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SN Computer Science
During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity anddoi:10.1007/s42979-020-00383-w pmid:33163975 pmcid:PMC7607889 fatcat:kljnouvklbhxjg7xe2nq7qbptq
more »... ctivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.
There are no such fish products available in the market of developing countries, including Bangladesh (Amanullah, 2019) . ... In the current study, total coliforms and fecal coliforms showed irregular growth behavior and were unsuccessful in remarking a progressive increase in Md. A. Karim et al. ...doi:10.15578/squalen.v15i3.483 fatcat:w4bcmiotijdihmyilcnbosfq6i
This Quantitative trait locus (QTL) analysis is a widely used statistical approach for the<br />detection of important genes in the chromosomes. Maximum likelihood (ML) based<br />interval mapping (IM) is one of the most popular approaches for QTL analysis. However,<br />it is relatively complex and computationally slower than regression based IM. Haley-<br />Knott (HK) and extended Haley-Knott (eHK) regression based IM save computation time<br />and produce similar results as ML-IM. However,doi:10.3329/rujse.v44i0.30401 fatcat:vlytwhhdfbbqhgecn6uuhhivou
more »... ese approaches are not robust against<br />phenotypic outliers. In this research, we have developed a robust regression based IM<br />approach by maximizing beta-likelihood function for intercross (F2) population. The<br />proposed method reduces to the HK-IM method when beta → 0. The tuning parameter<br />beta controls the performance of the proposed method. The simulation results show that<br />the proposed method improves performance over the existing IM approaches in the case<br />of data contaminations; otherwise, it shows almost the same results as the classical IM<br />approaches.
Garment Business today has become very competitive. Low price, less lead time, high costing, many competitors have made the market saturated. Depending on the level of journey, this business has come to a professional and scientific stage, where accurate planning, proper time management in production and operation, high skilled technical support, optimum cost-profit estimation are very important issues to survive. Now a day's it's impossible to run a garment manufacturing operation withoutdoi:10.5281/zenodo.4309662 fatcat:e5sf7e4wi5fl5nyyti5gtzpmyu
more »... tific and professional approach. Industrial Engineering concepts are developed on this demand. Industrial Engineering concepts are required in every stage in Costing, Product R&D, Planning, Supply Chain, Production management, Maintenance management, Layout plan, Productivity Improvement, Cutting Improvement, and Manpower Skill Development and so on. Industrial Engineering also more concepts are required in Initiative on lean manufacturing, learning of lean tools and looking for scope of implementation. Every Garment Owner now understands that only scientific and professional approach can make the profit. So demand for Industrial Engineering is very high, and still the availability of Industrial Engineers is very less than the demand.
The confrontation of COVID-19 pandemic has become one of the promising challenges of the world healthcare. Accurate and fast diagnosis of COVID-19 cases is essential for correct medical treatment to control this pandemic. Compared with the reverse-transcription polymerase chain reaction (RT-PCR) method, chest radiography imaging techniques are shown to be more effective to detect coronavirus. For the limitation of available medical images, transfer learning is better suited to classify patternsdoi:10.1101/2020.08.24.20181339 fatcat:jzzkx4ct3fdwrbcuc5e37hz6h4
more »... in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. CNN is used to extract complex features from samples and classified them using RNN. The VGG19-RNN architecture achieved the best performance among all the networks in terms of accuracy and computational time in our experiments. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize class-specific regions of images that are responsible to make decision. The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff.
Nowadays automatic disease detection has become a crucial issue in medical science with the rapid growth of population. Coronavirus (COVID-19) has become one of the most severe and acute diseases in very recent times that has been spread globally. Automatic disease detection framework assists the doctors in the diagnosis of disease and provides exact, consistent, and fast reply as well as reduces the death rate. Therefore, an automated detection system should be implemented as the fastest waydoi:10.1101/2020.06.18.20134718 fatcat:5j2ffil2v5c2jcenwmoz3zis6e
more »... diagnostic option to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short -term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 421 X -ray images including 141 images of COVID-19 is used as a dataset in this system. The experimental results show that our proposed system has achieved 97% accuracy, 91% specificity, and 100% sensitivity. The system achieved desired results on a small dataset which can be further improved when more COVID-19 images become available. The proposed system can assist doctors to diagnose and treatment the COVID-19 patients easily.
