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A Comparative Research On Sentiment Analysis of Tweets Using Deep and Machine Learning Models 5

A Comparative Research On Sentiment Analysis of Tweets Using...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Madhav, Kagolanu Venkata Chandra Mudunuri, Sravan Varma Reddy, Panyam Charithartha Beena, B.M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350383522

Social media platforms are rich sources of user-generated material that are updated in real time like twitter and may offer important insights into public opinion. Taking advantage of the dynamic nature of user-generated information in real-time on social media sites like Twitter, this research uses a strict approach that includes data collection, sentiment categorization, feature extraction, and preprocessing. Sentiment analysis is guaranteed to have a representative sample when Twitter datasets are used. We combine machine learning models with deep learning models, such as CNN, RNN, FNN, and BiLSTM. We performed a comparative study of different models inorder to find the accuracy so that we can achieve the target that we are aimed to. This analysis provides future study for the topic twitter sentimental analysis for finding more effective techniques for more bird's eye categorization through comprehensive learning of machine learning and deep learning models. © 2024 IEEE.

关键词： Deep Learning Models Machine Learning Models Twitter Sentimental Analysis

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Efficient Workload Distribution for Sustainable Server Utilization in Cloud Data Centers

Efficient Workload Distribution for Sustainable Server Utili...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Yadav, Monika Mishra, Atul J C Bose University of Science and Technology Ymca Dept. of Computer Engineering Faridabad India

ISBN: (纸本)9798350375237

Cloud service providers offer flexible infrastructure scaling options, allowing users to adjust resources based on their requirements and pay only for what they use. This paper proposes an approach to efficiently distribute workloads across host machines within a data center environment. This work proposes the concept of skewness as a measure of server utilization, identifying underutilized servers. When a server is found to be underused, its workload is redistributed to other host machines, enabling the underutilized server to be idled and potentially shut down. This strategy not only optimizes server utilization but also contributes to green computing initiatives by reducing power consumption and carbon footprint. Through Simulation, we demonstrate the effectiveness of our approach in achieving both scalability and sustainability in cloud computing environments. © 2024 IEEE.

关键词： Cloud computing

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Parkinson's Disease Diagnosis: Integrating Decision Trees and Random Forest 18

Parkinson's Disease Diagnosis: Integrating Decision Trees an...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Abhinandhan, S. Sreedevi, A.G. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. Computer Science and Engineering Chennai India

ISBN: (纸本)9789380544519

This research work attempts to create a powerful machine-learning model that can identify the prefrontal cortex of individuals with Parkinson's Disease by analyzing their voice recordings. The disorder of Parkinson's, a progressive neurodegenerative condition that is characterized by the presence of tremors, muscular rigidity, and coordination issues, is early detection and requires no invasiveness. Leveraging the advanced computational techniques, this research pioneered a two-phase approach that incorporated Decision Tres and random Forest to have more specific diagnoses. The amalgamation of these methods not only addresses the diagnostic issues, but also facilitates the formulation of individualized treatment strategies. Ultimately, these strategies have a preeminent impact on the aspect of liveliness of patients with Parkinson's disease. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Decision Trees Diagnosis healthcare Machine learning Parkinson's disease personalized treatment Quality of life Random Forest

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ThriveJIT: Dynamic Just-In-Time Compilation for Efficient Execution of Arithmetic Expressions 15

ThriveJIT: Dynamic Just-In-Time Compilation for Efficient Ex...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ramesh, Sreyas Sukanth, B.N. Jaswanth, Sathyavarapu Sri Sharma, Vishwash Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Just in Time (JIT) compiler has been used in many modern programming languages such as python and Java, it has also been implemented in various web browsers to improve the performances of code. In this paper, we give a thorough discussion about the Compiler optimization phases, with the main focus on Python and GraalVM based JIT-Just in Time Compiler and AOT-Ahead of Time Compiler. The study is done using a deep comprehensive exploration by understanding the underlying mechanism of how a compiler optimization works. Going forward the paper will also look on how compiler transform source codes into high efficient executable programs which is done by understanding the role of a JIT compiler and AOT compiler. The paper also focuses on comparing the results of various JIT and AOT compilers where ThriveJIT was the most efficient compiler when compared with Python Compilers © 2024 IEEE.

