Last Teen Pixels for Arabic Font Size and Style Recognition

Arabic printed script segmentation and recognition techniques change from font to other i.e. each font has particular properties calligraphic and structural which differ with other. Majority of segmentation system suffer in word or sub word segmentation into characters because they consider one algorithm to segment all kind of Arabic printed font, style and size. The goal of this work is to prepare a system of word or sub word Optical Font Arabic Recognition (OFAR) for different font size and style of Arabic printed script, in order to integrate it in global Arabic Optical Character Recognition (AOCR) to choose preferred and good segmentation algorithm. APTI database was used to extract last ten pixels for each word or sub word to build new database of last 10 pixels for each word; OFAR is based upon this new database and our extraction approach called Pixels Continuity (PC) algorithm in different matrix direction and some histogram statistics to extract 20 features. Three KNN classifiers with K=5 and three different distances using Cityblock, Euclidean and Correlation based upon majority-vote are used to evaluate the system robustness. This classifier is compared in the first time with Back propagation Neural Network and Steerable Pyramid (SP) algorithm to re cognize three font families, then in the second time with Gaussian Mixture Models (GMMs) to recognize font and size. The average recognition results obtained was 99.55% about font and size and 98.17% for font, size and style recognition.

A Cross-Sectoral Investigation of the Energy–Environment–Economy Causal Nexus in Pakistan: Policy Suggestions for Improved Energy Management

This paper explored the energy–environment–economy (EEE) causal nexus of Pakistan, thereby reporting the causal determinants of the EEE nexus by employing the newly developed modified Peter and Clark (PC) algorithm. The modified PC algorithm was employed to investigate the causal ordering of energy consumption, CO2 emissions and economic growth across Pakistan’s domestic, industrial, transportation and agricultural sectors. An empirical comparison, i.e., following Monte Carlo simulation experiments demonstrates that the proposed modified PC algorithm is superior to the original PC proposition and can differentiate between true and spurious nexus causalities. Our results show that significant causality is running from energy consumption in industrial and agricultural sectors towards economic growth. There is no causal association between energy consumption and economic growth in the domestic and transportation sectors. On the other hand, causality runs from energy consumption in the transportation, domestic and industrial sectors towards CO2 emissions. It is concluded that energy consumption in industrial and agricultural sectors leads to economic growth alongside the associated CO2 emissions. On the other hand, the contribution of domestic and transportation sectors in economic growth is trivial with significant CO2 emissions. This paper provides novel empirical evidence of impacts of energy mismanagement at sectoral levels, economic output and environmental consequences; alongside policy recommendations for sustainable energy-based development on the national scale.

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River.

MRPC: An R Package for Inference of Causal Graphs

Understanding the causal relationships between variables is a central goal of many scientific inquiries. Causal relationships may be represented by directed edges in a graph (or equivalently, a network). In biology, for example, gene regulatory networks may be viewed as a type of causal networks, where X→Y represents gene X regulating (i.e., being causal to) gene Y. However, existing general-purpose graph inference methods often result in a high number of false edges, whereas current causal inference methods developed for observational data in genomics can handle only limited types of causal relationships. We present MRPC (a PC algorithm with the principle of Mendelian Randomization), an R package that learns causal graphs with improved accuracy over existing methods. Our algorithm builds on the powerful PC algorithm (named after its developers Peter Spirtes and Clark Glymour), a canonical algorithm in computer science for learning directed acyclic graphs. The improvements in MRPC result in increased accuracy in identifying v-structures (i.e., X→Y←Z), and robustness to how the nodes are arranged in the input data. In the special case of genomic data that contain genotypes and phenotypes (e.g., gene expression) at the individual level, MRPC incorporates the principle of Mendelian randomization as constraints on edge direction to help orient the edges. MRPC allows for inference of causal graphs not only for general purposes, but also for biomedical data where multiple types of data may be input to provide evidence for causality. The R package is available on CRAN and is a free open-source software package under a GPL (≥2) license.

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.