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SGGAA: a robust adversarial generation based on synthesizing point cloud and mesh space

Neural computing and Applications 2025年 1-23页

作者： Hu, Ruihan Yang, Rui Faculty of Computing Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Tianjin University Tianjin China

3D vision recognition offers a significantly more robust tool for achieving machine cognition compared to traditional 2D vision techniques. However, similar to the vulnerabilities present in 2D vision, many 3D vision recognition applications, including point cloud classification models, are susceptible to degradation when confronted with imperceptible outliers. Outliers have a considerable impact on the performance and accuracy of models, compromising their overall effectiveness. Existing 3D point cloud attack and defense strategies neglect outliers and overlook the crucial impact of 3D printability in real-world scenarios. Neglecting this aspect hinders the consideration of potential manipulation or alteration of 3D objects in the physical realm, posing significant implications for the security and robustness of 3D vision systems. This manuscript presents a novel and effective targeted attack framework called synthesizing geometry and geodesic attack (SGGAA). It offers an alternative adversarial training algorithm that bridges the gap between the digital and physical domains. SGGAA introduces a novel generalized distance metric, facilitating the design of powerful and simple techniques for generating adversarial samples and improving the training process. It focuses on the regularizer loss object in synthesizing attack modes for mesh and point cloud spaces, utilizing Hausdorff distance, geometry distance, and geodesic distance losses. Comprehensive evaluations, including hyperparameter tuning, qualitative analysis, and quantitative experiments, were conducted to assess SGGAA’s performance. The results demonstrated its superiority, with high attack success rates, excellent geometric similarity scores, and outperformance of state-of-the-art attack strategies across various 3D defense techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mesh generation

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Enhancing Citrus Fruit Classification and Quality Assessment Through Advanced Machine Learning Techniques 12th

Enhancing Citrus Fruit Classification and Quality Assessment...

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Menon, Sindhu Shree, Keerthi Reddy, Nagarjun Jangam, Bharath Bhoomika, B.K. School of Computing and Information Technology REVA University Bengaluru India

ISBN: (纸本)9789819788354

This research delves into the essential aspects of identifying crucial variables and establishing a comprehensive categorization system for citrus fruits based on their varieties and quality. Citrus fruits play a pivotal role in the global fruit trade, with both growers and consumers placing significant importance on the ability to recognize, classify, and assess them. The study involves a thorough analysis of key factors influencing the categorization and evaluation of citrus fruits. To ensure the credibility and distinctiveness of the findings, these criteria are determined through a meticulous blend of extensive study, laboratory analysis, and field observations. The investigation commences by scrutinizing the external characteristics of citrus fruits, including size, shape, and color. By quantifying these attributes, a foundational framework for classifying citrus fruits is established. Additionally, a comprehensive examination of the chemical composition of citrus fruits is conducted, emphasizing features such as sugar concentration, acidity, and fragrance compounds that significantly impact taste and overall quality. The paper presents comprehensive insights into the utilization of a machine learning model to train datasets for determining the quality of various citrus fruits, such as Orange, Lime, Lemon, Grapefruit, Pomelo, Tangerine, Kumquats, and Clementine, and classifying the same. The developed machine learning model using linear regression demonstrates an accuracy of 89% in distinguishing whether a given fruit is a citrus fruit and further categorizing it based on the provided data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Citrus fruits

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Enhanced sentiment analysis framework: Ensemble attention enhanced bidirectional long-short-term encoder for accurate classification of consumer reviews

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Alexandria Engineering Journal 2025年 127卷 265-283页

作者： Almalki, Ali Jaber Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia

The fast growth of today's technology on social media, the web, etc caused the ongoing and fast creation of opinionated textual data. In this context, internet reviews by consumers determine upcoming consumers whether can buy a particular product or not. Because the thoughts regarding the products are expressed by commenting on reviews. The sentiment analysis is more supportive for comprehending feedback and the attitude of consumers regarding the product's characteristics. More sentiment classification tasks are carried out by the researchers but attained low level output due to difficulty in learning contextual words. To rectify this type of issue, current work focuses on developing a sentiment analysis framework by progressive techniques. The novel namely Ensemble Attention Enhanced Bidirectional Long-short-term Encoder (EAE-BiLE) is developed. The developed method analyzes online reviews and gains reviews from Amazon and the second task is to form high-quality input. The BERT model generates contextual word embeddings and Bi-directional Long-Short-Term Memory (Bi-LSTM) processes these embeddings in order to capture both backward and forward dependencies from the text. The novel method makes the accurate classification by focusing on important and relevant parts in the text using an attention mechanism that assigns different weights. Detailed experiments that expose the performance of the developed method are performed in this work. This requires a few evaluation metrics that can measure the effectiveness of the developed method, which is accomplished by F1-score, recall, accuracy, and precision. The developed EAE-BiLE framework yields higher rates of 97.2 %, 97.2 %, 99.1 %, and 98.4 % for precision, F1-score, accuracy, and recall. Its error performance is also low, which represents the developed method makes the precise classification. The developed EAE-BiLE excels with higher effectiveness than prior techniques for efficacious sentiment classification. © 2025 T

关键词： Emotion Recognition

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Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction 5th

Comparative Study of Classification Algorithms Using Big Dat...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Asiri, Mona Hassan AL-Malaise AL-Ghamdi, Abdullah S. Fayoumi, Ayman G. Ragab, Mahmoud Department of Information Systems Collage of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Information Systems Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah34801 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819771776

Flight delays are the most pressing cause of concern in the airline sector because they can weaken and harm airlines, passengers, and airports. Flight delay prediction has become an essential step in decision-making processes within commercial aviation. Due to the developments of recent technologies, there are many prediction models for flight delays;however, more development of accurate prediction models is still needed. Machine learning, statistical models, and other techniques can be used to create quick and accurate flight delay prediction models. Furthermore, methods for feature selection can be developed to get rid of dimensionality issues. This study aimed to compare four classification algorithms using "big data analytics" to predict flight delays, a function that can improve the whole airline sector. The selected classification algorithms were the "Gaussian Naïve Bayes," "Gradient Boosted Tree" classifier, "Logistic Regression," and "Random Forest" classifier. Many assessment techniques were applied to each one of the suggested models to show the better performance of these models. The comparison used the resulting confusion matrix to summarize prediction results for the following classification problems: "F1 Score", "Accuracy," "Recall," "Precision," "Area Under ROC," and "Area Under PR." The assessment results showed the enhanced outcomes of the "Gradient Boosted Tree" Classifier with existing techniques, and all results of "Logistic Regression" and "Naïve Bayes" are equal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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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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Modified Neural Network Used for Host Utilization Predication in Cloud computing Environment

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Computers, Materials & Continua 2025年第3期82卷 5185-5204页

作者： Arif Ullah Siti Fatimah Abdul Razak Sumendra Yogarayan Md Shohel Sayeed Centre for Intelligent Cloud Computing Multimedia UniversityMelaka75450Malaysia Faculty of Information Science and Technology Multimedia UniversityMelaka75450Malaysia

Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client *** essential aspect of cloud computing that improves resource allocation techniques is host load *** difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response *** solve this issue and accurately predict cloud capacity,cloud data centers use prediction *** permits dynamic cloud scalability while maintaining superior service *** host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this ***,the suggested hybrid model is input is subjected to the vector auto regression *** data in many variables that,prior to analysis,has been filtered to eliminate linear *** that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing *** next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series *** key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant *** systems require accurate prediction due to the increasing degrees of unpredictability in data *** of this,two actual load traces were used in this study’s assessment of the *** example of the load trace is in the typical dispersed *** comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.

关键词： Cloud computing datacenter virtual machine(VM) predication algorithm

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Cost-Effective Edge Data Caching With Failure Tolerance and Popularity Awareness

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IEEE Transactions on Mobile computing 2025年第6期24卷 5357-5369页

作者： Luo, Ruikun Zhang, Zujia He, Qiang Xu, Mengxi Chen, Feifei Dai, Xiaohai Wu, Song Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Deakin University School of Information Technology GeelongVIC3125 Australia

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existing edge data caching (EDC) methods prioritize popular data and aim to minimize users’ data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enable uncertainty-aware edge data caching (uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Stepwise monogamous pairing genetic algorithm method applied to a multi-depot vehicle routing problem with time windows

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Neural computing and Applications 2025年第16期37卷 9639-9668页

作者： Lim, Ting Yee Ng, Xin Ju Tan, Choo Jun Lim, Chee Peng Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology 77 Lorong Lembah Permai Tiga Penang Tanjong Bungah11200 Malaysia Department of Computing Technologies Swinburne University of Technology John Street HawthornVIC3122 Australia

