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A Cost-Sensitive LSTM Model for Driving Risk Assessment from Vehicular Trajectory Data 100

A Cost-Sensitive LSTM Model for Driving Risk Assessment from...

引用

100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Osho Pranay Kumar, Pradeep Chakraborty, Suchetana Indian Institute of Technology Jodhpur Department of Computer Science and Engineering India

ISBN: (纸本)9798331517786

Identifying risky driving behavior is crucial for early hazard detection, encouraging safer driving practices, and minimizing accident risks. Driving patterns, characterized by sensory data, can be used to classify risky behavior. However, effective classification into risk categories relies on supervised learning methods that require labeled data. The challenge lies in the high cost and difficulty of obtaining accurate groundtruth labels for these signatures. As a result, most datasets lack risk labels. Additionally, because risky incidents are infrequent during regular driving, the dataset collected from studies becomes imbalanced, containing fewer instances of risky events. This imbalance poses a significant challenge, as it biases the model towards the majority class, increasing the likelihood of costly misclassifications where risky instances are incorrectly identified as safe. To address this, we propose a three-stage method. First, we identify driving events indicative of risky behavior from the trajectory data and mathematically formulate them as potential risk indicators. Using these indicators, we then employ clustering to assign appropriate risk labels to the data. Finally, we tackle the class imbalance problem using a cost-sensitive LSTM model that combines a custom loss function with LSTM architecture to prioritize accurately classifying risky instances. Our method outperforms other state-of-the-art approaches with high accuracy, precision, F1 score, and recall of 98.13%, 95.2%, 96.6%, and 96.35%, respectively, effectively managing an imbalanced dataset. © 2024 IEEE.

关键词： Risk assessment

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A Survey on Recent Developments in Software Vulnerability Detection 1

A Survey on Recent Developments in Software Vulnerability De...

引用

1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Rajput, Sachin Kumar, Kuldeep National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350365016

In recent years, the rise of cyber threats and attacks has led to significant financial losses for software industries and individuals worldwide. Early detection of software vulnerabilities is crucial for mitigating future monetary risks. As a result, deep learning methodologies, such as pre-trained models, graph neural networks, transformer-based models, convolutional neural networks, and recurrent neural networks, have gained prominence for their ability to learn features and respond to new data. This paper provides a detailed discussion of recent deep learning methodologies for software vulnerability detection, delving into their approaches, limitations, and employed metrics. The key findings of the review highlight the predominant focus on the National Vulnerability Database, and similar repositories of open-source code, potential challenges in performance when confronted with real-world data, the limited scope of research on types, and the opportunity for exploring multilingual vulnerability detection methodologies. © 2024 IEEE.

关键词： Adversarial machine learning

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Point-To-Set Principle and Constructive Dimension Faithfulness 49

Point-To-Set Principle and Constructive Dimension Faithfulne...

引用

49th International Symposium on Mathematical Foundations of computer Science, MFCS 2024

作者： Nandakumar, Satyadev Pulari, Subin Akhil, S. Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9783959773355

Hausdorff Φ-dimension is a notion of Hausdorff dimension developed using a restricted class of coverings of a set. We introduce a constructive analogue of Φ-dimension using the notion of constructive Φ-s-supergales. We prove a Point-to-Set Principle for Φ-dimension, through which we get Point-to-Set Principles for Hausdorff dimension, continued-fraction dimension and dimension of Cantor coverings as special cases. We also provide a Kolmogorov complexity characterization of constructive Φ-dimension. A class of covering sets Φ is said to be "faithful" to Hausdorff dimension if the Φ-dimension and Hausdorff dimension coincide for every set. Similarly, Φ is said to be "faithful" to constructive dimension if the constructive Φ-dimension and constructive dimension coincide for every set. Using the Point-to-Set Principle for Cantor coverings and a new technique for the construction of sequences satisfying a certain Kolmogorov complexity condition, we show that the notions of "faithfulness" of Cantor coverings at the Hausdorff and constructive levels are equivalent. We adapt the result by Albeverio, Ivanenko, Lebid, and Torbin [1] to derive the necessary and sufficient conditions for the constructive dimension faithfulness of the coverings generated by the Cantor series expansion, based on the terms of the expansion. © Satyadev Nandakumar, Subin Pulari, and Akhil S.

