The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.

The aim of the study is to examine the role of social capital in the formation of mobile financialservice users’ adoptive behaviours toward service providers in the context of the financialtechnological regime of Ghana. The study used a total of 417 sample data from a self-administeredquestionnaire to achieve its objectives. Structural equation modeling was employed to analyze thedirect effects of social capital and the mediation role of cognitive-based trust and affective-based truston mobile financial service. The results of the study demonstrate that social capital, cognitive-basedand affective-based trust collaboratively impact on the adoptive lifestyle of mobile financial serviceusers. Specifically, both cognitive-based and affective-based trust mediate between social capital andmobile financial inclusion to inform users’ behavioral intentions. The results however, reveals thataffective-based trust plays a significant role in shaping mobile financial service users’ behavioralintentions than cognitive-based trust. In detail, the study indicates that social capital directly impactson the adoptive behavior of mobile financial service users. It is therefore, recommended that in thepresent era of Covid-19 pandemic and its attendant social distancing protocols, mobile financialservice providers should invest heavily in social media channels to develop their corporate socialcapital brand in order to deepen their bondage with users. The study makes a vital contribution to theevolving debate on mobile financial inclusion, information system and consumer psychology fromAfrican perspective. The study acknowledges the contributions of prior studies to the afore-mentionedstrands of discipline and the chasm in literature. Specifically, this study is the first of its kind toempirically examine the influential functions of social capital, cognitive-based trust and affective-basedtrust on mobile financial inclusion from the len of the Ghanaian financial technological landscape.The study will equip industry players with the needed know-how to enhance their profitability andoperational competitiveness.

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In an effort to foster the growth of Android in the mobile operating system market and keep current consumers, Google has made millions of applications, some of which are free and others of which are paid apps, available in the Google Play store. Users have, however, regu- larly complained that the store is full of malicious apps and low quality apps, putting their devices and personal information at risk. Detec- tion of mobile applications vulnerabilities remains a significant challenge due to the constant evolution of methods to obfusticate and circumvent current detection and security schemes.The ability to correctly classify and categorize mobile applications, especially those built for Android, is crucial for separating malignant applications from benign ones thereby protecting the many more devices of unsuspecting users.This paper presents a deep neural network technique to classify android applications into legitimate and malware applications. Specifically,we first proposed applications classification model based on deep belief neural network classifier.The neural network was built and trained on real dataset to classify android-based applications using TensorFlow library and imple- mented on python programming language. We further trained and tested our neural network’s classification performance against that of four tra- ditional deep feed-forward neural networks and seven baseline models based on machine learning algorithms on the same data. According to experimental results, a deep belief neural network-based model could accurately categorize Android apps into benign and malicious cate- gories with 98.7% of the time. Compared to all previous deep learning and machine learning methods, this represents a significant improve- ment. Also,the categorization accuracy of the DBN model is better than that of numerous other models examined by earlier researchers.

Article Title our neural network's classification performance against that of four traditional deep feed-forward neural networks and seven baseline models based on machine learning algorithms on the same data. According to experimental results, a deep belief neural network-based model could accurately categorize Android apps into benign and malicious categories with 98.7% of the time. Compared to all previous deep learning and machine learning methods, this represents a significant improvement. Also,the categorization accuracy of the DBN model is better than that of numerous other models examined by earlier researchers.