LexDeep:Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic product.Sentiment analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social media.However,limited research has been conducted in this domain using theLexDeep approach.This study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 pandemic.First,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)thepandemic.Subsequently,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the tweets.The performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and analysis.The resultsdemonstrated that all LexDeep models with simple embedding outperformedthe SVM.This confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific sentiments.In terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe affected.Future research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.

Spatial and Temporal Variation of Particulate Matter (PM10 and PM2.5) and Its Health Effects during the Haze Event in Malaysia

This study aims to assess and compare levels of particulate matter(PM10 and PM2.5)in urban and industrial areas in Malaysia during haze episodes,which typically occur in the south west monsoon season.The high concentrations of atmospheric particles are mainly due to pollution from neighbouring countries.Daily PM concentrations were analysed for urban and industrial areas including Alor Setar,Tasek,Shah Alam,Klang,Bandaraya Melaka,Larkin,Balok Baru,and Kuala Terengganu in 2018 and 2019.The analysis employed spatiotemporal to examine how PM levels were distributed.The data summary revealed that PM levels in all study areas were right-skewed,indicating the occurrence of high particulate events.Significant peaks in PM concentrations during haze events were consistently observed between June and October,encompassing the south west monsoon and inter-monsoon periods.The study on acute respiratory illnesses primarily focused on Selangor.Analysis revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma(AEBA)and acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with values of 260.500 and 185.170,respectively.Similarly,for outpatient cases of AEBA and AECOPD,Klang had the highest average values of 41.67 and 14.00,respectively.Shah Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass burning.The statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions,particularly from June to September,as shown in the bar diagram.Haze episodes were associated with more healthcare utilization due to haze-related respiratory illnesses,seen in higher inpatient and outpatient visits(p<0.05).However,seasonal variability had minimal impact on healthcare utilization.These findings offer a comprehensive assessment of PM levels during historic haze episodes,providing valuable insights for authorities to develop policies and guidelines for effective monitoring and mitigation of the negative impacts of haze events.

Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification

Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects teenagers.The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation.Photogrammetry is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s back.Currently,detecting the curve of the spine is manually performed,making it a time-consuming task.To overcome this issue,it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of AIS.This research proposes a new integration of ESNN and Feature Extraction(FE)methods and explores the architecture of ESNN for the AIS classification model.This research identifies the optimal Feature Extraction(FE)methods to reduce computational complexity.The ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial.A comparison between the conventional classifier(Support Vector Machine(SVM),Multi-layer Perceptron(MLP)and Random Forest(RF))with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia(HUKM).This dataset consists of various photogrammetry images of the human back with different types ofMalaysian AIS patients to solve the scoliosis problem.The process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale images.This is then followed by feature extraction,normalization,and classification.The experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS classification.This demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images.

A Provably Secure and Efficient Remote Password Authentication Scheme Using Smart Cards

Communication technology has advanced dramatically amid the 21st century,increasing the security risk in safeguarding sensitive information.The remote password authentication(RPA)scheme is the simplest cryptosystem that serves as the first line of defence against unauthorised entity attacks.Although the literature contains numerous RPA schemes,to the best of the authors’knowledge,only few schemes based on the integer factorisation problem(IFP)and the discrete logarithm problem(DLP)that provided a provision for session key agreement to ensure proper mutual authentication.Furthermore,none of the previous schemes provided formal security proof using the random oracle model.Therefore,this study proposed an improved RPA scheme with session key establishment between user and server.The design of the proposed RPA scheme is based on the widely established Dolev-Yao adversary model.Moreover,as the main contribution,a novel formal security analysis based on formal definitions of IFP and DLP under the random oracle model was presented.The proposed scheme’s performance was compared to that of other similar competitive schemes in terms of the transmission/computational cost and time complexity.The findings revealed that the proposed scheme required higher memory storage costs in smart cards.Nonetheless,the proposed scheme is more efficient regarding the transmission cost of login and response messages and the total time complexity compared to other scheme of similar security attributes.Overall,the proposed scheme outperformed the other RPA schemes based on IFP and DLP.Finally,the potential application of converting the RPA scheme to a user identification(UI)scheme is considered for future work.Since RPA and UI schemes are similar,the proposed approach can be expanded to develop a provably secure and efficientUI scheme based on IFP and DLP.

Rapid and Flexible User-Defined Low-Level Hybridization for Metaheuristics Algorithm in Software Framework

The metaheuristics algorithm is increasingly important in solving many kinds of real-life optimization problems but the implementation involves programming difficulties. As a result, many researchers have relied on software framework to accelerate the development life cycle. However, the available software frameworks were mostly designed for rapid development rather than flexible programming. Therefore, in order to extend software functions, this approach involves modifying software libraries which requires the programmers to have in-depth understanding about the internal working structure of software and the programming language. Besides, it has restricted programmers for implementing flexible user-defined low-level hybridization. This paper presents the concepts and formal definition of metaheuristics and its low-level hybridization. In addition, the weaknesses of current programming approaches supported by available software frameworks for metaheuristics are discussed. Responding to the deficiencies, this paper introduces a rapid and flexible software framework with scripting language environment. This approach is more flexible for programmers to create a variety of user-defined low-level hybridization rather than bounded with built-in metaheuristics strategy in software libraries.

