Automatic Visual Features for Writer Identification: A Deep Learning Approach

© 2013 IEEE. Identification of a person from his writing is one of the challenging problems; however, it is not new. No one can repudiate its applications in a number of domains, such as forensic analysis, historical documents, and ancient manuscripts. Deep learning-based approaches have proved as the best feature extractors from massive amounts of heterogeneous data and provide promising and surprising predictions of patterns as compared with traditional approaches. We apply a deep transfer convolutional neural network (CNN) to identify a writer using handwriting text line images in English and Arabic languages. We evaluate different freeze layers of CNN (Conv3, Conv4, Conv5, Fc6, Fc7, and fusion of Fc6 and Fc7) affecting the identification rate of the writer. In this paper, transfer learning is applied as a pioneer study using ImageNet (base data-set) and QUWI data-set (target data-set). To decrease the chance of over-fitting, data augmentation techniques are applied like contours, negatives, and sharpness using text-line images of target data-set. The sliding window approach is used to make patches as an input unit to the CNN model. The AlexNet architecture is employed to extract discriminating visual features from multiple representations of image patches generated by enhanced pre-processing techniques. The extracted features from patches are then fed to a support vector machine classifier. We realized the highest accuracy using freeze Conv5 layer up to 92.78% on English, 92.20% on Arabic, and 88.11% on the combination of Arabic and English, respectively

Evaluation of handwritten Urdu text by integration of MNIST dataset learning experience

© 2019 IEEE. The similar nature of patterns may enhance the learning if the experience they attained during training is utilized to achieve maximum accuracy. This paper presents a novel way to exploit the transfer learning experience of similar patterns on handwritten Urdu text analysis. The MNIST pre-trained network is employed by transferring it's learning experience on Urdu Nastaliq Handwritten Dataset (UNHD) samples. The convolutional neural network is used for feature extraction. The experiments were performed using deep multidimensional long short term (MDLSTM) memory networks. The obtained result shows immaculate performance on number of experiments distinguished on the basis of handwritten complexity. The result of demonstrated experiments show that pre-trained network outperforms on subsequent target networks which enable them to focus on a particular feature learning. The conducted experiments presented astonishingly good accuracy on UNHD dataset

Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals

© 2013 IEEE. Background: EEG signals are extremely complex in comparison to other biomedical signals, thus require an efficient feature selection as well as classification approach. Traditional feature extraction and classification methods require to reshape the data into vectors that results in losing the structural information exist in the original featured matrix. Aim: The aim of this work is to design an efficient approach for robust feature extraction and classification for the classification of EEG signals. Method: In order to extract robust feature matrix and reduce the dimensionality of from original epileptic EEG data, in this paper, we have applied robust joint sparse PCA (RJSPCA), Outliers Robust PCA (ORPCA) and compare their performance with different matrix base feature extraction methods, followed by classification through support matrix machine. The combination of joint sparse PCA with robust support matrix machine showed good generalization performance for classification of EEG data due to their convex optimization. Results: A comprehensive experimental study on the publicly available EEG datasets is carried out to validate the robustness of the proposed approach against outliers. Conclusion: The experiment results, supported by the theoretical analysis and statistical test, show the effectiveness of the proposed framework for solving classification of EEG signals

Jejunal Perforation following Screening Colonoscopy

Colonoscopy is rarely associated with complications such as colonic perforation. Perforation of the small bowel is extremely rare, especially if the procedure is done without therapeutic interventions. Several factors are associated with this entity. Perforation of the ileum has been reported, but proximal jejunal perforation secondary to rupture of jejunal diverticulum during colonoscopy has not been reported. We present the case of an 88-year-old patient who developed abdominal pain after undergoing colonoscopy without any additional interventions. Urgent exploration revealed perforation of the proximal jejunum secondary to rupture of a jejunal diverticulum. No therapy or biopsies were undertaken during the colonoscopy, which are known predisposing factors

Differences in police, ambulance, and emergency department reporting of traffic injuries on Karachi-Hala road, Pakistan

<p>Abstract</p> <p>Background</p> <p>Research undertaken in developing countries has assessed discrepancies in police reporting of Road Traffic Injury (RTI) for urban settings only. The objective of this study was to assess differences in RTI reporting across police, ambulance, and hospital Emergency Department (ED) datasets on an interurban road section in Pakistan.</p> <p>Methods</p> <p>The study setting was the 196-km long Karachi-Hala road section. RTIs reported to the police, Edhi Ambulance Service (EAS), and five hospital EDs in Karachi during 2008 (Jan to Dec) were compared in terms of road user involved (pedestrians, motorcyclists, four-wheeled vehicle occupants) and outcome (died or injured). Further, records from these data were matched to assess ascertainment of traffic injuries and deaths by the three datasets.</p> <p>Results</p> <p>A total of 143 RTIs were reported to the police, 531 to EAS, and 661 to hospital EDs. Fatality per hundred traffic injuries was twice as high in police records (19 per 100 RTIs) than in ambulance (10 per 100 RTIs) and hospital ED records (9 per 100 RTIs). Pedestrian and motorcyclist involvement per hundred traffic injuries was lower in police records (8 per 100 RTIs) than in ambulance (17 per 100 RTIs) and hospital ED records (43 per 100 RTIs). Of the 119 deaths independently identified after matching, police recorded 22.6%, EAS 46.2%, and hospital ED 50.4%. Similarly, police data accounted for 10.6%, EAS 43.5%, and hospital ED 54.9% of the 1 095 independently identified injured patients.</p> <p>Conclusions</p> <p>Police reporting, particularly of non-fatal RTIs and those involving vulnerable road users, should be improved in Pakistan.</p

Hybrid Words Representation for Airlines Sentiment Analysis

© 2019, Springer Nature Switzerland AG. Social media sentimental analysis is interesting field with the aim to analyze social conservation and determine deeper context as they apply to a topic or theme. However, it is challenging as tweets are unstructured, informal and noisy in nature. Also, it involves natural language complexities like words with same meanings (Polysemy). Most of the existing approaches mainly rely on clean textual data, however Twitter data is quite noisy in real life. Aiming to improve the performance, in this paper, we present hybrid words representation and Bi-directional Long Short Term Memory (BiLSTM) with attention modeling resulting in improvement in tweet quality by not only treating the noise within the textual context but also considers polysemy, semantics, syntax, out of vocabulary (OOV) words as well as words sentiments within a tweet. The proposed model overcomes the current limitations and improves the accuracy for tweets classification as showed by the evaluation of the model performed on real-world airline related datasets

Identification of longevity-associated genes in long-lived Snell and Ames dwarf mice

Recent landmark molecular genetic studies have identified an evolutionarily conserved insulin/IGF-1 signal transduction pathway that regulates lifespan. In C. elegans, Drosophila, and rodents, attenuated insulin/IGF-1 signaling appears to regulate lifespan and enhance resistance to environmental stress. The Ames (Prop1df/df) and Snell (Pit1dw/dw) hypopituitary dwarf mice with growth hormone (GH), thyroid-stimulating hormone (TSH), and prolactin deficiencies live 40–60% longer than control mice. Both mutants are resistant to multiple forms of environmental stress in vitro. Taken collectively, these genetic models indicate that diminished insulin/IGF-l signaling may play a central role in the determination of mammalian lifespan by conferring resistance to exogenous and endogenous stressors. These pleiotropic endocrine pathways control diverse programs of gene expression that appear to orchestrate the development of a biological phenotype that promotes longevity. With the ability to investigate thousands of genes simultaneously, several microarray surveys have identified potential longevity assurance genes and provided information on the mechanism(s) by which the dwarf genotypes (dw/dw) and (df/df), and caloric restriction may lead to longevity. We propose that a comparison of specific changes in gene expression shared between Snell and Ames dwarf mice may provide a deeper understanding of the transcriptional mechanisms of longevity determination. Furthermore, we propose that a comparison of the physiological consequences of the Pit1dw and Prop1df mutations may reveal transcriptional profiles similar to those reported for the C. elegans and Drosophila mutants. In this study we have identified classes of genes whose expression is similarly affected in both Snell and Ames dwarf mice. Our comparative microarray data suggest that specific detoxification enzymes of the P450 (CYP) family as well as oxidative and steroid metabolism may play a key role in longevity assurance of the Snell and Ames dwarf mouse mutants. We propose that the altered expression of these genes defines a biochemical phenotype which may promote longevity in Snell and Ames dwarf mice