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Dates Fruit Recognition: From Classical Fusion to Deep Learning

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Computer Systems Science & Engineering 2022年第1期40卷 151-166页

作者： Khaled Marji Alresheedi suliman aladhadh Rehan Ullah Khan Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

There are over 200 different varieties of dates fruit in the ***,every single type has some very specific features that differ from the *** recent years,sorting,separating,and arranging in automated industries,in fruits businesses,and more specifically in dates businesses have inspired many research *** this regard,this paper focuses on the detection and recognition of dates using computer vision and machine *** experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates *** machine learning includes the Bayesian network,Support Vector Machine,Random Forest,and Multi-Layer Perceptron(MLP),while the Convolutional Neural Network is used for the deep learning *** feature set includes Color Layout features,Fuzzy Color and Texture Histogram,Gabor filtering,and the Pyramid Histogram of the Oriented *** fusion of various features is also extensively explored in this *** MLP achieves the highest detection performance with an F-measure of ***,deep learning shows better accuracy than the classical machine learning *** fact,deep learning got 2%more accurate results as compared to the MLP and the Random *** also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.

关键词： features Deep separating

Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

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Computers, Materials & Continua 2021年第12期69卷 3399-3411页

作者： suliman aladhadh Hidayat Ur Rehman Ali Mustafa Qamar Rehan Ullah Khan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Ainfinity Algorythma Abu DhabiUnited Arab Emirates Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

A tremendous amount of vendor invoices is generated in the corporate *** automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is *** paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash *** text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an *** chunk is later passed to the deep learning model,which performs text *** deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the *** model integrates feature extraction,modeling sequence,and transcription into a unified *** handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale ***,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical *** overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed *** model is thus generic and can be used for other similar recognition scenarios.

关键词： Character recognition text spotting long short-term memory recurrent convolutional neural networks

Automatic Crop Expert System Using Improved LSTM with Attention Block

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Computer Systems Science & Engineering 2023年第11期47卷 2007-2025页

作者： Shahbaz Sikandar Rabbia Mahum suliman aladhadh Computer Science Department University of Engineering and TechnologyTaxilaPakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia

Agriculture plays an important role in the economy of any *** half of the population of developing countries is directly or indirectly connected to the agriculture *** farmers do not choose the right crop for cultivation depending on their soil type,crop type,and climatic requirements like *** wrong decision of crop selection directly affects the production of the crops which leads to yield and economic loss in the *** parameters should be observed such as soil characteristics,type of crop,and environmental factors for the cultivation of the right *** decision-making is time-taking and requires extensive ***,there should be an automated system for the right crop recommendation to reduce human efforts and *** automated crop recommender system should take these parameters as input and suggest the farmer’s right ***,in this paper,a long short-term memory Network with an attention block has been *** proposed model contains 27 layers,the first of which is a feature input *** exist 25 hidden layers between them,and an output layer completes the *** these levels,the proposed model enables a successful recommendation of the ***,the dropout layer’s regularization properties aids in reduction of overfitting of the *** this paper,a customized novel long short-term memory(LSTM)model is proposed with a residual attention block that recommends the right crop to *** metrics used for the proposed model include f1-score,recall,precision,and accuracy attaining values as 95.69%,96.56%,96.9%,and 97.26%respectively.

关键词： Crop recommendation LSTM deep neural network

A Robust Approach for Detection and Classification of KOA Based on BILSTM Network

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Computer Systems Science & Engineering 2023年第11期47卷 1365-1384页

作者： Abdul Qadir Rabbia Mahum suliman aladhadh Computer Science Department UETTaxilaPakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia

A considerable portion of the population now experiences osteoarthritis of the knee,spine,and hip due to lifestyle ***,early treatment,recognition and prevention are essential to reduce damage;nevertheless,this time-consuming activity necessitates a variety of tests and in-depth analysis by *** overcome the existing challenges in the early detection of Knee Osteoarthritis(KOA),an effective automated technique,prompt recognition,and correct categorization are *** work suggests a method based on an improved deep learning algorithm that makes use of data from the knee images after segmentation to detect KOA and its severity using the Kellgren-Lawrence(KL) classification schemes,such as Class-I,Class-II,Class-III,and *** ResNet to segregate knee pictures,we first collected features from these images before using the Bidirectional Long Short-Term Memory(BiLSTM)architecture to classify *** that the technique is a pre-trained network and doesn’t require a large training set,the Mendeley VI dataset has been utilized for the training of the proposed *** evaluate the effectiveness of the suggested model,cross-validation has also been employed using the Osteoarthritis Initiative(OAI)***,our suggested technique is more resilient,which overcomes the challenge of imbalanced training data due to the hybrid architecture of our proposed *** suggested algorithm is a cuttingedge and successful method for documenting the successful application of the timely identification and severity categorization of *** algorithm showed a cross-validation accuracy of 78.57%and a testing accuracy of 84.09%.Numerous tests have been conducted to show that our suggested algorithm is more reliable and capable than the state-of-the-art at identifying and categorizing KOA disease.

关键词： KOA image classification knee osteoarthritis deep learning neural networks human computer interaction(HCI) medical imaging

Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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Computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab suliman aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning