A Mosque Among the Stars

A Mosque Among The Stars was the first anthology that dealt with the subject of Muslim characters and/or Islamic themes and Science Fiction

Automatic facial age estimation

The reliability of automatically estimating human ages, by processing input facial images, has generally been found to be poor. On other hand, various real world applications, often relating to safety and security, depend on an accurate estimate of a person’s age. In such situations, Face Image based Automatic Age Estimation (FI-AAE) systems which are more reliable and may ideally surpass human ability, are of importance as and represent a critical pre-requisite technology. Unfortunately, in terms of estimation accuracy and thus performance, contemporary FI-AAE systems are impeded by challenges which exist in both of the two major FI-AAE processing phases i.e. i) Age based feature extraction and representation and ii) Age group classification. Challenges in the former phase arise because facial shape and texture change independently and the magnitude of these changes vary during the different stages of a person’s life. Additionally, contemporary schemes struggle to exploit age group specific characteristics of these features, which in turn has a detrimental effect on overall system performance. Furthermore misclassification errors which occur in the second processing phase and are caused by the smooth inter-class variations often observed between adjacent age groups, pose another major challenge and are responsible for low overall FI-AAE performance. In this thesis a novel Multi-Level Age Estimation (ML-AE) framework is proposed that addresses the aforementioned challenges and improves upon state-of-the-art FI-AAE system performance. The proposed ML-AE is a hierarchical classification scheme that maximizes and then exploits inter-class variation among different age groups at each level of the hierarchy. Furthermore, the proposed scheme exploits age based discriminating information taken from two different cues (i.e. facial shape and texture) at the decision level which improves age estimation results. During the process of achieving our main objective of age estimation, this research work also contributes to two associated image processing/analysis areas: i) Face image modeling and synthesis; a process of representing face image data with a low dimensionality set of parameters. This is considered as precursor to every face image based age estimation system and has been studied in this thesis within the context of image face recognition ii) measuring face image data variability that can help in representing/ranking different face image datasets according to their classification difficulty level. Thus a variability measure is proposed that can also be used to predict the classification performance of a given face recognition system operating upon a particular input face dataset. Experimental results based on well-known face image datasets revealed the superior performance of our proposed face analysis, synthesis and face image based age classification methodologies, as compared to that obtained from conventional schemes

A Mosque Among the Stars

A Mosque Among The Stars was the first anthology that dealt with the subject of Muslim characters and/or Islamic themes and Science Fiction

The Impact of Leadership Styles on Employee Wellbeing and Resilience during COVID-19: A Partial Least Square Approach

Purpose:The purpose of this research is to investigate the impact of leadership styles on employee well-being and resilience in private universities in Peshawar during COVID-19. The role of leadership in reducing stress and improving mental and physical health was not investigated in COVID-19, and this area is particularly understudied in the Pakistani context.Methodology:Data has been collected from 203 faculty members of 10 private-sector universities in Peshawar using an adapted questionnaire. The respondents include lecturers, assistant professors, and full professors working in private-sector universities.Findings:Using the partial least square regression, it is found that charismatic leadership, intellectual stimulation, personal recognition, contingent reward, and management by exception have positive and significant relationships with employee well-being and resilience in private sector universities in Peshawar.Conclusion:The conclusion is that leaders should use both transformational and transactional leadership styles in their organizations. They should also pay attention to the well-being and resilience of their employees in the workplace

Experimental investigation on interply friction properties of thermoset prepreg systems

A comprehensive novel investigation into the characterisation of interply friction behaviour of thermoset prepregs for high-volume manufacturing (HVM) was conducted. High interply slipping rate and normal pressure typically used for HVM present challenges when preforming carbon fibre reinforced plastics (CFRP). The study involved multiple reinforcement architectures (woven and unidirectional (UD) with the same rapid-cure resin system) which were characterised using a bespoke interply friction test rig used to simulate processing conditions representative to press forming and double diaphragm forming. Under prescribed conditions, woven and UD prepregs exhibit significantly different frictional behaviour. Results demonstrated the UD material obeys a hydrodynamic lubrication mode. For the woven material, a rate-dependent friction behaviour was found at low normal pressure. At higher normal pressure however, the woven material exhibited a friction behaviour similar to that of a dry reinforcement and significant tow displacement was observed. Post-characterisation analysis of test-specimens showed significant resin migration towards the outer edges of the plies, leaving a relatively resin-starved contact interface. The findings generate new knowledge on interply friction properties of thermoset prepreg for HVM applications, yet reveal a lack of understanding of the influence of tow tensions as well as the pre-impregnation level for a range of processing conditions

