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Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

computers, Materials & Continua 2024年第3期78卷 3431-3448页

作者： Mahmood A.Mahmood Khalaf Alsalem Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakahKingdom of Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityGizaEgypt

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic *** detection of these diseases is essential for effective *** propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf *** proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf *** model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning *** the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and *** wavelet transformation,the augmented images are decomposed into three frequency *** pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning *** models were trained using the approximate images of the third-level sub-band of the wavelet *** the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout *** proposed model was evaluated using a dataset of images of healthy and infected olive *** achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the *** finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

关键词： Olive leaf diseases wavelet transform deep learning feature fusion

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MV-DUO: multi-variate discrete unified optimization for psychological vital assessments

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Neural Computing and Applications 2024年第31期36卷 19777-19793页

作者： Pethuraj, Mohamed Shakeel Burhanuddin, M.A. Dzakiyullah, Nur Rachman Department of Information System Faculty of Computer and Engineering Universitas Alma Ata Yogyakarta Indonesia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka Malaysia

Psychological vital assessments are required for monitoring health conditions and observing body reactions toward diseases and medications. Wearable sensors play a vital role in sensing body vitals and presenting them as signals for computer-based analysis. The problem relies on the signal decoding due to its input stream that turns out to be discrete/ continuous. Therefore for addressing the above specific issue, this article introduces MV-DUO (Multi-Variate Discrete Unified Optimization) method. This method addresses the above problem from a multi-variate perspective by sensing differential signals across healthy and unhealthy conditions. The healthy and unhealthy conditions are trained using neural learning by augmenting/ ceasing external vital data. The unification is performed using single-point artificial ecosystem-based optimization for identifying discrete sequences collaborated with continuous signals. The single-point reference is grouped based on the maximum continuity fitness observed under various sensing intervals. In this process, the non-grouped sequences are identified as unhealthy or discrete for which additional detection training and classification are required. Considerably the changes between successive sensing intervals are used for variations detection from unified high-fitness groups. Those grouped instances are used for training new vital changes observed at distinct intervals. This improves detection accuracy under controlled errors. For the varying sensing intervals, the proposed method achieves 14.13% high accuracy, 8.29% high grouping rate, 10.77% less error, and 10.07% less detection time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： computer aided analysis

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Towards the Development of a Copyright Risk Checker Tool for Generative Artificial Intelligence Systems

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Digital Government: Research and Practice 2024年第4期5卷 1-21页

作者： Billiris, Grace Gill, Asif Oppermann, Ian Niazi, Mahmood Faculty of Engineering and Information Technology University of Technology SydneyNSW Australia Computer Science University of Technology SydneyNSW Australia University of Technology SydneyNSW Australia King Fahd University of Petroleum and Minerals Department of Information and Computer Science Dhahran Saudi Arabia

Generative Artificial Intelligence (GAI) is fundamentally changing the ways of working and blurring the boundaries between human and machine-generated contents. While there is an increasing interest in the adoption of GAI systems, such as ChatGPT and DALL-E, there are also serious concerns about the copyright of the contents - the inputs or generated as outputs by the GAI systems. Such concerns need to be identified and assessed to ensure the ethical and responsible use of GAI systems. Thus, this article aims to address the key research challenge: "how to identify and assess GAI system's copyright concerns"? In response, we propose the development of a Copyright Risk Checker (CRC) Tool. This tool has been formulated and evaluated using a recognised design science research methodology, drawing on an analysis of 10 legal cases across Australia, the United Kingdom, the United States, and Europe. The CRC Tool has undergone evaluation through an experimental scenario, and the results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems. The outcomes of this preliminary assessment can be further examined by expert legal advisors for an in-depth analysis. The development of the CRC Tool provides a foundation for continued research and advancement in this significant area of study. © 2024 Copyright held by the owner/author(s).

关键词： Generative Artificial Intelligence Copyright Concern GAI Governance Copyright Regulations Independent Intellectual Effort Authorship

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Stylometry-driven framework for Urdu intrinsic plagiarism detection: a comprehensive analysis using machine learning, deep learning, and large language models

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Neural Computing and Applications 2025年第9期37卷 6479-6513页

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Abid, Adnan Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex

关键词： Deep learning

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Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

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computer Systems Science & Engineering 2023年第2期44卷 1679-1689页

作者： Abdullah Alshehri Nayeem Khan Ali Alowayr Mohammed Yahya Alghamdi Information Technology Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Computer Science Department Faculty of Science and Arts at BuljurashiAl Baha UniversityAl Baha65799Saudi Arabia

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior *** framework models the user behavior as sequences of events representing the user activities at such a *** represented sequences are thenﬁtted into a recurrent neural network model to extract features that draw distinctive behavior for individual ***,the model can recognize frequencies of regular behavior to proﬁle the user manner in the *** subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular *** importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the *** detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including ***,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reﬂect a normal network workﬂ*** contrast,the irregular patterns can trigger an alert for a potential *** framework has been fully described where the evaluation metrics have also been *** experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM *** paper has been concluded with pro-viding the potential directions for future improvements.

