Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks

With the rapid development of networks,users are increasingly seeking richer and high-quality content experience,and there is an urgent need to develop efficient content caching strategies to improve the content distribution efficiency of caching.Therefore,it will be an effective solution to combine content popularity prediction based on machine learning(ML)and content caching to enable the network to predict and analyze popular content.However,the data sets which contain users’private data cause the risk of privacy leakage.In this paper,to address this challenge,we propose a privacy-preserving algorithm based on federated learning(FL)and long short-term memory(LSTM),which is referred to as FL-LSTM,to predict content popularity.Simulation results demonstrate that the performance of the proposed algorithm is close to the centralized LSTM and better than other benchmark algorithms in terms of privacy protection.Meanwhile,the caching policy in this paper raises about 14.3%of the content hit rate.

Prediction of the Helix/Sheet Content of Proteins from Their Primary Sequences by Neural Network Method

The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by using the independent non-homologous protein database. It is shown that the average absolute errors for resubstitution test are 0.070 and 0.068 with the standard deviations 0.049 and 0.047 for the prediction of the content of α-helix and β-sheet respectively. For cross-validation test, the average absolute errors are 0.075 and 0.070 with the standard deviations 0.050 and 0.049 for the prediction of the content of α-helix and β-sheet respectively. Compared with the other methods currently available, the BP neural network method combined with the amino acid composition and the biased auto-correlation function features can effectively improve the prediction accuracy.

Distribution prediction of moisture content of dead fuel on the forest floor of Maoershan national forest, China using a LoRa wireless network

The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal

A genetic algorithm based on the nested intervals chaos search (NICGA) hasbeen given. Because the nested intervals chaos search is introduced into the NICGA to initialize thepopulation and to lead the evolution of the population, the NICGA has the advantages of decreasingthe population size, enhancing the local search ability, and improving the computational efficiencyand optimization precision. In a multi4ayer feed forward neural network model for predicting thesilicon content in hot metal, the NICGA was used to optimize the connection weights and thresholdvalues of the neural network to improve the prediction precision. The application results show thatthe precision of predicting the silicon content has been increased.

Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and downstream.This content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product profitability.Based on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm fruit.First,350 palm fruits with Tenera varieties(Elaeis guineensis Jacq.var.tenera),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content extraction.Then,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil content.Finally,a robust EMD-ANN model is generated by optimizing the lowest possible errors.Based on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±0.208.These results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally friendly.Therefore,the proposed technique has a promising potential to be applied in the oil palm industry.Measurements like this will lead to the effective and efficient management of oil palm production.

A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA

The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares(MWPLS)and genetic algorithm(GA)for the chlorophyll content detection of winter wheat canopy using spectroscopy technology.Firstly,the original spectral dataset was pre-processed by wavelet denosing,multiple scatter correction.Then,abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY.Finally,the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares(PLS)models were established for chlorophyll content prediction.For the model established by using all the wavebands in the region of 400-900 nm,its R_(c)^(2) and R_(v)^(2) were 0.4468 and 0.3821 respectively;its modeling root mean square error(RMSEM)and verification root mean square error(RMSEV)were 2.9057 and 1.7589 respectively.For the model established by using 151 wavebands selected by MWPLS,its R_(c)^(2) and R_(v)^(2) were 0.6210 and 0.5901 respectively;its RMSEM and RMSEV were 2.4007 and 1.6408 respectively.For the model established by using 36 wavebands selected by MWPLS+GA,its R_(c)^(2) and R_(v)^(2) were 0.7805 and 0.7497 respectively;its RMSEM and RMSEV were 1.8504 and 1.1315 respectively.The results show that wavelength selection can remove redundant information and improve model performance.The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction.

Quality assessment of processed Eucommiae Cortex based on the color and tensile force

Eucommiae Cortex(EC),the dried stem bark of Eucommia ulmoides Oliv,has been traditionally used to strengthen the muscle and bone tissues and improve liver and kidney functions in East Asian countries,including China,Japan,and Korea.Salty-fried EC(SFEC)is made by using mixing EC and saline together based on the protocol of Chinese Materia Medica Processing(CMMP)for clinical use.However,the clinical effectiveness of SFEC is directly impacted by the frying temperature and time.But precise techniques for evaluating the caliber of SFEC have yet to be developed.Thus,this study aimed to establish a fast and accurate quality-check method for SFEC decoction pieces.According to the frying temperature and time,four different categories of SFEC had been got according to the Chinese Pharmacopoeia method as Raw(R),Under(U),Moderately(M),and Overly(O).The red(R),green(G),blue(B),and light(L)color values of the decoction pieces were quantitated using the Photoshop software to determine the standard value range of the L color(raw[104.44±15.06],under[67.28±8.20],moderately[39.94±6.40],and over[15.02±5.03]).Additionally,the tensile strengths and pinoresinol diglucoside(PDG)levels of EC gum-silks were measured using the mechanical tensile test and HPLC,respectively.We also conducted Pearson correlation analysis on the L color value,EC gum-silk tension(FbcN),and PDG level,and established the following muliple-linear regression equation:the decline in PDG level(w)=0.829-0.001×FbeN-0.009×I.In conclusion,the L color value and FbeN could be used for cluster analysis of four categories of SFEC.Additinally,based on the correlation among the L color value,gum-silk tensile test,and PDG level,a rapid and accurate quality-control method for SFEC decoction pieces with different frying temperatures and durations was estab-lished.This method facilitates the manufacturing of efficacious SFEC.