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| 1 | Applied machine learning in greenhouse simulation;new application and analysis显示文摘Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot regions.The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it.So the main goal of this study is to select the best method between Artificial Neural Network(ANN)and Support Vector Machine(SVM)to estimate three different variables include inside air,soil and plant temperatures(Ta,Ts,Tp)and also energy exchange in a polyethylene greenhouse in Shahreza city,Isfahan province,Iran.The environmental factors which influencing all the inside temperatures such as outside air temperature,wind speed and outside solar radiation were collected as data samples.In this research,13 different training algorithms were used for ANN models(MLPRBF).Based on K-fold cross validation and Randomized Complete Block(RCB)methodology,the best model was selected.The results showed that the type of training algorithm and kernel function are very important factors in ANN(RBF and MLP)and SVM models performance,respectively.Comparing RBF,MLP and SVM models showed that the performance of RBF to predict Ta,Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 indices.The range of RMSE and MAPE factors for RBF model to predict Ta,Tp and Ts were between 0.07 and 0.12C and 0.28-0.50%,respectively.Generalizability and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data groups.The performance of best model(RBF)to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy. | Morteza Taki Saman Abdanan Mehdizadeh Abbas Rohani Majid Rahnama Mostafa Rahmati-Joneidabad | 2018 | Information Processing in Agriculture2018，5，2： | 8 |
| 2 | Prediction of representative deformation modulus of longwall panel roof rock strata using Mamdani fuzzy system显示文摘Deformation modulus is the important parameter in stability analysis of tunnels, dams and mining structures. In this paper, two predictive models including Mamdani fuzzy system(MFS) and multivariable regression analysis(MVRA) were developed to predict deformation modulus based on data obtained from dilatometer tests carried out in Bakhtiary dam site and additional data collected from longwall coal mines. Models inputs were considered to be rock quality designation, overburden height, weathering,unconfined compressive strength, bedding inclination to core axis, joint roughness coefficient and fill thickness. To control the models performance, calculating indices such as root mean square error(RMSE),variance account for(VAF) and determination coefficient(R2) were used. The MFS results show the significant prediction accuracy along with high performance compared to MVRA results. Finally, the sensitivity analysis of MFS results shows that the most and the least effective parameters on deformation modulus are weathering and overburden height, respectively. | Mohammad Rezaei Mostafa Asadizadeh Abbas Majdi Mohammad Farouq Hossaini | 2015 | International Journal of Mining Science and Technology2015，25，1： | 6 |
| 3 | Solar thermal simulation and applications in greenhouse显示文摘In this study,a comprehensive review focusing on key strategies of energy saving technologies based on simulation of heat and mass transfer and also artificial intelligent for climate controlling is presented.Following the brief and concise assessment of existing greenhouse systems in terms of their role in total energy consumption;effective shape and structure,energy-efficient and new technologies are analyzed in detail for potential utilization in greenhouses for notable reductions in energy consumption and also go toward the sustainability.The technologies considered within the scope of this research are mainly renewable and sustainable based solutions such as photovoltaic(PV)modules,solar thermal(T)collectors,hybrid PV/T collectors and systems,phase change material(PCM)and underground based heat storage techniques,energy-efficient heat pumps,alternative facade materials for better thermal insulation and power generation.The findings from the research clearly reveal that up to 70%energy saving can be achieved through appropriate retrofit of conventional greenhouses.Using of solar greenhouses in Europe is more popular than others.In some countries in Asia such as Iran,it is very restrict to invest on renewable projects because of cheap fossil fuels.So it is recommended beside of investments by private investors,the Iranian government should also invest in the extension of solar energy in greenhouse by setting up a specialized agency or contracting firms.Those should target the modeling and design the best shape of solar greenhouse for all agricultural areas to receive the maximum solar radiation and decrease the need of fossil fuels. | Morteza Taki Abbas Rohani Mostafa Rahmati-Joneidabad | 2018 | Information Processing in Agriculture2018，5，1： | 4 |
