Vectorization and Sentiment Analysis of Arabizi Text

Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology

Sentiment Analysis of Self Driving Car Dataset: A comparative study of Deep Learning approaches

Objectives: Language detection is the process of identifying a language associated with a text. The proposed system aims to detect the Dravidian language that is associated with the given text using different machine learning and deep learning algorithms. The paper presents an empirical analysis of the results obtained using the different models. It also aims to evaluate the performance of a language agnostic model for the purpose of language detection. Method: An empirical analysis of Dravidian language identification in social media text using machine learning and deep learning approaches with k-fold cross validation has been implemented. The identification of Dravidian languages, including Tamil, Malayalam, Tamil Code Mix, and Malayalam Code Mix, is performed using both machine learning (ML) and deep learning algorithms. The machine learning algorithms used for language detection are Naive Bayes (NB), Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). The supervised Deep Learning (DL) models used include BERT, mBERT and language agnostic models. Findings: The language agnostic model outperform all other models considering the task of language detection in Dravidian languages. The results of both the ML and DL models are analyzed empirically with performance measures like accuracy, precision, recall, and f1-score. The accuracy associated with different machine learning algorithms varies from 85% to 89%. It is evident from the experimental result that the deep learning model outperformed with an accuracy of 98%. Novelty: The proposed system emphasizes on the use of the language agnostic model to implement the process of detecting Dravidian languages associated with the given text which provides a promising result of 98% accuracy which is higher than the existing methodologies. Keywords: Language, Machine learning, Deep learning, Transformer model, Encoder, Decoder

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

Abstract: Automobile reviews provide a valuable opportunity for sentiment analysis in order to gain a better understanding of consumer opinions and improve product quality. As a result of the complexity of natural language and differences in user opinions, accurate and robust sentiment analysis by computer models remains challenging. Traditional machine learning algorithms such as Support Vector Machines (SVM) and Naive Bayes (NB) have been used for sentiment analysis on textual data. They can, however, be limited by the heterogeneity of user opinions and language variations. In contrast, deep learning models and natural language processing have shown great promise in sentiment analysis. In particular, the VADER (Valence Aware Dictionary and sentiment Reasoner) algorithm has been proposed as a powerful tool for sentiment analysis on social media texts, and has shown promising results in other domains as well. In this work, The aim is to achieve accurate sentiment analysis on automobile reviews by comparing the accuracy performance of SVM, NB, and the VADER algorithm.

Diseases of the airways and the other parts of the lung cause chronic respiratory diseases. The major cause of lung disease is tobacco smoke, along with risk factors such as dust, air pollution, chemicals, and frequent lower respiratory infections during childhood. Early detection of these diseases requires the analysis of medical images, which would aid doctors in providing effective treatment.This paper aims to classify lung X-ray images as benign or malignant and to identify the type of disease, such as Atelectasis, Infiltration, Nodule, and Pneumonia, if the disease is malignant. Machine learning (ML) approaches, combined with a multi-attribute decision-making method called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), are used to rank different classifiers. Additionally, the deep learning (DL) model Inception v3 is proposed. This method ranks the SVM with RBF as the best classifier among the others used in this approach. Furthermore, the results show that the deep learning model achieves the best accuracy of 97.05%, which is 11.8% higher than the machine learning approach using the same dataset.

Sentiment analysis of product reviews plays a crucial role in understanding consumer feedback, improving customer experience and making informed business decisions. This paper explores the application of machine learning and deep learning algorithms to effectively classify and analyse the sentiment of product reviews. Traditional machine learning techniques, such as Naïve Bayes, Support Vector Machines (SVM) and Random Forests are employed for sentiment classification based on manually engineered features. Simultaneously, deep learning approaches like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are leveraged to automatically learn complex representations from raw text data. The study compares the performance of these methods in terms of accuracy, precision, recall and F1-score. Additionally, pre-trained language models such as BERT are incorporated to enhance contextual understanding. Experimental results demonstrate that deep learning models particularly LSTM and BERT, outperform traditional machine learning techniques in capturing sentiments. This analysis provides valuable insights into the effectiveness of different algorithms in sentiment analysis tasks, paving the way for more advanced applications in natural language processing and customer sentiment evaluation.

