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Clustering Method of Driving Scenes based on Road Shape by using Road Map Images 4

Clustering Method of Driving Scenes based on Road Shape by u...

引用

4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Hashimoto, Kohjiro Yanagihara, Daichi Kuniyuki, Hiroshi Doki, Kae Funabora, Yuki Doki, Shinji Dept. Applied Information Engineering Suwa University of Science Nagano Chino city Japan Dept. Mechanical and Electrical Engineering Suwa University of Science Nagano Chino city Japan Dept. Electrical Engineering Aichi Institute of Technology Aichi Toyota city Japan Dept. Electrical Engineering and Computer Science Nagoya University Chikusa-ku Aichi Nagoya Japan

ISBN: (纸本)9798350369694

Currently, vehicles are equipped with various sensors that can collect a series of driving behavior data while driving. The driving behavior data obtained in this way can be used to analyze the causes of traffic accidents and evaluate driver skills. However, when performing the above analysis, a specific driving scene is defined and driving behavior data for that driving scene is required. On the other hand, the series of driving behavior data obtained during one driving operation is enormous, and it is not easy to pick up desired driving scene data from among them. This research aims to establish a driving scene clustering method for a series of driving behavior data. In this paper, a driving scene is defined as a scene in which road shape and operation are associated, and propose a driving scene clustering method considered road shape. Here, a road map image is acquired from position information obtained from a GPS sensor, and the relationship between the road map image and operation data is clustered based on Coupled-GP-HSMM. © 2023 IEEE.

关键词： Deep learning

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LIME Explainability on Flower Classification 6

LIME Explainability on Flower Classification

引用

6th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2022

作者： Nikith, Boda Venkata Nikhil, Masabattula Teja Sri Siddhartha, Mutyala Sai Murali, K. Amrita Vishwa Vidyapeetham Dept. Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Dept. Mathematics Bengaluru India

ISBN: (纸本)9781665456982

Artificial Intelligence is becoming more advanced with increasing complexity in generating the predictions and as a result it is becoming more challenging for the users to understand and retrace how the algorithm is predicting the outcomes. Artificial intelligence has also been contributing in making decisions. There are many flowers in the world so the botanist scientists need help in identifying or recognizing which type of flower. The paper presents an x-ray diagnostic model and the explained with Local interpretable model-agnostic explanations LIME method. The model is trained with various COVID as well as non-COVID images. Whereas chest X-rays are segmented to extract the lungs and the model predictions are tested with perturbated images that are generated using LIME. This paper opens a wide area of research in the field of XAI. © 2022 IEEE.

关键词： Lime

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying 4

Talk to your data: Enhancing Business Intelligence and Inven...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Zhu, Jerry Bazaz, Saad Ahmed Dutta, Srimonti Anuraag, Bhavaraju Haider, Imran Bandopadhyay, Srijita University of Waterloo Faculty of Mathematics ON Canada National University of Computer and Emerging Sciences Dept. of Computer Science Islamabad Pakistan Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Vellore Institute of Technology Dept. of Electronics and Communication Engineering India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India University of Engineering and Management Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350369786

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT's precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape. © 2023 IEEE.

关键词： Structured Query Language

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Combating Cyber Attacks by Malware Classification Using Deep Neural Networks

Combating Cyber Attacks by Malware Classification Using Deep...

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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Deepthi Tabitha Bennet Preethi Samantha Bennet Anitha Gracy. J Sharmila. P Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

The detection and classification of malware pose significant challenges in cybersecurity due to the ever-evolving nature of malicious threats. In this study, we propose a novel approach for classifying malware using deep neural networks (DNNs). Our dataset comprises of 25 distinct classes of malware samples. After developing and testing 4 DNNs, including ResNet50, InceptionResNetV2, VGG16, DenseNet169, a robust and efficient model capable of accurately identifying and categorizing malware instances is built using DenseNet169. The process involves feature extraction from byteplot *** proposed malware detection model uses DenseNet169 as it has shown very high accuracy (95%), precision, recall and F1-score for the *** research contributes to advancing the state-of-the-art in malware classification, providing insights into the efficacy of deep neural networks for combating sophisticated cyber threats in modern computing environments.

关键词： Training Accuracy Computational modeling Transfer learning Training data Feature extraction Malware Data models Testing Residual neural networks

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Development and Realization of Software Application for Syntax Checking of Karakalpak Language Text 3

Development and Realization of Software Application for Synt...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Saidov, Bobur R. Barakhnin, Vladimir B. Sharipov, Elbek J. Maksetbaev, Akilbek B. Ruzimov, Javokhir O. Abdullayev, Ruslan M. Novosibirsk State University Dept. of Mechanics and mathematics Novosibirsk Russia Novosibirsk State University It Department Novosibirsk Russia Federal Research Center for Information and Computational Technologies Novosibirsk Russia University of Information Technologies in Tashkent Dept. of Computer Engineering Tashkent Uzbekistan

ISBN: (纸本)9798331516321

This scientific work focuses on developing a software application to check the syntax of texts in the Karakalpak language. With the growing accessibility of the Internet in Karakalpakstan, this tool serves native speakers and learners alike, benefiting various fields. The study aims to design and implement a syntactic analysis application, centering on the parsing of Karakalpak texts. A comprehensive database of 31,243 words from the modern Karakalpak literary language was created to support this analysis. The application can evaluate both simple and complex sentences across genres, including scientific, newspaper, and fiction texts. Utilizing Python, a script for syntactic analysis was developed, and a website was created to facilitate user access. A simple and easy-to-use interface has been developed for users. Additionally, a comparative analysis of Turkic languages was conducted, with results presented in a table format. The algorithm developed is versatile and can also be adapted for use with Uzbek, Kazakh, and Tatar languages. © 2024 IEEE.

