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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud computing

A Hybrid Approach Using Cryptography and K-Least Significant...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Anshika Goyal Bhumika Gupta Dept. of Computer Science & Engineering VMSB Uttarakhand Technical University Dehradun India Dept. of Computer Science & Engineering G.B. Pant Institute of Engineering & Technology Pauri India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT).

关键词： Cloud computing Steganography Image coding Computational modeling data models Encoding Communications technology Encryption Cryptography Huffman coding

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of Information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP international Federation for Information Processing 2025.

关键词： Convex optimization

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Privacy Preservation of Cluster Integrity on Web-Scraped Hospital data

Privacy Preservation of Cluster Integrity on Web-Scraped Hos...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Hrishik Sai Bojnal Dharmisht SVK J Krishna Kaarthik Dhyaan Kotian Shashidhar Virupaksha Department of Computer Science and Engineering RV Institute of Technology and Management Bengaluru Karnataka India Visvesvaraya Technological University Belagavi Karnataka India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This paper proposes a method for anonymizing geospatial healthcare data that preserves the natural clustering of locations. A dataset is built with each healthcare facility's location, ratings, and review count by scraping it off an online maps platform. This enables the calculation of each facility's regional influence. The approach retains spatial clusters by restricting the displacements on each facility's geospatial coordinates to at most the minimum radius of the cluster while also factoring for the prominence of the facility in the region. This ensures that the shape and magnitude distribution of clusters is maintained, thereby preserving dataset quality. Impactful insights can be drawn from this anonymized dataset without having to identify any individual facility.

关键词： data privacy Hospitals Reviews Shape Layout Spatial databases Information filtering Geospatial analysis Protection Information integrity

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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6th international conference on Security and Privacy in New computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Federated learning

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Swin-VasMamba: A Topologically Constrained Model For 3D Vascular Segmentation

Swin-VasMamba: A Topologically Constrained Model For 3D Vasc...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Ziyu Li, Jiaxuan He, Xiangjian Xu, Qing Chen, Xin Zhou, Shoujun School of Computer Science University of Nottingham Ningbo China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9798350368741

Accurate 3D vascular segmentation is essential for diagnosing and treating vascular diseases. This task remains challenging due to the complexity of the 3D data and the morphological diversity of blood vessels. In recent years, state space models (SSMs) have received a great attention for its good performance while preserving global receptive field and consuming less computing resources and time. Inspired by this, we propose a model called Swin-VasMamba for 3D vascular segmentation. It consists of a network called CMU-Net and a topologically constrained loss function called dsh loss. We compare our model with several other advanced segmentation models based on CNN, Transformer and Mamba. The results show that Swin-VasMamba achieves a state-of-the-art performance, with the highest Dice coefficient of 0.880, the lowest 95th-percentile of Hausdorff Distance (HD95) of 0.673, and the lowest Average Surface Distance (ASD) of 0.159 on a benchmark dataset. © 2025 IEEE.

关键词： 3D Vascular segmentation CNN Mamba topological constraint

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Analysis for Predicting English Premier League Matches Using Clifford-Steerable Convolutional Networks

Analysis for Predicting English Premier League Matches Using...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Mohideen AbdulKader M S. Advay Vaithieswaran Tanuj Rao Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu Tamilnadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The most widely watched football league globally is the Barclays English Premier League (EPL), the future performance of which attracts attention from both scholars and practitioners in terms of outcome prediction. It is still challenging to provide accurate predictions per match result because of the many attempts tried in the past due to complicated interference of many factors that entice game dynamics. In order to predict the EPL matches, this paper proposed a Cubature Kalman Filter Haar Wavelet Transform Clifford-Steerable Convolutional Neural Network with Mud Ring Algorithm (CKFHWTCSCNNet-MRA). The data was downloaded from Kaggle and the OCRCKF was used for data pre-processing. For feature selection the Q-DHWT was used, which stands for Quantized Discrete Haar Wavelet Transform. CS-CNNs were then applied to predict match results while The Mud Ring Algorithm (MRA) was applied to the analysis of hyperparameters. The rationale and validation of the proposed model is established based on the performance measures employed in predicting wins/loses of the match which yielded an outstanding accuracy, specificity, sensitivity and recall of 99%, F1-Score of 99% and error of 0.25%.