Successful integration of renewable energy sources like wind power into smart grids largely depends on accurate prediction of power from these intermittent sources. Production of wind power cannot be controlled as the wind speed can vary based on weather conditions. Accurate prediction of wind power can assist smart grid that intelligently decides on the usage of alternative power sources based on demand forecast. Time series wind speed data are normally used for wind power prediction. In thisdoi:10.1186/s40807-017-0044-x fatcat:fhf4bccckbajlnfbhy75s5i4ni
more »... aper, we have investigated the usage of a set of secondary features obtained using deep learning for wind power prediction. Deep learning is a special form on neural network that is capable of capturing the structural properties of time series data in terms of a set of numeric features. More precisely, we have designed a two-stage autoencoder (a particular type of deep learning) and incorporated the structural features into a prediction framework. Using the structural features, we have achieved as high as 12.63% better prediction accuracy than traditionally used statistical features. © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The Majallat al-Aḥkām al-'Adliyyah, known as the Mejelle, is the code of Islamic civil transactions which was prepared under the auspices of the Ottoman Caliphate. This code was established based on the Ḥanafī School of Islamic law. However, people, who follow other than the Ḥanafī School, are reluctant to rely on the Mejelle even though they are interested to know the stands of their respective School of Islamic law on the articles of the Mejelle. Thus, considering the importance and relevancedoi:10.22452/js.vol28no3.2 fatcat:jnnmtvrg3fbb5n66o5vlcecbre
more »... of Salam contract to the contemporary context, the paper attempts to conduct a juristic evaluation of the articles of the Mejelle on salam contract along with an investigation of its contemporary applications. Salam is a sale contract in which the commodity's delivery is deferred until a defined period, while the buyer must settle the payment upon the contract's conclusion. Although the general principle says that sale of something which is not possessed and not present is not permissible, salam contract is made valid as an exception from this, considering the need of the people. The study follows a qualitative approach and uses the content analysis method to achieve the objectives. For data source and analysis, the study consults with related classical and contemporary literature. The study finds that in general, the articles of the Mejelle on salam contract conform to the prominent Schools of Islamic law. Among the notable contemporary applications of salam contract are salam financing, parallel salam, salam ṣukūk, salam in short selling, and so forth.
International Journal of Psychology and Counselling
Thus, patients Amanullah and Firdos 23 may actively involve towards illness or disease rather than just having feeling of being dependent or passive. ... maintain the confidentiality is very important in any official case; however, in the case of counselling in medical setting, it is important to share relevant information to the doctors, but to some extent Amanullah ...doi:10.5897/ijpc2017.0501 fatcat:kqgqb7vabzedtgxyq4ncngydte
In the contemporary period, there is hardly any transaction that is detached from its respective terms and conditions. This study aims to explore and analyze the issue of sale subject to a condition from the viewpoints of various schools of Islamic law. This study was accomplished through the scrutiny of the related primary sources of these schools. The researchers have found that the judgment of a sale with conditions is subject to the status of the related condition itself. Any condition thatdoi:10.22452/js.vol24no3.4 fatcat:pyq423ytnngvdcmhpcav4r6k2a
more »... conforms to the requirements of the contract and customs is valid; otherwise it is invalid. Similarly, the study posits that the ruling of the contemporary transaction with terms and conditions is subject to the status of relevant conditions. If the condition is consistent with the contract and Shariah, it is valid and effective. Any condition that contradicts the requisites of the contract and Shariah are invalid and accordingly invalidate the transaction as well.
This paper presents an analytical framework to develop a hierarchical energy management system (EMS) for energy sharing among neighbouring households in residential microgrids. The houses in residential microgrids are categorized into three different types, traditional, proactive and enthusiastic, based on the inclusion of solar photovoltaic (PV) systems and battery energy storage systems (BESSs). Each of these three houses has an individual EMS, which is defined as the primary EMS. Two otherdoi:10.3390/en10122098 fatcat:zb7jhuknyjaopbcpyvujlqxsqa
more »... Ss (secondary and tertiary) are also considered in the proposed hierarchical energy management framework for the purpose of effective energy sharing. The intelligences of each EMS are presented in this paper for the purpose of energy sharing in a residential microgrid along with the priorities. The effectiveness of the proposed hierarchical framework is evaluated on a residential microgrid in Australia. The analytical results clearly reflect that the proposed scheme effectively and efficiently shares the energy among neighbouring houses in a residential microgrid. procurement. An optimal control-based EMS is presented in  for automotive power systems with battery and supercapacitor storage devices to increase the overall operational efficiency. A cooperative home EMS for a house with the solar PV unit and BESS is proposed in  to make a balance between the generation and load while forecasting the solar irradiation. The EMSs as presented in     are only used to manage energy either for a single house with renewable energy sources (RESs) with energy storage systems (ESSs) or for RESs with local loads and the main power grid. For this reason, the structures of these EMSs in [7-10] are quite simple. However, the EMS will be more complicated if several houses with different features are connected together in order to share energy among themselves. The energy management of microgrids is discussed in [11, 12] where RESs are considered as distributed energy resources. In [11, 12] , the main objective of the EMS is to reduce the energy consumption without any prediction of demand and generation. An EMS, considering load and generation prediction, is proposed in  with the aim of utilizing the BESS in an optimal way for better grid support. In [14, 15] , the EMS for the microgrid is designed to reduce the utilization cost of the BESSs under the uncertainties in RESs through optimal generation scheduling. A similar approach for the EMS is presented in  for the least expensive pricing options in the local energy market and maximization of the energy utilization from the RESs. The EMSs in       are useful for managing power under different operating scenarios of microgrids. However, the RESs in these microgrids are lumped at some specific points, which is not the case in residential microgrids, as each house comprises RESs (especially solar PV systems) and BESSs. Thus, the EMS need to be designed in a different way that carries information from each house. The multi-agent frameworks are useful for carrying information from different entities or subsystems within a system, as well as sharing information among these entities    . A decentralized multi-agent framework is proposed in  for the energy management and autonomous control of microgrids. Similar EMSs based on multi-agent frameworks are proposed in  . A cognitive decision agent architecture is used in  to effectively manage the energy in microgrids. In  , the neighbourhood energy sharing is considered without considering any strategy for prioritizing the houses, charging or discharging the BESSs, etc. However, the effective energy management capability of an EMS depends on the intelligence of the agents. The multi-agent frameworks as discussed in       did not consider the detailed power generation and consumption features of different entities in a microgrid, and some of these approaches have also neglected the battery charging or discharging features. The hierarchical approaches are useful for sharing energy information among different entities in a microgrid. A hierarchical EMS is proposed in  where two layers are considered for static and dynamic frequency. Another hierarchical approach is used in  where the upper level of the EMS is used to manage the microgrids along with associated components, and the lower level is used for the optimization of operational costs. A multi-level EMS for multi-source electric vehicles is proposed in  based on an integrated rule-based meta-heuristic approach. In  , a hierarchical framework is presented as a day-ahead scheduling and double-layer intra-hour (master-client) adjustment system where these two layers are used to optimize the operational costs and tie-line power smoothing. A multi-source multi-product microgrid with a hierarchical EMS is proposed in  where the hierarchical framework includes supervisory, optimization and execution control layers. A hierarchical EMS based on master and slave control strategies is presented in  to maintain the power balance within the microgrid while considering the state of charge (SOC) of the battery. All these hierarchical approaches are proposed in [23-28]; the energy management at the household level is not considered, but rather some overall specific activities. This paper aims to design a hierarchical EMS for residential microgrids where the houses are categorized into three different types based on the inclusion of solar PV units and BESSs. Energy sharing priorities are also defined in this paper by considering both grid-connected and islanded operation of microgrids. In the proposed hierarchical framework, the levels are considered to
Head & Face Medicine
Esthesioneuroblatoma (Olfactory neuroblastoma) is a rare malignant neoplasm arising from the olfactory epithelium with bimodal age distribution between with first peak in second decades and second peak in sixth decade. Proptosis due to esthesioneuroblastoma is one of the rare causes. They have a long natural history characterized by frequent local or regional recurrence. Computed tomography and magnetic resonance imaging are the imaging modalities for diagnosing these tumors. Adoi:10.1186/1746-160x-9-19 pmid:23890074 pmcid:PMC3733952 fatcat:lnb7gs4gy5bfvgmz2kcxftp3y4
more »... approach with surgery and radiation therapy is an excellent treatment options for these tumors with chemotherapy being used to treat advanced or recurrent disease.
Moon THJM, Amanullah M, Akhter N, et al. Early cardiac change after menopause-an open level comparative study. Obstet Gynecol Int J. 2021;12(2):113-117. ...doi:10.15406/ogij.2021.12.00562 fatcat:lyhxthflvzhehg4dg7fvtv76x4
The effects of vacuum (VP) and 100% N2 modified atmosphere packaging (MAP) on the qualityand shelf-life of sliced pangasius catfish (Pangasianodon hypophthalmus) during refrigerated storage (4°C)were investigated up to 12 days. The values of pH, total volatile base nitrogen (TVB-N) and thiobarbituricacid reactive substance (TBARS) of sliced fish samples during storage under VP and MAP packaging werewithin the limit acceptable for chilled fish. Total viable count (TVC) of pangasius fish, on thedoi:10.52168/bjf.2020.32.09 fatcat:5ve2tmpdtvcwdholtlvxohzpby
more »... ther hand,gradually increased from the initial value of 4.32±0.04 to 8.30±0.13 log CFU/g on day 9 for non-sealedpack (control) and 7.64±0.12 and 8.34±0.07 log CFU/g for VP and MAP on day 12. There were nosignificant (p<0.05) differences in TVC values among the three packaging conditions during the storageperiod except on day 9 where significantly (p<0.05) lower TVC values were observed in the VP samplecompared to that of other samples. Based on the bacterial counts of 7 log CFU/g, which is considered as theupper acceptable limit for fresh and frozen fish, the shelf-life was determined as the excess of 6 days forcontrol pack and MAP samples, and excess of 9 days for VP sample. Therefore, VP is a good option toincrease the shelf-life of wet fish, which can be adopted by the superstores to display their products withextended shelf-life.
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