Evolutionary computation (EC) is a powerful tool for global optimization across various domains, including healthcare logistics. However, the prevalence of local optima in complex problems often hinders the ability of the populations to find optimal solutions, leading to premature convergence. More daunting is that complex optimization problems motivated from real-world challenges continue to pose huge hurdles for EC, with many remaining unresolved. To fill this gap, we propose a new framework named SW-MopGA (Stepwise Monogamous Pairing Genetic Algorithm), which hinges on two innovations. First, a controller for diversity maintenance is introduced into MopGA to generate perturbed opposite solution via opposition-learning when necessary. In doing so, the algorithm is able to maintain population diversity effectively, which alleviates the risk of premature convergence. Second, SW-MopGA proposes a two-step framework: Initially, the population undergoes evolution on a simplified version of the originally complex problem. Solutions derived from the simplified problem serve as building blocks (stepping stones) upon which more intricate blocks are constructed for the final problem—a form of incremental evolution. Numerical analysis on two real-world cases in the logistics healthcare sector demonstrates the effectiveness of SW-MopGA. The problems are formulated as a multi-depot vehicle routing problem with time windows (MDVRPTW). Overall, the combined effect of the two innovations resulted in up to 18% and 8% savings in terms of total travel time and distance, and vehicles used, respectively on an off-peak day scenario;while approximately 17% and 7% savings for the same criteria on a peak-day scenario, compared to the baseline MopGA. Additionally, SW-MopGA improved the best-known solution of three instances in the MDVRPTW benchmark dataset. Finally, algorithmic insights are provided. In general, our initial study shows encouraging outcomes for incremental evolution and its

关键词： Travel time

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An iterated greedy algorithm based on NSGA-II for distributed hybrid flow shop scheduling problem 3

An iterated greedy algorithm based on NSGA-II for distribute...

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3rd International Conference on Communications, information System, and Data Science, CISDS 2024

作者： Liu, Dandan Liang, Xu Zou, Zhiyuan School of Computing Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The distributed hybrid flow shop scheduling problems (DHFSP) widely exist in various industrial production processes, and thus have received widespread attention. However, studies on HFSP considering green objective in distributed production environment are quite limited. Therefore, this paper investigated a distributed hybrid flow shop scheduling problem with objectives of minimization the makespan and total energy consumption (TEC). To solve it, an iterated greedy algorithm based on NSGA-II (NSGAIG) is developed. In the proposed algorithm, a random initialization strategy is used to generate the initial solution. Then, starting from the population initialization, a multi-objective local search is carried out for the current optimal solution in the population to obtain the global optimal solution. Next, random variation method is used to increase the exploration space of the algorithm. Finally, the proposed NSGAIG algorithm is compared with other multi-objective optimization algorithms. Experimental results indicate that the proposed NSGAIG outperforms its compared algorithms in solving this problem. © 2025 SPIE.

关键词： Optimization algorithms

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Blockchain-Enabled Edge computing Techniques for Advanced Video Surveillance in Autonomous Vehicles

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Computers, Materials & Continua 2025年第4期83卷 1239-1255页

作者： Mohammad Tabrez Quasim Khair Ul Nisa Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBishaP.O.Box 551Saudi Arabia

The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system(STS).This system lets customers,video creators,and service providers directly connect with each ***-based STS devices need a lot of computer power to change different video feed quality and forms into different versions and structures that meet the needs of different *** the other hand,existing blockchains can’t support live streaming because they take too long to process and don’t have enough computer *** amounts of video data being sent and analyzed put too much stress on networks for vehicles.A video surveillance method is suggested in this paper to improve the performance of the blockchain system’s data and lower the latency across the multiple access edge computing(MEC)*** integration of MEC and blockchain for video surveillance in autonomous vehicles(IMEC-BVS)framework has been *** deal with this problem,the joint optimization problem is shown using the actor-critical asynchronous advantage(ACAA)method and deep reinforcement training as a Markov Choice Progression(MCP).Simulation results show that the suggested method quickly converges and improves the performance of MEC and blockchain when used together for video surveillance in self-driving cars compared to other methods.

关键词： Blockchain multiple access edge computing video surveillance autonomous vehicles

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