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Performance analysis of naive vehicular named data networks

International Journal of Wireless and Mobile Computing 2025年第3期28卷 234-244页

作者： Nithin Rao, R. Sharma, Rinki Department of Computer Science and Engineering Ramaiah University of Applied Sciences Karnataka Bangalore India

Vehicular Named Data Networks (VNDN) is a content centric approach for vehicle networks. The fundamental principle of addressing the content rather than the host, suits vehicular environment. There are numerous challenges such as interest/data flooding, routing, naming schemes, channel constraints and security, among others. The proposed work aims to analyse the behaviour of the naive VNDN under diverse scenarios, which is crucial for devising effective forwarding, caching and security strategies to enhance VNDN’s performance. In this paper, the naive VNDN architecture is implemented and its performance is evaluated by considering the metrics such as Throughput, Packet Delivery Ratio (PDR), Interest Satisfaction Delay (ISD), Interest Satisfaction Ratio (ISR), Copies of Data Packets Processed (CDPP) and Hop Count Difference (HCD). These metrics are tested against varying vehicle speed, network size and interests generated by each vehicle in the network. The simulation results provide insights that should be considered while developing the forwarding strategies and caching mechanisms that will improve the performance of VNDN. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： caching content forwarding data packets delay naming timestamp

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A novel word recognition system in Persian/Arabic handwritten words using stacking ensemble classifier of deep learning

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Multimedia Tools and Applications 2024年 1-32页

作者： Bozorgi, Donya Kheirkhah, Esmaeil Tavoli, Reza Sheibani, Reza Department of Computer Engineering Mashhad Branch Islamic Azad University Mashhad Iran Department of Computer Engineering Chalous Branch Islamic Azad University Chalous Iran

Recognizing words in handwritten documents is one of the most challenging tasks, because the writing style in such documents is highly diverse, and the words and sub-words share similar structural features while retaining distinct identities. This similarity in structure often leads to recognition errors and reduced accuracy. To address these challenges, this paper proposes a novel method for recognizing words in Persian/Arabic handwritten documents using a stacking ensemble classification approach. The proposed method leverages a combination of deep learning (DL) and machine learning (ML) techniques in different layers to achieve improved recognition performance. In the first layer, the training input is given to convolutional neural network. The output of the first layer is given to the classifiers of the second layer including machine learning classifiers. The output of the best decision of these classifiers is sent to the third layer classifier and the final prediction is made. The reason for using the multi-level architecture based on ensemble learning was the lack of correct recognition of some classes by simple classifications. On the other hand, the presence of some features in Persian/Arabic handwritten words, such as ascenders, descenders, holes, dots, and upward strokes, as well as different writing styles, has led to misrecognition. This model helps to correctly identify input samples with the correct combination of classifications in different layers. We have tested and evaluated the proposed method on several datasets, such as the Iranian cities dataset(Iranshahr), the IFN/ENIT Arabic dataset, and the IBN SINA dataset. The mention datasets include several handwritten word images in 1260, 530, 937 classes, respectively. The accuracy of the test set is 98.3%, 98.1% and 98.6% on the three data sets of Iranshahr, IFN/ENIT Arabic and IBN-SINA, respectively;which was much better than the previous models. © The Author(s), under exclusive licence to Springer S

关键词： Deep learning

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Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

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Intelligent Automation & Soft Computing 2023年第4期36卷 223-233页

作者： S.Sridevi Jeevaa Katiravan Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India

Scientiﬁc workﬂows have gained the emerging attention in sophisti-cated large-scale scientiﬁc problem-solving *** pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientiﬁc workﬂow *** the cloud is not a utopian environment,failures are inevitable that may result in experiencingﬂuctuations in the delivered *** a single task failure occurs in workﬂow based applications,due to its task dependency nature,the reliability of the overall system will be affected *** rather than reactive fault-tolerant approaches,proactive measures are vital in scientiﬁc workﬂ*** work puts forth an attempt to con-centrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm(IWDA)combined with an efﬁcient machine learning approach-Support Vector Regression(SVR)for task failure prognostica-tion which facilitates proactive fault-tolerance in the scheduling of scientiﬁc workﬂow *** failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workﬂ*** experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.

