National integrity strategy implementation in land administration to prevent corruption in Bangladesh.

Corruption in land administration is one of the challenges in implementing the national integrity strategy (NIS) in Bangladesh. Citizens visiting the Land Office have reported many instances of unpleasant experiences. In response to these issues, this study attempted to investigate the challenges of NIS implementation in land administration and possible solutions. Based on in-depth interviews with citizens and public officials, as well as document analysis, the study found that the NIS has helped to modernize land administration in recent years. The Bangladesh government, with the help of young land administration officials, has undertaken several measures to resolve this crisis. However, the improvement in service delivery is, in some ways, nominal compared to the volume of corruption in land administration. The likely reason is the nature of the country's societal patterns. In practice, local and administrative politics continue to have a significant influence. Therefore, despite good initiatives and appropriate measures, land administration officials have failed to implement the NIS properly. Several other problems are making NIS implementation challenging, such as the lack of skilled manpower and technical support, the tendency to break rules, and other forms of corruption discussed in this study. However, the study found that an alternative approach, namely, "local solutions for local problems" could have more success than the NIS approach. Therefore, this study argues that dynamic, sustainable, and corruption-free land administration in Bangladesh requires a combined approach involving local solutions, innovation, and NIS implementation.

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.

Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light. In this paper, we propose PanClassif, a method that requires a very few and effective genes to detect cancer from RNA-seq data and is able to provide performance gain in several wide range machine learning classifiers. We have taken 22 types of cancer samples from The Cancer Genome Atlas (TCGA) having 8287 cancer samples and 680 normal samples. Firstly, PanClassif uses k-Nearest Neighbour (k-NN) smoothing to smooth the samples to handle noise in the data. Then effective genes are selected by Anova based test. For balancing the train data, PanClassif applies an oversampling method, SMOTE. We have performed comprehensive experiments on the datasets using several classification algorithms. Experimental results shows that PanClassif outperform existing state-of-the-art methods available and shows consistent performance for two single cell RNA-seq datasets taken from Gene Expression Omnibus (GEO). PanClassif improves performances of a wide variety of classifiers for both binary cancer prediction and multi-class cancer classification. PanClassif is available as a python package (https://pypi.org/project/panclassif/). All the source code and materials of PanClassif are available at https://github.com/Zwei-inc/panclassif.