| 4 | 高超音速双楔形升力面的主动热气动弹性控制问题的研究(英文)显示文摘非线性热弹性和气动不稳定性问题在再入大气层运载工具和高超音速飞行器上都是极为重要的问题,应该在飞行器的设计阶段给予特别的重视。尤其是热力学问题更为重要,因为温度环境的变化可对高超音速飞行器结构的静力学与动力学特性产生显著影响,进而导致动力学不稳定乃至于灾难性的结构疲劳破坏。为了深入理解这类'热'结构的动力学行为,本文分析一种双楔形升力面,该升力面在沉浮和俯仰两个自由度方向都有自由间隙与三次非线性结构刚度。三阶空气动力学活塞理论被用来计算施加于升力面上的非线性非定常气动载荷。还考虑了由于升力面气动加热产生的轴向应力所导致的扭转刚度的损失。根据高速气流产生的隔热层温度可以预估气动加热的影响。主动控制是今年来兴起的主要用于航空宇航结构系统的新技术,为了抑制颤振边界上和颤振后的动力响应,我们采用了线性和非线性主动控制策略,给出了建模方法及其仿真结果。值得强调的是,当扭转刚度损失较大时有可能引发升力面的动力学不稳定状态。此外,主动控制策略还可以拓展颤振边界,并且能将不稳定极限环振动转变为稳定极限环振动,或者可以将这两种状态的转捩点推向更高的马赫数。 | Laith K Abbas 陈前 Piergiovanni Marzocca Gürdal Zafer Abdalla Mostafa | 2008 | Chinese Journal of Aeronautics2008，21，1： | 3 |
| 5 | First derivative spectrophotometric determination of uranium(Ⅵ)and vanadium(Ⅴ)in natural and saline waters and some synthetic matrices using PAR and cetylpyridinum chloride显示文摘 | Abbas M N Homoda A M Mostafa G A E | 2001 | Anal Chim Acta2001，436，2： | 1 |
| 6 | First Derivative Spectrophotometric Determination of Uranium (Ⅵ) and Vanadium( Ⅴ ) in Natural and Saline Waters and Some Synthetic Matrices Using PAR and Cetylpyridinum Chloride显示文摘 | Abbas M N Homoda A M Mostafa G A E | 2001 | Analytica Chimica Acta2001，436，2： | 1 |
| 7 | Determination of Traces of Nitrite and Nitrate in Water by Solid Phase Spectrophotometry显示文摘 | Abbas M N Mostafa G A | 2000 | Anal Chim Acta2000，410，： | 1 |
| 8 | An ensemble deep learning model for cyber threat hunting in industrial internet of things显示文摘By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)platforms.Such vast heterogeneous data increase the challenges of security risks and data analysis procedures.As IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to operate.In this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is proposed.In this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data dimension.In addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and performance.To solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection model.In this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on data.The results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,respectively.Moreover,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model. | Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour | 2023 | Digital Communications and Networks2023，9，1： | 1 |
| 9 | Cetylpyridiniumi-odomercurate PVC membrane ion selective electrode for determination of cetypfidinum cation in ezafluor mouthwash and as a detector for some potentiometric titrations显示文摘 | ABBAS M N MOSTAFA G A E HOMODA A M A | 2000 | Talanta2000，53，2： | 1 |
| 10 | Brain aging in normal Egyptians: cognition, education, personality, genetic and immunological study显示文摘 | Osamah Elwan Obsis Madkour Fadia Elwan Mervat Mostafa Azza Abbas Helmy Maged Abdel-Naseer Sanaa Abdel Shafy Nervana El Faiuomy | 2003 | Journal of the Neurological Sciences2003，，1： | 1 |
| 11 | Role of alpha-!ipoic acid in the management of anemia in patients with chronic renalfall- ure undergoing hemodialysis 显示文摘 | E1-Nakib GA Mostafa TM Abbas TM | 2013 | Int J Nephrol Renovasc Dis2013，6，： | 1 |
| 12 | Determination of traces of nitrite in water by solid phase spectrophotometry显示文摘 | Abbas M N Mostafa G A | 2000 | Anal Chim Acta2000，410，12： | 1 |
| 13 | On the use of hierarchical color moments for image indexing and retrieval显示文摘 | Mostafa T Abbas H M Wahdan A A | 2002 | IEEE Transactions on Systems Man and Cybernetics2002，7，6： | 1 |
| 14 | The effect of organizational culture on the knowledge management implementation processes from the viewpoint of Education Dept employees显示文摘 | Jafari Sepideh Abbaspour Abbas Azizishomami Mostafa | 2013 | Interdisciplinary Journal of Contemporary Research in Business2013，5，1： | 1 |
| 15 | Prevalence of HIV infection and the correlates among beggars in Tehran, Iran显示文摘 | Seyed Ahmad Seyed Alinaghi Abbas Ostad Taghi Zadeh Hossein Zaresefat Mehdi Hajizadeh Seyed Najmeddin Mohamadi Koosha Paydary Sahra Emamzadeh Fard Mostafa Hosseini | 2013 | Asian Pacific Journal of Tropical Disease2013，，1： | 1 |
| 16 | PVC membrane ion selective electrode for the determina- tion of pentachlorophenol in water, wood and soil using tetrazolium pentachlorophenolate显示文摘 | Abbas M N Mostafa G A E Homoda A M A | 2001 | Talanta2001，55，： | 1 |
| 17 | On the Use of Hierarchical Color Moments for Image Indexing and Retrieval显示文摘 | Mostafa T Abbas H M Wahdan A A | 2002 | IEEE International Conference on Systems Man and Cybernetics2002，7，： | 1 |
| 18 | Does prospective permutation scan statistics work well with cutaneous leishmaniais as a high-frequency or malaria as a low-frequency infection in Fars province, Iran?显示文摘Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis(CL) or for malaria in Fars province, Iran in 2016. Methods: Using time-series data including 29 177 CL cases recorded during 2010-2015 and 357 malaria cases recorded during 2010-2015, CL and malaria cases were predicted in 2016. Predicted cases were used to verify if they followed uniform distribution over time and space using space-time analysis. To testify the uniformity of distributions, permutation scan statistics was applied prospectively to detect statistically significant and non-significant outbreaks. Finally, the findings were compared to determine whether permutation scan statistics worked better for CL or for malaria in the area. Prospective permutation scan modeling was performed using SatScan software. Results: A total of 5 359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied timeseries models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria(Pv was set to 0.1). The results indicated two significant prospective spatial-temporal outbreaks for CL(P<0.5) including Most Likely Clusters, and one non-significant outbreak for malaria(P>0.5) in the area. Conclusions: Both CL and malaria follow a space-time trend in the area, but prospective permutation scan modeling works better for detecting CL spatial-temporal outbreaks. It is not far away from expectation since clusters are defined as accumulation of cases in specified times and places. Although this method seems to work better with finding the outbreaks of a high-frequency disease; i.e., CL, it is able to find non-significant outbreaks. This is clinically important for both high-and low-frequency infections; i.e., CL and malaria. | Abbas Rezaianzadeh Marjan Zare Hamidreza Tabatabaee Mohsen Ali-Akbarpour Hossain Faramarzi Mostafa Ebrahimi | 2018 | Asian Pacific Journal of Tropical Biomedicine2018，8，10： | 0 |
| 19 | Monitoring hawksbill turtle nesting sites in some protected areas from the Persian Gulf显示文摘Iranian nesting populations of the critically endangered hawksbill turtle(Eretmochelys imbricate) are some of the most important in the Indian Ocean. In this study, four of the most important hawksbill nesting grounds in the Persian Gulf, situated within three Iranian marine protected areas, were surveyed during nesting season,including Nakhiloo, Ommolgorm and Kharko Islands and the mainland beaches of the Naiband Marine-Coastal National Park(NMCNP). We present GIS maps of these key nesting grounds and describe sand texture of key nesting zones, along with conservation recommendations. About 9.2(28.3%) out of 32.5 km of all shores surveyed in this study were used by nesting hawksbill turtles follows: Nakhiloo: 1.4 km(52% of potential nesting area);Ommolgorm: 1.94 km(40%);Kharko: 3.4 km(28%), and NMCNP: 2.46 km(18.9%). The average nesting density was calculated as 131 nests/km at Nakhiloo, 76 nests/km at Ommolgorm, 7 nests/km at Kharko, and 15 nests per km at NMCNP. Highest nesting density was observed in Nakhiloo and Ommolgorm. It is thought that high hawksbill nesting density in these islands seems likely a result of limiting adequate nesting shores rather than the size of population, and also low density in Kharko and NMCNP more related to past and current pressures and low population density. With the exception of Ommolgorm Island, sands at the nesting grounds were well sorted.Grain size indicated that female hawksbill turtles in the Iranian Persian Gulf nest in sands that are generally mixed, with mean grain size ranging from coarse sands(0.4Φ;~0.5–1 mm) to fine sands(2Φ;~0.25 mm). We provide and discuss conservation recommendations and suggestions for future. | Majid Askari Hesni Mohsen Rezaie-Atagholipour Somaye Zangiabadi Mohammad Amin Tollab Mostafa Moazeni Hosein Jafari Mohammad Talebi Matin Ghasem Ghorbanzadeh Zafarani Mahtab Shojaei Abbas Motlaghnejad | 2019 | Acta Oceanologica Sinica2019，38，12： | 0 |
| 20 | Prediction of COVID-19 Transmission in the United States Using Google Search Trends显示文摘Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality. | Meshrif Alruily Mohamed Ezz Ayman Mohamed Mostafa Nacim Yanes Mostafa Abbas Yasser El-Manzalawy | 2022 | Computers, Materials & Continua2022，，4： | 0 |
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