关键词： Failure prediction intelligent water drops support vector regression proactive fault-tolerance scientiﬁc workﬂows precision accuracy resource provisioning

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A machining center selection study using neural networks

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Neural Computing and Applications 2025年 1-38页

作者： Ic, Yusuf Tansel Yıldız, Burak Kececi, Barış Sert, Mustafa Department of Industrial Engineering Baskent University Ankara06970 Turkey Department of Computer Engineering Baskent University Ankara06970 Turkey Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States

Machining centers (MCs) are crucial high-technology machine tools that are widely used in the manufacturing industry. Since their high investment necessities, the selection of the appropriate MC for a company is an important task so that the company’s competitive advantages can be continued in the future. In the literature, there are many studies for MC selection using multi-criteria decision-making (MCDM) models and their fuzzy versions. This type of selection model must use selection criteria weighting procedures that are mainly based on subjective preferences. On the other hand, some attempts are provided in the literature to set objective decision-making models, such as the design of an experiment (DoE)-based Technique for Order Preferences by Similarity to the Ideal Solution (TOPSIS) model. In this study, we improved the DoE-based TOPSIS approach using neural network-based solutions and compared their capability of the MC selection studies against the classical MCDM and DoE-TOPSIS models. The proposed model can handle the newly generated scenarios using the Neural Networks. The feedforward neural network (FFNN)-based model utilizes machine learning techniques to overcome some of the challenges by modeling the complex relationships among various decision criteria without relying on human judgment for weighting. As a form of artificial neural network, FFNNs learned from large datasets and captured non-linear patterns, making them well-suited for the MC selection problem. Their capability to process high-dimensional inputs with numerous variables and interactions aligns effectively with the complexities of the MC selection problem. Therefore, the proposed FFNN model is very competitive against the DoE-TOPSIS and classical TOPSIS models. © The Author(s) 2025.

关键词： Cutting

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IWM-LSTM encoder for abstractive text summarization

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Multimedia Tools and Applications 2025年第9期84卷 5883-5904页

作者： Gangundi, Ravindra Sridhar, Rajeswari Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli620015 India

Sequence-to-sequence models are fundamental building blocks for generating abstractive text summaries, which can produce precise and coherent summaries. Recently proposed, different text summarization models aimed to enhance summarization performance through the use of copying mechanisms, reinforcement learning, and multiple-level encoders. However, there has been limited research on improving the summarization output by modifying the structure of the long short-term memory (LSTM) cell. We introduced an improved version of LSTM called improved working memory LSTM (IWM-LSTM). IWM-LSTM removes the output gate and enhances the input and forget gates by incorporating cell state information into these gates. In our sequence-to-sequence model for text summarization, we replaced the LSTM encoder with a bi-directional IWM-LSTM, resulting in better summaries with minimal training time and less computational intensiveness. Additionally, we utilized Bidirectional encoder representations from transformers (BERT) embeddings to enhance the rouge score. The CNN/DailyMail dataset is used to train and test the model performance. The proposed model achieves better Recall-oriented understudy for gisting evaluation (ROUGE) scores than state-of-the-art models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Advancing Activity Recognition with Multimodal Fusion and Transformer Techniques

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IEEE Sensors Journal 2025年第11期25卷 19632-19649页

作者： Aidarova, Saltanat Nurmakhan, Temirlan Myrzakhan, Rakhat Fazli, Siamac Yazici, Adnan Nazarbayev University School of Engineering and Digital Sciences Department of Computer Science Astana Kazakhstan

In the field of Human Activity Recognition (HAR), the precise identification of human activities from time-series sensor data is a complex yet vital task, given its extensive applications across various industries. This study introduces an advanced HAR technique that markedly improves activity recognition by combining multimodal sensor fusion with a transformer-based attention mechanism. Our methodology begins with rigorous preprocessing of the raw data from multiple sensors, focusing on cleaning and normalizing the data to create ideal conditions for subsequent analysis. We then apply an innovative sensor fusion strategy alongside a transformer-based attention mechanism to accurately and comprehensively detect human activities. The effectiveness of our method was rigorously evaluated on two widely recognized benchmark datasets in HAR research, Extrasensory and UCI-HAR, both known for their complexity and broad usage. The evaluation concentrates on the model’s ability to precisely classify primary human activities, particularly in near-real-time situations. The findings demonstrate that our model excels in accuracy and adeptly identifies primary human activities, marking a notable progression in HAR technology. This enhancement highlights the strong potential of our proposed approach, combining sensor fusion and attention mechanisms to advance activity recognition in real-life settings. © 2001-2012 IEEE.