关键词： Accuracy Games Predictive models Prediction algorithms Feature extraction Discrete wavelet transforms Kalman filters Convolutional neural networks Multiresolution analysis Sports

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Enhancing Federated Learning Robustness in Non-IID data Environments via MMD-Based Distribution Alignment 25th

Enhancing Federated Learning Robustness in Non-IID Data Env...

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25th international conference on Parallel and Distributed computing, Applications and technologies, PDCAT 2024

作者： Ma, Xiao Shen, Hong Lyu, Wenqi Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia

ISBN: (纸本)9789819642069

Federated learning(FL), due to its distributed nature, is highly susceptible to malicious attacks. Although various Byzantine-robust FL methods exist, they often fail to maintain robustness in practical scenarios due to the non-independent and identically distributed (Non-IID) nature of client data. Moreover, existing FL methods often suffer from weight divergence caused by heterogeneous data distributions across clients. To address these issues, we propose a novel federated learning framework that aligns local data distributions across different clients to enhance robustness for Non-IID data in adversarial environments. It contains a feature transformation layer that incorporates Maximum Mean Discrepancy (MMD) as a regularization term to avoid weight divergence through aligning local and global data distributions without sharing raw data. Our approach dynamically updates the statistical information of both local and global data, including the mean and variance, ensuring that local models are closely aligned with the global model throughout training. Experimental results on MNIST and CIFAR-10 datasets demonstrate that our proposed framework significantly improves robustness both in the absence of attacks and against untargeted attacks such as sign-flipping and additive noise. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Energy-Efficient Autism Spectrum Disorder data Collection Mechanism using Wireless Sensor Networks and Internet-of-Things

Energy-Efficient Autism Spectrum Disorder Data Collection Me...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Vijaya Shetty S Srujana S.N P. Ramesh Naidu Jyoti Neeli Sarojadevi H Ramya Prakash Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

A patient's cognitive health may be better assessed using smart monitoring and assisted living technologies. Social skills, repetitive habits, verbal and nonverbal communication, and adjusting to new environments are among the challenges children on the autism spectrum face. Because of their reduced cognitive capacities, children with autism spectrum disorder (ASD) present a significant public health challenge because it is difficult to comprehend their feelings. The main purpose of this research is to assist autistic newborns in adapting to their environment via Internet-of-things (IoT) technologies and wireless sensor networks. The study indicates an Internet of Things (IoT)-WSNs system for data collection, using an energy-efficient cluster-based routing optimization (EECRO) method that sends data to an IoT gateway server as quickly as possible, allowing the guardian to access the data in real-time. The simulation outcome indicates the EECRO can significantly reduce energy consumption to deliver messages from autistic children to their guardians.

关键词： Wireless sensor networks Autism Pediatrics Energy consumption Computational modeling Routing Energy efficiency Real-time systems Internet of Things Optimization

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CNN LIPNET : Automated Lip Reading Using Deep Convolutional Neural Networks

CNN LIPNET : Automated Lip Reading Using Deep Convolutional ...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： VPV Datta Ganesh O Sai Likith Kumar P. Jithendra S. Satheesh Kumar Computer Science and Information Technology Institute of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Lipreading, or the process of interpreting the speech by analyzing lip movements, is a critical solution when auditory cues are limited or even unavailable. This project introduces the LipNet, a vision-based neural network that uses CNNs and LSTM networks to decode lip movements into text. The model is trained on the Grid Corpus dataset, with data split into 80% for training and 20% for testing. Keyframes extracted from video sequences are processed to capture both spatial features using CNNs and temporal patterns with LSTMs. A SoftMax layer outputs accurate textual representations of speech. Achieving a notable 97.44% average accuracy, LipNet demon- strates robust performance across different speakers, accents, and speaking styles. It has broader implications on human- computer interaction, especially in assistive technologies for the hearing impaired, environments with high noise interference, and silent communication, among others. The model improves focus on crucial visual cues through the integration of attention mechanisms.

关键词： Training Adaptation models Accuracy Lips Computational modeling Training data Feature extraction data models Convolutional neural networks Testing

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