Search

In recent years, there has been a growing interest in biometric recognition based on finger dorsal patterns, making it a significant area of research. This paper introduces a biometric classification system that utilizes dorsal finger middle creases. The viability of this trait is assessed through the implementation of a new method known as multi block circular shift combination local binary pattern (MBCSC-LBP). The MBCSC-LBP approach involves dividing the image into multiple blocks to enhance robustness and capture both local and global information, thereby extracting discriminative features. These features from each block are then concatenated to form a comprehensive feature vector. To evaluate the accuracy of the proposed MBCSC-LBP feature extractor, a support vector machine (SVM) with a linear kernel is utilized. The classification accuracy achieved by this method is 96.22% indicating a promising performance. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current research was planned and conducted at Institute of Agricultural Extension, Education and Rural Development, University of Agriculture, Faisalabad, Pakistan during the year 2019-20 and restricted to district Gujranwala with respect to potato crop. The main objective of this study was to compare the extension services provided by the public and private sector along with different teaching methodologies adopted by them. However, 36 respondents from each tehsil of district Gujranwala were selected through convenient sampling technique. Thereby, getting a sample size of 144 for the purpose of data collection for which interviews were conducted. Data were analyzed through SPSS. Results were explained through weighted score and mean values of all variables. According to results it was found that both sectors were giving useful knowledge to farmers with different teaching methods. It was also concluded that farmers had desire to take information from public sector but they have to go to private sector for products. Furthermore, it was also concluded that performance of public sector was better than private sector. [ABSTRACT FROM AUTHOR]

This paper presents comparative evaluation of an application of deep convolutional neural networks (dCNNs) to age invariant face recognition. To this end, we use four distinct dCNN models, the AlexNet, VGGNet, GoogLeNet and ResNet. We assess their performance to recognize face images across aging variations, firstly by fine-tuning the models and secondly using them as face feature extractor. We also suggest a novel synthesized aging augmentation technique suitable for age-invariant face recognition using dCNNs. The face recognition experiments are conducted on three challenging FG-NET, MORPH and LAG aging datasets, and results are benchmarked with a simple CNN. The comparative study allows us to answer (i) when and why transfer learning or feature extraction strategies are useful in age-invariant face recognition scenarios, (ii) the potential of aging synthesized augmentation to increase accuracy and (iii) the choice of appropriate feature normalization and distance metrics to be used with deeply learned features. The extensive experiments, and valuable insights presented in this study can be extended to the design of effective age-invariant face recognition algorithms. [ABSTRACT FROM AUTHOR]

Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data. [ABSTRACT FROM AUTHOR]

The training needs of rice growers in Punjab, Pakistan were assessed regarding the production technology used in growing rice. Total 332 rice growers were selected through random sampling technique from tehsil Nowshera Virkan, district Gujranwala and interviewed accordingly through a face to face interview technique. Data was analyzed using Statistical Package for Social Sciences (SPSS). Frequency, percentages, weighted mean and chi-square techniques were run on the data. Production technology was divided into two sections (i) major aspects and (ii) sub-aspects. Major aspects were 8 in number and 46 in sub-aspects. Findings indicated that almost 60 and 14.2% of the respondents had a low and medium level of training need. 21.5% of respondents had a high level of training needs. The average score of 128.28 indicated the medium level of training needs for the sampled farmers. Among major aspects, weed management and seed and seedlings embarked high level of training need. Whereas, in sub-aspects, 30 aspects show a low level of training, 12 in medium and 04 at a high level. Chi- square confirmed that socio-economic attributes and training need was no different. However, lower educational level in the area as 47.9% of respondents had no education asks for the due attention from the department of extension to formulate training programs for the farmers materializing the need and socio-economic position of the farmers. [ABSTRACT FROM AUTHOR]

Facial palsy caused by nerve damage results in loss of facial symmetry and expression. A reliable palsy grading system for large-scale applications is still missing in the literature. Although numerous approaches have been reported on facial palsy quantification and grading, most employ hand-crafted features on relatively smaller datasets which limit the classification accuracy due to non-optimal face representation. In contrast, convolutional neural networks (CNNs) automatically learn the discriminative features facilitating the accurate classification of underlying tasks. In this paper, we propose to apply a typical deep network on a large dataset to extract palsy-specific features from face images. To prevent the inherent limitation of overfitting frequently occurring in CNNs, a generative adversial network (GAN) is applied to augment the training dataset. The deeply learned features are then used to classify the palsy disease into five benchmarked grades. The experimental results show that the proposed approach offers superior palsy grading performance compared to some existing methods. Such an approach is useful for palsy grading at large scale, such as primary health care. [ABSTRACT FROM AUTHOR]

Bilateral facial asymmetry is frequently exhibited by humans but its combined evaluation across demographic traits including gender and ethnicity is still an open research problem. In this study we measure and evaluate facial asymmetry across gender and different ethnic groups and investigate the differences in asymmetric facial dimensions among the subjects from two public face datasets, the MORPH and FERET. To this end, we detect 28 facial asymmetric dimensions from each face image using an anthropometric technique. An exploratory analysis is then performed via a multiple linear regression model to determine the impact of gender and ethnicity on facial asymmetry. Post-hoc Tukey test has been used to validate the results of the proposed method. The results show that out of 28 asymmetric dimensions, females differ in 25 dimensions from males. African, Asian, Hispanic and other ethnic groups have asymmetric dimensions that differ significantly from those of Europeans. These findings could be important to certain applications like the design of facial fits, as well as guidelines for facial cosmetic surgeons. Lastly, we train a neural network classifier that employs asymmetric dimensions for gender and race classification. The experimental results show that our trained classifier outperforms the support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. [ABSTRACT FROM AUTHOR]

Demographic estimation of human face images involves estimation of age group, gender, and race, which finds many applications, such as access control, forensics, and surveillance. Demographic estimation can help in designing such algorithms which lead to better understanding of the facial aging process and face recognition. Such a study has two parts—demographic estimation and subsequent face recognition and retrieval. In this paper, first we extract facial-asymmetry-based demographic informative features to estimate the age group, gender, and race of a given face image. The demographic features are then used to recognize and retrieve face images. Comparison of the demographic estimates from a state-of-the-art algorithm and the proposed approach is also presented. Experimental results on two longitudinal face datasets, the MORPH II and FERET, show that the proposed approach can compete the existing methods to recognize face images across aging variations. [ABSTRACT FROM AUTHOR]

Present study focused on the factors affecting citrus growers’ perception regarding production and protection problems in Tehsil Toba Tek Singh, Pakistan. Study was conducted during 2019. For the collection of data from the selected 120 respondents, a well-planned interview schedule was prepared. The observation technique was also used to conduct a qualitative in-depth investigation. For drawing conclusions and making suggestions, the data was analyzed using the statistical package for social sciences (SPSS). It was found that adoption of intercropping, crop rotation, adequate extension services and costly labor were the major problem to control insect pest and diseases. But, cost of pesticides spraying, non-availability of machinery, lack of adoption of recommendation and judicious use of pesticides were the major hurdles regarding to chemical control of insect pest and diseases. Analysis showed that citrus growers’ education and income negatively associated with their protection and protection problems. But, size of land holding and farm size (citrus) positively associated with their protection and protection problems. It was also observed that lack of technical knowledge was an issue faced by the citrus growers. It was recommended, extension workers should arrange training workshop for citrus growers at village/union council level. [ABSTRACT FROM AUTHOR]