A Hybridized Forecasting Model for Metal Commodity Prices: An Empirical Model Evaluation

Appropriate decision on perfect commodity prediction under market’s constant fluctuations intensifies the need for efficient methods. The main objective of this study is to apply different optimization algorithms such as conventional particle swarm optimization (PSO), bat algorithm (BAT) and ant colony optimization (ACO) algorithms on back propagation neural network (BPNN) to enhance the accuracy of prediction and minimize the error. In this paper, a model has been proposed for volatility forecasting using PSO algorithm to train the BPNN and predict the commodities’ closing price. The proposed PSO-BPNN model is considered as best forecasting model compared to BPNN, BAT-BPNN and ACO-BPNN. The experiment has been carried out upon five publicly available metal datasets (gold, silver, lead, aluminium, and copper) to forecast the price return volatility of those five metals challenging the effectiveness. Here, three technical indicators and four filters, such as; moving average convergence/divergence (MACD), williams %R (W%), bollinger (B), least mean squares (LMS), finite impulse response (FIR), Kalmanand recursive least square (RLS) have been applied for providing an additional degree of freedom to train and test the classifiers. From the experimental result analysis it has been found that the proposed PSO-BPNN produces promising output while comparing with BPNN, BAT-BPNN and ACO-BPNN

A Hybridized Forecasting Model for Metal Commodity Prices: An Empirical Model Evaluation

945-950Appropriate decision on perfect commodity prediction under market’s constant fluctuations intensifies the need for efficient methods. The main objective of this study is to apply different optimization algorithms such as conventional particle swarm optimization (PSO), bat algorithm (BAT) and ant colony optimization (ACO) algorithms on back propagation neural network (BPNN) to enhance the accuracy of prediction and minimize the error. In this paper, a model has been proposed for volatility forecasting using PSO algorithm to train the BPNN and predict the commodities’ closing price. The proposed PSO-BPNN model is considered as best forecasting model compared to BPNN, BAT-BPNN and ACO-BPNN. The experiment has been carried out upon five publicly available metal datasets (gold, silver, lead, aluminium, and copper) to forecast the price return volatility of those five metals challenging the effectiveness. Here, three technical indicators and four filters, such as; moving average convergence/divergence (MACD), williams %R (W%), bollinger (B), least mean squares (LMS), finite impulse response (FIR), Kalmanand recursive least square (RLS) have been applied for providing an additional degree of freedom to train and test the classifiers. From the experimental result analysis it has been found that the proposed PSO-BPNN produces promising output while comparing with BPNN, BAT-BPNN and ACO-BPNN

Visualization of Early Events in Acetic Acid Denaturation of HIV-1 Protease: A Molecular Dynamics Study

Protein denaturation plays a crucial role in cellular processes. In this study, denaturation of HIV-1 Protease (PR) was investigated by all-atom MD simulations in explicit solvent. The PR dimer and monomer were simulated separately in 9 M acetic acid (9 M AcOH) solution and water to study the denaturation process of PR in acetic acid environment. Direct visualization of the denaturation dynamics that is readily available from such simulations has been presented. Our simulations in 9 M AcOH reveal that the PR denaturation begins by separation of dimer into intact monomers and it is only after this separation that the monomer units start denaturing. The denaturation of the monomers is flagged off by the loss of crucial interactions between the α-helix at C-terminal and surrounding β-strands. This causes the structure to transit from the equilibrium dynamics to random non-equilibrating dynamics. Residence time calculations indicate that denaturation occurs via direct interaction of the acetic acid molecules with certain regions of the protein in 9 M AcOH. All these observations have helped to decipher a picture of the early events in acetic acid denaturation of PR and have illustrated that the α-helix and the β-sheet at the C-terminus of a native and functional PR dimer should maintain both the stability and the function of the enzyme and thus present newer targets for blocking PR function

Analysis of Breath-Holding Capacity for Improving Efficiency of COPD Severity-Detection Using Deep Transfer Learning

Air collection around the lung regions can cause lungs to collapse. Conditions like emphysema can cause chronic obstructive pulmonary disease (COPD), wherein lungs get progressively damaged, and the damage cannot be reversed by treatment. It is recommended that these conditions be detected early via highly complex image processing models applied to chest X-rays so that the patient’s life may be extended. Due to COPD, the bronchioles are narrowed and blocked with mucous, and causes destruction of alveolar geometry. These changes can be visually monitored via feature analysis using effective image classification models such as convolutional neural networks (CNN). CNNs have proven to possess more than 95% accuracy for detection of COPD conditions for static datasets. For consistent performance of CNNs, this paper presents an incremental learning mechanism that uses deep transfer learning for incrementally updating classification weights in the system. The proposed model is tested on 3 different lung X-ray datasets, and an accuracy of 99.95% is achieved for detection of COPD. In this paper, a model for temporal analysis of COPD detected imagery is proposed. This model uses Gated Recurrent Units (GRUs) for evaluating lifespan of patients with COPD. Analysis of lifespan can assist doctors and other medical practitioners to take recommended steps for aggressive treatment. A smaller dataset was available to perform temporal analysis of COPD values because patients are not advised continuous chest X-rays due to their long-term side effects, which resulted in an accuracy of 97% for lifespan analysis

