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Guardian X: Smart Copilot for Real-time Automobile accidents detection

Guardian X: Smart Copilot for Real-time Automobile accidents...

引用

Image processing and Capsule networks (ICIPCN), international conference on

作者： B. Perumal B.Naveen Kumar Reddy T. Dani Akash Y. Naresh Kumar N. Charan Kumar Reddy Y.J. Lakshmi Murthy Department of E.C.E Kalasalingam Academy of Research & Education Krishnan koil Virudhnagar India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Real-time accident detection systems are essential for reducing the impact of traffic accidents in the field of vehicle safety. this study presents “Guardian X” a smart copilot system to instantly detect accidents and trigger timely reaction mechanisms. the proposed system makes use of a variety of sensors and modules, such as the GPS module for exact location tracking, the GSM module for communication, the ADXL345 accelerometer for collision detection, and a buzzer for instant alerts. When Guardian X detects an accident, it sets off a sequence of events that alert emergency services and provide them precise location information and possibly even save lives. By combining various technologies into an integrated system, an innovative method of accident detection is presented. the proposed method has the potential to save lives and minimize the degree of injuries by cutting down the amount of time that passes between an accident and emergency response. the effectiveness and efficiency of Guardian X as a real-time accident detection system are highlighted in this study by describing the system’s design, implementation, and testing.

关键词： Smart cities Vehicle safety Emergency services Real-time systems sensor systems Automobiles Predictive analytics

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Synthetic data generation for watermark extraction from 3D printed objects 2022

Synthetic data generation for watermark extraction from 3D p...

引用

5th international conference on Image and Graphics processing, ICIGP 2022

作者： Zhang, Xin Jia, Ning Ivrissimtzis, Ioannis Surrey University Surrey Guildford United Kingdom Durham University Durham United Kingdom

ISBN: (纸本)9781450395465

the use of computer generated images to train Deep Neural networks is a viable alternative to real images when the latter are scarce or expensive. In this paper, we study the effectiveness of various rendering techniques in the generation of training data for the task of watermark retrieval from 3D printed objects. We created eight training sets, each one with a different lighting environment, and tested them on three different network architectures, ResNet, U-Net and a combined architecture developed by us. the test set consisted of photos of 3D printed objects produced from the same CAD models used to generate the training set. Our results show that the effect of the lighting environment is comparable in significance to that of the network architecture. We also show that both light probes, and modelled lighting environments, can give good results. In the case of light probes, we identified as two significant factors affecting performance the similarity between the light probe and the test environment, as well as the light probe's resolution. © 2022 ACM.

关键词： Deep neural networks

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Hindi Document Extractive Summarization: Neural Method on A New Data Set 5

Hindi Document Extractive Summarization: Neural Method on A ...

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5th international conference on Computational Intelligence and networks, CINE 2022

作者： Tawatia, Kunal Jain, Nishant Kundu, Suman Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Jodhpur342037 India

ISBN: (纸本)9781665464659

Extractive summarization is one of the vital tasks in text analysis and natural language processing. Although Hindi is one of the world's highly speaking languages and produces thousands of online documents daily, most existing text summarization works focus on the English language. A Neural network-based summarizer is popular for abstractive summarization but has not been explored for extractive one except in a few recent studies. the present work uses a neural extractive summarizing model to develop a Hindi language extractive summarizer. the main contribution of the paper is two-fold. First, we generated a new Hindi-based text summarization data set from a popular Hindi news channel AajTak. the code to generate the data set is available at https://***/sonaa-hindi-text. then we use this data set to train a Neural Extractive Summarization model. the model also learns the word embeddings while learning itself. the ROUGE-2-Fl and ROUGE-1-Fl results on test data show promising output with a score of 20.02 and 39.81, respectively. © 2022 IEEE.

关键词： Text processing

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Delay Estimation of Initial Access procedure for 5G mm-Wave Cellular networks 17

Delay Estimation of Initial Access procedure for 5G mm-Wave ...

