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Deep-Learning-Based Risk Prediction with Urban Sensing Data for Consumer Driving Safety 13

Deep-Learning-Based Risk Prediction with Urban Sensing Data ...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Lin, Kun-Yu Liu, Pei-Yi Hu, Chih-Lin Huang, Sheng-Zhi Chen, Yung-Hui Hui, Lin National Central University Department of Communication Engineering Taoyuan City320317 Taiwan Tamkang University Department of Computer Science and Information Engineering New Taipei City251301 Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taiwan

ISBN: (纸本)9798350355079

With the provision of IoT-driven and user-provided sensing data sources in smart cities, we take advantage of deep learning techniques to analyze the spatio-temporal traffic data and predict traffic risks for driving safety. Our study continues to collect the traffic data from multiple sensing sources, and meanwhile adopts both CNN and LSTM to interpret the data collection in spatial and temporal dimensions. Thus, a novel traffic risk prediction scheme based on CNN and LSTM, named TRP-CL, is proposed to generate a traffic warning map of risks and hazard situations on a grid-scaled city map. Not only a theoretic formation but also an experimental implementation of the TRP-CL scheme are made to show the practical feasibility. © 2024 IEEE.

关键词： crowdsensing intelligent transportation system (ITS) IoT machine learning (ML) risk prediction Traffic data

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PQTD-TM: Integrating information Retrieval with Machine Learning for effective Placement Topic Discovery 2024

PQTD-TM: Integrating Information Retrieval with Machine Lear...

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Sardana, Neetu Kumar, Prabudh Goyal, Kritank Rishi Jain, Nehal Arora, Anuja Varshney, Deepika Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

Technical interviews, particularly coding rounds, are pivotal in shaping career trajectories, especially for roles in top-tier companies like Amazon, Google, and Microsoft. Understanding the patterns and nuances of coding problems and their associated tags is essential for effective interview preparation. To aid candidates in excelling in these assessments, we analyzed coding problems encountered in such interviews. We compile a comprehensive dataset of coding problems and their associated tags, sourced from real technical interviews, ensuring reliability and relevance. We propose a topic prediction approach for placement related questions, PQTD-TM (Placement question topic discovery using TF IDF and Machine Learning). The proposed technique integrates information retrieval technique (TF-IDF) with six machine learning classifiers for prediction. We evaluated the predictive performance of various machine learning models, including Logistic Regression, Decision Trees, Naive Bayes, KNN, One-vs-Rest Classifier, and Random Forest. Random forest attained the best prediction accuracy of 93%. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Design and Practical Realisation of a Multichannel Bandpass Filter for the Current DVB-T2 Band 15

Design and Practical Realisation of a Multichannel Bandpass ...

引用

15th International Conference Elektro, ELEKTRO 2024

作者： Adamec, Bohumil Machaj, Juraj Zak, Xaver Brida, Peter University of Zilina Faculty of Electrical Engineering and Information Technology Department of Multimedia and Information-Communication Technology Zilina Slovakia

ISBN: (纸本)9798350372359

In the paper, a unique concept of a multichannel Radio Frequency (RF) bandpass filter operating in the current DVB-T2 band is proposed. Based on the analysis of the presented filter concept the prototype was designed and realized. Subsequently, the measurement of the scattering parameters and verification of the filter properties in practical conditions were carried out. The proposed and realised bandpass filter consists of a section for DC signal, two equivalent RF bandpass sections and an additional RF part for improved suppression of unwanted out-of-band signals. In addition to these three basic parts, the structure contains coupling elements that ensure the appropriate connection of individual sections with the elimination of their influence on each other. A prototype of the proposed bandpass filter operating at 533 MHz with a fractional bandwidth of 9.4%, which includes 6 consecutive DVB-T2 channels, is presented. © 2024 IEEE.

关键词： Scattering parameters

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Block chain and Deep Learning Based Secure communication using SAE-LSTM &salp Swarm Optimizer for Multivariate Industrial IoT-oriented Infrastructure 15

Block chain and Deep Learning Based Secure Communication usi...