关键词： Geometry

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A Contemporary Review of Interpolation-based Reversible Data Hiding 2

A Contemporary Review of Interpolation-based Reversible Data...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Pandey, Supriya Pandey, Shrawan Kumar Tripathi, Abhinandan Buddha Institute of Technology Department of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Reversible data hiding (RDH) has become a critical topic in modern information security, as it allows for secure communication by embedding secret data into a cover medium. Importantly, RDH also ensures that the hidden data can be extracted while fully preserving the real content at the receiver's end, a process known as lossless recovery. RDH is particularly well-suited for applications in medical imaging, such as MRI and CT scans, as well as military uses. In recent years, several RDH techniques have been developed, including difference expansion, histogram shifting (HS), prediction error expansion (PEE), and interpolation. This review paper focuses on various RDH strategies that leverage interpolation techniques to achieve high visual quality (VQ) and data embedding capacity, with ongoing efforts by researchers to enhance both simultaneously. © 2024 IEEE.

关键词： EC PSNR RDH

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Energy-Aware Priority-Based Task Scheduling for Edge Devices in Internet of Things 5

Energy-Aware Priority-Based Task Scheduling for Edge Devices...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Pawar, Vijaykumar Naik Mini, S. National Institute of Technology Department of Computer Science and Engineering Goa India

ISBN: (纸本)9798350376753

Billions of networked devices interact and collaborate with one another via the Internet as part of the Internet of Things (IoT). The performance and energy efficiency of IoT environments are greatly enhanced by effective task scheduling, especially for edge devices. In this paper, we present an energyaware, priority-based task scheduling algorithm specifically for edge devices in IoT networks, which prioritizes tasks according to their urgency and resource requirements while optimizing energy consumption to prolong device lifetime and lower operating costs. The model takes into consideration the dynamic nature of task demands as well as the varying energy profiles of edge devices. Simulations validate the efficacy of our approach, revealing that it achieves a substantial reduction in energy consumption while maintaining competitive task completion rates. © 2024 IEEE.

关键词： Scheduling algorithms

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Priority-Based MPTCP Scheduler for 5G and Beyond Heterogeneous Links 16

Priority-Based MPTCP Scheduler for 5G and Beyond Heterogeneo...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Gumasthi, Nikhil B. Baswade, Anand M. Indian Institute of Technology Bhilai Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Modern cellular devices often have dual interfaces for providing high-speed data transfer. This dual-interface design enhances versatility and user convenience. Dual interface technology allows 5G NR devices to connect to the 5G core using two separate base stations. One base station operates in the mmWave frequency range, and the other operates in the sub-6 GHz frequency range. However, millimeter-wave (mmWave) signals face challenges in terms of limited penetration through obstacles, such as buildings, and susceptibility to atmospheric absorption, leading to reduced coverage and potential signal degradation in adverse weather conditions. On the other hand, sub-6 GHz waves offer better penetration through obstacles and a broader scope compared to mmWave, making them suitable for providing reliable and expansive wireless connectivity in urban and indoor environments. Both mmWave and sub-6 GHz frequencies have distinct advantages and limitations;mmWave provides high data rates but has limited coverage, while sub-6 GHz offers a broader range but at lower data rates. However, in a heavy data transfer scenario, frequent loss in the mmWave link while transmitting packets can lead to packet drops (wireless medium), reducing the congestion window size and increasing the flow completion time. To mitigate this, we propose to use analytical hierarchy process (AHP) and grey rational analysis (GRA) based priority allocation mechanism to schedule packets on the most reliable link, thereby reducing the packet drops and flow completion time. © 2024 IEEE.

关键词： 5G NR Multipath TCP (MPTCP) network simulator-3 (NS-3) network simulator-3-mmWave priority scheduling

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MediMind: A Comprehensive Health Prediction and Record-Keeping Platform 6th

MediMind: A Comprehensive Health Prediction and Record-Keep...