Sentiment analysis (or opinion mining), is a natural language processing (NLP) technique that harnesses Information from text data that is naturally subjective. Opinions, emotions, and attitudes are things that this information can contain. In recent years, there has been an increasing focus on sentiment analysis because of the explosive growth of unstructured data on the internet. Sentiment analysis tasks have seen state of the art performance using deep learning models. However, these models can be computationally intensive and high dimensional can produce problems of overfitting when working with relatively limited data. In order to overcome these limitations, a new hybrid model is proposed using a combination of LSTM network for feature extraction and Random Forest classifier for the final sentiment classification improving the accuracy of the sentiment analysis, while reducing computational cost. In this paper, the proposed hybrid model is designed and implemented, and evaluated on a publicly available dataset of movie reviews.

In recent years, ride-hailing platforms such as Uber have significantly transformed the transportation industry by offering convenient, flexible, and technology-driven services. These platforms have made travel more accessible by enabling users to book rides instantly through mobile applications. However, customer satisfaction is not always consistent, as users frequently report issues related to pricing, driver behavior, service reliability, and application performance. Understanding these concerns is essential for improving service quality and enhancing user experience.This study focuses on analyzing customer reviews collected from Uber using sentiment analysis techniques. Since customer feedback is typically unstructured and large in volume,manual analysis is inefficient. Therefore, Natural Language Processing (NLP) techniques are used to process and interpret the textual data. The research applies both machine learning and deep learning models, including Logistic Regression, Naïve Bayes, Random Forest, and Long Short-Term Memory (LSTM), to classify reviews into positive, negative, and neutral sentiments. The dataset used in this study consists of approximately 12,000 customer reviews, which provide a diverse representation of user experiences. Data preprocessing techniques such as cleaning, tokenization, stopword removal, and stemming are applied to improve data quality. The experimental results demonstrate that deep learning models, particularly LSTM,outperform traditional machine learning approaches in terms of accuracy and contextual understanding. The proposed model achieved an overall accuracy of 90%, indicating its effectiveness in correctly classifying customer sentiments. The analysis also reveals key factors influencing customer satisfaction, including frequent ride cancellations, surge pricing, driver-related issues, and technical problems within the application. Overall, this research highlights the importance of sentiment analysis as a powerful tool for understanding customer opinions in ride-hailing platforms. The insights obtained from this study can help companies make data-driven decisions, improve service quality, and enhance customer satisfaction.

Sentiment analysis of news headlines is an important factor that investors consider when making investing decisions. We claim that the sentiment analysis of financial news headlines impacts stock market values. Hence financial news headline data are collected along with the stock market investment data for a period of time. Using Valence Aware Dictionary and Sentiment Reasoning (VADER) for sentiment analysis, the correlation between the stock market values and sentiments in news headlines is established. In our experiments, the data on stock market prices are collected from Yahoo Finance and Kaggle. Financial news headlines are collected from the Wall Street Journal, Washington Post, and Business-Standard website. To cope with such a massive volume of data and extract useful information, various embedding methods, such as Bag-of-words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), are employed. These are then fed into machine learning models such as Naive Bayes and XGBoost as well as deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Various natural language processing, andmachine and deep learning algorithms are considered in our study to achieve the desired outcomes and to attain superior accuracy than the current state-of-the-art. Our experimental study has shown that CNN (80.86%) and LSTM (84%) are the best performing models in relation to machine learning models, such as Support Vector Machine (SVM) (50.3%), Random Forest (67.93%), and Naive Bayes (59.79%). Moreover, two novel methods, BERT and RoBERTa, were applied with the expectation of better performance than all the other models, and they did exceptionally well by achieving an accuracy of 90% and 88%, respectively.

