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Influence of Magnetic Treatment on Salts Leaching： From Soils of Different Salinity Levels

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Journal of Agricultural Science and Technology(B) 2014年第6期4卷 434-439页

作者： Khalida Abdul-Karim Hassan Farhad Ali Hashim sarwar mohammed Rasheed Ahmed Department of Soil and Water Faculty of Agriculture and Forestry Duhok University Duhok 42001 Kurdistan Region Iraq

Leaching behavior of individual ions from three saline soils （S1 = 85.0 dS/m, S2 = 53.92 dS/m and S3 = 38.14 dS/m） under magnetic water （MW） treatment or non-magnetized well water （WW） was investigated in column study. After five pore volumes, soil electrical conductivity （EC） decreased to a range between 2.99-3.29 dS/m under the two water treatments. The rate of soluble cations leached was Na＋〉 Ca2＋〉 Mg2＋〉 K＋ and CI- 〉 SO42- 〉 HCO3- for anions under the two water treatments. The greater amount of salt leached was during the first two pore volumes and was higher under MW treatment. Among soils, the higher the initial EC, the greater the amount of the salt leached. Measurements of the exchangeable Na~ performed at the end of leaching experiment showed a high decrease in soil exchangeable Na＋ compared to the initial values of sodium adsorption ratio （SAR） and exchangeable sodium percentage （ESP） in the soil indicated that there was no prospects of soil sodification after leaching with five pore volumes.

关键词： MW soil salinit leaching

A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

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Computers, Materials & Continua 2023年第4期75卷 959-976页

作者： MZeeshan sarwar mohammed Hamad Alatiyyah Ahmad Jalal Mohammad Shorfuzzaman Nawal Alsufyani Jeongmin Park Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D *** enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given *** cues can improve the quality of object detection and *** main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D *** proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not ***,the proposed RGB-D-based system uses depth cues for segmentation with the help of *** it extracts multiple features from the segmented *** selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting *** achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 ***,their results are compared and the proposed model with CNN outperforms other state-of-the-art *** proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.

关键词： Artificial intelligence convolutional neural network depth images interactive-object detection machine learning

Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

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Computers, Materials & Continua 2022年第10期73卷 1437-1448页

作者： Mohammad Alamgeer Hanan Abdullah Mengash Radwa Marzouk Mohamed K Nour Anwer Mustafa Hilal Abdelwahed Motwakel Abu sarwar Zamani mohammed Rizwanullah Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Early detection of lung cancer can help for improving the survival rate of the *** imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung *** recently developed deep learning(DL)models can be employed for the effectual identification and classification of *** article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT *** proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT *** proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature ***,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT ***,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch *** demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches.

关键词： Biomedical images lung cancer deep learning machine learning metaheuristics hyperparameter tuning

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

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Computer Systems Science & Engineering 2023年第4期45卷 737-751页

作者： Saud S.Alotaibi Eatedal Alabdulkreem Sami Althahabi Manar Ahmed Hamza mohammed Rizwanullah Abu sarwar Zamani Abdelwahed Motwakel Radwa Marzouk Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social ***,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process *** OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into ***,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification ***,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM *** major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual *** addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction ***,BLSTM model is employed for the effectual detection and classification of ***,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

关键词： Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning

Intelligent Cybersecurity Classification Using Chaos Game Optimization with Deep Learning Model

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Computer Systems Science & Engineering 2023年第4期45卷 971-983页

作者： Eatedal Alabdulkreem Saud S.Alotaibi Mohammad Alamgeer Radwa Marzouk Anwer Mustafa Hilal Abdelwahed Motwakel Abu sarwar Zamani mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent *** Machine Learning(ML)and Deep Learning(DL)classification models are useful in effective identification and classification of *** addition,the involvement of hyper parameters in DL models has a significantly influence upon the overall performance of the classification *** this background,the current study develops Intelligent Cybersecurity Classification using Chaos Game Optimization with Deep Learning(ICC-CGODL)*** goal of the proposed ICC-CGODL model is to recognize and categorize different kinds of attacks made upon ***,ICC-CGODL model primarily performs min-max normalization process to normalize the data into uniform *** addition,Bidirectional Gated Recurrent Unit(BiGRU)model is utilized for detection and classification of ***,CGO algorithm is also exploited to adjust the hyper parameters involved in BiGRU model which is the novelty of current work.A wide-range of simulation analysis was conducted on benchmark dataset and the results obtained confirmed the significant performance of ICC-CGODL technique than the recent approaches.

关键词： Deep learning chaos game optimization cybersecurity chaos game optimization cyberattack

Intelligent Machine Learning Based EEG Signal Classification Model

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Computers, Materials & Continua 2022年第4期71卷 1821-1835页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd N.Al-Wesabi mohammed Abdullah Al-Hagery Anwer Mustafa Hilal Abu sarwar Zaman Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and *** systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,***(EEG)is a technique commonly applied in capturing brain *** is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and *** classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and *** this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI *** Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG ***,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG ***,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was *** outcomes were examined under distinct *** experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

关键词： Brain computer interface EEG recognition human computer interface machine learning parameter tuning FSVM

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

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Computers, Materials & Continua 2022年第6期71卷 5699-5715页

作者： Mesfer Al Duhayyim Taiseer Abdalla Elfadil Eisa Fahd NAl-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abu sarwar Zamani mohammed Rizwanullah Radwa Marzouk Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Corresponding Author:Manar Ahmed Hamza.Email:ma.hamza@psau.edu.sa

The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal *** understandings obtained from the data can assist municipal authorities handle assets and services *** the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research ***,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of *** of the commonly available wastes are paper,paper boxes,food,glass,*** order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and *** to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of *** this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart *** goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL *** IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object *** addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a ***,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet *** order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques

关键词： Smart cities deep reinforcement learning computer vision image classification object detection waste management

Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

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Computers, Materials & Continua 2022年第11期73卷 3367-3382页

作者： Hadil Shaiba Radwa Marzouk Mohamed K Nour Noha Negm Anwer Mustafa Hilal Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Abu sarwar Zamani mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the ***,weather constitutes a key role in all regular human *** forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from ***,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather *** research works have been conducted on weather *** drawbacks of existing approaches are that they are less effective,inaccurate,and *** overcome these issues,this paper proposes an enhanced and reliable weather forecasting *** well as developing weather forecasting in remote *** data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather *** aim of this study is to demonstrate its ability to predict weather forecasts as soon as *** evaluation shows our ensemble technique achieves 95%prediction ***,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

关键词： Weather forecasting KNN random forest gradient boosting decision tree naive bayes bernoulli

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第5期36卷 1915-1930页

作者： Rab Nawaz Bashir Imran sarwar Bajwa Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed mohammed Alghamdi Adel ABahaddad Khalid Ali Almarhabi Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science and IT Islamia University BahawalpurPakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Soil salinity is a serious land degradation issue in *** is a major threat to agriculture *** irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation *** the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)*** relationship between environmental conditions and ET rate is hard to be deﬁned by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)***-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity *** proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)*** validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm *** applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.

关键词： Leaching fraction saline soil evapotranspiration machine learning calibrated evapotranspiration artiﬁcial intelligence blaney criddle method