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17th Annual IEEE international conference on Distributed Computing in sensor Systems (IEEE DCOSS)

作者： Desai, Soham Malandra, Filippo SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA

ISBN: (纸本)9781665439299

5G aims to provide ultra-low latency communication and very high Giga-bit data transmissions. the so called mmWave frequencies are used to achieve such high data rates, thanks to the larger bandwidth availability compared to the frequency spectrum currently used for cellular networks up to LTE. the mmWave spectrum, used for the first time in cellular networks, introduces a number of challenges and imposes a new paradigm in terms of architectures and connection protocols. In particular, additional latency is introduced in the initial attach procedure, which requires extra steps with respect to previous technologies. It is therefore important to thoroughly analyze all components of the initial access (IA) delay in 5G mmWave networks. Among the 5G IA delay components, this work mainly focuses on carrier frequency scanning, cell search, beamforming procedures, derivation of system information, and random access (RACH) procedure. We provide details and a methodology to estimate the delay in these steps of the initial access procedure for 5G mmWave networks.

关键词： Cellular networks Protocols 5G mobile communication Array signal processing Microprocessors Computer architecture Delay estimation

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Fine Tuning LLMs for Low Resource Languages

Fine Tuning LLMs for Low Resource Languages

引用

Image processing and Capsule networks (ICIPCN), international conference on

作者： Shreyas Joshi Muhammad Shahnawaz Khan Aditya Dafe Kavita Singh Vedant Zope Tanish Jhamtani Department of Computer Technology YCCE Nagpur Maharashtra India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Large Language Models (LLMs) hold immense potential, but their data hunger can limit its performance in processing languages with limited resources. this research study explores the techniques for fine-tuning LLMs specifically for low-resource settings. this study highlights the need for language-specific approaches that satisfy the unique characteristics of each language. Further, this study analyzes various fine-tuning techniques, including parameter-efficient methods (like Low Rank Adaption (LoRA) and Quantized Low Rank Adaption (QLoRA)), instruction tuning, and Representation Fine-Tuning (ReFT). By promoting these strategies, this work aims to bridge the language gap and empower low-resource languages within the LLM landscape.

关键词： Bridges Adaptation models Large language models Image processing Benchmark testing Human in the loop Cultural differences

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Deep Learning Based Approach to Generate Realistic Data for ADAS Applications 5

Deep Learning Based Approach to Generate Realistic Data for ...

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5th international conference on Computer, Communication, and Signal processing, ICCCSP 2021

作者： Soni, Rajat Kumar Nair, Binoy B Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering India

ISBN: (纸本)9781665432771

Quantity, quality and diversity of datasets are prerequisites for training the deep-learning autonomous driving models. One major issue identified from the literature is the lack of realistic training data which eventually leads to a less robust model. Simulators can help in dealing with reducing reality gaps, however, commonly available simulators generate data that are far removed from the real world scenarios. the model proposed in this study is based on video-To-video synthesis and image synthesis methods using Generative Adversarial networks (GANs). the results indicate improved realism. Kanade-Lucas-Tomasi (KLT) and Fr'echet Inception Distance (FID) based temporal coherence evaluation metrics have also been proposed as a possible alternative to human perception driven evaluations. © 2021 IEEE.

关键词： Generative adversarial networks

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AI in the Detection of Breast Cancer and Related Ethical and Legal Issues 5th

AI in the Detection of Breast Cancer and Related Ethical and...

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5th international Ethical Hacking conference, eHaCON 2024

作者： De Mukhopadhyay, Keya Guha, Tapas Basu, Abhisikta Department of Biotechnology Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science Engineering Techno International New Town India University of Engineering and Management Kolkata India

ISBN: (纸本)9789819784561

Breast Cancer accounts for a significant percentage of malignancy among women globally. It is a disorder where abnormal breast cells develop uncontrollably and form tumours when left untreated, the tumour could spread throughout the body and become fatal. So, early detection is critical for survival. the clinical outcomes of breast cancer can be significantly improved with an accurate and timely diagnosis. Significant advancements in breast cancer diagnosis and customized treatments have been made possible, by the emergence of Artificial Intelligence particularly in the field of image analysis, the K-Nearest Neighbors algorithm is one of the simplest and easily interpretable supervised ML algorithms, which can determine whether the cancer lumps are malignant or benign. KNN, SVM, and GRNN are machine learning algorithms that make use of an openly accessible dataset, features taken from images, data pre-processing techniques, and Principal Component Analysis (PCA) in order to improve accuracy and decrease diagnostic time. Artificial Intelligence can identify both missed cancers and breast tissue features, which in turn, can help to predict future cancer development. Some Artificial Intelligence algorithms excel at predicting patients with a high risk of interval cancer, which is aggressive. When no cancer was clinically detected by screening mammography, even Artificial Intelligence algorithms trained for short time horizons can forecast the chance of developing cancer for up to five years in the future. the WHO reports that low- and middle-income nations account for over 80% of fatalities from breast and cervical cancer. In the developing economic scenario of India, one in 28 women is likely to develop breast cancer. the Artificial Intelligence detection model is a cost-effective precautionary measure for the early detection of breast cancer in women. the data protection laws should be convenient enough to carry out the test with the informed consent of the patients

