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Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Levels of the selected major and minor elements (Na, K, Ca, Mg, Ni, Cr, Cd, Pb, Zn and Fe) in 11 summer fruits grown in Pakistan were estimated by the flame AAS method based on HNO₃/HClO₄ wet‐digestion method. The metal concentrations are expressed as X for triplicate sub‐samples with a standard deviation of ±1.0‐1.5 per cent. Of all the minor elements analyzed, Fe was found to be the dominant metal on mean basis as compared with other metals in fruits, its concentration being 14.25mg/kg. The increasing order of minor‐element concentration was: Cd<Pb<Zn<Cr<Ni<Fe. While for major elements, K concentration was found to be maximum as 409.7mg/kg and increasing order was: Ca<Na<Mg<K. In general, minor‐element concentrations were found to surpass the safe limit laid down by the World Health Organization. Fruits were found to be a rich resource of major elements.

Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest.

In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count.

The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data.

The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems.

Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts.

This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

The novel Coronavirus (COVID-19) pandemic, which started in late December 2019, has spread to more than 200 countries. As no vaccine is yet available for this pandemic, government and health agencies are taking draconian steps to contain it. This pandemic is also trending on social media, particularly on Twitter. The purpose of this study is to explore and analyze the general public reactions to the COVID-19 outbreak on Twitter.

This study conducts a thematic analysis of COVID-19 tweets through VOSviewer to examine people’s reactions related to the COVID-19 outbreak in the world. Moreover, sequential pattern mining (SPM) techniques are used to find frequent words/patterns and their relationship in tweets.

Seven clusters (themes) were found through VOSviewer: Cluster 1 (green): public sentiments about COVID-19 in the USA. Cluster 2 (red): public sentiments about COVID-19 in Italy and Iran and a vaccine, Cluster 3 (purple): public sentiments about doomsday and science credibility. Cluster 4 (blue): public sentiments about COVID-19 in India. Cluster 5 (yellow): public sentiments about COVID-19’s emergence. Cluster 6 (light blue): public sentiments about COVID-19 in the Philippines. Cluster 7 (orange): Public sentiments about COVID-19 US Intelligence Report. The most frequent words/patterns discovered with SPM were “COVID-19,” “Coronavirus,” “Chinese virus” and the most frequent and high confidence sequential rules were related to “Coronavirus, testing, lockdown, China and Wuhan.”

The methodology can be used to analyze the opinions/thoughts of the general public on Twitter and to categorize them accordingly. Moreover, the categories (generated by VOSviewer) can be correlated with the results obtained with pattern mining techniques.

This study has a significant socio-economic impact as Twitter offers content posting and sharing to billions of users worldwide.

According to the authors’ best knowledge, this may be the first study to carry out a thematic analysis of COVID-19 tweets at a glance and mining the tweets with SPM to investigate how people reacted to the COVID-19 outbreak on Twitter.

The purpose of this paper is to establish some acceptable trends in the contamination of roadside vegetation and to define a safety limit regarding the effects of metal contamination arising from various toxic metals deposited on leaves of the plants and in the bulk of the fruits.

Distribution of essential and non‐essential elements on the surface of leaves and in bulk of fruits of specific areas of Multan (Pakistan) was estimated and correlated with World Health Organization (WHO) standards. The metal concentrations are expressed as X ± SD for triplicate sub samples with the SD of ± 1.0‐1.5 per cent. The maximum metal levels in bulk of various fruits were calculated for FE, followed by Cu, Zn and Co.

In samples from roadside leaves Fe (823 mg/kg) was found to be the dominant metal, whereas the observed threshold level was found for Co (17.25 mg/kg). The non‐essential elements in various fruits, the Cr was found to be the dominate (16 mg/kg) on mean basis as compared with other metals in fruits. The increasing order of non‐essential metals on the surface of roadside leaves was Li < Ni < Sr < Pb < Cr. The results revealed that metal concentration decreases with increase in distance from roadside (10, 30 and 50 m) with negative correlation coefficient.

This paper shows that the metals concentration in case of all fruit samples fall within the permissible safe limit, whereas the metal concentrations on the surface of roadside leaves were found to surpass the safe limits laid down by the WHO. It is consequently suggested that edible portions of vegetation and fruits near highways should be consumed cautiously.