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

作者： MathalaiRaj, J. Sivaranjani, S. Rajalakshmi, J. Jayachandran, T. Maheswari, M. Kavin Kumar, K. Chandrasekaran, Gokul Nadar Saraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India Sethu Institute of Technology Department of Computer Science and Engineering Virudhnagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhunagar India Nandha Engineering College Department of Electronics and Communication Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350370249

The rapid development of the Industrial Internet of Things (IIoT) imposes the needs for digitization of industrial procedures so as to enhance the efficiency of the network. Cyber-Physical System (CPS) has important role in applications of industries and infrastructure towards IoT. There are several challenges in IIoT environment such as centralization, scalability, privacy, security and communication latency. To overcome these issues, Deep learning based Sparse Autoencoder-Long Short-Term Memory (SAE-LSTM) with Salp swarm optimizer SSA based IoT-oriented infrastructure is used to detect the attacks. The major objective of this work is to propose a deep learning and blockchain based smart contract technique for a secure communication in smart city using Salp Swarm Optimizer algorithm for determining data privacy and security for the IIoT's application service. Blockchain & data aggregation with Proof-of-Work (PoW) generates a distributed network for communication phase of CPS. The protocols to forward the data in the network is established by Software-Defined Networking (SDN). Smart contract is employed to execute the transaction. The suggested technique is implemented and the performance is carried out by estimating Accuracy, Precision, Recall and F1 Score. © 2024 IEEE.

关键词： Blockchain and data aggregation with Proof-of-Work (PoW) Decision Sparse Autoencoder-Long Short-Term Memory (DSAE-LSTM) Industrial IoT Salp swarm optimizer SSA Smart contract

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A 72 mW, 50 MHz Bandwidth Low-IF CMOS Receiver Front End with Improved Linearity and Dynamic Range 10

A 72 mW, 50 MHz Bandwidth Low-IF CMOS Receiver Front End wit...

引用

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

作者： Khatoon, Apsana Misra, Prasanna Kumar Indian Institute of Information Technology Department of Electronics and Communication Engineering Prayagraj India

ISBN: (纸本)9798331533229

The design of receiver frontend using CMOS technology has been a challenge for long distance communication due to low value of received RF input power and noise associated with MOSFETs. In this paper, a CMOS receiver frontend architecture is proposed that can detect up to -95 dBm of RF input power levels while consuming a power of 72 mW. The two-stage low noise amplifier (LNA), mixer, filter and 4-stage variable gain amplifier (VGA) was integrated for detection of wide range of RF signals and to offer better dynamic range for receiver for further processing. The VGA was incorporated to amplify the weak signal of the filter output when low input RF power was received (-95 dBm). The noise figure of the receiver is found to be 2.45 dB with a 1 dB compression point value of -27 dB. The receiver design can be used for sub-6 GHz 5G wireless systems and Wireless LAN with a maximum bandwidth of 50 MHz. © 2024 IEEE.

关键词： CMOS integrated circuits

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Virtual Fitness Trainer using Artificial Intelligence 2024

Virtual Fitness Trainer using Artificial Intelligence

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Gurbuxani, Shrey Gupta, Lakshay Madan, Kapil Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

An AI wellness coach is a software program that guides users through physical health routines by leveraging MediaPipe, OpenCV, and Python features. The program uses OpenCV computer vision techniques to monitor user progress and provide feedback on procedure and frame. To prepare the video data and provide real-time assessments, MediaPipe is used. Additionally, the app makes use of machine learning algorithms to provide users with tailored wellness recommendations and promote adoption. When these advances are combined, clients have an incredibly intelligent and practical way to advance their physical fitness. © 2024 Copyright held by the owner/author(s).