The rising prevalence of harassment in Middle Eastern countries is mirrored in literary works from the region. However, extracting data from these texts to understand the typology and frequency of the cases poses a significant challenge due to human cognitive limitations and potential biases. Thus, this study aims to use natural language processing (NLP) approaches to propose a machine learning framework for text mining of sexual harassment content in literary texts. The data source for this study consists of twelve Middle Eastern novels. The proposed framework involves the classification of physical and non-physical types of sexual harassment using a machine-learning model. Lexicon-based sentiment and emotion detection are applied to sentences containing instances of sexual harassment for data labelling and analysis. Finally, a long short-term memory-gated recurrent unit (LSTM-GRU) deep learning model is built to classify the sentiment characteristics that induce sexual harassment. The proposed model achieved an accuracy of 75.8% while outperforming five other algorithms. Additionally, a sentiment classification with three labels—negative, positive, and neutral—was developed using an LSTM-GRU RNN deep learning model. The accuracy of this model was 84.5%. Most statements, even those involving physical sexual harassment, which had greater levels of sexual harassment, had negative sentiments, according to lexicon-based sentiment analysis. This study contributes to the field of text mining by providing a novel approach to identifying instances of sexual harassment in literature in English from the Middle East. The use of machine learning models and sentiment analysis techniques allows for more accurate identification and classification of different types of sexual harassment. Furthermore, this study sheds light on the prevalence of sexual harassment in Middle Eastern countries and highlights the need for further research and action to address this issue.

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions, or feedback that relates to the usage of computational linguistics, text analytics, and natural language processing. With the rise of social media posts, it is becoming more challenging to evaluate brief, casual, and non-structured texts to optimize consumer feedback and spot patterns. Meanwhile, social commerce involves social media for social interaction in assisting customers and merchants to do business transactions. From a social media perspective, the informal Malay Text is less explored by the researchers. Thus, it will directly yield difficulties in conducting and preparing the SA processes. Cross-Industry Standard Process for Data Mining (CRISP-DM) was adapted as a reference model for the methodology of this work with machine learning approaches in classifying the informal Malay textual data based on sentiment. The dataset was extracted from the Facebook platform of Pos Laju Malaysia pages. The comparison of the classification technique performances was analyzed in identifying the most accurate classifier for SA, within three different machine learning classifiers was experimented by using 1200 instances from an informal Malay textual dataset. The results of Decision Tree (J48), Support Vector Machine (SVM), and Naïve Bayes (NB) were analyzed and discussed. The result of the highest accuracy of Ten-Fold Cross-Validation is 69.7% and meanwhile, for the Percentage Split method, the highest accuracy result is 70.9%. It shows that Support Vector Machine (SVM) is the best classifier compared to other classifiers of text classification based on sentiment.

Forecasting interest rates is a fundamental task in financial planning, investment strategies, and policy-making. Traditional statistical models, while widely used, often fail to adequately capture complex non-linear relationships and temporal dependencies inherent in financial data. This study addresses these limitations by exploring the potential of machine learning models to improve the accuracy and reliability of interest rate forecasts. The primary objective of this research is to evaluate and compare the performance of multiple machine learning models, including linear regression, support vector machines, and deep learning techniques, in predicting interest rate trends. Historical data spanning two decades was collected and preprocessed, ensuring data quality and consistency. The models were trained and tested on this dataset using well-defined evaluation metrics such as mean absolute error and root mean squared error to ensure robust performance assessments. The results revealed that machine learning approaches, particularly deep learning models, outperformed traditional methods in capturing complex patterns and delivering more accurate forecasts. The findings further discuss the practical implications of implementing machine learning techniques in real-world financial contexts, highlighting both opportunities and challenges. In conclusion, this study provides actionable insights and a robust framework for integrating machine learning into interest rate forecasting, contributing to the advancement of predictive modeling in finance.