Framework for transfer learning: Maximization of quadratic mutual information to create discriminative subspaces

In the area of pattern recognition and computer vision, Transfer learning has become an emerging topic in recent years. It is motivated by the mechanism of human vision system that is capable of accumulating previous knowledge or experience to unveil a novel domain. Learning an effective model to solve a classification or recognition task in a new domain (dataset) requires sufficient data with ground truth information. Visual data are being generated in an enormous amount every moment with the advance of photo capturing devices. Most of these data remain unannotated. Manually collecting and annotating training data by human intervention is expensive and hence the learned model may suffer from performance bottleneck because of poor generalization and label scarcity. Also an existing trained model may become outdated if the distribution of training data differs from the distribution where the model is tested. Traditional machine learning methods generally assume that training and test data are sampled from the same distribution. This assumption is often challenged in real life scenario. Therefore, adapting an existing model or utilizing the knowledge of a label-rich domain becomes inevitable to overcome the issue of continuous evolving data distribution and the lack of label information in a novel domain. In other words, a knowledge transfer process is developed with a goal to minimize the distribution divergence between domains such that a classifier trained using source dataset can also generalize over target domain. In this thesis, we propose a novel framework for transfer learning by creating a common subspace based on maximization of non-parametric quadratic mutual information (QMI) between data and corresponding class labels. We extend the prior work of QMI in the context of knowledge transfer by introducing soft class assignment and instance weighting for data across domains. The proposed approach learns a class discriminative subspace by leveraging soft-labeling. Also by employing a suitable weighting scheme, the method identifies samples with underlying shared similarity across domains in order to maximize their impact on subspace learning. Variants of the proposed framework, parameter sensitivity, extensive experiments using benchmark datasets and also performance comparison with recent competitive methods are provided to prove the efficacy of our novel framework

Non-Performing Loans: A Catastrophic Phenomena in Banking Sector of Bangladesh

Nonperforming loan (NPL) is one of the most cataclysmic phenomena for the entire banking industry in Bangladesh. NPLs in the banking sector have experienced a monstrous escalation of 300% in the last decade and statistically this figure is more than 1000 billion of Bangladeshi Taka (BDT). Even though international standards of loan classification and provisioning system is being adopted, the management of NPL is found unproductive. Fundamentally, deficiency of good governance, weak supervision, corruption, political interference in approving loans, culture of impunity and professional ineptness of bankers to deal with the pressing issue have played an instrumental role for the swift upsurge of NPLs. Pertaining to preventive measures, prominence needs to be placed on credit screening, loan surveillance, stringent law enforcement, centralized loan authorization system, strong monetary policy and strong loan review functionaries. Therefore, this study has emphasized on the challenges of NPL, evocative ways for improving the debt recovery environment and cracking the NPL problems in order to safeguard a sustainable banking sector of the country.Keywords: non-performing loan, loan classifications, provisioning, good governance, sustainable bankingDOI: 10.7176/EJBM/12-27-14Publication date:September 30th 202

Data-Driven Process Reengineering and Optimization Using a Simulation and Verification Technique

Process reengineering (PR) in manufacturing organizations is a big challenge, as shown by the high rate of failure. This research investigated different approaches to process reengineering to identify limitations and propose a new strategy to increase the success rate. The proposed methodology integrates data as a procedure for process identification (PI) and mapping and incorporates process verification to analyze the changes made in a specific process. The study identifies interdependency within the manufacturing process (MP) and proposes a generic process reengineering approach that uses simulation and analysis of production line data as a method for understanding the changes required to optimize the process. The paper discusses the methodology implementation technique as well as process identification and the process mapping technique using simulation tools. It provides an improved data-driven process reengineering framework that incorporates process verification. Based on the proposed model, the study investigates a production line process using the WITNESS Horizon 21 simulation package and analyse the efficiency of data-driven process reengineering and process verification in terms of implementing changes

Growth and development patterns in Mustard (Brassica spp.) as influenced by sowing time

Mustard is Bangladesh's leading oil crop, produced only during the winter (rabi) season. The sowing date is a key factor determining mustard's optimum growth and development. Because of global warming, gradual changes in season and weather parameters over time is creating a challenge in mustard cultivation. Thus, the present investigation assessed the role of different planting dates on several modern mustard varieties to disclose the optimum growth indicators necessary for elevated biological yield (BY) and harvest index (HI). Three planting times, viz. 31st October (D1), 10th November (D2),  20th November (D3) and six varieties viz. Binasarisha-4 (V1), Binasarisha-9 (V2), Binasarisha-10 (V3), BARI Sarisha-14 (V4), BARI Sarisha-16 (V5), BARI Sarisha-17 (V6) were put on a replicated factorial randomized complete block design (RCBD) during rabi 2019 at BINA Sub-station farm, Magura. At the final harvest stage, outcomes depicted that highest and lowest total dry mass (g/plant) was produced by treatment D3× V5 (64.03) and D1× V1 (15.34), maximum and minimum absolute growth rate (mg/plant/day) by D1× V5 (2389.10) and D2× V1 (184.50), most and least relative growth rate (mg/g/day) in D1× V4 (53.34) and D2× V1 (3.55), maximum and least crop growth rate (g/m2/day) with D1× V3 (55.60) and  D3× V4 (20.04). BY was the peak (8.13, 8.71, 8.77 t/ha) under all plantings (D1, D2, D3) with V5 variety, but HI (44.96%) was most in variety V4 with D2 sowing. Therefore, correlation studies showed a significant positive relationship between biological yield and harvest index. Overall, BARI Sarisha-16 performed well in all three sowing times, and remarkably, BY was rising with delayed planting in the case of Binasarisha-9, Binasarisha-10, and BARI Sarisha-14. This implies that delayed planting might not hamper yield but boost yield to some extent