Application of Intuitionistic Z-Numbers in Supplier Selection

Intuitionistic fuzzy numbers incorporate the membership and non-membership degrees.In contrast,Z-numbers consist of restriction components,with the existence of a reliability component describing the degree of certainty for the restriction.The combination of intuitionistic fuzzy numbers and Z-numbers produce a new type of fuzzy numbers,namely intuitionistic Z-numbers(IZN).The strength of IZN is their capability of better handling the uncertainty compared to Zadeh's Z-numbers since both components of Z-numbers are charac-terized by the membership and non-membership functions,exhibiting the degree of the hesitancy of decision-makers.This paper presents the application of such numbers in fuzzy multi-criteria decision-making problems.A decision-making model is proposed using the trapezoidal intuitionistic fuzzy power ordered weighted average as the aggregation function and the ranking function to rank the alternatives.The proposed model is then implemented in a supplier selection problem.The obtained ranking is compared to the existing models based on Z-numbers.The results show that the ranking order is slightly different from the existing models.Sensitivity analysis is performed to validate the obtained ranking.The sensitivity analysis result shows that the best supplier is obtained using the proposed model with 80%to 100%consistency despite the drastic change of criteria weights.Intuitionistic Z-numbers play a very important role in describing the uncertainty in the decision makers’opinions in solving decision-making problems.

An Alternative Algorithm for Vehicle Routing Problem with Time Windows for Daily Deliveries

This study attempts to solve vehicle routing problem with time window (VRPTW). The study first identifies the real problems and suggests some recommendations on the issues. The technique used in this study is Genetic Algorithm (GA) and initialization applied is random population method. The objective of the study is to assign a number of vehicles to routes that connect customers and depot such that the overall distance travelled is minimized and the delivery operations are completed within the time windows requested by the customers. The analysis reveals that the problems experienced in vehicle routing with time window can be solved by GA and retrieved for optimal solutions. After a thorough study on VRPTW, it is highly recommended that a company should implement the optimal routes derived from the study to increase the efficiency and accuracy of delivery with time insertion.

Prelude to <i>Natphoric Kansei </i>Engineering Framework

Consumers’ emotion has become imperative in product design. In affective design field, Kansei Engineering (KE) has been recognized as a technology that enables discovery of consumer’s emotion and formulation of guide to design products that win consumers in the competitive market. Albeit powerful technology, there is no rule of thumb in its analysis and interpretation process. KE expertise is required to determine sets of related Kansei and the significant concept of emotion. Many research endeavours become handicapped with the limited number of available and accessible KE experts. This work is performed to simulate the role of experts with the use of Natphoric algorithm and thus provides solution to the complexity and flexibility in KE. The algorithm is designed to learn the process by implementing training datasets taken from previous KE research works. A framework for automated KE is then designed to realize the development of automated KE system.

Mixed Convection of Non-Newtonian Erying Powell Fluid with Temperature-Dependent Viscosity over a Vertically Stretched Surface

The viscosity of a substance or material is intensely influenced by the temperature,especially in the field of lubricant engineering where the changeable temperature is well executed.In this paper,the problem of temperature-dependent viscosity on mixed convection flow of Eyring Powell fluid was studied together with Newtonian heating thermal boundary condition.The flow was assumed to move over a vertical stretching sheet.The model of the problem,which is in partial differential equations,was first transformed to ordinary differential equations using appropriate transformations.This approach was considered to reduce the complexity of the equations.Then,the transformed equations were solved using the Keller box method under the finite difference scheme approach.The validation process of the results was performed,and it was found to be in an excellent agreement.The results on the present computation are shown in tabular form and also graphical illustration.The major finding was observed where the skin friction and Nusselt number were boosted in the strong viscosity.

MELex: The Construction of Malay-English Sentiment Lexicon

Currently,the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon.Thus,this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis.In this study,a new lexicon for sentiment analysis is constructed.A detailed review of existing approaches has been conducted,and a new bilingual sentiment lexicon known as MELex(Malay-English Lexicon)has been generated.Constructing MELex involves three activities:seed words selection,polarity assignment,and synonym expansions.Our approach differs from previous works in that MELex can analyze text for the two most widely used languages in Malaysia,Malay,and English,with the accuracy achieved,is 90%.It is evaluated based on the experimentation and case study approaches where the affordable housing projects in Malaysia are selected as case projects.This finding has given an implication on the ability of MELex to analyze public sentiments in the Malaysian context.The novel aspects of this paper are two-fold.Firstly,it introduces the new technique in assigning the polarity score,and second,it improves the performance over the classification of mixed language content.

Axisymmetric stagnation-point flow over a stretching/shrinking plate with second-order velocity slip

The axisymmetric stagnation point flow over a stretching/shrinking surface with second-order slip and temperature jump is studied numerically.The governing partial differential equations are transformed into ordinary(similarity)differential equations.These equations along with the corresponding boundary conditions are solved numerically using a boundary value problem solver bvp4c in Matlab software.It is observed that dual(first and second)solutions exist for the similarity equations.The effects of different parameters on the velocity and the temperature distributions as well as the skin friction coefficient and the Nusselt number are analyzed and discussed.