关键词： Cybersecurity deep learning machine learning user behavior analytics

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Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

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computers, Materials & Continua 2023年第5期75卷 4419-4444页

作者： Arwa Saif Fadel Osama Ahmed Abulnaja Mostafa Elsayed Saleh Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computer Sciences and EngineeringHodeidah UniversityYemen

Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline *** solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE *** methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of ***,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a *** paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text *** LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its ***,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training *** paper examined the effect of data augmentation on the multi-task model for Arabic *** experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC *** results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation

关键词： Arabic aspect extraction arabic sentiment classification AraBERT multi-task learning data augmentation

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Wireless Sensor Security Issues on Data Link Layer:A Survey

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computers, Materials & Continua 2023年第5期75卷 4065-4084页

作者： Muhammad Zulkifl Hasan Zurina Mohd Hanapi Muhammad Zunnurain Hussain Department of Communication Technology and Network Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdangSelangor43400Malaysia

A computer network can be defined as many computing devices connected via a communication medium like the *** network development has proposed how humans and devices communicate *** networks have improved,facilitated,and made conventional forms of communication ***,it has also led to uptick in-network threats and *** 2022,the global market for information technology is expected to reach$170.4 ***,in contrast,95%of cyber security threats globally are caused by human *** networks may be utilized in several control systems,such as home-automation,chemical and physical assault detection,intrusion detection,and environmental *** proposed literature review presents a wide range of information on Wireless Social Networks(WSNs)and Internet of Things(IoT)*** aim is first to be aware of the existing issues(issues with traditional methods)and network attacks on WSN and IoT systems and how to defend *** second is to review the novel work in the domain and find its *** goal is to identify the area’s primary gray field or current research divide to enable others to address the ***,we concluded that *** Rapid Spanning Tree Protocol(RSTP)messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol(BPDU)*** research divides our future research into solutions and newly developed techniques that can assist in completing the lacking *** this research,we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview.

关键词： Wireless sensor networks(WSN) internet of things(IoT) industrial revolution 4.0(IR4.0) computer networks network security

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High-Secured Image LSB Steganography Using AVL-Tree with Random RGB Channel Substitution

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computers, Materials & Continua 2024年第10期81卷 183-211页

作者： Murad Njoum Rossilawati Sulaiman Zarina Shukur Faizan Qamar Center of Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Computer Science and Cyber Security Faculty of Engineering and Information TechnologyBirZeit UniversityBirZeitRamallahP.O.Box 14Palestine

Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden *** property makes it difficult for steganalysts’powerful data extraction tools to detect the hidden data and ensures high-quality stego image ***,using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical *** addition,these numbers may cluster in certain *** hidden data in these clustered pixels will reduce the image quality,which steganalysis tools can ***,this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego *** paper employs the AdelsonVelsky and Landis(AVL)tree data structure algorithm to implement the randomization pixel selection technique for data *** AVL tree algorithm provides several advantages for image ***,it ensures balanced tree structures,which leads to efficient data retrieval and insertion ***,the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels,thereby preserving the stego image *** data structure employs the pixel indicator technique for Red,Green,and Blue(RGB)channel *** green channel serves as the foundation for building a balanced binary ***,the sender identifies the colored cover image and secret *** sender will use the two least significant bits(2-LSB)of RGB channels to conceal the data’s size and associated *** next step is to create a balanced binary tree based on the green *** the channel pixel indicator on the LSB of the green channel,we can conceal bits in the 2-LSB of the red or blue *** first four levels of the data structure tree will mask the data size,while subsequent levels will conceal the remaining digits of secret *** embedding the bits i

关键词： Image steganography pixel random selection(PRS) AVL tree peak signal-to-noise ratio(PSNR) imperceptibility capacity

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Multispectral point cloud superpoint segmentation

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Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Code switching: exploring perplexity and coherence metrics for optimizing topic models of historical documents

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 103-118页

作者： Yusof, Muhammad Abdullah Saee, Suhaila Faculty of Computer Science and Information Technology University Malaysia Sarawak Sarawak Malaysia

The Latent Dirichlet Allocation (LDA) model has two important hyperparameters that control the document-topic distribution known as alpha (α), and topic-word distribution known as beta (β). It is important to find the suitable values for both hyperparameters to achieve an accurate topic cluster. Using a single evaluation method to determine the optimal hyperparameters values is insufficient due to the size and complexity of the dataset. Thus, an experiment was conducted to study the relationship between the hyperparameters with perplexity, coherence scores and to establish a baseline for further topic modelling studies. It is the first study that focuses on multiple languages in Sarawak Gazette data for topic modelling. The study was conducted on LDA using Gensim package. The result shows that while perplexity scores were good indicator of the model’s ability to predict new or hidden data, the word cluster within topic does not always reflect the similarity or relationships between words which compromised topic interpretation. The lowest perplexity score was observed when α was set to 5 and β to 0.4. The coherence evaluation indicated the optimal number of topics for each set of hyperparameter values although the relationship with hidden words remains unclear. The coherence score is highest when the number of topics was 5 and 4. In conclusion, the perplexity scores are effective indicators of word prediction accuracy for each hyperparameter setting. While coherence captures the optimal number of topics needed to produce high-coherence word cluster within a topic. Combining both evaluation methods ensures optimal results, producing topics that are both accurate and interpretable. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Hyperparameter Latent Dirichlet Allocation perplexity topic coherence topic modelling

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