogram ***,the features like white mass,grey mass,texture,and shape are extracted from the *** features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality *** texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution *** neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of *** on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor

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Enhancing sarcasm detection through grasshopper optimization with deep learning based sentiment analysis on social media

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1785-1791页

作者： Srinu, Nidamanuri Sivaraman, K. Sriram, M. School of Computing Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai 600073 India

Detecting sarcasm in social media presents challenges in natural language processing (NLP) due to the informal language, contextual complexities, and nuanced expression of sentiment. Integrating sentiment analysis (SA) with sarcasm detection enhances the understanding of text meaning. Deep learning (DL), utilizing neural networks to grasp lexical and contextual features, offers a method for sarcasm detection. However, current DL-based sarcasm detection methods often overlook sentiment semantics, a crucial aspect for improving detection outcomes. Therefore, this study develops a new sarcasm detection using grasshopper optimization algorithm with DL (SD-GOADL) technique. The SD-GOADL technique aims to explore the patterns that exist in social media data and detect sarcasm. To obtain this, the SD-GOADL technique undergoes data pre-processing and Glove based word embedding technique. Next, the classification of sarcasm takes place using deep belief network (DBN) system. For enhancing the detection results of the DBN approach, the SD-GOADL technique uses GOA for hyperparameter selection process. The stimulation outcome of the SD-GOADL technique is tested on a sarcasm dataset and the results highlight the significant performance of the SD-GOADL technique compared to recent models. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Deep learning Grasshopper optimization algorithm Natural language processing Sarcasm detection Sentiment analysis

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Two kinds of average approximation accuracy

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 481-490页

作者： Qingzhao Kong Wanting Wang Dongxiao Zhang Wenbin Zhang Department of Science Jimei UniversityXiamenChina Digital Fujian Big Data Modeling and Intelligent Computing Institute Jimei UniversityXiamenChina Department of Computer Science Michigan Technological UniversityHoughtonMichiganUSA

Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set *** overcome this,two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is *** first is the relative average approximation accuracy,which is based on all sets in the universe and has several basic *** second is the absolute average approximation accuracy,which is based on undefinable sets and has yielded significant *** also explore the relationship between these two types of average approximation ***,the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.

关键词： rough sets rough set theory

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computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

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computer Systems science & Engineering 2023年第8期46卷 1811-1826页

作者： M.Uvaneshwari M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of Computing Technologies School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign *** benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in *** recognition of BT is highly significant to protecting the patient’s ***,the BT can be identified through the magnetic resonance imaging(MRI)scanning *** the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the ***,ML has prevailed against standard image processing *** studies denote the superiority of machine learning(ML)techniques over standard ***,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)*** accomplish the detection of brain tumor effectively,a computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research ***,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull ***,mayfly optimization with the Kapur’s thresholding based segmentation process takes *** feature extraction proposes,local diagonal extreme patterns(LDEP)are *** last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification *** accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research *** experimental validation of the proposed model demonstrates its promising performance over other existing methods.

关键词： Brain tumor machine learning segmentation computer-aided diagnosis skull stripping

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Generative adversarial networks with stochastic gradient descent with momentum algorithm for video-based facial expression

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3703-3722页

作者： Cherian, Aswathy K. Vaidhehi, M. Arshey, M. Briskilal, J. Simpson, Serin V. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of AI & amp School of Computer Science & amp

Video data is an asset that may be used in various settings, such as a live broadcast on a personal blog or a security camera at a manufacturing facility. Both of these examples are examples of how video data can be used. It is becoming increasingly common practice across a wide range of applications to use a machine learning appliance as a tool for processing video. Recent years have seen significant advancements made in the field of machine learning in computer vision. These advancements have been achieved. The presentation of humans is approached or even surpassed in areas such as item identification, object categorization, and image segmentation. Despite this, challenging difficulties exist, such as identifying human emotions. This study aims to recognize human emotions by analyzing still images and motion pictures taken from motion pictures using numerous machine learning procedures. To accomplish this, neural networks constructed based on Generative Adversarial Networks (GAN) were used to classify each face picture obtained from a frame into one of the seven categories of facial emotions we chose. To communicate feelings, videos are mined for informative aspects such as audio, single, and multiple video frames. During this process stage, separate instances of the OpenSMILE and Inception-ResNet-v2 models extract feature vectors from the audio and frames. After that, numerous classification models are trained using stochastic gradient descent with the impetus approach (SGDMA). The findings from each of the pictures are compiled into a table, and from that, it is determined which facial expression was seen on-screen the most often throughout the film. The classification of audio feature vectors is accomplished with the application of GAN-SGDMA. The Inception-ResNet-v2 algorithm is utilized to recognize feelings conveyed by still photographs. The findings of several experiments suggest that the presented distributed model GAN-SGDMA could significantly boost the sp

关键词： Attention model Face recognition Generative Adversarial Networks (GAN) Stochastic gradient descent with momentum algorithm (SGDMA) Video surveillance

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Generating Schrodinger's Kitten State from Coherent Pulses via Deterministic Photon Subtraction 16

Generating Schrodinger's Kitten State from Coherent Pulses v...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Pasharavesh, Abdolreza Bajcsy, Michal Institute for Quantum Computing University of Waterloo WaterlooON Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

We propose a scheme to generate Schrodinger's kitten states by subtracting single photons from coherent pulses through single-photon Raman interaction. Our findings suggest fidelities exceeding 99%, offering a pro... 详细信息

ISBN: (纸本)9798350372076

关键词： Photons

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A SPRINT-Based Single Photon Subtractor for PNS Attacks on QKD

A SPRINT-Based Single Photon Subtractor for PNS Attacks on Q...

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2024 Photonics North, PN 2024

作者： Pasharavesh, Abdolreza Bajcsy, Michal Institute for Quantum Computing University of Waterloo WaterlooON Canada Electrical and Computer Engineering Department University of Waterloo WaterlooON Canada

ISBN: (纸本)9798350366365

We present a photon subtraction scheme designed to deterministically extract single photons from multiphoton states within arbitrary input pulses of light using single-photon Raman interaction (SPRINT) [1]. The proposed system comprises two cascaded Lambda-type atoms with transitions selectively coupled to distinct modes of a single chiral waveguide. Through numerical simulations, we evaluate the device's performance in a potential application involving photon-number-splitting (PNS) attacks against quantum key distribution (QKD). © 2024 IEEE.

关键词： quantum cryptography

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Netflix Stock Price Prediction Using LSTM Based RNN 1st

Netflix Stock Price Prediction Using LSTM Based RNN

引用

1st International Conference on Intelligent computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Mohamed Usman Ali, A. Madhi Perkin, T. Department of Computer Science Engineering Hindustan Institute of Technology and Science Chennai India School of Computing Sciences Department of Computer Science Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9783031759567

Stock market forecast is a complex process on account of the clamorous, individual, complex and changeable character of the stock price occasion succession. Due to the growing number of consumers and new rules achieved apiece Netflix Corporation to stop giving passwords, the stock price of Netflix has existed unsteady currently. This project uses a Long Short-Term Memory (LSTM) model to think the stock price of Netflix. The LSTM model is a type of repeating interconnected system (RNN) that can efficiently capture worldly dependencies later succession dossier. Historical stock price data for Netflix is composed and pre-treated expected used as input for the LSTM model. The LSTM model is therefore prepared on the ancient dossier and proven on a grasped-beginning of dossier to judge allure predicting depiction. The results show that the LSTM model can efficiently envision the stock price of Netflix accompanying a large size of accuracy. This project explains the potential for utilizing LSTM models to forecast stock prices and supplies valuable observations for financiers curious in Netflix’s stock. Using LSTM located RNN, the model has the thought of the earlier dossier and can efficiently deceive new data to envision correct results for the active data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Costs

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