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| 1 | Randomized clinical trial of routine on‐table cholangiography during laparoscopic cholecystectomy显示文摘 | O. A.Khan S.Balaji G.Branagan D. H.Bennett N.Davies | 2011 | Br J Surg2011，，3： | 1 |
| 2 | 枯草芽孢杆菌对角粉的微生物降解以及在皮革加工中的应用:一个一举两得的方法显示文摘角粉水解蛋白是经过高蒸汽压处理牲畜的生角和蹄子而获得的。它通过天然枯草芽抱杆菌菌株的生物降解,能够产生水溶性多肽的混合物,这里特指为细菌分解角粉蛋白(BDHH)。BDHH易储存,在32℃±3℃和相对湿度为40%~80%下也不易腐败。这种材料被成功的应用在改善皮革鞣制过程中铬的消耗,并且在复鞣中也被用作鞣胶原纤维来填补空隙。在皮革加工中.使用BDHH可以减少铬鞣过程中铬盐的消耗,从而使废液中铬的排放量从30%~35%大大减少到少于10%,并且减少了铬盐的花费和商业铬鞣过程中的污染负荷。在复鞣过程中,使用BDHH作为鞣制皮革的填料能提升表皮坚固度。 | S.BALAJI R.KARTHIKEYAN M.SENTHIL KUMAR 唐国庆(译) M.SENTHIL KUMAR(等) | 2009 | 北京皮革(中外鞋讯)(下)2009，，8： | 0 |
| 3 | Deep Learning Based Energy Consumption Prediction on Internet of Things Environment显示文摘The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts. | S.Balaji S.Karthik | 2023 | Intelligent Automation & Soft Computing2023，，7： | 0 |
| 4 | Energy Prediction in IoT Systems Using Machine Learning Models显示文摘The Internet of Things (IoT) technology has been developed fordirecting and maintaining the atmosphere in smart buildings in real time. Inorder to optimise the power generation sector and schedule routine maintenance,it is crucial to predict future energy demand. Electricity demandforecasting is difficult because of the complexity of the available demandpatterns. Establishing a perfect prediction of energy consumption at thebuilding’s level is vital and significant to efficiently managing the consumedenergy by utilising a strong predictive model. Low forecast accuracy is justone of the reasons why energy consumption and prediction models havefailed to advance. Therefore, the purpose of this study is to create an IoTbasedenergy prediction (IoT-EP) model that can reliably estimate the energyconsumption of smart buildings. A real-world test case on power predictionsis conducted on a local electricity grid to test the practicality of the approach.The proposed (IoT-EP) model selects the significant features as input neurons,the predictable data is selected as output nodes, and a multi-layer perceptronis constructed along with the features of the Convolution Neural Network(CNN) algorithm. The analysis of the proposed IoT-EP model has higheraccuracy of 90%, correlation of 89%, and variance of 16% in less training timeof 29.2 s, and with a higher prediction speed of 396 (observation/sec). Whencompared to existing models, the results showed that the proposed (IoT-EP)model outperforms with a satisfactory level of accuracy in predicting energyconsumption in smart buildings. | S.Balaji S.Karthik | 2023 | Computers, Materials & Continua2023，，4： | 0 |
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