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Enhanced Road Safety Through Intelligent Lane Detection and Driver Drowsiness Monitoring 15

Enhanced Road Safety Through Intelligent Lane Detection and ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunpandian, J. Gunasekaran, V. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350370249

The innovative system outlined in the provided research demonstrates a significant stride towards addressing the growing concern of road safety. Lane departure and driver drowsiness are two major causes of road accidents. In this paper, we present an observation of road lane and drowsiness detection system that uses a camera to capture real-time video footage of the road ahead and applies image processing techniques to detect and track road lanes. The system's road lane detection module employs the canny edge detection algorithm and Hough transform to detect lane lines accurately. The drowsiness detection module of the system analyzes the driver's facial features and tracks the driver's eye movements, blink frequency to identify signs of drowsiness. If the driver found to be sleepy, it issues an audio alert to the driver. The system's capability to detect and track road lanes with precision, along with its ability to identify signs of driver drowsiness, underscores its potential as a promising solution for enhancing road safety. © 2024 IEEE.

关键词： Canny edge algorithm drowsiness lane detection road safety vehicles

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A hybrid wrapper technique enabled Network Intrusion Detection System for Software defined networking based IoT networks 3

A hybrid wrapper technique enabled Network Intrusion Detecti...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramaiah, Mangayarkarasi Padma, Adla Vishnukumar, Ravula Rahamathulla, Mohemmed Yousuf Chithanuru, Vasavi Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

In the age of smart era, usage of IoT devices is inevitable. As the number of IoT devices increases, the amount of data they generate also increases, which in turn leads to security breaches. Continuous monitoring through SDN is the appropriate solution to mitigate security breaches. SDN introduces a new architecture where the control plane operates independently of the data plane. While this design allows applications in the base plane to interact with network components via the controller, it also creates security concerns. An attacker could exploit vulnerabilities in the control plane, potentially leading to significant data breaches. Amid a lot of research in various venues to address the security aspects of SDN, a simple and robust Intrusion detection system is necessary for the current context. Hence, a hybrid-wrapped enabled feature selection RF-RFE has been included in determining the highly influential features upon the derived features and a fine-tuned machine learning-based model to detect the anomalies at the earliest possible in the context of SDN has been trained and tested on SDN-IoT dataset. The tested results reveal that RF-based RF-RFE-SDNIoT-NIDS obtained 99% of its binary and multi-classification accuracy using minimal features compared to other machine learning models. © 2024 IEEE.

关键词： Intrusion detection

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Enhanced Traffic Management in IoT-Integrated Internet of Vehicles (IoV) with Optimized Routing and Deep Learning Algorithm 5th

Enhanced Traffic Management in IoT-Integrated Internet of Ve...

引用

5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Sahoo, Arundhati Tripathy, Asis Kumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

The integration of the Internet of Things (IoT) into vehicular networks has paved the way for the exciting development of the Internet of Vehicles (IoV) within Intelligent Transportation Systems (ITS). These breakthroughs bring about enhancements in urban transportation services, including improved quality and interaction, reduced costs, more efficient resource utilization, and enhanced traffic management capabilities. The incorporation of IoV into transportation has the potential to address various traffic-related challenges in smart cities. Nevertheless, optimizing routing and monitoring network traffic presents significant challenges, primarily due to limited node-to-node communication capabilities. To tackle these problems, this study proposes the BiLSTM-GreedyNet, which combines the Improved Greedy Perimeter Stateless Routing Algorithm with the Bi-LSTM neural network technology in a 5G network environment. The system delivers impressive results, as evidenced by simulations in MATLAB, boasting a remarkable 98.22% accuracy rate, a low 34.2 MJ energy consumption, and a high Packet Delivery Ratio (PDR) of 91%, all tested with a network of 100 nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Dynamic and Energy Efficient Data Traffic Scheduling for Earlier Forest Fire Detection using WSN 2

Dynamic and Energy Efficient Data Traffic Scheduling for Ear...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Swetha, R. Navaneethan, C. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In general, data traffic volume is significant in sensor networks, and communication often occurs with variable capacity during early forest fire detection using Wireless Sensor Networks (WSN). Initially, optimal routes are determined by selecting the best prime nodes. Subsequently, packets are dynamically scheduled based on the severity of the data traffic associated with detecting forest fires. The selection of dominant weighted nodes is a crucial initial stage, and these nodes are chosen for optimal routing, taking into consideration factors such as distance and energy criteria. The second stage involves the dynamic scheduling of packets, ensuring that they are sent based on packet priority, queue index value, and the urgency of forest fire detection. This process aims to minimize congestion and prevent unnecessary transmission delays, contributing to the efficient and timely detection of forest fires using WSN. © 2024 IEEE.