关键词： Ahead Of Time (AOT) Compilation Compiler Optimization Phases GraalVM Just In Time (JIT) Compilation Native-Image Numba

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Solar Radiation Prediction: A Comprehensive Analysis of Machine Learning Models and Interpretability Techniques

Solar Radiation Prediction: A Comprehensive Analysis of Mach...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Shah, Kriti Priya Sah, Subodh Narayan Dharmaraj, Kadam Prajwal Nair, Priyanka C. Sampath, Nalini Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382693

Solar energy is an imminent renewable energy resources. Solar radiation is one of the important factors of solar energy systems. Obtaining sun radiation data for many regions is still difficult due to either absence of solar radiation measuring equipment's or due to their high cost and limited availability. This study contributes to the current machine learning approaches to find out the solar radiation value for given climatic parameters. The main goal of this study is to analyze a high-accuracy solar radiation forecasting model using regression based models as Artificial Neural Network (ANN), Neural Network Autoregressive with Exogenous Input (NNARX), XgBoost, Multilayer perceptron (MLP) and AdaBoost. The results are evaluated using MAE, MSE, R2 and RMSE. Based on the evaluation metrics the best performing algorithm for this study is XgBoost. The results are interpreted here for each algorithms based on explainable AI - LIME (Local Interpretable Model agnostic Explanations). Additionally, time series analysis was done for this study to have a better understanding of the solar radiation pattern over time. © 2024 IEEE.

关键词： Multilayer neural networks

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Enhancing Low-Resolution Face Recognition with Self Attention Network and Resolution Augmentation Approach 2

Enhancing Low-Resolution Face Recognition with Self Attentio...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Soni, Ravindra Kumar Nain, Neeta Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur India

ISBN: (纸本)9798331504106

Unconstrained low-resolution face recognition is a very hard and demanding task in the field of computer vision. Due to the scarcity of abundant visual features in low-resolution (LR) images, it is very challenging to extract discriminant key features to improve the identification performance of deep learning networks. The size of the LR images also poses a constraint on the recognition rate, and if it is tiny in size, then it hardens the recognition task more. For boosting the identification rate on unconstrained low resolution faces, a Residual Branch Self Attention Network (RBSaN) is designed. The RBSaN encompasses a self attention module where pixel-wise convolution is applied to channels for extracting the discriminant features. Resolution-augmented images are generated at multiple scales from the high-resolution (HR) images to capture the resolution aspects. RBSaN has achieved identification accuracy on the TinyFace dataset and Surveillance Cameras Face (SCface) database comparable to state-of-the-art (SOTA) models. Heat-map visualization witnesses the effectiveness of RBSaN to locate the key discriminant feature areas in low-resolution images for separating the identities in embedding space. Extensive experimental analysis has achieved qualitative results comparable to SOTA models. ©2024 IEEE.

关键词： Face recognition

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Enhancing Data Security through the Synergy of AI and AES Encryption: A Comprehensive Study and Implementation

Enhancing Data Security through the Synergy of AI and AES En...

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2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Budhewar, Anupama Bhumgara, Sayonee Tekavade, Abhishek Nandkar, Jayditya Zanwar, Atharva MIT ADT University MIT School of Computing Dept. of Computer Science Engineering Pune India

ISBN: (纸本)9798350362879

In today's digital era, safeguarding data is crucial. This paper examines the combination of Artificial Intelligence (AI) and Advanced Encryption Standard (AES) for enhanced data security. We integrate the k-Nearest Neighbors (k-NN) algorithm with AES encryption to fortify data protection across various domains. This study highlights the strengths and limitations of both AI and AES, and demonstrates how k-NN's pattern recognition capabilities enhance AES's robustness against evolving threats. The practical implementation for secure file transmissions and confidential data storage is also discussed. © 2024 IEEE.

