A comparative clinical study between intra-caesarean and interval intra uterine copper device insertion in caesarean deliveries

Background: Health and family welfare of Indian Ministry, emphasis on postpartum IUCD insertion. Here we conducted a clinical study comparing intra-caesarean and interval CuT-380A insertion in caesarean deliveries.Methods: A systematic study with 150 patients in each group, recruited clients alternately. Group A Intra-Caesarean Cu-T insertion and Group B Interval Cu-T insertion in caesarean deliveries. Groups were followed up at 6th week and 6th month post insertion with a set of parameters. Missed strings, expulsion and infection rates were the primary outcome measures.Results: Infection rate is higher in Group A (2.3%) at 6th week, and at 6th month infection rate is higher in Group B (1.8%). Missed strings are higher in intra-caesarean than in interval insertion method both at 6th week and 6th month follow up p=0.000, hence significant. Expulsion rate is higher in Group A (2.5%) at 6th week, and at 6th month expulsion rate is higher in Group B (1.9%). There are no complications such as uterine perforation or contraceptive failures in both the groups during the study period. By analysis, there are no significant differences in infection and expulsion rates between the groups. For missed strings there is significant difference between the groups with more missed strings in intra-caesarean insertion method.Conclusions: To conclude, intra-caesarean method is equally effective as interval IUCD insertion method without added complications in caesarean deliveries, with advantage of high motivation, good compliance, safety and ease for the provider to deliver services.

Assessment of Intermittent Leather based on Image Score Pattern

The process of intermittent leather inspection is being predominantly carried out with the support of human intervention based on homogenous distribution of colors. However, results of the observations between one experts to another expert may be different in opinion. Therefore, to emphasis some sort of supporting hand to the experts while taking decision, the authors have introduced to align intermitted leather images based on the Image Score Pattern algorithm. Which separates defect versus non-defect intermittent leather images from feature image datasets namely DGF, DLO, DGFLO and DLOGF consisting of 32 features generated from Gray Level Co-occurrence Matrix, Simple Linear Iterative Clustering and Minimum Spanning Tree Clustering from the training and testing datasets of about 1132 and 404 generated respectively. Gradient Boosting has implemented in finding the key feature among the Contrast, Dissimilarity, Homogeneity, Energy, Correlation and Angular Second Moment. The results of the classifier Support Vector Machine for these datasets confirms the accuracy of 84% for the proposed Image Score Pattern algorithm. The other performance measures such as Error Rate, Recall, False Positive Rate, Specificity, Precision and Prevalence  are also confirming that proposed method is performing in aligning of intermittent leathe

Aspergillus niger Fungus Detection using Transfer Learning Technique and Modified Backpropagation Algorithm with Inertia and Legendre Polynomial

622-632Looking at the loss due to health problems from fungal diseases in one hand and the benefits from its industrial/agricultural use, rapid automated fungal species identification is the need of the hour. Hence, proposed a fast identification of fungal species by a 15 minutes staining procedure followed by an artificial-intelligence-based image classification technique. In this modern era, deep architectures have shown a significant performance on computer vision problems. Instead of developing a new model from scratch, the pre-trained convolutional neural network models are available to obtain the appropriate features from input samples using the transfer-learning technique. This work utilizes the transfer-learning approach for feature extraction and classification performed using the proposed modified third-term Backpropagation (BP) algorithm. This proposed algorithm contains Inertia as a third factor in the weight updation rule expanded in the form of the Legendre polynomial to overcome the limitations of the traditional Backpropagation algorithm. The effectiveness of the proposed classifier compared to the results of the existing cutting-edge algorithms namely, Backpropagation algorithm, Backpropagation algorithm using Momentum, and softmax classifier. Compare to the existing models, the proposed model scored a high testing accuracy of 97.27%

OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK

Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. One major advantage of SAR is that it can generate imagery under all weather conditions. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish ‘oil spills’ from ‘look-alikes’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes

Assessment of Intermittent Leather based on Image Score Pattern

605-614The process of intermittent leather inspection is being predominantly carried out with the support of human intervention based on homogenous distribution of colors. However, results of the observations between one experts to another expert may be different in opinion. Therefore, to emphasis some sort of supporting hand to the experts while taking decision, the authors have introduced an algorithm based on Image Score Pattern to distinguish between defect versus non-defect intermittent leather images. About 32 features generated from Gray Level Co-occurrence Matrix, Simple Linear Iterative Clustering and Minimum Spanning Tree Clustering from the training and testing datasets of about 1132 and 404 generated. The results of the classifier Support Vector Machine has confirmed the accuracy of 84% for the proposed Image Score Pattern method for these datasets. Similarly, other performance measures such as Precision, Recall, F1-Score, Specificity and Error Rate are also confirming that proposed method is performing in aligning of intermittent leather

Faculty Perception on Library Facilities: A Survey on NAAC Accredited Autonomous Arts and Science Colleges in Coimbatore City

The present article has made an attempt to study the faculty perception on Library faculties in Autonomous Arts and Science Colleges in one of the biggest cities in Tamil Nadu, India. There colleges got accredited by National Assessment and Accreditation Council of India, hence the standard of education and faculties in these colleges are generally behind to be the best. The study used a questionnaire and the results revealed that faculty have a low perception on the collection, services, ICT facilities, manpower and infrastructure facilities of libraries in these colleges