关键词： Fires

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GS-Net:U-Net with Gated Spatial Convolution for Retinal Image Segmentation 9

GS-Net:U-Net with Gated Spatial Convolution for Retinal Imag...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Cao, Guogang Zhang, Yunqing Yan, Rugang Zhou, Zhilin Zhang, Sai Liang, Wanying Zhong, Tao Yang, Zhaojun Shanghai Institute of Technology School of Computer Science & Information Engineering Shanghai201418 China

ISBN: (纸本)9798350363043

Optical Coherence Tomography Angiography (OCTA) plays a crucial role in the early diagnosis and continuous monitoring of eye diseasesThis study presents GS-Net, aimed at accurately segmenting various retinal structures, including capillaries, arteries, veins, and the foveal avascular zone (FAZ). The core of GS-Net is an improved U-Net based on gated spatial convolution. The advantages of gated spatial convolution include dynamic feature selection and enhanced representation capabilities. Extensive experiments demonstrate that GS-Net outperforms existing state-of-the-art methods on two subsets of the publicly available OCTA-500 dataset. Compared to Intersection over Union (IoU), the average IoU scores are 77.91% and 83.68%, respectively. The proposed GS-Net shows excellent performance in OCTA image segmentation, providing an efficient and accurate multi-task segmentation solution for ophthalmic diagnosis. © 2024 IEEE.

关键词： Image segmentation

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Multifunctional Pre-processing Approach for Diabetes Mellitus Prediction Using Ensemble Learning 3

Multifunctional Pre-processing Approach for Diabetes Mellitu...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Divya, S. Sudha, M. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Diabetes mellitus when untreated can result in a number of health issues. It is a metabolic disease marked by abnormal blood glucose levels. Early detection of diabetes improves a person's long-term health by halting its progression. Making better medical decisions can be greatly aided through the implementation into practice automated techniques that can precisely identify pre- and diabetic situations. In this paper Synthetic Minority Oversampling Technique and Adaptive Oversampling Technique is used to address class imbalance problem and Chi-square test, Recursive feature elimination methods are involved in detecting and extracting relevant components from unprocessed data. These techniques are applied to the framework and compared using different ensemble learning classifiers such as XGBoost, AdaBoost, LightGBM and CATBoost. The result shows us CATBoost with ADASYN and RFE provides us with better accuracy of 0.96%. © 2024 IEEE.

关键词： Adaptive boosting

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A Hybrid Approach to Validate and Retrieve Fingerprint Image for Authenticating Database Sensitive information Access and to Verify Some of Topological Multi-Granular Rough Sets Properties on Authenticated Database Fields 2

A Hybrid Approach to Validate and Retrieve Fingerprint Image...

引用

2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Raghavan, R. John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Content based image retrieval has already begun to turn into an intriguing and exceptionally needed field to meet with the client prerequisite. Fingerprint approvals likewise have become a fascinating field to principally uphold the confirmation framework. Notwithstanding foundations regulating guaranteed about information straightforwardly, affiliations are also obliging them to examine the fingerprint endorsement. Despite establishments overseeing ensured about data, directly associations are similarly obliging them to research the fingerprint check. Organizations and foundations both are typically utilizing the idea of fingerprint for the participation and presence reason. Nevertheless, establishments presently managing secured information as like institutes and additionally going along with them to investigate the fingerprint authentication. More than this usual action there are some particular applications where the authentication would be considerable to help the application than using a traditional pattern of password. We proposed a fingerprint authentication system for the scientific based industry database access system. We removed the noise if any found in the input fingerprint using anisotropic Gauss filtering. The noise free fingerprint image is not further passed to the binarization phase as the need of binarization and some form of the drawback of binarization is replaced with a thinning algorithm. The thinned fingerprint image is followed by the matching process and once it is matched, then only the sensitive and highly confidential information are allowed to access from the database. The testing is done using OpenCV and FVC 2004 dataset to assess the projected retrieval method. Further the matched fingerprints are used to prove some of the internal structure properties of multi-granular rough sets (MGRS) on authenticated database fields. The accuracy obtained in this hybrid approach is found to be more satisfactory and hence the security towards acce

