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Individualized Driving Intention Prediction With Inverse Reinforcement Learning

IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 8125-8139页

作者： Liu, Siqi Li, Xinyang Chen, Jiansheng Guo, Chenghao Wu, Jiehui Luo, Qifeng Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Advanced Driver Assistance Systems (ADAS) are designed to prevent collisions, identify the condition of drivers while operating vehicles, and provide additional information to enhance drivers’ awareness of potential hazards on the road. Today, ADAS are capable of predicting drivers’ actions several seconds in advance, preparing for potential future hazards to prevent accidents or reduce injuries to occupants. Most previous works have achieved prediction results by analyzing and processing a vast amount of driving data from multiple drivers, based on the macro intention preferences of multiple drivers and external environmental features, collectively referred to as the generalized intention prediction network. This network utilizes extensive driving data to predict the common driving intentions of the overall driving population, without considering individualized driving styles. However, according to our research, different drivers exhibit distinct latent preferences in real-world driving scenarios. The generalized intention prediction network is influenced by these latent preferences, resulting in poor generalization capabilities and inaccurate predictions across different drivers. In this study, we propose a individualized driver intention prediction network. Based on Inverse Reinforcement Learning (IRL), it extracts individualized driving intention feature preferences that influence driving intentions from the driver’s historical behavior to improve generalized prediction results and achieve individualized driving intention prediction. We demonstrate that preferences vary among different drivers in the driving domain, leading to biases in model predictions. Upon experimental validation, the method we have proposed demonstrates remarkable efficacy on both the Brain4Cars and IESDD datasets, thereby showcasing its enhanced applicability in real-world scenarios. © 2000-2011 IEEE.

关键词： Advanced driver assistance systems

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Analyzing Hierarchical Relationships and Quality of Embedding in Latent Space

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 843-858页

作者： Chatterjee, Ankita Mukherjee, Jayanta Das, Partha Pratim Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Existing learning models partition the generated representations using hyperplanes which form well defined groups of similar embeddings that is uniquely mapped to a particular class. However, in practical applications, the embedding space does not form distinct boundaries to segregate the class representations. There exists interaction among similar classes which cannot be visually determined in high-dimensional space. Moreover, the structure of the latent space remains obscure. As learned representations are frequently reused to reduce the inference time, it is important to analyse how semantically related classes interact among themselves in the latent space. Therefore, we propose a boundary estimation algorithm that minimises the inclusion of other classes in the embedding space to form groups of similar representations and compare the quality of these class embeddings for various models in an already encoded space. These groups are overlapping to denote ambiguous embeddings that cannot be mapped to a particular class with high confidence. The algorithm determines which representations to be included or discarded to form well defined regions, separating discriminating, ambiguous and rejected embeddings to depict a particular class. Later, we construct relation trees to evaluate the hierarchical relationships formed among the classes, and compare it with the WordNet ontology using phylogenetic tree comparison methods. © 2020 IEEE.

关键词： Embeddings

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Atrous Convolution-Based Fusion Attention Mechanism for Brain Tumor Segmentation

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IEEE Access 2025年 13卷 92611-92621页

作者： Jadhav, Abhishek Rasool, Akhtar Gyanchandani, Manasi Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India

Accurate medical image segmentation is pivotal for advanced diagnostic and therapeutic planning, especially for intricate tasks such as brain tumor delineation. However, existing segmentation methods often struggle with challenges such as heterogeneous tumor morphologies, diffuse boundaries, and low-contrast regions, limiting their reliability in complex medical imaging scenarios. In this work, we introduce an Atrous Convolution-Based Fusion Attention Mechanism, a novel framework that combines local and global attention through an innovative fusion block. Local attention leverages atrous convolutions to capture fine-grained spatial features at multiple scales, while global attention extracts long-range contextual dependencies across the image. These complementary features are dynamically fused via a novel fusion block, ensuring balanced representation and robust feature aggregation for precise segmentation. The proposed model is rigorously evaluated on the BraTS 2019, 2020, and 2023 datasets, demonstrating consistent and significant improvements across key segmentation metrics compared to baseline models. Quantitatively, the model achieves an absolute average increase of 0.2522, 0.1873, and 0.1934 in the Dice Coefficient, and 0.3071, 0.2365, and 0.2369 in the Intersection over Union (IoU) for enhancing tumor, tumor core, and whole tumor, respectively, across all datasets. The results highlight robustness and adaptability of the proposed architecture, underlining its potential to address critical challenges in brain tumor segmentation and advance the state-of-the-art in medical image analysis. © 2013 IEEE.

关键词： Image segmentation

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CRAQL: a novel clustering-based resource allocation using the Q-learning in fog environment

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International Journal of Cloud Computing 2024年第3期13卷 243-266页

作者： Ahlawat, Chanchal Krishnamurthi, Rajalakshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can lead to resource starvation and unable to complete the task assignment within a specific time. Hence, to enhance the efficiency of the fog resources, it is critical to perform proper resource allocation. This work targets to provide the solution to the resource allocation problem with a novel clustering-based resource allocation using the Q-learning (CRAQL) model. For this purpose, the problem is defined as a decision-making problem and formulated as Markov decision process (MDP). Next, to find the optimal resource, an enhanced optimal resource allocation (EORA) algorithm is proposed and detailed study is performed to analyse the impact of various performance parameters. Simulation results show the comparison of the EORA versus conventional Brute force method by varying the performance parameters such as learning rate and number of trials. The experimental results exhibit optimal solutions with significant improvement in learning rate at an average probability of 0.5 within limited epochs. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Reinforcement learning

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Neural network-guided motion blur estimation and patch representation method for face hallucination

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Neural Computing and Applications 2025年 1-28页

作者： Kumar, Banti Rajput, Shyam Singh Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Face hallucination (or super-resolution) is a technique of generating high-resolution face images from low-resolution (LR) inputs. The current face hallucination algorithms struggle with motion blur, a common issue in captured images due to various factors, including camera defocusing, objects in motion, etc. To address this problem, a new motion blur robust face hallucination algorithm via neural network-guided motion blur estimation and patch representation (NNMEPR) is proposed in this paper. The NNMEPR algorithm first estimates the motion blur kernels i.e., length (λ) and angle (α) from a motion-blurred LR test faces using a neural network fitting approach with scale conjugate gradient (SCG) learning algorithm. Then, the estimated factor is embedded in the LR dictionary face images to make them compatible with the test images and mitigate the impact of motion blur from the reconstruction process. Moreover, the proposed algorithm employs a neighboring position patch representation in the correlation coefficient calculation process to sustain the sharp edges and texture details in the resulting HR face images. Experiments on standard datasets and locally captured real-world faces demonstrate the superior performance of the proposed NNMEPR algorithm over the existing state-of-the-art techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion estimation

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HA-FGOVD: Highlighting Fine-Grained Attributes via Explicit Linear Composition for Open-Vocabulary Object Detection

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IEEE Transactions on Multimedia 2025年 27卷 3171-3183页

作者： Ma, Yuqi Liu, Mengyin Zhu, Chao Yin, Xu-Cheng University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Open-vocabulary object detection (OVD) models are considered to be Large Multi-modal Models (LMM), due to their extensive training data and a large number of parameters. Mainstream OVD models prioritize object coarse-grained category rather than focus on their fine-grained attributes, e.g., colors or materials, thus failed to identify objects specified with certain attributes. Despite being pretrained on large-scale image-text pairs with rich attribute information, their latent feature space does not highlight these fine-grained attributes. In this paper, we introduce HA-FGOVD, a universal and explicit method that enhances the attribute-level detection capabilities of frozen OVD models by highlighting fine-grained attributes in explicit linear space. Our approach uses a LLM to extract attribute words in input text as a zero-shot task. Then, token attention masks are adjusted to guide text encoders in extracting both global and attribute-specific features, which are explicitly composited as two vectors in linear space to form a new attribute-highlighted feature for detection tasks. The composition weight scalars can be learned or transferred across different OVD models, showcasing the universality of our method. Experimental results show that HA-FGOVD achieves state-of-the-art performance on the FG-OVD benchmark and demonstrates promising generalization on the OVDEval benchmark, suggesting that our method addresses significant limitations in fine-grained attribute detection and has potential for broader fine-grained detection applications. © 1999-2012 IEEE.

关键词： Coarse-grained modeling

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Revolutionizing signature scheme: the enhanced Edward Elgamal extreme performance accumulate signature approach for IoT and blockchain applications

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Soft Computing 2025年第3期29卷 1473-1496页

作者： Anusha, R. Saravanan, R. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

Digital signatures, essential for establishing trust in the digital realm, have evolved in their application and importance alongside emerging technologies such as the Internet of Things (IoT), Blockchain, and cryptocurrency. These advancements necessitate improvements in performance, security, and efficiency. This article examines and compares the Elliptic Curve Digital Signature Algorithm with the Hyper Elliptic Curve Digital Signature Algorithm and the Edwards Curve Digital Signature Algorithm. We highlight its superior capabilities for blockchain and IoT applications and advocate for its potential to deliver immediate enhancements in security and performance. Our study introduces a novel digital signature scheme specifically designed to enhance non-repudiation in blockchain ecosystems. Utilizing the Optimized Extreme Performance Edwards Curve Accumulated Signature scheme, our approach significantly reduces signing and verification times by 10% and 13%, respectively, compared to traditional signatures. Additionally, it offers a 10% boost in transaction throughput and block validation efficiency. Experiments conducted within various blockchain-integrated IoT setups demonstrate the scheme's effectiveness, consistently achieving improvements across diverse IoT sensor data. This highlights the innovative contribution of our scheme to the efficiency and security of blockchain technology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Blockchain Edward curve Elliptic curve Hyperelliptic curve IoT Optimization

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Attention enabled viewport selection with graph convolution for omnidirectional visual quality assessment

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Multimedia Tools and Applications 2025年第14期84卷 12925-12948页

作者： C, Nandhini M, Brindha Department of Computer Science and Engineering National Institute of Technology TamilNadu Tiruchirappalli620015 India

Omnidirectional images provide an immersive viewing experience in a Virtual Reality (VR) environment, surpassing the limitations of traditional 2D media beyond the conventional screen. This VR technology allows users to interact with visual information in an exciting and engaging manner. However, the storage and transmission requirements for 360-degree panoramic images are substantial, leading to the establishment of compression frameworks. Unfortunately, these frameworks introduce projection distortion and compression artifacts. With the rapid growth of VR applications, it becomes crucial to investigate the quality of the perceptible omnidirectional experience and evaluate the extent of visual degradation caused by compression. In this regard, viewport plays a significant role in omnidirectional image quality assessment (OIQA), as it directly affects the user’s perceived quality and overall viewing experience. Extracting viewports compatible with users viewing behavior plays a crucial role in OIQA. Different users may focus on different regions, and the model’s performance may be sensitive to the chosen viewport extraction strategy. Improper selection of viewports could lead to biased quality predictions. Instead of assessing the entire image, attention can be directed to areas that are more importance to the overall quality. Feature extraction is vital in OIQA as it plays a significant role in representing image content that aligns with human perception. Taking this into consideration, the proposed ATtention enabled VIewport Selection (ATVIS-OIQA) employs attention based view port selection with Vision Transformers(ViT) for feature extraction. Furthermore, the spatial relationship between the viewports is established using graph convolution, enabling intuitive prediction of the objective visual quality of omnidirectional images. The effectiveness of the proposed model is demonstrated by achieving state-of-the-art results on publicly available benchmark datasets, n

关键词： Convolution

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UCD_Net: dilated convolution-enhanced upsampling fusion for advanced lung disease classification

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Multimedia Tools and Applications 2025年 1-19页

作者： Sharma, Suchit Shelke, Nitin Arvind School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India

Deep Learning has recently been in trend when it comes to medical image analysis as it uses Convolution Neural Network (CNN), which utilizes multi-layer processing to extract intricate and complex features from the data. Similar to other research work, we used Chest X-ray (CXR) images as our dataset and classified them into five categories of lung disease, namely bacterial pneumonia, COVID-19, normal, pneumothorax, and tuberculosis. This study proposes a novel CNN, UCD_Net, which uses a convolutional upsampling layer featuring bicubic interpolation along with a stack of convolution layers before inputting the images into the fine-tuned Xception model and resorts to multiple dilated convolution layers before predicting output. This 2D CNN model is used to achieve optimal feature extraction within minimal time and space constraints. The dataset was divided into a ratio of 0.66 and 0.33 for the training set and testing set, respectively. The best performance is recorded by our proposed model, UCD_Net, with a training accuracy equal to 99.23%, a validation accuracy equal to 98.05%, a recall of 97.8%, a precision of 97.2%, and a fi-score equal to 97.6%. The model performance on the same dataset is also compared to four state-of-the-art architectures, namely ResNet 50, VGG16, Xception, and ResNet152. After our UCD_Net, the best performance was seen using ResNet 50, with accuracy, recall, and precision of 95.45%, 94.5%, and 95.2%, respectively. Training the model on a larger dataset and subsequent fine-tuning can lead to more research and model development. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8639-8658页

作者： Karthika, K. Rangasamy, Devi Priya Anna University Chennai India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. © 2024 – IOS Press. All rights reserved.

关键词： Sensitive data

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