关键词： Python

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Enhancing Handwritten Devanagari Character Recognition via GAN-Generated Synthetic Data 2024

Enhancing Handwritten Devanagari Character Recognition via G...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Sinha, Khushi Gupta, Richa Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

Devanagari, one of the oldest scripts in India, remains prevalent in both Indian and Nepalese texts. However, there is a significant need to digitize these documents. While several efforts have been made for the recognition of the Roman script, fewer studies focus on the Devanagari script due to its inherent complexity. Furthermore, existing publicly available databases lack the diversity and variations to effectively train models for handling unseen data. Thus, a research gap exists in this area. To address the gap, this work proposes a model that utilizes Generative Adversarial Networks (GANs) to generate synthetic images. These images are then used to train Convolutional Neural Networks (CNNs). The model's performance is evaluated using metrics such as accuracy, precision, recall and f1-score. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Student Placement Prediction Using Machine Learning Algorithms 2nd

Student Placement Prediction Using Machine Learning Algorith...

引用

2nd International Conference on Emerging Trends in engineering and technology, EGTET 2022

作者： Sajwan, Samarth Bhardwaj, Rudraksh Mishra, Revaan Jaiswal, Shruti Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9789819943616

One of the most perplexing issues confronting higher education institutions today is how to increase student placement performance. Placement estimation becomes more complex as the number of educational institutions increases. Educational organizations explore more efficient technologies to assist them in improving their decision-making practices, as well as in developing creative methods. Providing new insight into instructional processes is a critical component in resolving quality issues. Machine learning methods are used to extract information from historical data contained in the libraries of educational organizations. Our model will generate a recommendation system that forecasts the placement level of a student. This model assists an organization’s selection cell in identifying prospective students, assessing their technical and interpersonal abilities, and assisting them in developing them. Students in their pre-final and final years of B. Tech programs may also use this work to determine their individual placement status and probability of achieving it. This enables them to exert additional effort in order to get placements in organizations with higher hierarchies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Vehicle Accident Detection and Reporting System using Internet of Things 5

Vehicle Accident Detection and Reporting System using Intern...

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5th International Conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Goyal, Atul Gupta, Priyansh Pandey, Kavita Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350379945

The rising number of vehicles on the road has led to a concerning increase in accidents, as reported by the Indian Government's Ministry of Road Transport and Highways. In many cases, prompt medical assistance can save lives. While numerous solutions exist to address this issue, most are limited to two-wheelers. In contrast, this research study presents a vehicle-independent solution that employs sensors to detect accidents in any vehicle type, making it highly effective and cost-efficient. The proposed model automatically identifies and report accidents in real-time, enhancing the road safety. It utilizes advanced sensors and communication technology to promptly notify emergency services and relevant authorities for timely response and assistance. © 2024 IEEE.

关键词： Vehicle detection

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Synthesis of Wilms tumor images using Generative Adversarial Networks 11

Synthesis of Wilms tumor images using Generative Adversarial...

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11th IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Jha, Manika Gupta, Richa Saxena, Rajiv Jaypee Institute of Information Technology Department of Electronics and Communication Engineering Noida India

ISBN: (纸本)9798350308433

Wilms tumor or Nephroblastoma is paediatric cancer, that can be treated if diagnosed at an early stage. However, no appropriate image dataset is publicly available except the Wilms MRI dataset of Mathematical Image Analysis Group, Saarland University, Germany, which only contain 28 multisequence MR scans. As, huge amount of data is required to train a machine or deep learning model, image synthesis could be beneficial. The absence of professionals who can label data was another problem. In order to meet the data requirements two different Generative Adversarial Networks (GANs)-Wasserstein GAN (WGAN) and Pix2Pix GAN have been utilized. These GAN architectures generate high-quality and realistic MRI images in terms critical evaluation metrics. These architectures may potentially benefit the detection of rare diseases which lack sufficient size of medical datasets. With the help of image synthesis and precise amount of data, effective computer-Aided diagnostic models could be developed. © 2024 IEEE.

关键词： Generative adversarial networks

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