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Syntactic Simplification of English Sentences Using Parse Trees 2024

Syntactic Simplification of English Sentences Using Parse Tr...

引用

16th International Conference on Contemporary computing, IC3 2024

作者： Keerthan Kumar, T.G. Adhith, Adhith Jayadeep, Nitin Gokulraj, M. Koolagudi, Shashidhar G. Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Surathkal India

ISBN: (纸本)9798400709722

In the realm of Natural Language Processing, the quest to simplify text is paramount, aiming to enhance accessibility and comprehension for all individuals. This study delves into the realm of English text simplification through a distinctive approach leveraging syntactic parse trees, known as syntactic simplification. Our methodology tackles two key aspects: the first involves streamlining compound sentences linked by coordinating conjunctions, while the second focuses on simplifying those connected by subordinating conjunctions, namely complex sentences. With our innovative approach, we achieved a notable BLEU score of 81.9, surpassing previous endeavors in the field. In an era marked by information saturation and diverse literacy levels, the need for clear and intelligible text is undeniable. This work not only sheds light on effective text simplification but also underscores its vital role in fostering universal understanding and communication. © 2024 Copyright held by the owner/author(s).

关键词： Syntactics

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Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieval in Congested Road Networks Using Predicted Speeds 10

Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieva...

引用

10th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2024

作者： Sondhi, Kartikey Dalapati, Poulami Kumar, Saurabh Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789897587030

Efficient transportation systems are crucial for the ever-growing smart cities. With the increasing urbanization and growth in vehicular traffic, congestion has become a significant challenge. This research paper addresses the critical issue of identifying the fastest, least congested path in road transport networks, aiming to enhance overall travel efficiency and reduce the negative impact of traffic congestion. The study employs an improved version of the Lifelong Planning A∗ (LPA∗) that helps find the fastest route between two points in dynamic changing environments. The proposed methodology is called the Lifelong Dynamic Timed A∗(LTA∗) algorithm with an optimal bound weight factor integrated with it to make the search more guided and efficiently predict optimized traffic paths to provide real-time recommendations. To validate the effectiveness of the developed algorithm, extensive simulations and case studies are conducted on a small area inWashington as well as on GridWorlds. The experimental results show that LTA∗, within accurate weight bounds, always managed to find the fastest path, and in some cases, the time taken was close to half of that produced by A∗. Copyright © 2024 by SCITEPRESS - science and technology Publications, Lda.

关键词： Traffic congestion

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Diabetic Retinopathy Detection Using Deep Learning

Diabetic Retinopathy Detection Using Deep Learning

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Keerthi, B. Powmika, K Kalai Narendra, Pranudev Karthik, N. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

- In this research, we present DRNet, a deep learning model for automated detection and grading of diabetic retinopathy (DR) using fundus images. DR is a leading cause of vision loss in diabetic individuals, emphasizing the importance of early detection. Our model combines the ResNet architecture for feature extraction with the U-Net architecture for lesion segmentation. Trained on a large dataset of 666,383 fundus images from 121,342 diabetic patients, DRNet incorporates realtime image quality assessment, lesion detection, and grading of DR severity. Evaluation on both local and external datasets, totaling 409,455 images, demonstrates the effectiveness of DRNet, achieving an area under the receiver operating characteristic curves (AUC) of 0.90-0.936 for lesion detection and 0.96-0.98 for DR severity grading. Our results suggest that DRNet has the potential to enhance DR screening programs by improving accuracy and efficiency in diagnosis, thereby benefiting patient outcomes and healthcare systems. © 2024 IEEE.

关键词： Deep learning

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Smart Metro Railway Ticketing System

Smart Metro Railway Ticketing System

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Nandini, N.S. Bhoomika, N. Lavanya, K.V. Monika, H.L. Suchithra, H.M. Bgs Institute of Technology Computer Science and Engineering Karnataka BG Nagara India

ISBN: (纸本)9798350359688

In one's life, transportation is vital. The primary objective of this paper is to dispose of dailyand massive troubles related to sporting a price ticket while touring and to promote a more tranquil journey enjoy. For this purpose, however no longer exclusively within the context of metro railways, we are suggesting a biometric-based totally to the ticketing system. When registering, purchasing tickets, and authenticating the fingerprint at the day of journey, we recollect everyone's fingerprint to create a completely unique identification. This lets in her or him to travel to their desired area on a sure day and on the desired teach. An Arduino interface can be created using the fingerprint sensor, and this. © 2024 IEEE.

