Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

Current and further trajectories in designing functional materials for solid oxide electrochemical cells:A review of other reviews

Complex oxides are an important class of materials with enormous potential for electrochemical appli-cations.Depending on their composition and structure,such complex oxides can exhibit either a single conductivity(oxygen-ionic or protonic,or n-type,or p-type electronic)or a combination thereof gener-ating distinct dual-conducting or even triple-conducting materials.These properties enable their use as diverse functional materials for solid oxide fuel cells,solid oxide electrolysis cells,permeable membranes,and gas sensors.The literature review shows that the field of solid oxide materials and related electro-chemical cells has a significant level of research engagement,with over 8,000 publications published since 2020.The manual analysis of such a large volume of material is challenging.However,by examining the review articles,it is possible to identify key patterns,recent achievements,prospects,and remaining obstacles.To perform such an analysis,the present article provides,for the first time,a comprehensive summary of previous review publications that have been published since 2020,with a special focus on solid oxide materials and electrochemical systems.Thus,this study provides an important reference for researchers specializing in the fields of solid state ionics,high-temperature electrochemistry,and energyconversiontechnologies.

Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics.In recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related conversations.Nevertheless,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of misinformation.To address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related tweets.Election-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,andmisinformation.ElecBERT is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in English.Themodel outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on ElecSent-English.The performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case study.The ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of 6.13.This paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media analysis.However,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and understanding.In light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral process.To achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country levels.LEPM predicts the support and opposing strength of each political party/candidate.To determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/posts.The sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case study.By applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state level.Moreover,at the country level,the LEPM model outperforms traditional polling results.With a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other methodologies.Leveraging the extensive social media data,the LEPM model provides nuanced insights into voter behavior,enabling policymakers to make informed decisions and facilitating in-depth analyses of elections.The study emphasizes the importance of using social media data for reliable election prediction and offers implications for enhancing prediction accuracy and understanding voter sentiment and behavior.

Liver Ailment Prediction Using Random Forest Model

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the world.Previous research has indicated that liver disease is more frequent in younger people than in older ones.When the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is difficult.Using several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early stages.As a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early on.The dataset was picked from the University of California,Irvine repository.RF’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and G-measure.RF has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is 72.1739.The widely acknowledged result of this work can be used as a starting point for subsequent research.As a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain

Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agriculturalists spend a huge amount of money.However,the manual detection method of plant diseases is complicated and time-consuming.Consequently,automated systems for plant disease detection using machine learning(ML)approaches are proposed.However,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data.To address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural networks.In the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained architectures.It is capable to obtain the feature extraction of the given data with fine-tuning details.After convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural Network.The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step.The achieved average accuracy of our method is more than 96%and outperforms the recent techniques.

Automated Test Case Generation from Requirements: A Systematic Literature Review

Software testing is an important and cost intensive activity in software development.The major contribution in cost is due to test case generations.Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure.Requirement-based testing includes functional and nonfunctional requirements.The objective of this study is to explore the approaches that generate test cases from requirements.A systematic literature review based on two research questions and extensive quality assessment criteria includes studies.The study identies 30 primary studies from 410 studies spanned from 2000 to 2018.The review’s nding shows that 53%of journal papers,42%of conference papers,and 5%of book chapters’address requirementsbased testing.Most of the studies use UML,activity,and use case diagrams for test case generation from requirements.One of the signicant lessons learned is that most software testing errors are traced back to errors in natural language requirements.A substantial amount of work focuses on UML diagrams for test case generations,which cannot capture all the system’s developed attributes.Furthermore,there is a lack of UML-based models that can generate test cases from natural language requirements by rening them in context.Coverage criteria indicate how efciently the testing has been performed 12.37%of studies use requirements coverage,20%of studies cover path coverage,and 17%study basic coverage.

An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

Liver-Tumor Detection Using CNN ResUNet

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018.There are several imaging tests like Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver.These tests are costly and time-consuming.This paper proposed that image processing through deep learning Convolutional Neural Network(CNNs)ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods.The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest(ROI).This study uses ResUNet,an updated version of U-Net and ResNet Models that utilize the service of Residential blocks.We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients.The results showed the True Value Accuracy around 99%and F1 score performance around 95%.This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.

