Extraction of intersecting palm-vein and palmprint features for cancellable identity verification

A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification.Capitalising on the unique geometrical relationship between the two biometric modalities,the cross-modality intersecting points provides a stable set of features for identity verification.To facilitate flexibility in template changes,a template transformation is proposed.While maintaining non-invertibility,the template transformation allows transformation sizes beyond that offered by the con-ventional means.Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.

Image enhancement with intensity transformation on embedding space

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.

Enhancing Building Facade Image Segmentation via Object-Wise Processing and Cascade U-Net

The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades,as buildings contribute significantly to energy consumption in urban environments.However,conventional image segmentation methods often struggle to capture fine details such as edges and contours,limiting their effectiveness in identifying areas prone to energy loss.To address this challenge,we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model,Cascade U-Net.Object-wise processing isolates components of the facade,such as walls and windows,for independent analysis,while Cascade U-Net incorporates contour information to enhance segmentation accuracy.The methodology involves four steps:object isolation,which crops and adjusts the image based on bounding boxes;contour extraction,which derives contours;image segmentation,which modifies and reuses contours as guide data in Cascade U-Net to segment areas;and segmentation synthesis,which integrates the results obtained for each object to produce the final segmentation map.Applied to a dataset of Korean building images,the proposed method significantly outperformed traditional models,demonstrating improved accuracy and the ability to preserve critical structural details.Furthermore,we applied this approach to classify window thermal loss in real-world scenarios using infrared images,showing its potential to identify windows vulnerable to energy loss.Notably,our Cascade U-Net,which builds upon the relatively lightweight U-Net architecture,also exhibited strong performance,reinforcing the practical value of this method.Our approach offers a practical solution for enhancing energy efficiency in buildings by providing more precise segmentation results.

Computing the User Experience via Big Data Analysis:A Case of Uber Services

As of 2020,the issue of user satisfaction has generated a significant amount of interest.Therefore,we employ a big data approach for exploring user satisfaction among Uber users.We develop a research model of user satisfaction by expanding the list of user experience(UX)elements(i.e.,pragmatic,expectation confirmation,hedonic,and burden)by including more elements,namely:risk,cost,promotion,anxiety,sadness,and anger.Subsequently,we collect 125,768 comments from online reviews of Uber services and perform a sentiment analysis to extract the UX elements.The results of a regression analysis reveal the following:hedonic,promotion,and pragmatic significantly and positively affect user satisfaction,while burden,cost,and risk have a substantial negative influence.However,the influence of expectation confirmation on user satisfaction is not supported.Moreover,sadness,anxiety,and anger are positively related to the perceived risk of users.Compared with sadness and anxiety,anger has a more important role in increasing the perceived burden of users.Based on these findings,we also provide some theoretical implications for future UX literature and some core suggestions related to establishing strategies for Uber and similar services.The proposed big data approach may be utilized in other UX studies in the future.

Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

Metaverse is one of the main technologies in the daily lives of several people,such as education,tour systems,and mobile application services.Particularly,the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere.To provide an improved service,it is important to analyze online reviews that contain user satisfaction.Several previous studies have utilized traditional methods,such as the structural equation model(SEM)and technology acceptance method(TAM)for exploring user satisfaction,using limited survey data.These methods may not be appropriate for analyzing the users of mobile applications.To overcome this limitation,several researchers perform user experience analysis through online reviews and star ratings.However,some online reviews occasionally have inconsistencies between the star rating and the sentiment of the text.This variation disturbs the performance of machine learning.To alleviate the inconsistencies,Valence Aware Dictionary and sEntiment Reasoner(VADER),which is a sentiment classifier based on lexicon,is introduced.The current study aims to build a more accurate sentiment classifier based on machine learning with VADER.In this study,five sentiment classifiers are used,such as Naïve Bayes,K-Nearest Neighbors(KNN),Logistic Regression,Light Gradient Boosting Machine(LightGBM),and Categorical boosting algorithm(Catboost)with three embedding methods(Bag-of-Words(BoW),Term Frequency-Inverse Document Frequency(TF-IDF),Word2Vec).The results show that classifiers that apply VADER outperform those that do not apply VADER,excluding one classifier(Logistic Regression with Word2Vec).Moreover,LightGBM with TF-IDF has the highest accuracy 88.68%among other models.

CBOE Volatility Index Forecasting under COVID-19:An Integrated BiLSTM-ARIMA-GARCH Model

After the outbreak of COVID-19,the global economy entered a deep freeze.This observation is supported by the Volatility Index(VIX),which reflects the market risk expected by investors.In the current study,we predicted the VIX using variables obtained fromthe sentiment analysis of data on Twitter posts related to the keyword“COVID-19,”using a model integrating the bidirectional long-term memory(BiLSTM),autoregressive integrated moving average(ARIMA)algorithm,and generalized autoregressive conditional heteroskedasticity(GARCH)model.The Linguistic Inquiry and Word Count(LIWC)program and Valence Aware Dictionary for Sentiment Reasoning(VADER)model were utilized as sentiment analysis methods.The results revealed that during COVID-19,the proposed integrated model,which trained both the Twitter sentiment values and historical VIX values,presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models.

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.

ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

Environmental,social,and governance(ESG)factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value.Recently,non-financial indicators have been considered as important for the actual valuation of corporations,thus analyzing natural language data related to ESG is essential.Several previous studies limited their focus to specific countries or have not used big data.Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG.To address this problem,in this study,the authors used data from two platforms:LexisNexis,a platform that provides media monitoring,and Web of Science,a platform that provides scientific papers.These big data were analyzed by topic modeling.Topic modeling can derive hidden semantic structures within the text.Through this process,it is possible to collect information on public and academic sentiment.The authors explored data from a text-mining perspective using bidirectional encoder representations from transformers topic(BERTopic)—a state-of-the-art topic-modeling technique.In addition,changes in subject patterns over time were considered using dynamic topic modeling.As a result,concepts proposed in an international organization such as the United Nations(UN)have been discussed in academia,and the media have formed a variety of agendas.

Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric technique.The main challenges in gait recognition are the change in in-person view angle and covariant factors.The major covariant factors are walking while carrying a bag and walking while wearing a coat.Deep learning is a new machine learning technique that is gaining popularity.Many techniques for HGR based on deep learning are presented in the literature.The requirement of an efficient framework is always required for correct and quick gait recognition.We proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this work.The proposed framework consists of four primary steps.In the first step,the database is normalized in a video frame.In the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s nature.After that,we trained both modified models using transfer learning and extracted the features.The IACO algorithm is used to improve the extracted features.IACO is used to select the best features,which are then passed to the Cubic SVM for final classification.The cubic SVM employs a multiclass method.The experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time.

Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

In the field of natural language processing(NLP),the advancement of neural machine translation has paved the way for cross-lingual research.Yet,most studies in NLP have evaluated the proposed language models on well-refined datasets.We investigatewhether amachine translation approach is suitable for multilingual analysis of unrefined datasets,particularly,chat messages in Twitch.In order to address it,we collected the dataset,which included 7,066,854 and 3,365,569 chat messages from English and Korean streams,respectively.We employed several machine learning classifiers and neural networks with two different types of embedding:word-sequence embedding and the final layer of a pre-trained language model.The results of the employed models indicate that the accuracy difference between English,and English to Korean was relatively high,ranging from 3%to 12%.For Korean data(Korean,and Korean to English),it ranged from 0%to 2%.Therefore,the results imply that translation from a low-resource language(e.g.,Korean)into a high-resource language(e.g.,English)shows higher performance,in contrast to vice versa.Several implications and limitations of the presented results are also discussed.For instance,we suggest the feasibility of translation from resource-poor languages for using the tools of resource-rich languages in further analysis.

A Structural Topic Model for Exploring User Satisfaction with Mobile Payments

This study explored user satisfaction with mobile payments by applying a novel structural topic model.Specifically,we collected 17,927 online reviews of a specific mobile payment(i.e.,PayPal).Then,we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment.Consequently,we discovered that“lack of reliability”and“poor customer service”tend to appear in negative reviews.Whereas,the terms“convenience,”“user-friendly interface,”“simple process,”and“secure system”tend to appear in positive reviews.On the basis of information system success theory,we categorized the topics“convenience,”“user-friendly interface,”and“simple process,”as system quality.In addition,“poor customer service”was categorized as service quality.Furthermore,based on the previous studies of trust and security,“lack of reliability”and“secure system”were categorized as trust and security,respectively.These outcomes indicate that users are satisfied when they perceive that system quality and security of specific mobile payments are great.On the contrary,users are dissatisfied when they feel that service quality and reliability of specific mobile payments is lacking.Overall,our research implies that a novel structural topic model is an effective method to explore mobile payment user experience.

Smart Devices Based Multisensory Approach for Complex Human Activity Recognition

Sensors based Human Activity Recognition(HAR)have numerous applications in eHeath,sports,fitness assessments,ambient assisted living(AAL),human-computer interaction and many more.The human physical activity can be monitored by using wearable sensors or external devices.The usage of external devices has disadvantages in terms of cost,hardware installation,storage,computational time and lighting conditions dependencies.Therefore,most of the researchers used smart devices like smart phones,smart bands and watches which contain various sensors like accelerometer,gyroscope,GPS etc.,and adequate processing capabilities.For the task of recognition,human activities can be broadly categorized as basic and complex human activities.Recognition of complex activities have received very less attention of researchers due to difficulty of problem by using either smart phones or smart watches.Other reasons include lack of sensor-based labeled dataset having several complex human daily life activities.Some of the researchers have worked on the smart phone’s inertial sensors to perform human activity recognition,whereas a few of them used both pocket and wrist positions.In this research,we have proposed a novel framework which is capable to recognize both basic and complex human activities using builtin-sensors of smart phone and smart watch.We have considered 25 physical activities,including 20 complex ones,using smart device’s built-in sensors.To the best of our knowledge,the existing literature consider only up to 15 activities of daily life.

Advances in wireless sensor networks under AI-5 G for augmented reality

Due to the recent rapid development in the 5 G technology,the usage of sensor networks especially wireless sensor networks(WSNs)has boosted advances in the augmented reality(AR),supporting decision making in AR environments.Such decision-making needs support and consideration of artificial intelligence(AI)techniques capable of adapting to changes in AR environments for creating systems that evolve autonomously over time.Currently,it is important to apply new information fusion techniques that allow for the processing of information at low and high levels to improve the accuracy of such systems.