关键词： Subways

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A Novel Approach for Leukemia Classification using Multi-Neural Networks 1

A Novel Approach for Leukemia Classification using Multi-Neu...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Kumar, J Senthil Ramalakshmi, B. KIT-Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372748

Leukemia is a type of cancer that originates in the bone marrow and affects the blood-forming cells. These abnormal cells, typically white blood cells, multiply uncontrollably, hindering the production of normal blood cells. Because of its various genetic and molecular properties, leukemia, a complex and heterogeneous group of blood malignancies, provides major hurdles in correct subtype categorization. Traditional categorization approaches often fail to reflect the complexities of leukemia subgroups. In this paper, this research offer the Multi-Neural Network (MNN), a ground-breaking strategy for addressing these difficulties by exploiting hierarchical information and merging specialized neural networks. The dataset was collected from Kaggle repository. After collecting dataset this research use Non adaptive threshold for Image denoising. After denoising, this research use Adam optimization algorithm for optimization process. After optimization this research use HOG algorithm for feature selection. The proposed MNN architecture is made up of a unique collection of neural networks, each adapted to a certain hierarchical level of leukemia subtypes. An Improved Convolutional Neural Network (CNN), a DenseNet, and an improved VGG19 are among the specialized networks. These networks are painstakingly developed to extract distinguishing information from leukemia cell pictures, enhancing classification accuracy. This research uses the cross-entropy loss function in combination with the Adam optimization approach to improve the performance of our MNN even more. This combination improves the training process, allowing our MNN to discover complex patterns and correlations from the leukemia dataset. © 2024 IEEE.

关键词： Cells

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Velocity Powered Sprint: Gesture Controlled Car Racing Game 2

Velocity Powered Sprint: Gesture Controlled Car Racing Game

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Mamatha Bai, B.G. Singh, Priyanshu Pramod Tuttagunta, Sowmya Goutam, Vachaspati Nitte Meenakshi Institute of Technology Computer Science and Engineering Karnataka Benguluru India

ISBN: (纸本)9798350372892

An innovative real-time car racing game controlled by hand gestures is introduced, employing OpenCV and advanced AI techniques. The primary goal is to offer players an immersive gaming experience by allowing them to steer a virtual car along a racing track using intuitive hand movements. The system utilizes OpenCV's computer vision algorithms, analyzing live video feeds from a webcam to instantly detect and interpret hand gestures. These gestures, including commands like 'move forward,' 'move backward,' 'turn left,' and 'turn right,' act as input signals for directing the car within the game. To enhance gesture recognition accuracy, AI methods are integrated, training a model with machine learning algorithms such as Convolutional Neural Networks (CNNs) to precisely categorize various hand gestures. The identified gestures are then translated into control signals for the virtual car, resulting in a seamless and captivating gaming experience. Featuring visually stunning graphics, challenging racecourses, and competitive adversaries, this project exemplifies the synergy of computer vision, AI, and gaming technologies, offering a user-friendly and immersive hand gesture-controlled car racing game. It not only provides entertainment but also opens up avenues for potential applications in gaming, virtual reality, and human-computer interaction. © 2024 IEEE.

关键词： Gesture recognition

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Predicting Water Potability: Leveraging Machine Learning Techniques 2

Predicting Water Potability: Leveraging Machine Learning Tec...

引用

2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Laya, N. Shruthi Shetty, J. Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

In recent times, the spotlight has been on understanding and forecasting water quality, owing to the variety of pollutants that pose potential harm. This research aims to advance strategies for managing and minimizing water pollution risks by examining the application of machine learning models for water quality classification. The algorithms under scrutiny include Logistic Regression, K-Nearest Neighbors Classifier, Support Vector Machine, Decision Tree, and Random Forest Classifier. These models underwent testing on a wate-potability dataset with 10 features, and their performance was assessed using various accuracy metrics, precision, fl-score and recall, where parameter tuning is also applied lastly for increasing better performace. The findings indicate that the proposed models effectively categorize water quality, with the Random Forest Classifier emerging as the most accurate in making predictions with 80% before parameter tuning and with 81% after parameter tuning. The. pkl pickle library has been employed to save the random forest classifier, ensuring its accessibility and usefulness in the future. Through this process, the ultimate model has been stored, facilitating its application in forecasting outcomes on unseen datasets in upcoming *** research where the prediction is made on the dataset to categorize the water as potable or non-potable. © 2024 IEEE.

