DeBERTa-GRU: Sentiment Analysis for Large Language Model

Modern technological advancements have made social media an essential component of daily life.Social media allow individuals to share thoughts,emotions,and ideas.Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the text.Sentiment analysis is essential in business and society because it impacts strategic decision-making.Sentiment analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance correlations.The execution time increases due to the sequential processing of the sequence models.However,the calculation times for the Transformer models are reduced because of the parallel processing.This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations.In particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment analysis.Using the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics correctly.The proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing

Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them.Deep learning has gained momentum for identifying activities through sensors,smartphones or even surveillance cameras.However,it is often difficult to train deep learning models on constrained IoT devices.The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing,which we call DL-HAR.The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on less-powerful edge devices for recognition.The idea is to conduct the training of the model in the Cloud and distribute it to the edge nodes.We demonstrate how the DL-HAR can perform human activity recognition at the edge while improving efficiency and accuracy.In order to evaluate the proposed framework,we conducted a comprehensive set of experiments to validate the applicability of DL-HAR.Experimental results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models.

A Deep Learning-Based Recognition Approach for the Conversion of Multilingual Braille Images

Braille-assistive technologies have helped blind people to write,read,learn,and communicate with sighted individuals for many years.These technologies enable blind people to engage with society and help break down communication barriers in their lives.The Optical Braille Recognition(OBR)system is one example of these technologies.It plays an important role in facilitating communication between sighted and blind people and assists sighted individuals in the reading and understanding of the documents of Braille cells.However,a clear gap exists in current OBR systems regarding asymmetric multilingual conversion of Braille documents.Few systems allow sighted people to read and understand Braille documents for self-learning applications.In this study,we propose a deep learning-based approach to convert Braille images into multilingual texts.This is achieved through a set of effective steps that start with image acquisition and preprocessing and end with a Braille multilingual mapping step.We develop a deep convolutional neural network(DCNN)model that takes its inputs from the second step of the approach for recognizing Braille cells.Several experiments are conducted on two datasets of Braille images to evaluate the performance of the DCNN model.The rst dataset contains 1,404 labeled images of 27 Braille symbols representing the alphabet characters.The second dataset consists of 5,420 labeled images of 37 Braille symbols that represent alphabet characters,numbers,and punctuation.The proposed model achieved a classication accuracy of 99.28%on the test set of the rst dataset and 98.99%on the test set of the second dataset.These results conrm the applicability of the DCNN model used in our proposed approach for multilingual Braille conversion in communicating with sighted people.

A Secure NDN Framework for Internet of Things Enabled Healthcare

Healthcare is a binding domain for the Internet of Things(IoT)to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the Internet.The current IP-based Internet architecture suffers from latency,mobility,location dependency,and security.The Named Data Networking(NDN)has been projected as a future internet architecture to cope with the limitations of IP-based Internet.However,the NDN infrastructure does not have a secure framework for IoT healthcare information.In this paper,we proposed a secure NDN framework for IoTenabled Healthcare(IoTEH).In the proposed work,we adopt the services of Identity-Based Signcryption(IBS)cryptography under the security hardness Hyperelliptic Curve Cryptosystem(HCC)to secure the IoTEH information in NDN.The HCC provides the corresponding level of security using minimal computational and communicational resources as compared to bilinear pairing and Elliptic Curve Cryptosystem(ECC).For the efficiency of the proposed scheme,we simulated the security of the proposed solution using Automated Validation of Internet Security Protocols and Applications(AVISPA).Besides,we deployed the proposed scheme on the IoTEH in NDN infrastructure and compared it with the recent IBS schemes in terms of computation and communication overheads.The simulation results showed the superiority and improvement of the proposed framework against contemporary related works.

Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of deaths.Therefore,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.This study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of COVID-19.The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners.Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,2020.The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method.The results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.

A Cost-Effective Approach for NDN-Based Internet of Medical Things Deployment

Nowadays,healthcare has become an important area for the Internet of Things(IoT)to automate healthcare facilities to share and use patient data anytime and anywhere with Internet services.At present,the host-based Internet paradigm is used for sharing and accessing healthcare-related data.However,due to the location-dependent nature,it suffers from latency,mobility,and security.For this purpose,Named Data Networking(NDN)has been recommended as the future Internet paradigm to cover the shortcomings of the traditional host-based Internet paradigm.Unfortunately,the novel breed lacks a secure framework for healthcare.This article constructs an NDN-Based Internet of Medical Things(NDN-IoMT)framework using a lightweight certificateless(CLC)signature.We adopt the Hyperelliptic Curve Cryptosystem(HCC)to reduce cost,which provides strong security using a smaller key size compared to Elliptic Curve Cryptosystem(ECC).Furthermore,we validate the safety of the proposed scheme through AVISPA.For cost-efficiency,we compare the designed scheme with relevant certificateless signature schemes.The final result shows that our proposed scheme uses minimal network resources.Lastly,we deploy the given framework on NDN-IoMT.