3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

Recent advancements in applications of chitosan-based biomaterials for skin tissue engineering

The use of polymer based composites in the treatment of skin tissue damages,has got huge attention in clinical demand,which enforced the scientists to improve the methods of biopolymer designing in order to obtain highly efficient system for complete restoration of damaged tissue.In last few decades,chitosan-based biomaterials have major applications in skin tissue engineering due to its biocompatible,hemostatic,antimicrobial and biodegradable capabilities.This article overviewed the promising biological properties of chitosan and further discussed the various preparation methods involved in chitosan-based biomaterials.In addition,this review also gave a comprehensive discussion of different forms of chitosan-based biomaterials including membrane,sponge,nanofiber and hydrogel that were extensively employed in skin tissue engineering.This review will help to form a base for the advanced applications of chitosan-based biomaterials in treatment of skin tissue damages.

Energy Management of a Battery Storage and D-STATCOM Integrated Power System Using the Fractional Order Sliding Mode Control

At present,the power system is more inclined towards disturbances,such as voltage variations and unbalanced load conditions,due to the grid's complexity and load growth.These challenges emphasize the integration of the compensating devices,such as battery storage(BS)and D-STATCOMs.In this regard,this current paper exhibits a novel energy management system(EMS)of a combined BS and D-STATCOM to compensate the power system during disturbances.The EMS is based on a fractional order sliding mode control(FOSMC)to drive the voltage source converters(VSCs)such that the active power is independently absorbed/injected by the BS,whereas the reactive power is independently absorbed/injected by the D-STATCOM depending upon the disturbance situation.FOSMC is a robust non-linear controller in which the Riemann-Liouville(RL)function is employed to design the sliding surface and the exponential reaching law is used to minimize the chattering phenomenon.The stability of the FOSMC in the proposed EMS is proved using the Lyapunov candidate function.In order to validate the performance of the proposed EMS,a model of a 400 V,180 kVA radial distributor along with a BS and D-STATCOM is simulated in MATLAB/Simulink environment in two test cases.The results prove that the proposed EMS with FOMSC effectively compensates the power system under voltage variations and unbalanced load conditions with rapid tracking,fast convergence and upright damping.Furthermore,the results have been compared with the classical proportional integral(PI)control and fixed frequency SMC(FFSMC),and they demonstrate the superiority of the proposed EMS with FOSMC in power system applications.

Hyperspectral anomaly detection:a performance comparison of existing techniques

Anomaly detection in Hyperspectral Imagery(HSI)has received considerable attention because of its potential application in several areas.Numerous anomaly detection algorithms for HSI have been proposed in the literature;however,due to the use of different datasets in previous studies,an extensive performance comparison of these algorithms is missing.In this paper,an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different categories.In addition,this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available datasets.Results indicate that attribute and edge-preserving filtering-based detection(AED),local summation anomaly detection based on collaborative representation and inverse distance weight(LSAD-CR-IDW)and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector(LSUNRSORAD)perform better as indicated by the mean and median values of area under the receiver operating characteristic(ROC)curves.Finally,this paper studies the effect of various dimensionality reduction techniques on anomaly detection.Results indicate that reducing the number of components to around 20 improves the performance;however,any further decrease deteriorates the performance.

A flexible nickel phthalocyanine resistive random access memory with multi-level data storage capability

Metal phthalocyanine is considered one of the most promising candidates for the design and fabrication of flexible resistive random access memory(RRAM)devices due to its intrinsic flexibility and excellent functionality.However,performance degradation and the lack of multi-level capability,which can directly expand the storage capacity in one memory cell without sacrificing additional layout area,are the primary obstacles to the use of metal phthalocyanine RRAMs in information storage.Here,a flexible RRAM with pristine nickel phthalocyanine(Ni Pc)as the resistive layer is reported for multi-level data storage.Due to its high trap-concentration,the charge transport behavior of the device agrees well with classical space charge limited conduction controlled by traps,leading to an excellent performance,including a high on-off current ratio of 10^(7),a long-term retention of 10^(6)s,a reproducible endurance over6000 cycles,long-term flexibility at a bending strain of 0.6%,a write speed of 50 ns under sequential bias pulses and the capability of multi-level data storage with reliable retention and uniformity.