Integration of Data-Driven Process Re-Engineering and Process Interdependence for Manufacturing Optimization Supported by Smart Structured Data

Process re-engineering and optimization in manufacturing industries is a big challenge because of process interdependencies characterized by a high failure rate. Research has shown that over 70% of approaches fail because of complexity as a result of process interdependencies during the implementation phase. This paper investigates data from a manufacturing operation and designs a filtration algorithm to analyze process interdependencies as a new approach for process optimization. The algorithm examines the data from a manufacturing process to identify limitations through cause and effect relationships and implements changes to achieve an optimized result. The proposed cause and effect approach of re-engineering is termed the Khan-Hassan-Butt (KHB) methodology, and it can filter the process interdependencies and use those as key decision-making tools. It provides an improved process optimization framework that incorporates data analysis along with a cause and effect algorithm to filter out the process interdependencies as an approach to increase output and reduce failure factors simultaneously. It also provides a framework for filtering the manufacturing data into smart structured data. Based on the proposed KHB methodology, the study investigated a production line process using the WITNESS Horizon 22 simulation package and analyzed the efficiency of the proposed approach for production optimization. A case study is provided that integrated the KHB methodology with data-driven process re-engineering to analyze the process interdependencies to use them as decision-making tools for production optimization

A Computational analysis on Lectin and Histone H1 protein of different pulse species as well as comparative study with rice for balanced diet

The issue of balanced nutrition is of great concern to human. Meat and fish are the best sources of protein. The affordability of these resources for people in developing countries is less. Thus, there is an increasing interest in pulses and its derivates as an alternative to fish and meat. Lectin and histone H1 are the most common proteins in various pulses and our interest is in identifying the dominant essential amino acids in them for use as supplements. However, actin and lectin are common among Oryza Sativa and cicer arietinum. We describe the amount of lectin and histone H1 in cicer arietinum, Lens culinaris and Pisum sativum in a comparative manner. cicer arietinum was found to contain more essential amino acids than Lens culinaris and Pisum sativum. The secondary structures of lectin and histone H1 protein were analyzed to gain functional inferences in these species. The comparative study shows the relatively poor presence of the amino acid methionine in most pulses. However, Oryza Sativa was found to contain sufficient methionine. The study shows that pulses (especially cicer arietinum) were a suitable alternative source to meat and fish for Lectin and Histone H1 balance. Hence, pulses could be suggested with rice for balanced protein diet

Optimum and late sowing of mustard varieties show similar seed yield

Appropriate planting time of mustard (Brassica sp.) during winter determines the growth yield and quality of a particular cultivar. Since the shift of winter period over the last few years driven by climate change, a transformation in mustard cultivation is also observed. Thus, to study the extent of these variations we studied 6 mustard varieties (V1- Binasarisha-4, V2- Binasarisha-9, V3- Binasarisha-10, V4- BARI Sarisha-14, V5- BARI Sarisha-16 and V6- BARI Sarisha-17) under 3 different planting dates (D1-31st October, D2-10th November and D3-20th November) in the Magura district of Bangladesh to evaluate yield differences over sowing times. Field experimentation was set followed RCB (Randomized complete block) design. Data on growth and yield parameters were collected at various days after sowing. Outcomes noted that, most number of siliqua/plant was obtained with treatment combination D3 × V5 (190.33), siliqua length with D3 × V2 (7.95 cm), number of seeds per siliqua by D3 × V6 (36.30), thousand grain weight by D1 × V1 (3.90 g). Hence, correlation study suggested that, seed yield was positively related to number of siliqua/plant, siliqua length and number of seeds/siliqua. Though, BARI Sarisha-16 (V5) delivered top seed and stover yield in all sowing dates. But interaction effects depicted that planting on 10th (2.00 t/ha) and 20th November (1.99, 1.94 t/ha) gave similar seed yield like 31st October planting (2.31 t/ha); in addition, stover yield (6.70, 6.83 t/ha) also remained at peak with the later plantings (D2, D3). An increase in the tendency of life duration was noticed when sown on 20th November for most treatment combination. Overall, delayed sowing of mustard didn’t affect the yield and related attributes rather it accelerated to some attributes. Hence, rescheduling of optimum sowing time for mustard is now a time demanding concern with regard to weather change

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Factors associated with partner referral among patients with sexually transmitted infections in Bangladesh

Understanding the demographic, behavioural and psychosocial factors associated with partner referral for patients with sexually transmitted infections (STIs) is important for designing appropriate intervention strategies. A survey was conducted among STI clients in three government and three non-governmental organization-operated clinics in Dhaka and Chittagong city in Bangladesh. Demographic and psychosocial information was collected using a questionnaire guided by the Attitude-Social Influence-Self Efficacy model. Partner referral data were collected by verification of referral cards when partners appeared at the clinics within one month of interviewing the STI clients. Of the 1339 clients interviewed, 81% accepted partner referral cards but only 32% actually referred their partners; 37% of these referrals were done by clients randomly assigned to a single counselling session vs. 27% by clients not assigned to a counselling session (p Sexually transmitted infections Partner referral Psychosocial factors Bangladesh Attitudes