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Enhanced Object Detection and Classification via Multi-Method Fusion

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Computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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Computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally Saud S.Alotaibi abdulwahab Alazeb Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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Computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

用于提升全水解性能的超低含量Ru掺杂NiMoO_(4)@Ni_(3)(PO_(4))_(2)核壳纳米结构

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Chinese Journal of Catalysis 2024年第5期60卷 360-375页

作者： Adel Al-Salihy 梁策 abdulwahab Salah Abdel-Basit Al-Odayni 卢子昂陈孟新刘倩倩徐平哈尔滨工业大学化工与化学学院新能源转化与存储关键材料工业和信息化部重点实验室黑龙江哈尔滨150001 东北师范大学化学学院动力电池国家地方联合工程实验室吉林长春130024 沙特国王大学牙科学院修复性牙科科学系沙特阿拉伯利雅得

随着全球能源危机和环境污染日益加剧,开发可持续的绿色能源技术已经成为当务之急.其中,水分解制氢作为一种清洁能源的生产方式,因其零排放和高能量密度的特点,而受到广泛的研究和关注.析氢反应(HER)和析氧反应(OER)是水分解过程的关键... 详细信息

随着全球能源危机和环境污染日益加剧,开发可持续的绿色能源技术已经成为当务之急.其中,水分解制氢作为一种清洁能源的生产方式,因其零排放和高能量密度的特点,而受到广泛的研究和关注.析氢反应(HER)和析氧反应(OER)是水分解过程的关键步骤,它们的反应效率直接影响整个制氢过程的能量转换效率.因此,开发高效的双功能电催化剂对于推动水分解技术的发展具有重要意义.本文制备了一种超低Ru掺杂的新型核壳纳米结构电催化剂,该催化剂生长在泡沫镍(NF)上,简称Ru-NiMoO_(4)@Ni_(3)(PO_(4))_(2)/NF.首先,通过水热法在NF上均匀生长了水合NiMoO_(4)纳米柱,并通过共价相互作用,确保其在垂直方向上有序排列.随后,采用超低浓度的RuCl_(3)对NiMoO_(4)纳米柱进行蚀刻,并通过磷酸化反应引入(PO_(4))^(3+)离子形成了Ni_(3)(PO_(4))_(2)壳层.这种设计克服了以往Ru易团聚的难题,保障了Ru的均匀分布,从而合成Ru-NiMoO_(4)@Ni_(3)(PO_(4))_(2)/NF催化剂.通过X-射线衍射、X-射线光电子能谱及透射电子显微镜等对催化剂的化学组分和微观结构进行了分析,证实其成功制备.元素分析结果表明,催化剂中Ru的含量仅为1.94 wt%,显著降低了其制备成本.电化学测试结果表明,该催化剂在HER和OER中均表现出较好的催化活性.在10和100 mA cm^(−2)电流密度下,HER的过电位分别为−14.8和−57.1 mV,Tafel斜率为35.8 mV dec^(-1),优于商业Pt/C催化剂;在100 mA cm^(−2)电流密度下,OER过电位为259.7 mV,Tafel斜率为21.6 mV dec^(−1),优于商业RuO_(2)催化剂.在全水解测试中,该催化剂在10和100 mA cm^(−2)时所需的电池电压分别为1.43和1.68 V,与其他自支撑材料相比表现出良好的性能.此外,该催化剂在150 h性能测试过程中表现出较好的稳定性.采用密度泛函理论计算研究了催化剂具有较好活性的内在机制.结果表明,其具有低水离解能垒(ΔGb=0.46 eV),接近零的HER自由吸附能(∆G*H=0.02 eV),低的OER自由吸附能量(ΔG*OOH−ΔG*OH=2.74 eV)和接近费米能级的高密度态.这些特性共同促进了电解水反应的高效进行.综上,本文研究开发的Ru-NiMoO_(4)@Ni_(3)(PO_(4))_(2)/NF双功能催化剂具有较好的催化活性、低电位需求和长期稳定性,为氢气生产开辟了新途径,也为优化双功能水分解催化剂提供了新视角,并为未来可持续能源应用发展提供一定的参考.

关键词：电催化核壳结构掺杂密度泛函理论析氢反应析氧反应分解水

Utilizing Machine Learning with Unique Pentaplet Data Structure to Enhance Data Integrity

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Computers, Materials & Continua 2023年第12期77卷 2995-3014页

作者： abdulwahab Alazeb Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia

Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative *** detection systems are essential for keeping databases *** in technology will lead to significant changes in the medical field,improving healthcare services through real-time information ***,reliability and consistency still need to be *** against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data ***-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is *** research presents a role-based access control architecture for an anomaly detection ***,the Structured Query Language(SQL)queries are stored in a new data structure called *** pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual *** identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).

