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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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Survey and Tutorial on Hybrid Human-Artificial Intelligence

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Tsinghua science and Technology 2023年第3期28卷 486-499页

作者： Feifei Shi Fang Zhou Hong Liu Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Software Engineering East China Normal UniversityShanghai 200062China Faculty of Computing Ulster UniversityNewtownabbey BT370QBUK

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

关键词： hybrid human-artificial intelligence(H-AI) Internet of Things(IoT) artificial intelligence(AI)

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Exploring ensemble optimized voting and stacking classifiers through Cross-validation for early detection of suicidal ideation

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 335-349页

作者： Shukla, Shiv Shankar Prasad Singh, Maheshwari Prasad Department of Computer Science Engineering National Institute of Technology Patna Bihar Patna India

Detecting behavioral changes associated with suicidal ideation on social media is essential yet complex. While machine learning and deep learning hold promise in this regard, current studies often lack generalizability due to single dataset reliance. Traditional embedding techniques struggle with semantic analysis,leading to challenges in achieving high accuracy models and conventional validation methods have data drift limitations. To address these challenges, this study proposes a novel evaluation approach using natural language processing across diverse platforms like Twitter and Reddit. By integrating BERT embedding, adept at handling semantic nuances, with an optimized Stacked Classifier combining different base classifiers and XGBoost as the meta-classifier, the model excels in swiftly detecting signs of suicidal ideation compared to the Voting Classifier, i.e., the combination of Decision Tree, Random Forest, Gradient Boost and XGBoost and several machine learning models. Additionally, the study explores advanced embedding techniques like MUSE and LLM, and deep learning models including Bi-LSTM, Bi-GRU, and Text-CNN for *** ensemble approach aims to create a model that is not only interpretable but also robust, reducing computational complexity and enhancing resilience against noisy data - common challenges faced in text classification tasks. Through K-fold validation, which involves partitioning the dataset into k equal-sized subsets or "folds"and training the model k times, using k-1 folds for training and one-fold for testing each time, the proposed model achieves impressive accuracy rates of 97% on Reddit and 96% on Twitter datasets, underscoring its effectiveness in identifying suicidal ideation across social media platforms. © 2024 - IOS Press. All rights reserved.

关键词： Semantics

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Prioritization and offloading in P4 switch integrated with NFV

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Telecommunication Systems 2024年第3期86卷 571-584页

作者： Neha, Farhin Faiza Lai, Yuan-Cheng Hossain, Md. Shohrab Lin, Ying-Dar Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

The architecture of integrating software Defined Networking (SDN) with Network Function Virtualization (NFV) is excellent because the former virtualizes the control plane, and the latter virtualizes the data plane. As Programming Protocol-independent Packet Processors (P4) become popular, the architecture integrating SDN with NFV may shift from traditional switches to P4 switches. In this architecture, which integrates P4 switch and NFV (P4 + NFV), network functions can be provided in both P4 switches (PNF) and NFV (VNF). Thus, to minimize packet delay, an offloading problem between P4 switches and NFV in this P4 + NFV should be addressed. This paper tackles this offloading problem and figures out the prioritization mechanism between newly arriving packets and packets that require VNF for minimizing packet delay. We model and analyze the P4 + NFV architecture using an M/M/1 queuing model with non-preemptive priority. Also, we propose an optimization solution based on gradient descent to find the optimal offloading probability of going to VNF. Results show that optimal offloading from P4 switch to NFV can reduce the average packet delay from 13.74 to 40.73%, when packets requiring VNF are given higher priority than newly arriving packets. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： software defined networking

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Enhancing E-Commerce Text Classification: A GRU-Based Approach for Improved Product Understanding 5th

Enhancing E-Commerce Text Classification: A GRU-Based Approa...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Nazmul Abdal, Md. Shaown, Shahanaz Islam Islam, Khairum Aman, Rutba Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Uttara University Dhaka Bangladesh Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9789819791118

In the burgeoning landscape of e-commerce, the ability to accurately classify product texts is paramount for enhancing user experience and driving business success. Traditional approaches to text classification often struggle with the nuances and complexities inherent in e-commerce product descriptions. In this paper, we propose a novel approach utilizing Gated Recurrent Unit (GRU) to address these challenges and improve product understanding in e-commerce text classification tasks. Our model leverages the inherent sequential nature of product descriptions, effectively capturing long-range dependencies and semantic relationships within the text. We use a standard dataset in extended trials to demonstrate the superiority of our GRU-based approach over conventional methods in terms of classification accuracy and robustness across diverse product categories. Furthermore, we conduct comprehensive analyses to gain insights into the inner workings of our model and its ability to learn meaningful representations of e-commerce text data. The performance of the model is compared using several cutting-edge techniques, including Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) in order to show that our model is superior at correctly classifying e-commerce texts. The experimental findings show that the suggested model performs competitively in classifying e-commerce texts, surpassing other approaches with an accuracy of 98.35%. Our findings underscore the potential of GRU-based approaches for advancing the state-of-the-art in e-commerce text classification, offering promising avenues for future research and practical applications in the domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marketplaces

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Hate Speech Detection Using Recurrent Neural Networks (RNN) 5th

Hate Speech Detection Using Recurrent Neural Networks (RNN)

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Pokkuluri, Kiran Sree Sivakoti, Ramadevi Raja Rao, P.B.V. Murthy, P.T.S. Pamarthi, Nagaraju Varma, Ch. Phaneendra Gurujukota, Ramesh Babu Usha Devi N, S.S.S.N. Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada Kakinada India

