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Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

Computer Modeling in Engineering & Sciences 2024年第4期139卷 759-782页

作者： Raveena Selvanarayanan Surendran Rajendran Youseef Alotaibi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is hard to control because wind,rain,and insects carry *** researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest *** the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate *** overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate *** proposed methodology selects CBD image datasets through four different stages for training and *** to train a model on datasets of coffee berries,with each image labeled as healthy or *** themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed *** of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions *** inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of *** evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is *** involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its *** comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

关键词： Computer vision coffee berry disease colletotrichum kahawae XG boost shapley additive explanations

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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Computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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A Multi-Stream Scrambling and DNA Encoding Method Based Image Encryption

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Computer Systems Science & Engineering 2023年第11期47卷 1321-1347页

作者： Nashat Salih Abdulkarim Alsandi Dilovan Asaad Zebari Adel Al-Zebari Falah Y.H.Ahmed Mazin Abed Mohammed Marwan Albahar Abdulaziz Ali Albahr Information Technology Duhok Private Technical InstituteDuhokIraq Department of Computer Science College of ScienceNawroz UniversityDuhok42001Iraq Department of Information Technology Technical College of Informatics AkreDuhok Polytechnic UniversityDuhokIraq Faculty of Computing and Information Technology Sohar UniversityOman College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Science Umm Al Qura UniversityP.O.Box 715MeccaSaudi Arabia College of Applied Medical Sciences King Saud Bin Abdulaziz University for Health SciencesAl Ahsa31982Saudi Arabia King Abdullah International Medical research Center Al-Ahsa36276Saudi Arabia

Information security has emerged as a key problem in encryption because of the rapid evolution of the internet and ***,the progress of image encryption techniques is becoming an increasingly serious issue and considerable *** space of the key,encryption-based low confidentiality,low key sensitivity,and easily exploitable existing image encryption techniques integrating chaotic system and DNA computing are purposing the main problems to propose a new encryption technique in this *** our proposed scheme,a three-dimensional Chen’s map and a one-dimensional Logistic map are employed to construct a double-layer image encryption *** the confusion stage,different scrambling operations related to the original plain image pixels are designed using Chen’s map.A stream pixel scrambling operation related to the plain image is ***,a block scrambling-based image encryption-related stream pixel scrambled image is *** the diffusion stage,two rounds of pixel diffusion are generated related to the confusing image for intra-image ***’s map,logistic map,and DNA computing are employed to construct diffusion operations.A reverse complementary rule is applied to obtain a new form of DNA.A Chen’s map is used to produce a pseudorandom DNA sequence,and then another DNA form is constructed from a reverse pseudorandom DNA ***,the XOR operation is performed multiple times to obtain the encrypted *** to the simulation of experiments and security analysis,this approach extends the key space,has great sensitivity,and is able to withstand various typical *** adequate encryption effect is achieved by the proposed algorithm,which can simultaneously decrease the correlation between adjacent pixels by making it near zero,also the information entropy is *** number of pixels changing rate(NPCR)and the unified average change intensity(UACI)both are very near to optimal values.

关键词： Grayscale image encryption stream scrambling-confusion DNA encoding XOR operation chaotic systems

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The Impact of Check Bits on the Performance of Bloom Filter

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Computers, Materials & Continua 2022年第12期73卷 6037-6046页

作者： Rehan Ullah Khan Ali Mustafa Qamar Suliman A.Alsuhibany Mohammed Alsuhaibani Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Bloom filter(BF)is a space-and-time efficient probabilistic technique that helps answermembership ***,BF faces several *** problems with traditional BF are generally ***,a large number of false positives can return wrong content when the data is ***,the large size of BF is a bottleneck in the speed of querying and thus uses large *** order to solve the above two issues,in this article,we propose the check bits *** the implementation perspective,in the check bits approach,before saving the content value in the BF,we obtain the binary representation of the content ***,we take some bits of the content value,we call these the check *** bits are stored in a separate array such that they point to the same location as the ***,the content value(data)is stored in the BF based on the hash function *** retrieval of data from BF,the reverse process of the steps ensures that even if the same hash functions output has been generated for the content,the check bits make sure that the retrieval does not depend on the hash output *** thus helps in the reduction of false *** the experimental evaluation,we are able to reduce more than 50%of false *** our proposed approach,the false positives can still occur,however,false positives can only occur if the hash functions and check bits generate the same value for a particular *** chances of such scenarios are less,therefore,we get a reduction of approximately more than 50%false positives in all *** believe that the proposed approach adds to the state of the art and opens new directions as such.

关键词： Bloom filter big data network processing optimization check bits

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