关键词： Cryptography

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Accent Detection in Indian Languages through Convolutional Neural Network based Spectrogram Analysis 15

Accent Detection in Indian Languages through Convolutional N...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Ch. Rahul A. N. Singh, Harsh Kumar Prabhu, H.Suhas Jambha, Aniketh V. Jyotsna, C. Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

India has a diverse verbal landscape, comprising 22 major languages and approximately 1600 dialects. It is essential to recognise and comprehend the distinct features of each language and accent as it helps in promoting effective communication, learning sociolinguistic phenomena and language learning. This study, focuses especially on distinguishing between Hindi and non-Hindi languages. Unlike previous works, the dataset used in this work is newly curated, offering a fresh perspective on dynamic audio processing. Accent detection is made accurate by utilising audio preprocessing, which extracts Mel-frequency cepstral coefficients (MFCCs) and then runs them through convolutional neural networks (CNN). This process adds variation to the understanding of the diversity of languages. A standard existing MobileNet model demonstrates superior performance as compared to other existing models with an accuracy of 82%. Additionally, LIME (Local Interpretable Model-agnostic Explanations) analysis is applied to understand the model's predictions, providing insights into the regions of the spectrograms influencing the classification. Through this innovative method, accent barriers are not only removed but also transformed into bridges, facilitating improved communication, enhanced educational opportunities, and promoting a more connected India. © 2024 IEEE.

关键词： Accent Recognition Convolutional Neural Network Mel Frequency Spectrograms

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Pomegranate Grading Using Different Feature Extraction and Machine Learning Approaches

Pomegranate Grading Using Different Feature Extraction and M...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Gopi Kiran, V. Vidula, N.A. Hemanth Babu, T. Dinesh Kumar, K. Amrita School Of Computing Amrita Vishwa Vidyapeetham Dept. Of Computer Science &engineering Bengaluru India

ISBN: (纸本)9798350364828

In the preparation and packing of pomegranates, sorting is an essential stage. Pomegranates are currently hand sorted into quality groups. Manual sorting, however, takes a lot of time and is not precise. Furthermore, it isn't recommended to sort fruits of export quality manually. These problems can be resolved by automated sorting devices more precisely and effectively than by hand sorting. The objective of this study is to use digital image processing and machine intelligence to remove these problems with manual quality evaluation. As a result, the sorting procedure requires a computing facility with a machine vision system. This paper makes use of spatial domain features, frequency domain features and Histogram of Oriented Gradients (HOG) features to create datasets from the images by extracting their attributes, using which various Machine Learning classifiers are trained to classify pomegranate photos. It was found that HOG features outperformed spatial domain and frequency domain features, as an accuracy of 93.51% was obtained by training an ANN model with a dataset containing HOG features. © 2024 IEEE.

关键词： Grading

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Search Engine Optimization (SEO) Auditing Tool 8

Search Engine Optimization (SEO) Auditing Tool

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Pattedar, Nischal V. Yoshitha, Chunduri Jayarama Krishna, A. Prakashini, G. Vishwa Ullas, S. Vineetha, K.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331505462

The Search Engine Optimization (SEO) Auditing Tool is a feature-rich Java-based tool made with the power of Data Structures. It caters to help digital marketers and website managers optimize their online presence by performing in-dept. SEO analysis. This tool performs category identification, backlink analysis, broken link detection, page load time analysis, and user interface analysis. Auditing evaluates for the best practices to be adopted in improving the ranking performance on search engines. SEO Auditing revolves within the analysis of Technical SEO, On-Page SEO, Off-Page SEO, User Interface (UX/UI) and Competitor Analysis. The tool explores all categories within Auditing to briefly estimate the quality of a website and inclusion of Competitor analysis to this tool would add best possible comparison and evaluation of websites. © 2024 IEEE.

关键词： Search engines

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