ISBN: (纸本)9783031770807

Hate speech detection is a vital task in the context of content moderation, aiming to identify and mitigate harmful language in online platforms. This abstract presents a methodology utilizing Recurrent Neural Networks (RNNs) for hate speech detection. We have processed a heterogeneous dataset containing occurrences of hate speech and non-hate speech & preprocessed the text data, and represent words as vectors using pre-trained word embedding’s. Our chosen RNN architecture, adept at capturing sequential dependencies, processes the contextual information within the text. During training, the model optimizes using a binary cross-entropy loss function and undergoes validation for hyper parameter tuning. The effectiveness of the RNN-based hate speech detection model is evaluated using accuracy, F1 score, precision and recall. This methodology provides a robust framework for combating hate speech in digital spaces, contributing to a safer and more inclusive online. We have compared our work with various base line methods with several parameters and achieved an accuracy of 96.89%. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Recurrent neural networks

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Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

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Intelligent Automation & Soft Computing 2023年第6期36卷 2757-2771页

作者： Adel Alshamrani Abdullah Alshahrani Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology *** this paper,we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber *** present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning(MARL).Our proposed approach is inspired by the multiarmed bandits(MAB)learning technique for multiple agents to cooperate in decision making or to work *** study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their *** find that this game can be modeled from an individual player’s perspective as a restless MAB *** discover further results when the MAB takes the form of a pure birth process,such as a myopic optimal policy,as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.

关键词： Multiarmed bandits reinforcement learning multiagents intrusion detection systems

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis 1

Panoptic Region Slicing Segmentation and Optimized Alexnet-B...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Pareddy, Sudha V. Virupakshappa PDA College of Engineering Department of Computer Science & Engineering Kalaburagi India Sharnbasva University Department of Computer Science & Engineering Kalaburagi India

ISBN: (纸本)9798350371802

The early and accurate diagnosis of melanoma, a potentially harmful skin cancer, is essential in enhancing the survival of patients. This paper describes a new method for detecting melanoma called Panoptic Region Slicing Segmentation (PRS2), which employs an AlexNet-based optimized convolution neural network (PRS2-OCNN). The proposed system integrates several advanced techniques to improve the efficiency and accuracy of melanoma detection. The first step is to preprocess the dermoscopic image with a 2D Fusion Filter to improve its quality and prepare it for further analysis. The Panoptic Region Slicing Segmentation (PRS2) method is then used to emphasize the boundary regions, allowing for more precise localization of melanoma-affected areas. We use Threshold Histogram Evaluation (THE) to assess color variations within segmented regions, which effectively characterizes melanoma-specific color patterns. The features were extracted using Spread Spectral Menzies' Feature Selection (SSMFS), which reduced dimensionality and increased the efficiency of the subsequent analysis. The optimized convolution neural network, derived from the influential AlexNet architecture, serves as the foundation of our proposed approach. By fine-tuning the AlexNet-OCNN on the reduced feature set, we can improve its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN categorizes melanoma classes based on their risk severity, allowing dermatologists to make informed decisions about timely and appropriate treatment. A broad and diversified dataset of dermoscopic images was used in the experimental evaluations. By thoroughly analyzing the melanoma-affected regions, the proposed system performed better than existing methods in terms of detection accuracy. © 2024 IEEE.

关键词： Diagnosis

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Minimum spanning tree cycle intersection problem on outerplanar graphs

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Discrete Applied Mathematics 2024年 343卷 328-339页

作者： Su, Tai-Yu Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan

We study the minimum spanning tree cycle intersection (MSTCI) problem on outerplanar graphs in this paper. Consider a connected simple graph G=(V,E) and any spanning tree T=(V,ET) of G, it is well-known that each non-tree edge e∈E∖ET induces a fundamental cycle in T∪{e}. We say that two distinct fundamental cycles intersect if they share at least one edge. The MSTCI problem seeks a spanning tree such that the number of pairwise cycle intersections is minimized. We provide a linear-time algorithm to solve the problem on outerplanar graphs by exploiting the cycle-cut duality on plane graphs. Besides, we show that the minimum cycle intersection number is bounded by O(n) for outerplanar graphs with n vertices. © 2023 Elsevier B.V.

关键词： Graphic methods

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A Dual-Stage Semi-Supervised Pre-Training Approach for Medical Image Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 556-565页

作者： Aralikatti, Rajath C Pawan, S.J. Rajan, Jeny National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal575025 India

Deep neural networks have played a vital role in developing automated methods for addressing medical image segmentation. However, their reliance on labeled data impedes the practicability. Semi-Supervised learning is gaining attention for its intrinsic ability to extract valuable information from labeled and unlabeled data with improved performance. Recently, consistency regularization methods have gained interest due to their efficient learning procedures. They are, however, confined to data or network-level perturbations, negating the benefit of having both forms in a single framework. In light of this, we ask an intriguing but unexplored question: Can we have both network-level and data-level perturbation in the semi-supervised framework? To this end, we present a holistic approach that integrates data-level perturbation in the model pre-training stage, followed by implicit network-level perturbation in the fine-tuning stage. Furthermore, we incorporate networks with manifold learning paradigms throughout the training to facilitate the formation of robust data representations by ensuring local and global semantic affinities adhering to the theory of consensus. Notably, this may be the first attempt in the semi-supervised medical image segmentation archetype to use data and network-level perturbation with a model pre-training strategy. We extensively validated the efficacy of the proposed framework on three benchmark datasets, namely the Automated Cardiac Diagnosis Challenge, ISIC-2018, and Left Atrial Segmentation Challenge datasets, subjected to severely low-sampled labeled data. Notably, in ACDC (4%), ISIC-2018 (5%), and LA (6%) labeled cases, the proposed method outperforms the second-best method by 2.95%, 1.31%, and 0.71% in the Dice Similarity Metric. © 2023 IEEE.

关键词： Feature extraction

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