Tasnim Assali, Zayneb Trabelsi Ayoub, Sofiane Ouni
{"title":"Apply Distributed CNN on Genomics to accelerate Transcription-Factor TAL1 Motif Prediction","authors":"Tasnim Assali, Zayneb Trabelsi Ayoub, Sofiane Ouni","doi":"10.1109/TechDev57621.2022.00008","DOIUrl":"https://doi.org/10.1109/TechDev57621.2022.00008","url":null,"abstract":"Big Data works perfectly along with Deep learning to extract knowledge from a huge amount of data. However, this processing could take a lot of training time. Genomics is a Big Data science with high dimensionality. It relies on deep learning to solve complicated problems in certain diseases like cancer by using different DNA information such as the transcription factor. TAL1 is a transcription factor that is essential for the development of hematopoiesis and of the vascular system. In this paper, we highlight the potential of deep learning in the field of genomics and its challenges such as the training time that takes hours, weeks, and in some cases months. Therefore, we propose to apply a distributed deep learning implementation based on Convolutional Neural Networks (CNN) that showed good results in decreasing the training time and enhancing the accuracy performance with 95% by using multiple GPU and TPU as accelerators. We proved the efficiency of using a distributed strategy based on data-parallelism in predicting the transcription-factor TAL1 motif faster.","PeriodicalId":171777,"journal":{"name":"2022 11th International Conference on Computer Technologies and Development (TechDev)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115173311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prediction of Coke Oven Gas Production with Multivariable Input Based on LSTM Neural Network","authors":"P. Zhao, Yaocong Zhang, Jialin Yang, Shujun Liu, Wenting Wang, Tong Xu","doi":"10.1109/TechDev57621.2022.00009","DOIUrl":"https://doi.org/10.1109/TechDev57621.2022.00009","url":null,"abstract":"Coke-oven is not a closed, stable and continuous reaction device in the actual production process. It has periodic operation procedures and the transformation of production rhythm. These factors lead to the strong nonlinear and uncertain characteristics of coke-oven production process. It is difficult to accurately model the gas generation process from the reaction mechanism. In this paper, a digital twin model for forecast of coke-oven gas generationcoke-oven in metallurgical industry based on long short term memory network (LSTM) is proposed. The forecasting models of coke-oven gas based on neural network does not need to study the mechanism of complex chemical reaction. It only needs to determine the correlation characteristics of parameters, and then conduct model training through sufficient historical data, so as to obtain accurate predictive results, which can provide data support for advanced applications such as abnormal working condition early warning and integrated energy management system.","PeriodicalId":171777,"journal":{"name":"2022 11th International Conference on Computer Technologies and Development (TechDev)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116953830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of Photovoltaic Building Integrated Auxiliary Design Platform Based on BIM System","authors":"J. Wang","doi":"10.1109/TechDev57621.2022.00013","DOIUrl":"https://doi.org/10.1109/TechDev57621.2022.00013","url":null,"abstract":"As a more important element in green building, Photovoltaic (PV) building not only contributes to the realization of energy saving and emission reduction, but also promotes the healthy development of the industry. Therefore, this paper designs an auxiliary platform for PV building integration based on BIM system. The study uses PVsyst, a PV system design software, to clarify key parameters such as the installation and selection of inverters and PV modules, and then simulates the PV power generation. The information model of this residence is constructed using eQUEST software to analyze the building energy consumption in three aspects: PV power generation, air conditioning energy efficiency, and envelope structure. The results show that PV buildings can promote the realization of energy self-sufficiency and, combined with BIM technology, can play a role in promoting the development of green and energy-efficient buildings.","PeriodicalId":171777,"journal":{"name":"2022 11th International Conference on Computer Technologies and Development (TechDev)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117185693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}