关键词： Diseases

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Natural Language processing and Bayesian networks for the Analysis of Process Safety Events 5

Natural Language Processing and Bayesian Networks for the An...

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5th international conference on System Reliability and Safety (ICSRS)

作者： Valcamonico, Dario Baraldi, Piero Zio, Enrico Politecn Milan Energy Dept Milan Italy Politecn Milan Energy Fepartment Milan Italy PSL Univ Paris MINES ParisTech Ctr Rech Sur Risques & Crises CRC Sophia Antinopolis France

ISBN: (纸本)9781665400497

Adequate Process Safety Management System (PSMS) is fundamental to ensure the safety of the operation of process industry assets and prevent the occurrence of Process Safety Events (PSEs), such as unplanned or uncontrolled releases of product. In this work, we consider as source of information a repository of reports of PSEs and we aim at the identification of the factors that influence PSE occurrence and severity. A methodology based on the combination of Term Frequency Inverse Document Frequency (TFIDF) and Normalized Pointwise Mutual Information (NPMI) is developed for the automatic extraction of keywords from PSE reports, and a Bayesian Network (BN) is developed for modeling PSEs consequences.

关键词： process safety management system natural language processing Bayesian networks

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A Study of Query Expansion Strategies Effectiveness based on Natural Language processing

A Study of Query Expansion Strategies Effectiveness based on...

引用

Image processing and Capsule networks (ICIPCN), international conference on

作者： Hemendra Shanker Sharma Ashish Sharma Department of Computer Engineering & Applications GLA University NH#2 Delhi Mathura Highway Mathura UP India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

the Information Retrieval (IR) process starts with the query entered by the user in the system. the IR system is mostly used in the search string in text utilities and web search engines. For IR algorithms to efficiently retrieve pertinent documents, the documents are usually converted into an appropriate representation. Query expansion and Natural Language processing (NLP) are the most researched fields in Artificial Intelligence (AI). the query expansion process provides the additional information that is understood by the user in their query. this research study explores several NLP query expansion techniques that utilize word embedding methods for autonomous query expansion including deep learning, machine learning, and ontology approach. this research also discusses the importance of query expansion to improve search efficacy and provides a coherent view of NLP-based query expansion methods that leverage a range of data sources and methodologies.

关键词： Deep learning Soft sensors Image processing Ontologies Information retrieval Natural language processing Grammar

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Analyzing energy efficiency of flying objects in Wireless sensor and Mobile Ad Hoc networks

Analyzing energy efficiency of flying objects in Wireless Se...

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Recent Trends in Computer Science and Technology (ICRTCST), international conference on

作者： Shams Tabrez Siddiqui Adeel Ahmad Jorair Ahmad Mohammad Haseebuddin Alighazi Siddiqui Abu Salim Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Electrical and Electronics Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

the convergence of flying objects, Mobile Ad Hoc networks (MANETs), and Wireless sensor networks (WSNs) has been made feasible by the widespread proliferation of wireless communication technology. this study delves into the historical development and significant achievements of WSNs and MANETs, with a focus on the incorporation of drones and UAVs to enhance network capabilities. Our main goals involve conducting thorough assessments of communication protocols, routing algorithms, and network architectures in the presence of flying objects. Our particular emphasis is on maximizing energy efficiency. Examining the dynamics of flying objects in WSNs and MANETs, this analysis delves into the various challenges and opportunities that arise. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. the study explores existing protocols such as LEACH, AODV and OLSR, highlighting their benefits and challenges in flying objects. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability.

关键词： Wireless communication Wireless sensor networks Scalability Machine learning Standardization Energy efficiency Routing protocols

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