Analysis of Breath-Holding Capacity for Improving Efficiency of COPD Severity-Detection Using Deep Transfer Learning

Air collection around the lung regions can cause lungs to collapse. Conditions like emphysema can cause chronic obstructive pulmonary disease (COPD), wherein lungs get progressively damaged, and the damage cannot be reversed by treatment. It is recommended that these conditions be detected early via highly complex image processing models applied to chest X-rays so that the patient’s life may be extended. Due to COPD, the bronchioles are narrowed and blocked with mucous, and causes destruction of alveolar geometry. These changes can be visually monitored via feature analysis using effective image classification models such as convolutional neural networks (CNN). CNNs have proven to possess more than 95% accuracy for detection of COPD conditions for static datasets. For consistent performance of CNNs, this paper presents an incremental learning mechanism that uses deep transfer learning for incrementally updating classification weights in the system. The proposed model is tested on 3 different lung X-ray datasets, and an accuracy of 99.95% is achieved for detection of COPD. In this paper, a model for temporal analysis of COPD detected imagery is proposed. This model uses Gated Recurrent Units (GRUs) for evaluating lifespan of patients with COPD. Analysis of lifespan can assist doctors and other medical practitioners to take recommended steps for aggressive treatment. A smaller dataset was available to perform temporal analysis of COPD values because patients are not advised continuous chest X-rays due to their long-term side effects, which resulted in an accuracy of 97% for lifespan analysis

Performance of the Belle II Silicon Vertex Detector

The Belle II experiment at the SuperKEKB collider of KEK (Japan) will accumulate 50 ab−1 of e+e− collision data at an unprecedented instantaneous luminosity of 8 ×1035 cm−2s−1, about 40 times larger than its predecessor. The Belle II vertex detector plays a crucial role in the rich Belle II physics program, especially for time-dependent measurements. It consists of two layers of DEPFET-based pixels and four layers of double sided silicon strips detectors(SVD). The vertex detector has been recently completed and installed in Belle II for the physics run started in spring 2019. We report here results on the commissioning of the SVD and its performance measured with the first collision data set

Data quality monitors of vertex detectors at the start of the Belle II experiment

The Belle II experiment features a substantial upgrade of the Belle detector and will operate at the SuperKEKB energy-asymmetric e+e− collider at KEK in Tsukuba, Japan. The accelerator completed its first phase of commissioning in 2016, and the Belle II detector saw its first electron-positron collisions in April 2018. Belle II features a newly designed silicon vertex detector based on double-sided strip layers and DEPFET pixel layers. A subset of the vertex detector was operated in 2018 to determine background conditions (Phase 2 operation). The collaboration completed full detector installation in January 2019, and the experiment started full data taking. This paper will report on the final arrangement of the silicon vertex detector part of Belle II with a focus on online monitoring of detector conditions and data quality, on the design and use of diagnostic and reference plots, and on integration with the software framework of Belle II. Data quality monitoring plots will be discussed with a focus on simulation and acquired cosmic and collision data

Alignment for the first precision measurements at Belle II

On March 25th 2019, the Belle II detector recorded the first collisions delivered by the SuperKEKB accelerator. This marked the beginning of the physics run with vertex detector. The vertex detector was aligned initially with cosmic ray tracks without magnetic field simultaneously with the drift chamber. The alignment method is based on Millepede II and the General Broken Lines track model and includes also the muon system or primary vertex position alignment. To control weak modes, we employ sensitive validation tools and various track samples can be used as alignment input, from straight cosmic tracks to mass-constrained decays. With increasing luminosity and experience, the alignment is approaching the target performance, crucial for the first physics analyses in the era of Super-BFactories. We will present the software framework for the detector calibration and alignment, the results from the first physics run and the prospects in view of the experience with the first data

Data quality monitors of vertex detectors at the start of the Belle II experiment

The Belle II experiment features a substantial upgrade of the Belle detector and will operate at the SuperKEKB energy-asymmetric e+e− collider at KEK in Tsukuba, Japan. The accelerator completed its first phase of commissioning in 2016, and the Belle II detector saw its first electron-positron collisions in April 2018. Belle II features a newly designed silicon vertex detector based on double-sided strip layers and DEPFET pixel layers. A subset of the vertex detector was operated in 2018 to determine background conditions (Phase 2 operation). The collaboration completed full detector installation in January 2019, and the experiment started full data taking. This paper will report on the final arrangement of the silicon vertex detector part of Belle II with a focus on online monitoring of detector conditions and data quality, on the design and use of diagnostic and reference plots, and on integration with the software framework of Belle II. Data quality monitoring plots will be discussed with a focus on simulation and acquired cosmic and collision data