引用

6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Naik, Darshana A. Sharma, Aditya Mahajan, Danish Khandelwal, Dhruv Gajya, Arpit Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9783031707889

The "MediMind" project signifies a revolutionary fusion of predictive analytics, centered on diabetes and heart disease risk assessment, coupled with state-of-the-art skin disease prediction and robust Electronic Health Record (EHR) management. By employing advanced machine learning models like Logistic Regression and Support Vector Machines (SVM) for diabetes and heart disease predictions, alongside the cutting-edge Convolutional Neural Network (ResNet-50) for precise skin disease classification, "MediMind" achieves unparalleled accuracy and applicability. The predictive models exhibit exceptional performance metrics, boasting high accuracy, precision, recall, and F1-score. Seamless integration into Electronic Health Records (EHR) ensures immediate implementation in clinical decision-making, providing healthcare professionals with precise insights into diabetes and heart disease risks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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Interpreting Fake Reviews Using Machine Learning and Deep Learning

Interpreting Fake Reviews Using Machine Learning and Deep Le...

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World Conference on Information Systems for Business Management, ISBM 2023

作者： Bhat, Mohammad Qazim Jayalakshmi, D.S. Mallegowda, M. Geetha, J. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819983452

The issue of fake reviews on online platforms has increasingly become a huge concern in recent years, with the potential to mislead consumers and negatively impact businesses. In this paper, we present a comprehensive approach to detecting fake reviews using both supervised and unsupervised learning techniques. Our approach includes classic machine learning algorithms, deep learning techniques such as RNN and attention networks, as well as state-of-the-art models like BERT and GPT. We leverage a labeled dataset of restaurant reviews from *** to train and evaluate our models. We also compare the performance of supervised and unsupervised learning techniques and identify the most effective and explainable models for detecting fake reviews. Our results show that our approach achieves high accuracy in detecting fake reviews, and the interpretation of our models offers valuable insights into the factors that contribute to the identification of fake reviews. We believe our work contributes to the ongoing effort of combating fake reviews and provides a practical and effective solution for businesses and consumers to identify trustworthy reviews. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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NLP Based Video SparkNotes Assistant 4

NLP Based Video SparkNotes Assistant

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Bhilare, Amol Gaikwad, Namrata Kurapati, Aditya Dabade, Ronak Saklani, Daksh Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798331515720

This research presents a novel method of automatic video summarising and note-generating using Natural Language Processing (NLP) and audio recognition techniques. The exponential rise in internet video footage has increased demand for effective summarising methods. The proposed approach uses speech recognition to transcribe audio content, which is further processed with natural language processing (NLP) algorithms to provide succinct summaries. Furthermore, the system creates notes based on the main ideas that are taken from the transcript of the movie. The spoken words in the video are converted to text using voice recognition technology. After that, this study interpret and condense this material using specialized computer techniques, making it more concise yet nonetheless useful. Furthermore, our technology creates notes that highlight significant scenes or topics from the video. © 2024 IEEE.

关键词： Natural language processing systems

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SecureHD: Designing Low-Cost Reliable and Security Aware Hardware Accelerators During High-Level Synthesis for Computationally Intensive Application Frameworks 10

SecureHD: Designing Low-Cost Reliable and Security Aware Har...

引用

10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Chaurasia, Rahul Sengupta, Anirban Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331533229

Reliable and low-cost hardware accelerator design and its security countermeasures are crucial to safeguard not only the end consumer but also the intellectual property (IP) vendor and system-on-chip (SoC) integrator. The reusable hardware accelerator-based design paradigm has become popular for its usage in several consumer electronics (CE) and computing systems. This is because the usage of hardware accelerators enables higher performance and efficacy by accelerating the underlying computationally-intensive application frameworks. In the proposed work, secure hardware accelerators for digital signal processing (DSP), multimedia and machine learning frameworks are designed (at zero design overhead) during high-level synthesis (HLS) as it offers lesser design complexity than lower levels of chip design. Further, HLS facilitates to integration of modules for design space exploration and integrating multi-layer security. The proposed methodologies offer the following: (a) hardware accelerator design for various computationally intensive application frameworks (b) negligible design cost overhead (c) seamless detective control against counterfeiting (d) higher tamper tolerance (e) low probability of false positive (f) higher strength of obfuscation to thwart reverse engineering attack. © 2024 IEEE.

关键词： High level synthesis

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