The low-income groups in developing nations need microcredits to support their family needs. As banks avoid providing microcredits due to high costs, microfinance institutions are the last resort for this segment of society. The cost of borrowing for the borrowers is indeed high. However, these microfinance institutions play a significant role in financial inclusion. In Muslim countries where financial inclusion takes a hit as a portion of society does not want to indulge in usury transactions, Islamic microfinance institutions play a vital role. In this chapter, the focus is on the Islamic microfinance institutions and their role in achieving the objectives of Shari'ah (maqasid al-Shari'ah) along with the fulfillment of goal of financial inclusion. A case study of Akhuwat Foundation found that the institution offers different interest-free microcredit products along with free healthcare and clothing to the needy segment of society. In this way, not only that the financial inclusion is achieved but also the objectives of Shari'ah are fulfilled. The study provides key facts to the academia and microfinance industry in achieving financial inclusion and fulfilling maqasid al-Shari'ah altogether, in which the banking sector is lacking.

The purpose of this paper is to present an exploration of multiple slips and temperature dependent thermal conductivity effects on the flow of nano Williamson fluid over a slendering stretching plate in the presence of Joule and viscous heating aspects. The effectiveness of nanoparticles is deliberated by considering Brownian moment and thermophoresis slip mechanisms. The effects of magnetism and radiative heat are also deployed.

The governing partial differential equations are non-dimensionalized and reduced to multi-degree ordinary differential equations via suitable similarity variables. The subsequent non-linear problem treated for numerical results. To measure the amount of increase/decrease in skin friction coefficient, Nusselt number and Sherwood number, the slope of linear regression line through the data points are calculated. Statistical approach is implemented to analyze the heat transfer rate.

The results show that temperature distribution across the flow decreases with thermal conductivity parameter. The maximum friction factor is ascertained at stronger magnetic field.

In the current paper, the magneto-nano Williamson fluid flow inspired by a stretching sheet of variable thickness is examined numerically. The rationale of the present study is to generalize the studies of Mebarek-Oudina and Makinde (2018) and Williamson (1929).

The purpose of this paper is to examine the combined effects of thermal radiation and magnetic field of molybdenum disulfide nanofluid in a channel with changing walls. Water is considered as a Newtonian fluid and treated as a base fluid and MoS₂ as nanoparticles with different shapes (spherical, cylindrical and laminar). The main structures of partial differential equations are taken in the form of continuity, momentum and energy equations.

The governing partial differential equations are converted into a set of nonlinear ordinary differential equations by applying a suitable similarity transformation and then solved numerically via a three-stage Lobatto III-A formula.

All obtained unknown functions are discussed in detail after plotting the numerical results against different arising physical parameters. The validations of numerical results have been taken into account with other works reported in literature and are found to be in an excellent agreement. The study reveals that the Nusselt number increases by increasing the solid volume fraction for different shapes of nanoparticles, and an increase in the values of wall expansion ratio α increases the velocity profile f′(η) from lower wall to the center of the channel and decreases afterwards.

In this paper, a numerical method was utilized to investigate the influence of molybdenum disulfide (MoS₂) nanoparticles shapes on MHD flow of nanofluid in a channel. The validity of the literature review cited above ensures that the current study has never been reported before and it is quite new; therefore, in case of validity of the results, a three-stage Lobattoo III-A formula is implemented in Matlab 15 by built in routine “bvp4c,” and it is found to be in an excellent agreement with the literature published before.

The purpose of this paper is twofold: to explain access to formal and informal credit in agriculture of Vietnam; and to compare the effectiveness between regular econometrics and machine learning techniques.

The multinomial logit (MNL) regression model and the random forest (RF) technique are employed for comparison purposes. To avoid heteroskedasticity, the robust covariance matrix is computed to estimate the sandwich estimator which in turn provides an asymptotic covariance matrix for biased estimators. Additionally, multicollinearity is tested among independent variables with variance inflation factors less than 3. Adequacy approach and sensitivity analysis are used to determine relevant levels of predictors. For models comparison, statistical evaluation metrics including Cohen’s κ, mean absolute error, root mean squared error and relative absolute error are employed.

The discrepancy between sensitivity analysis and adequacy approach revealed that MNL is more compatible for explaining determinants of credit participation. Due to insignificant differences in the evaluation metrics between models, the winner of choice is undetermined. Among other determinants, collateral, farmsize, income, procedure, literacy and all risk variables stand out to be critical factors when deciding borrowing schemes. While financially literate farmers tend to acquire loans from both sources, borrowing decisions against different risk sources depend on risk type and famers’ own desire to borrow.

Results of the MNL model are more consistent with literatures, which reinforce the role of collateral in the local credit scheme. Besides, financial literacy and farmers’ perception on different risk sources also influence how farmers’ borrowing strategies vary among sources.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.