关键词： Database systems

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Enhancing Video Protection: A Reversible Digital Watermarking approach using Swin features and Arnold Transform enabled Multi-Aggregation Model 3

Enhancing Video Protection: A Reversible Digital Watermarkin...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dilip, Mali Satish Agilandeeswari, L. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Nowadays, the widespread internet technology brings out rapid advancement in the use of videos for sharing huge amounts of secret data. Conversely, the privacy of transmitted digital content is jeopardized by digital image processing techniques that are capable of producing flawless image or video duplication. Moreover, providing security to digital records in the form of videos is a highly challenging task, which requires a fusion-based watermarking technique to protect the data from illegitimate access and fraudulent usage. In this investigation, a tiny Swin and Arnold Transform-enabled Multi-aggregation model was introduced to protect video based on digital watermarking characteristics at a very low computational cost. Here, the Swin-T features are extracted from the input video, over which the Arnold transform and multi-aggregation model is developed in two phases. The best embedding location is identified by using multi aggregation model initially at the embedding phase and the inverse Arnold transform for secret image (VIT Logo) embedding in a binary embedding location to obtain the embedded video. The secret image is extracted from the attacked embedded video using the Arnold transform-multi-aggregation model in the extraction phase. The developed data-hiding technique is also evaluated to compute the robustness using the evaluation parameters. The simulation results reveal that the developed scheme achieved Peak-Signal-to-Noise Ratio (PSNR) and Similarity Index Measure (SSIM) values of 64.57 ∼dB and 0.9993 respectively. © 2024 IEEE.

关键词： Embeddings

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Facial Attendance Tracker using Enhanced Mean Local Binary Pattern 2

Facial Attendance Tracker using Enhanced Mean Local Binary P...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Charanya, R. Jayasurya, Arnold David Krithika, L.B. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350384277

Facial Attendance Tracker is a mobile application where a student can fetch his/her attendance by scanning his/her face from his/her own mobile. The faculty will project the Dynamic QR code when he/she wants to take the attendance. The students have to login into the mobile application using the login credentials and scan the dynamic QR code provided by the faculty. Once, the QR code is scanned successfully, it will redirect to the camera where the student can fetch his/her face. The system comprises of acquiring the image, followed by sending the image to the database, detection of face, pre-processing, extraction of features, classifying the features and post- processing stage. To detect the face from the image, Viola-Jones algorithm has been used and Enhanced Mean Local Binary Pattern (EMLBP) algorithm is used to extract features as it outperforms the other algorithms in a huge margin. © 2024 IEEE.

关键词： Local binary pattern

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Long-range Sequential Modeling Mamba For 3D Abdominal Multi-Organ Segmentation 9

Long-range Sequential Modeling Mamba For 3D Abdominal Multi-...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Cao, Guogang Yan, Rugang Zhang, Yunqing Zhou, Zhilin Liang, Wanying Yang, Zhaojun Zhang, Sai Zhong, Tao Shanghai Institute of Technology School of Computer Science & Information Engineering Shanghai201418 China

ISBN: (纸本)9798350363043

The automatic segmentation of abdominal organs in CT images is a critical task in medical image processing, aiding in improved diagnosis, treatment planning, and disease monitoring. However, due to blurred organ boundaries, varying sizes and shapes, and complex anatomical structures in abdominal CT images, the automatic segmentation of multiple organs remains challenging. To address these issues, this paper proposes a novel U-shaped model called MUX-Net, which combines state-space models with ConvNet architecture. We introduce the Mixed Vision Strategy Module (MambaUX) in the encoder stage, serving as the backbone of the encoder. By integrating the state-space model Mamba and large-kernel volumetric depthwise separable convolutions, we leverage the complementary advantages of different visual encoder strategies. This enhances the segmentation model's ability to extract both local and global features in 3D images. The effectiveness of the hybrid approach largely owes to Mamba's ability to capture long-term dependencies in sequential data and the large receptive field provided by the large convolutions. We evaluated the proposed method on a challenging public dataset for abdominal multi-organ segmentation: MICCAI Challenge 2022 AMOS. Experimental results show that MUX-Net achieved a Dice and NSD score of 0.8816 and 0.7699 on the AMOS2022 dataset, outperforming other mainstream segmentation methods. © 2024 IEEE.

关键词： computerized tomography

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