On the estimation of face recognition system performance using image variability information

The type and amount of variation that exists among images in facial image datasets significantly affects Face Recognition System Performance (FRSP). This points towards the development of an appropriate image Variability Measure (VM), as applied to face-type image datasets. Given VM, modeling of the relationship that exists between the image variability characteristics of facial image datasets and expected FRSP values, can be performed. Thus, this paper presents a novel method to quantify the overall data variability that exists in a given face image dataset. The resulting Variability Measure (VM) is then used to model FR system performance versus VM (FRSP/VM). Note that VM takes into account both the inter- and intra-subject class correlation characteristics of an image dataset. Using eleven publically available datasets of face images and four well-known FR systems, computer simulation based experimental results showed that FRSP/VM based prediction errors are confined in the region of 0 to 10%

Analysis of genetic diversity in chickpea (Cicer arietinum L.) cultivars using random amplified polymorphic DNA (RAPD) markers

Genetic diversity of seven chickpea (Cicer arietinum L.) cultivars of Pakistani origin was analyzed by using random amplified polymorphic DNA (RAPD) markers, an extremely effective method to determine the variations among the chickpea cultivars. Polymerase chain reaction (PCR) conditions were optimized for RAPD and the conditions which gave the optimized results were selected for further amplifications. Using nine random decamers for seven genotypes of chickpea, 63 bands were amplified. Out of 63 bands, 50 were polymorphic in all the seven chickpea cultivars. The numbers of RAPD fragments generated per primer ranged from 3 to 11. However, majority of the primers amplified 7 to 11 fragments. The Jaccard’s similarity coefficients ranged from 0.333 to 0.651. Maximum similarity (65.1%) was observed between PK G-3 and PK G-4 and the lowest similarity (33.3%) was observed between PK G-3 and PK G-7. A dendrogram was constructed by using the unweighted pair group arithmetic mean arrangement (UPGMA) that was based on similarity coefficients. Seven chickpea cultivars were clustered in two distinct groups of which two cultivars (PK G-6 and PK G-7) stood separately in the dendrogram. The results from this study may be useful to maximize the selection of diverse parent cultivars and to broaden the germplasm base in the future for chickpea breeding programs. The information generated from this study can also be used in identifying efficient strategies for the sustainable management of the genetic resources of chickpea crop.Keywords: Random amplified polymorphic DNA (RAPD), polymerase chain reaction (PCR), chickpea cultivars, genetic diversit

Customer churn prediction using composite deep learning technique

Customer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers. When dissatisfied customers find their present company\u27s services inadequate, they frequently migrate to another service provider. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. Deep neural networks were also used in these efforts to extract features without taking into account the sequence information. In view of these issues, the current study provides an effective method for predicting customer churn based on a hybrid deep learning model termed BiLSTM-CNN. The goal is to effectively estimate customer churn using benchmark data and increase the churn prediction process\u27s accuracy. The experimental results show that when trained, tested, and validated on the benchmark dataset, the proposed BiLSTM-CNN model attained a remarkable accuracy of 81%

Peringkat Daerah Rawan Pangan Berdasarkan Data Spasial Di Provinsi Aceh1 (Analise of Food Insecurity Base on Spatial in Nanggroe Aceh Darussalam Province)

Tujuan penelitian ini dalah untuk mengelompokkan daerah rawan pangan dan memetakanwilayah rawan pangan tingkat kabupaten/kota di Provinsi Aceh, mengidentifikasi karakteristik danfaktor-faktor penyebab rawan pangan pada setiap wilayah. Penelitian dilaksanakan di ProvinsiAceh yang meliputi 23 kabupaten/kota selama 8 bulan. Penelitian menggunakan metode survey,analisis secara deskriptif terhadap data sekunder yang meliputi : data pertanian, kesehatan, dan sosialekonomi. Hasil penelitian menunjukkan ada dua tingkatan wilayah rawan pangan di Provinsi Acehyaitu; tingkat kerawanan pangan sedang (21,7%), dan tingkat kerawanan tinggi (78,3%).Jumlah Kabupaten/kota dengan kategori kerawanan pangan tinggi lebih dari 3 kali lipatdibandingkan dengan daerah tingkat kerawanan sedang