关键词： Database intrusion detection system data integrity machine learning pentaplet data structure

Combining ability of Egyptian cotton(Gossypium barbadense L.)reveals genetic potential for improved yield and fiber quality

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Journal of Cotton Research 2024年第2期7卷 109-122页

作者： Abdelghany Ahmed M. El-Banna Aly A.A. Lamlom Sobhi F El-Sorady Gawhara A. Salama Ehab A.A. Ren Honglei Shaibu abdulwahab S. Yehia Waleed M.B. Crop Science Department Faculty of AgricultureDamanhour UniversityDamanhour 22516Egypt Plant Production Department Faculty of Agriculture Saba BashaAlexandria UniversityAlexandria 21531Egypt Agricultural Botany Department Faculty of Agriculture Saba BashaAlexandria UniversityAlexandria 21531Egypt Heilongjiang Academy of Agricultural Sciences Soybean Research InstituteHarbin 150086China Department of Agronomy Bayero University KanoKano 700001Nigeria Cotton Breeding Department Cotton Research InstituteAgriculture Research CenterGizaEgypt

Background As the most widely cultivated fiber crop,cotton production depends on hybridization to unlock the yield potential of current varieties.A deep understanding of genetic dissection is crucial for the cultivation of enhanced hybrid plants with desired traits,such as high yield and fine fiber *** this study,the general combining ability(GCA)and specific combining ability(SCA)of yield and fiber quality of nine cotton parents(six lines and three testers)and eighteen F1 crosses produced using a line×tester mating design were *** The results revealed significant effects of genotypes,parents,crosses,and interactions between parents and crosses for most of the studied ***,the effects of both additive and non-additive gene actions played a notably significant role in the inheritance of most of the yield and fiber quality *** F1 hybrids of(Giza 90×Aust)×Giza 86,Uzbekistan 1×Giza 97,and Giza 96×Giza 97 demonstrated superior performance due to their favorable integration of high yield attributes and premium fiber quality *** analysis revealed that lint yield has the highest positive direct effect on seed cotton yield,while lint percentage showed the highest negative direct effect on seed cotton *** component analysis identified specific parents and hybrids associated with higher cotton yield,fiber quality,and other agronomic *** This study provides insights into identifying potential single-and three-way cross hybrids with superior cotton yield and fiber quality characteristics,laying a foundation for future research on improving fiber quality in cotton.

关键词： Gossypium barbadense L. Combining ability Seed cotton yield Fiber quality Cluster analysis Path analysis

Role of alternate and fixed partial root-zone drying on water use efficiency and growth of maize(Zea mays L.)in gypsiferous soils

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International Soil and Water Conservation Research 2023年第1期11卷 145-158页

作者： abdulwahab Abdulrazak Al-Kayssi Agricultural Machinery and Equipment Department College of AgricultureTikrit UniversitySalahaddinTikritIraq

Alternate partial root-zone drying(APRD)is a water-saving method but can regulate crop physiological responses.A pot experiment has been conducted to study the efficiency of partial and fixed root-zone drying on the growth and production of maize(Zea mays L)in addition to the water use efficiency in soils with different gypsum *** experimental treatments include three irrigation treatments,*** Irrigation(CI),Alternate Partial Root-zone Drying(APRD)and Fixed Partial Root-zone Drying(FPRD),and three soils with different gypsum content"(60.0[G1],153.7[G2],and 314.2[G3]g kg^(-1))".The vegetative growth,root dry mass and physiological indices(leaf relative water content,carotenoid concentration,proline)have been studied during three stages of maize plant growth(jointing,tasselling,and maturing).The Results showed that compared to CI,APRD and FPRD increased water use efficiency by 38.93 and 14.94%based on dry seed *** addition,compared to CI,APRD increased maize seed yield by 4.62-20.71%,while FPRD decreased yield by 19.24-5.28%for the gypsiferous soils G2 and G3,*** has a slight effect on leaf water potential,leaf relative water content,carotenoid and proline activities from jointing to maturing stages at the three gypsiferous *** suggest that APRD could make maize plants use water even more productively with better adaptation to water shortages in the gypsiferous soils.

关键词： Partial root-zone drying Water use eficiency Cypsiferous soils Maize growth stages Maize grain yield

Multiclass Classification for Cyber Threats Detection on Twitter

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Computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter