Scene based Classification of Aerial Images using Convolution Neural Networks

1087-1094The advent of computer vision and evolution of high-end computing in remote sensing images have embellish various researchers for unprecedented development in remotely sensed aerial images. The requirement to extract essential information stimulated anatomization of aerial images for its usefulness. Deep learning provides state of the art solutions for widely explored visual recognition system and has emerged as an evolutionary area, being applicable to large scale image processing applications. Convolutional Neural Networks (CNNs), an essential component of deep learning algorithms consists of increasing the depth and connections in the processing layers to learn various features of data at different abstract levels. In this paper, we present an outlook for classifying and extracting the features of aerial images using CNN. We propose a CNN architecture based on various parameters and layers for classification. CNN has been evaluated on two publicly available aerial data sets: UC Merced Land Use and RSSCN7. Experimental results show that the proposed CNN architecture is competent and efficient in terms of accuracy as performance evaluation parameter in comparison with conventional classifiers like Bag of Visual Words (BOVW)

To Compare the Effects of the Hold-Relax Technique and Foam Roller Exercise On Hamstring Muscle Tightness, Dynamic Balance and Jump Performance Among Students of Health Sciences in Jalandhar City

Background: Flexibility is a vital component of fitness required for desirable musculoskeletal functioning. Flexibility dysfunction is a widespread problem, especially in case of hamstring group of muscles. Tightness of hamstring may result in imbalances of muscle strength, dysfunction of anatomical kinetic chains and reduction in optimal performance. As reduced flexibility generates a vicious circle of ROM, impaired performance and pain there is a need to find an effective technique. Objective: To compare the effects of the hold-relax technique and foam roller exercise on hamstring muscle tightness, dynamic balance and jump performance among students of health sciences in Jalandhar city.  Study design: Comparative design, Quasi-Experimental in nature. Method: 60 students, both male and female with age between 18-27 years, were selected for study and subsequently segregated into three groups with 20 subjects per group. Group A was given warm up. Group B was given hold-relax PNF Technique in addition to warm up and Group C received foam roller exercise in addition to warm up. Baseline data was recorded on 1st day pre-intervention, 5th day and 10th day post-intervention. 10 sessions per subject were given over 2 weeks. Hamstring muscle tightness, dynamic balance and jump performance was evaluated by Active knee extension test, Y balance test and vertical jump test respectively. Results: The result showed significant improvement in hamstring muscle tightness, dynamic balance and jump performance using Foam roller exercise. Conclusion: The present study concludes that foam roller exercise is most effective in improving hamstring muscle tightness, dynamic balance and jump performance

Scene Based Classification of Aerial Images using Convolution Neural Networks

The advent of computer vision and evolution of high-end computing in remote sensing images have embellish various researchers for unprecedented development in remotely sensed aerial images. The requirement to extract essential information stimulated anatomization of aerial images for its usefulness. Deep learning provides state of the art solutions for widely explored visual recognition system and has emerged as an evolutionary area, being applicable to large scale image processing applications. Convolutional Neural Networks (CNNs), an essential component of deep learning algorithms consists of increasing the depth and connections in the processing layers to learn various features of data at different abstract levels. . In this paper, we present an outlook for classifying and extracting the features of aerial images using CNN. We propose a CNN architecture based on various parameters and layers for classification. CNN has been evaluated on two publicly available aerial data sets: UCMerced Land Use and RSSCN7. Experimental results show that the proposed CNN architecture is competent and efficient in terms of accuracy as performance evaluation parameter in comparison with conventional classifiers like Bag of Visual Words (BOVW)

A genetic algorithm based optimized convolutional neural network for face recognition

Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR

Ensemble Learning for Disease Prediction: A Review

Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016–2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets

Colonic atresia associated with biliary atresia

Colonic atresia (CA) is an uncommon type of intestinal atresia commonly associated with other anomalies, while biliary atresia (BA) is also rare but usually an isolated anomaly. The pathogenesis for either of the anomalies is unclear. The co-occurrence of both pathologies has not been mentioned in the literature. We here discuss the management of CA with BA and the review of pertinent literature

Malignant Mucosal Melanoma of Nasal Cavity (melanic variant)

Malignant melanoma of nasal mucosa and paranasal sinuses is a very rare tumor, which can be melanin producing or non melanin producing ie amelanotic variant. Rhinosinusal mucosal melanoma constitutes less than 1% of all melanic tumors and 2-8% of overall cancers developing in nasal fossae and paranasal sinuses. Patient gets medical attention very late in the course of disease due to ignorance of early mild/nonspecific symptoms in the way of high degree of local and distant invasion by the disease, and this finally leads to poor prognosis. Studies show average 5 year survival as 20-30% for cutaneous and 10-15% for mucosal melanomas. Here, we present a case of 60 yrs old male presenting with right nasal mass associated with off and on nasal bleeding. On suspicion of malignancy he was investigated, confirmed by Biomarkers study and operated after final diagnosis, Later, patient was referred to Radiation oncology for appropriate treatment

Pre-Operative Clinico-Cytological Diagnosis of Thyroid Mass Lesions (A Correlating Study)

Introdution; thyroid nodules are the common thyroid endocrine disorders particularly seen in areas, deficient in iodine content in soil, that includes Sub-Himalayan areas of India, where prevalence may be as high as 40%. Thyroid enlargement (nodular or diffuse) requires many investigations like clinical history, physical examination, Thyroid profile (T3, T4 and TSH), transcutaneous Ultrasonography and Scintiography with Tc 99m. Now a days, FNAC has surpassed most of the tests and also has become an important diagnostic tool for various thyroid abnormalities (benign or malignant). It is a highly effective method for selectingpatients for surgery, identification of benign thyroid nodules except carcinomas and differentiating benign from malignant lesions respectively. Cytological sampling without aspiration is called fine needle capillary sampling (FNCS) and capillary biopsy (FNCB).Aim and objective; To study cytomorphological smear patterns in various non neoplastic and neoplastic thyroid disorders on FNAC .And to compare diagnostic efficacy of smears in fine needle sampling (Non Aspiration) and fine needle aspiration.Materials and methods; Study was undertaken to find, role of FNAC in preoperative diagnosis of Thyroid lesions in the Department of Pathology in M.M.I M S&R Mullana, Ambala (HR) that included eighty patients over a period of one year. USG guided FNAC was done as required. Diagnostic efficacy of smears in fine needle sampling (Non Aspiration) and fine needle aspiration compared. CONCLUSION; Fine needle sampling is important diagnostic modality in sampling of superficial as well as deep seated lesions. For cystic lesions, FNAC is the procedure of choice as FNCS would lead to spillage of contents. For solidthyroid lesions, FNCS often yields specimen of text book quality and also suits children. To increase the probability of a diagnostic sample, both FNAC and FNCS may be used in selected cases, in which one technique supplements the other. So FNAC of the thyroid is a useful tool for making a correct diagnosis in majority of cases, based on clinico-cytological correlation. By combining two techniques, a better diagnostic accuracy can be achieved

A randomized control trial to evaluate the effect of local instillation of mitomycin-C at the porta after kasai portoenterostomy in patients of biliary atresia

Background: Kasai portoenterostomy (KPE) is the initial treatment for biliary atresia (BA). Even after initial jaundice clearance, a significant number of children presented with the reappearance of symptoms due to ongoing fibrosis involving porta and intrahepatic ducts. Mitomycin-C (MMC) is an antifibrotic agent, and the study hypothesized that local application of MMC at porta can decrease fibrosis, which can improve jaundice clearance and lead to better native liver survival (NLS). Materials and Methods: This prospective randomized control trial included children with BA, who were allocated to groups A or B. The patients in both groups underwent standard KPE; in addition, a 5 French infant feeding tube (IFT) was placed near the porta through the Roux limb in Group B children. During the postoperative period, MMC was locally instilled over the porta in Group B children through IFT. Postoperative jaundice clearance and NLS were assessed and compared. Results: A total of 27 children were enrolled in the study, 16 in Group A and 11 in Group B. Both groups were comparable preoperatively. Although the NLS was not statistically significant in Group B, the survival was quite higher, that was 91%, 81%, and 73% at 6 months, 1 year, and 2 years, respectively, compared to 63%, 50%, and 38% in Group A. Conclusion: Children in Group B clinically showed an early jaundice clearance and a better trend of serial bilirubin levels as well as longer NLS than Group A, but it was not statistically significant. The procedure was technically easy, and no complication was encountered related to surgical technique or MMC instillation