关键词： Sensor data fusion

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Automatic bug assignments without texts: a study

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Frontiers of computer Science 2024年第4期18卷 211-213页

作者： Zexuan LI Kaixin HUANG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

1 Introduction Automatic bug assignment has been well-studied in the past *** textual bug reports usually describe the buggy phenomena and potential causes,engineers highly depend on these reports to fix ***,researchers spend much effort on processing bug reports,aiming for obtaining key information and/or clues for reproducing bugs,analyzing their root causes,assigning them to developers/maintainers,and fixing these ***,our previous research[1]reveals that noises in texts bring adverse impacts to automatic bug assignments unexpectedly,mainly due to insufficiency of classical Natural Language Processing(NLP)techniques.

关键词： bug assignment maintain

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From black box AI to XAI in neuro-oncology: a survey on MRI-based tumor detection

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Discover Artificial Intelligence 2025年第1期5卷 1-21页

作者： Asmita Mittal, Praveen Department of Computer Engineering and Application GLA University Uttar Pradesh Mathura Bharthia India

Brain tumor detection remains a critical focus in neuro-oncology, requiring precise and efficient diagnostic methods to support timely clinical decisions. Magnetic Resonance Imaging (MRI) is the modality of choice for this task, owing to its exceptional soft tissue contrast and spatial resolution. Recent advancements in deep transfer learning have driven transformative progress in automating tumor detection, achieving notable improvements in accuracy, scalability, and computational efficiency. This paper systematically reviews state-of-the-art transfer learning techniques, including pre-trained neural networks, domain adaptation approaches, fine-tuning methodologies, and ensemble learning frameworks. It also provides an in-depth analysis of widely used MRI datasets, highlighting persistent challenges such as data imbalance, overfitting, and variability across imaging domains. Emerging trends in model optimization, architectural innovation, and generalization strategies are discussed, offering a forward-looking perspective on the future of automated tumor detection. Through a detailed comparative evaluation, this review identifies the strengths and limitations of existing methods, while outlining potential directions for research aimed at developing robust, interpretable, and clinically deployable systems for brain tumor diagnosis. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Secure signal and image transmissions using chaotic synchronization scheme under cyber-attack in the communication channel

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Soft Computing 2025年第1期29卷 339-353页

作者： Nobakht, Shaghayegh Ahmadi, Ali-Akbar Industrial Automation Laboratory Electrical and Computer Engineering Department Kharazmi University Tehran Iran

In this paper, problem of secure message (signal and image) transmission is studied. The message is encrypted by masking it with a chaotic system state and then transmitted to receiver-side via a communication channel. It is assumed that the communication channel may be prone to a cyber-attack. The transmission system dynamics in presence of the cyber-attack in the communication channel is modeled by a singular dynamical system. Then, at the receiver-side, a novel observer is designed to recover the message together with the cyber-attack signal. Estimation of the cyber-attack is crucial for monitoring the communication channel and enable a network supervisor to mitigate the adverse consequences and make an appropriate strategy. It is assumed neither restrictions on the message nor on the cyber-attack signals. This makes the proposed scheme, the best solution in the real applications. Moreover, comparing to the existing methods which two public and private channels are required, only one communication channel is used in this paper for transmission the encrypted signal which results in a cheaper solution. Solving the optimization problem can be easily performed with the help of the existing toolboxes such as YALMIP and CVX software in MATLAB. Finally, numerical simulations for signal and image transmission are presented to justify the applicability and superior performance of the proposed scheme. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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Optimization of Magnetization State Manipulation in Variable-Flux PMSMs

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IEEE Transactions on Industry Applications 2025年第3期61卷 3862-3876页

作者： Orviz, Marcos Tiemblo, Javier G. Laborda, Diego F. Reigosa, David Guerrero, Juan Manuel University of Oviedo Department of Electrical Electronic Computer and System Engineering Gijon33204 Spain

Variable-flux permanent magnet synchronous machines (VF-PMSMs) were proposed to avoid the additional losses of conventional PMSMs during flux weakening (FW) operation at high speeds, as they allow dynamic manipulation of the magnetization state (MS) of the permanent magnets (PMs). However, the effect of eddy currents induced in the PMs during MS manipulation has not yet been analyzed;this issue is evaluated in this paper. Different current waveforms (i.e., pulse, trapezoidal, and half-sinusoidal signals) will be compared regarding MS manipulation capabilities and energy consumption considering PMs eddy current effects, to identify the most advantageous one. © 1972-2012 IEEE.

关键词： Permanent magnets

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