| 1 | Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT显示文摘Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years now.Confidential information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the IIoT.There are also significant security risks,concerns,and difficulties associated with cloud computing.To address these challenges,we propose merging BC and SDN into a cloud computing platform for the IIoT.This paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT applications.The proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and scalable.Customers in the industrial sector benefit from the dispersed or decentralized,and efficient environment of BC.Additionally,we described an SDN method to improve the durability,stability,and load balancing of cloud infrastructure.The efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself. | Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari | 2023 | Digital Communications and Networks2023，9，2： | 0 |
| 2 | Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs显示文摘People who have trouble communicating verbally are often dependent on sign language,which can be difficult for most people to understand,making interaction with them a difficult endeavor.The Sign Language Recognition(SLR)system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal person.The existing study related to the Sign Language Recognition system has some drawbacks,such as a lack of large datasets and datasets with a range of backgrounds,skin tones,and ages.This research efficiently focuses on Sign Language Recognition to overcome previous limitations.Most importantly,we use our proposed Convolutional Neural Network(CNN)model,“ConvNeural”,in order to train our dataset.Additionally,we develop our own datasets,“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”,both of which have ambiguous backgrounds.“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”both include images of Bangla characters and numerals,a total of 24,615 and 8437 images,respectively.The“ConvNeural”model outperforms the pre-trained models with accuracy of 98.38%for“BdSL_OPSA22_STATIC1”and 92.78%for“BdSL_OPSA22_STATIC2”.For“BdSL_OPSA22_STATIC1”dataset,we get precision,recall,F1-score,sensitivity and specificity of 96%,95%,95%,99.31%,and 95.78%respectively.Moreover,in case of“BdSL_OPSA22_STATIC2”dataset,we achieve precision,recall,F1-score,sensitivity and specificity of 90%,88%,88%,100%,and 100%respectively. | Muhammad Aminur Rahaman Kabiratun Ummi Oyshe Prothoma Khan Chowdhury Tanoy Debnath Anichur Rahman Md.Saikat Islam Khan | 2024 | Biomimetic Intelligence & Robotics2024，4，1： | 0 |
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