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| 1 | Tissue culture independent transformation for Corchorus olitorius显示文摘 | Abu Ashfaqur Sajib Md. Shahidul Islam Md. Shamim Reza Arpita Bhowmik Layla Fatema Haseena Khan | 2008 | Plant Cell, Tissue and Organ Culture2008，，3： | 1 |
| 2 | Novel Parameter Estimation Method for Chirp Signals Using Bowtie Chirplet and Discrete Fractional Fourier Transform 显示文摘 | Mostayed A Kim S K Sajib S Z K | 2008 | Second International Conference on Future Generation Communication and Networking Symposia2008，3，1315： | 1 |
| 3 | High Performance Computing for a Financial Application Using Fast Fourier Transform显示文摘 | Ruppa K Thulasiram Parimala Thulasiraman | 2005 | Springer-Verlag GmbH2005，36，48： | 1 |
| 4 | A Simple,Efficient and Rapid Method for Good Quality DNA Extraction from Rice Grains显示文摘An efficient and good DNA extraction protocol should be simple, affordable and yield enough DNA with high quality. Rice(Oryza sativa L.) DNA extraction methods often use seedlings or leaves rather than the grains and tend to be time-consuming, involve multiple steps, and use hazardous chemicals and expensive enzymes. Rice grains offer several benefits over seedlings and leaves as a source of DNA for genetic analysis. However, these benefits are underutilized because the bulk of a rice grain is made up of starch. It is particularly important, but difficult to get rid of the starch while extracting DNA from rice grains. This co-precipitated polysaccharide is a known inhibitor of DNA polymerase activity in polymerase chain reaction(PCR). We describe here a very simple and highly affordable Chelex~?-100 based DNA extraction method from rice grains. It does not require any hazardous chemicals or enzymes. This method reproducibly extracts DNA with good purity indices(A_(260)/A_(230) and A_(260)/A_(280) values), but requires only a few steps. | Abu Ashfaqur SAJIB Mohammad Ashraful Islam BHUIYA Roksana HUQUE | 2017 | Rice science2017，24，2： | 1 |
| 5 | 基于IR-VGG的多分类皮肤病实时诊断显示文摘恶性的皮肤病变在早期阶段的治愈率极高,基于深度学习的皮肤病诊断研究近年来受到持续关注,其诊断准确率较高,然而计算资源消耗大,且依赖于医院大型计算设备。为在物联网移动设备上实现快速准确皮肤病诊断,提出一种基于IR-VGG(inverted residual visual geometry group)的多分类皮肤病实时诊断系统,使用轮廓检测算法分割出皮肤病图像病灶区域,并用反转残差块替换VGG16第一层卷积块以降低网络参数权重和内存开销;将原图像和分割后的病灶图像输入IR-VGG网络,通过全局和局部特征提取后,输出皮肤病诊断结果。实验结果表明,IR-VGG网络结构在SkinData-1和SkinData-2皮肤病数据集上的准确率分别可达到94.71%和85.28%,并且可以有效降低复杂度,使诊断系统较容易在物联网移动设备上进行皮肤病实时诊断。 | 谈玲 荣杉山 夏景明 Sajib Sarker 马雯杰 | 2021 | 物联网学报2021，5，3： | 1 |
| 6 | Jamdani纱丽的自动化织造研究显示文摘Jamdani纱丽是孟加拉国最具地理标志性的手工产品,也是孟加拉国文化遗产的典型象征。随着Jamdani纱丽开始融入全球时尚产业,它作为孟加拉国独特的传统服饰具有一定的研究价值。传统的Jamdani纱丽织造采用纯手工编织的方法,该方法生产效率低下,不能满足现今的市场需求。文章总结了Jamdani纱丽的外观色彩效果、织物组织结构和纹样特征,通过选择合适的经纬密和组织在提花机上实现了Jamdani纱丽在提花机上的全自动织造。该方法提高了织造效率,极大地缩短生产时间,为Jamdani纱丽的大批量生产提供了可能性,有助于提高Jamdani纱丽在工业化市场中的竞争力。 | Sajib Kumar Paul 张鲁燕 刘珈利 梁明进 田伟 祝成炎 | 2019 | 丝绸2019，56，7： | 1 |
| 7 | Tissue culture independent transformation for Corchorus olitorius显示文摘 | Abu Ashfaqur Sajib Md Shahidul Islam Md Shamim Reza | 2008 | Plant Cell Tiss Organ Cult2008，95，： | 1 |
| 8 | Mechanical bowel preparation versus no preparation before colorectal surgery: A randomized prospective trial in a tertiary care institute显示文摘 | Asis Saha Firoz Chowdhury Amitesh Jha Sajib Chatterjee Anjan Das Parvin Banu | 2014 | Journal of Natural Science, Biology and Medicine2014，，： | 1 |
| 9 | Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks显示文摘The novel coronavirus 2019(COVID-19)rapidly spreading around the world and turns into a pandemic situation,consequently,detecting the coronavirus(COVID-19)affected patients are now the most critical task for medical specialists.The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide,resulting in the number of infected cases is expanding.Therefore,a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method,which hinders the spreading of coronavirus.In this paper,the study suggests a Deep Convolutional Neural Network-based multi-classification framework(COV-MCNet)using eight different pre-trained architectures such as VGG16,VGG19,ResNet50V2,DenseNet201,InceptionV3,MobileNet,InceptionResNetV2,Xception which are trained and tested on the X-ray images of COVID-19,Normal,Viral Pneumonia,and Bacterial Pneumonia.The results from 4-class(Normal vs.COVID-19 vs.Viral Pneumonia vs.Bacterial Pneumonia)demonstrated that the pre-trained model DenseNet201 provides the highest classification performance(accuracy:92.54%,precision:93.05%,recall:92.81%,F1-score:92.83%,specificity:97.47%).Notably,the DenseNet201(4-class classification)pre-trained model in the proposed COV-MCNet framework showed higher accuracy compared to the rest seven models.Important to mention that the proposed COV-MCNet model showed comparatively higher classification accuracy based on the small number of pre-processed datasets that specifies the designed system can produce superior results when more data become available.The proposed multi-classification network(COV-MCNet)significantly speeds up the existing radiology based method which will be helpful for the medical community and clinical specialists to early diagnosis the COVID-19 cases during this pandemic. | Sajib Sarker Ling Tan Wenjie Ma Shanshan Rong Osibo Benjamin Kwapong Oscar Famous Darteh | 2021 | Journal on Internet of Things2021，3，2： | 0 |
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