SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification

Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early detection and effective treatment. To address this challenge, we employed deep learning techniques to identify patterns in chest X-rays that correspond to different diseases. We conducted experiments on the "ChestX-ray14" dataset using various pre-trained CNNs, transformers, hybrid(CNN+Transformer) models and classical models. The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%. By combining the predictions of all trained models using a weighted average ensemble where the weight of each model was determined using differential evolution, we further improved the AUROC to 85.4%, outperforming other state-of-the-art methods in this field. Our findings demonstrate the potential of deep learning techniques, particularly ensemble deep learning, for improving the accuracy of automatic diagnosis of thoracic diseases from chest X-rays.Comment: Accepted in International Conference on Computer and Information Technology (ICCIT) 202

Study of fusion reactions of light nuclei at low energies using complex nucleon-nucleus potential function

Nuclear fusion reactions, at energies, far below the Coulomb barrier play a significant role in the synthesis of light elements in the primordial nucleosynthesis as well as in the interior of compact stellar objects. Many different kinds of nuclear reactions are occurring simultaneously inside the stellar core depending upon the density and temperature conditions of the nuclear plasma along with other relevant parameters of these stars. Nuclear fusion reactions in the energy range ($E\sim$ 1 eV to few keV) can be explained successfully by quantum mechanical tunneling through the mutual Coulomb barrier of interacting nuclei. The measurement of the cross-sections at extremely low energy is quite difficult because of the larger width of the Coulomb barrier, which results in a very small value of the reaction cross-section. Hence, any improvement in the data on astrophysical S-factors for the light nuclei fusion may give a better picture of the elemental abundance in nucleosynthesis. In this work, we have theoretically investigated the energy dependence of fusion cross-sections and astrophysical S-factors for fusion reaction of light nuclei like D-D and p-$^{11}$B using complex Gaussian nuclear potential with adjustable depth and range parameters plus the mutual Coulomb interaction of the interacting nuclei. Numerical computation of the observables is done in the framework of the selective resonant tunneling model approach. The results of our calculation are compared with those found in the literature.Comment: 13 Pages, 5 Figure

STUDY OF HOMOGENEITY, POROSITY AND INTERNAL DEFECTS IN AERATED AND EPS AGGREGATE POLY BRICKS USING NEUTRON RADIOGRAPHY TECHNIQUE

A powerful non-destructive testing (NDT) technique is adopted to study the internal defects and elemental distribution/homogeneity and porosity of aerated brick and EPS aggregate poly brick samples. In the present study the internal defects like homogeneity, porosity, elemental distribution, EPS aggregate and aerator distributor in the test samples have been observed by the measurement of gray value/optical density of the neutron radiographic images of these samples. From this measurement it is found that the neutron intensity/optical density variation with the pixel distance of the AOI of the NR images in both expanded polystyrene (EPS) aggregate poly brick and aerated brick samples comply almost same in nature with respect to the whole AOI but individually each AOI shows different nature from one AOI to another and it confirms that the elemental distribution within a AOI is almost homogeneous. Finally it was concluded that homogeneity, elemental distribution in the EPS aggregate poly brick sample is better than that of the aerated brick sample

Yields gap evaluation of wheat grown in Piedmont plain and Floodplain soils of Bangladesh through compositional nutrient diagnosis (CND) norm

Mineralnutrient stress is one of the major yield gap factors, especially in floodplain and piedmont plain soil. The compositional nutrient diagnosis (CND) provides a plant nutrient imbalance index in statistical distribution patterns, which is important for adjusting the soil-plant systems specific fertilization for maintaining sustainable soil fertility. This study calculated the CND norms of wheat (Triticum aestivum L.) and identified optimum wheat yield target of high-yielding subpopulation in farmers' fields. It also categorized the most yield limiting nutrient(s) for wheat grown. Popular high-yielding wheat was grown in 62 farmers' fields, maintaining farmers' nutrient management plan (FP) and improved nutrient management plan (INM). Nutrient composition analysis was done from 62 young foliar composite samples, collected at 7th leaves stage (vegetative stage).The CND generic model gave 3.47 Mg ha–1 as minimum cutoff yield of the high-yield subpopulation. Nitrogen was identified as the core yield limiting nutrient for wheat in piedmont and floodplain soils. However, the yield limiting nutrients for wheat grown in the studied are were established the following series: N > S > K, Mg >P, Ca and Mn >Fe >B >Zn respectively. The CND generic model, allowed us to suggest thatN, P, K, Mn, B were the factors discriminating high- from low–yielding subpopulation in piedmont plain and floodplain soils of Bangladesh