关键词： Application programs

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Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images

Deep Learning-and Transfer Learning-based Models for COVID-1...

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2023 International Conference on Advances in Electronics, Control and Communication Systems, ICAECCS 2023

作者： Mazari, Ahmed Cherif Kheddar, Hamza University of Medea Lsea Lab Dept. of Mathematics and Computer Science Faculty of Science Medea2600 Algeria University of Medea Lsea Lab Dept. of Electrical Engineering Faculty of Technology Medea2600 Algeria

ISBN: (纸本)9781665463102

Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type). © 2023 IEEE.

关键词： Image classification

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging 18

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khalid, Hassan Bin Siddique, Abu Bakar Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain. © 2023 IEEE.

关键词： Hidden Markov models

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Exploratory Evaluation of Speech Content Masking 15

Exploratory Evaluation of Speech Content Masking

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15th ITG Conference on Speech Communication

作者： Williams, Jennifer Pizzi, Karla Noé, Paul-Gauthier Das, Sneha Electronics and Computer Science University of Southampton United Kingdom Fraunhofer AISEC Garching Germany Technical University Munich Germany University of Avignon France Dept. of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (纸本)9783800761654

Most recent speech privacy efforts have focused on anonymizing acoustic speaker attributes but there has not been as much research into protecting information from speech content. We introduce a toy problem that explores an emerging type of privacy called "content masking" which conceals selected words and phrases in speech. In our efforts to define this problem space, we evaluate an introductory baseline masking technique based on modifying sequences of discrete phone representations (phone codes) produced from a pre-trained vector-quantized variational autoencoder (VQ-VAE) and re-synthesized using WaveRNN. We investigate three different masking locations and three types of masking strategies: noise substitution, word deletion, and phone sequence reversal. Our work attempts to characterize how masking affects two downstream tasks: automatic speech recognition (ASR) and automatic speaker verification (ASV). We observe how the different masks types and locations impact these downstream tasks and discuss how these issues may influence privacy goals. © VDE VERLAG GMBH Berlin Offenbach.

关键词： Toys

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Work in Progress: Enhancing Students' Learning Experience in Formal Methods through Practical Assignments

Work in Progress: Enhancing Students' Learning Experience in...

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IEEE World engineering Education Conference (EDUNINE)

作者： Ruth Schorr Dept. of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

Formal methods are crucial for ensuring higher integrity levels for safety-critical systems. However, teaching these methods can be quite challenging. Students often show low motivation and are primarily focused on passing formal methods courses with minimal effort. Performance in compulsory formal methods courses is usually below average, with students perceiving the subject as overly mathematical and lacking practical relevance. To address these challenges and enrich the learning experience, we have integrated mandatory group homework assignments into our teaching framework. Students are required to work collaboratively on case studies and present their solutions during class. This work-in-progress paper provides an experience report on enhancing the learning possibilities of master’s students in a model checking course at the Frankfurt University of applied Sciences (FRA-UAS).

关键词： Model checking engineering education

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Performance Comparison of Single and Dual Loop Control Scheme for Grid Connected Inverter with LCL Filter

Performance Comparison of Single and Dual Loop Control Schem...

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2022 International Conference on Recent Advances in Electrical engineering and computer Sciences, RAEE and CS 2022

作者： Mateen, Abdul Sarwar, Muhammad Hussain, Babar Abid, Muhammad Pakistan Institute of Engineering and Applied Sciences Dept. of Electrical Engineering Islamabad Pakistan Iowa State University Dept. of Electrical and Computer Engineering AmesIA United States

ISBN: (数字)9781665491037

ISBN: (纸本)9781665491020

Normally, utility-connected inverters are equipped with LCL filters to reduce the output current harmonics. But the system that is equipped with an LCL filter has a stability problem and requires a more complex control scheme. This paper presents a detailed performance comparison of single loop and dual loop control structure for grid-connected inverters with an LCL filter. The dual loop control structure effect on the resonance of the LCL is also discussed. The outer loop regulates the current and ensures the tracking of reference, while the inner loop improves stability and dampens the resonance. In this paper, grid current is taken for the outer loop feedback variable, and different parameters Ic and IL are used as inner loop feedback. Stability analysis and harmonic impedance offered to the grid voltage by inner feedback variables Ic and IL are discussed in detail. The effect of resistive damping on stability is also discussed in this paper. Generally, it is very difficult to measure the grid impedance exactly, so the performance of the control schemes is also evaluated over a range of grid side inductances. Simulation results using Simulink show the effectiveness of the proposed strategy. © 2022 IEEE.

关键词： Stability

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