关键词： Potable water

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Practical Defense Against Adversarial WiFi Sensing 18

Practical Defense Against Adversarial WiFi Sensing

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Shankar, Yamini Chakraborty, Ayon Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391725

WiFi-based sensing, a non-intrusive technology that leverages existing communication infrastructure, has become widely used for environmental monitoring by extracting Channel State Information (CSI). However, the vulnerability of these systems to adversarial attacks, a core challenge within Integrated Sensing and Communication (ISAC), highlights the need for practical and robust defenses. In this work, we introduce a practical black-box defense strategy designed to protect CSI data from adversarial manipulation, significantly reducing an attacker's classification accuracy from 98% to 17% while preserving communication quality. Our approach achieves a minimal median Signal-to-Noise Ratio (SNR) difference of 1 dB, ensuring stable throughput and reliable system performance. This defense represents a crucial step forward in securing WiFi-based sensing systems, offering a resilient, low-impact solution that safeguards both sensing integrity and communication efficacy. © 2024 IEEE.

关键词： Wi-Fi

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Lane-changing decision-making for autonomous driving based on the Deep Deterministic Policy Gradient algorithm 9

Lane-changing decision-making for autonomous driving based o...

引用

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

作者： Yang, Du Xu, Ao Yang, Wenhua Shao, Yao Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

During vehicle operation, obstacles or other vehicles may require lane changes, necessitating the coordination of both lateral and longitudinal control. In predictable environments, rule-based lane-changing methods are relatively simple to implement. However, such methods fail to respond effectively to sudden, unforeseen obstacles. To address this issue, an automatic lane-change approach based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. In the reinforcement learning simulation process, the state space is defined by the distance between the vehicle and the lane boundaries, as well as the surrounding vehicles, while the action space consists of the vehicle's steering angle and longitudinal acceleration. The reward function incorporates several factors: collision avoidance, with different lane-changing distances considered for safety;steering rate and jerk for comfort;and overall driving stability. For a one-way, two-lane road, the highway-env framework is used to construct a visual simulation environment. By comparing lane-changing trajectories in simplified traffic flow scenarios, the study investigates the impact of driving style on the lane-change process and the time required for lane changes. Experimental results demonstrate that the proposed method enables the autonomous vehicle to learn lane-changing strategies that balance safety and comfort. © 2024 IEEE.

关键词： Steering

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Improved Particle Swarm optimization Task Scheduling Algorithm Based on Multiple Swarm Molecular Dynamics Theory 24

Improved Particle Swarm optimization Task Scheduling Algorit...

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5th International Conference on computer Information and Big Data Applications, CIBDA 2024

作者： Liu, Zilai Yu, Liang Li, Qiqian College of Computer Science Guangdong University of Science and Technology Guangdong AIoT Application Innovation Joint Laboratory AIoT Edge Computing Engineering Technology Research Center Dongguan523000 China

ISBN: (纸本)9798400718106

In light of the objective to optimize task scheduling in Cyber-physical systems (CPS) by establishing an efficient mapping between system resources and user tasks, a novel particle swarm optimization task scheduling algorithm is proposed. This algorithm incorporates the principles of multiple swarm molecular dynamics theory, thereby enhancing its performance. By integrating molecular dynamics theory and KMTOA into the particle swarm optimization algorithm, the proposed approach aims to achieve improved task time span and load balancing capabilities. To achieve this, the algorithm initially divides the particle population into distinct sub-populations. Subsequently, different gravitational rules are applied to guide the evolution of particles in each sub-population. This division and rule implementation greatly enhance the diversity of the population in the early stages and enhance the local search ability in the later stages. Simulation and comparative experiments have been conducted to evaluate the effectiveness of the improved task scheduling algorithm. The results demonstrate that the algorithm exhibits superior task time span and load balancing capabilities, effectively enhancing the efficiency of resource mapping. © 2024 ACM.

关键词： Cyber Physical System

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