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Novel Audio Mosaic Using LPC-Coefficient and Excitation-Codeword Permutations

IEEE Signal Processing Letters 2024年 31卷 1690-1694页

作者： Tsai, Morris C.-H. Wu, Hsiao-Chun Huang, Scott C.-H. Chang, Shih Yu National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan City32003 Taiwan San Jose State University Department of Applied Data Science San JoseCA95192 United States

In this letter, a novel light-weight audio mosaic scheme using permutations of linear predictive coding (LPC) coefficients as LPC has been widely adopted in many audio codecs (coder-encoders) nowadays. We establish the theoretical secrecy analysis with respect to the degree of the original LPC polynomial and the minimum-phase population of permuted LPC polynomials. The mosaic (destructuring) performance in terms of DCT-KLD (Kullback-Leibler divergence of discrete cosine transform) is also evaluated through simulations. In comparison with the existing audio-mosaic method using waveform permutations, our proposed new audio-mosaic scheme using LPC-coefficient permutations can achieve a much better mosaic performance (a much higher DCT-KLD) subject to the same key size. © 1994-2012 IEEE.

关键词： Cryptography

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INTERPRETABLE AUTOMATED LEARNING: EVOLVING FUZZY NETWORKS TO EXPLICIT applications OF AI

INTERPRETABLE AUTOMATED LEARNING: EVOLVING FUZZY NETWORKS TO...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Rathi, Vikas Vijayalakshmi, V.J. Mahesh, Bhandari Ninoria, Shalini Gupta, Ankur Kumar Premananthan, G. Department of Computer Science & Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Electrical And Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Vishwakarma Institute of Information Technology Pune India CCSIT TMU India School of Computer Science and Applications IIMT University Meerut India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore6410 India

ISBN: (纸本)9798350355338

One of the biggest developments in intelligent machines has been the development of evolving fuzzification. They are flexible system designs created using evolving methods. Fluid simulation now has excellent capabilities to use in a variety of data scientific situations as a result of this combination. The goal with this addition is to create a policy document that provides a thorough study of the evolving hazy ecosystems field of study. In order to comprehend the present setting of this subject and its importance, queries are asked and answered with some of this goal in mind. It will be made clear why evolving neural nets are significant from an explicable perspective, even before they started, how much they are utilized for, and where scholars should devote themselves in this field going forward. People must really be crucial to the developing field of data-driven schooling. © 2024 IEEE.

关键词： Adversarial machine learning

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Seg-UResNet: A Deep Hybrid Convolutional Neural Network of Road Scenes for semantic segmentation

Seg-UResNet: A Deep Hybrid Convolutional Neural Network of R...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Faska, Zahra Khrissi, Lahbib Haddouch, Khalid El Akkad, Nabil Sidi Mohamed Ben Abdellah University Laboratory of Engineering Systems and Applications Ensa Fez Morocco Sidi Mohamed Ben Abdellah University Lisac Department of Computer Science Fsdm Fez Morocco

ISBN: (纸本)9798350365306

Comprehending the circumstances is an essential element of every autonomous driving system. computer vision harbors the task of semantic segmentation as one of its core components wherein each pixel in an image is labeled with precise labels. The task has found significant applications in areas like autonomous driving plus analysis of images and videos, not to mention medical imaging and scene understanding. The progress made in semantic segmentation through the utilization of deep learning techniques has been truly extraordinary. Our study introduces a groundbreaking approach known as hybrid evolutionary weighted ensemble, which combines the strengths of two deep models. Two distinct segmentation models will be developed using U-Net and ResNet34 as the decoder and the encoder respectively for the first model using transfer learning technique, we are able to create individual segmentation models. Additionally, we consider SegNet another highly regarded convolutional neural network for image segmentation as our second deep model. With the goal of enhancing the accuracy of the deep learning model, we propose an evolutionary leap towards the convergence of the DL model accuracy will be achieved through the introduction of a hybrid weighted ensemble model. This shall be done by combining ResNet34, an individual U-Net-based DTL model, with SegNet model and then executing fine-tuning. To assess the effectiveness of the hybrid network, we conducted an evaluation using the widely recognized and demanding CamVid dataset. Through our experiments, we were able to showcase that the hybrid network outperforms other popular semantic segmentation algorithms by a significant margin, solidifying its position as a cutting-edge solution. © 2024 IEEE.

关键词： Semantic Segmentation

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PropFND: Propagation Based Fake News Detection

PropFND: Propagation Based Fake News Detection

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International Conference on Advances and applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Verma, Pawan Kumar Agrawal, Prateek Department of Computer Engineering & Applications GLA University Uttar Pradesh Mathura India Department of Computer Science & Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9789811948305

In the recent years, social media acts as double-edged sword for the society as it is being used for exchanging real as well as fake news. Large number of researchers are involved for the detection of fake news using user credibility, content and propagation-based features. This paper proposes PropFND (Propagation based Fake News Detection) model to classify news as real or fake based on the combination of propagation pattern and user profile features. For the training of proposed model, we used combined features and applied several classifiers and finally concluded that Support Vector Machine (SVM) gives an improved result. This proposed model gives the improved accuracy of 93.81% which is higher than state-of-the-art model. After experimental analysis we noticed that real news propagates for long duration as compared to the fake news. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning 1

Real Time Prediction Model for Air Pollution and Air Quality...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kumar, Rakesh Sharma, Bhisham Shekhar, Shashi Dhaou, Imed Ben Singhal, Saurabh GLA University Department of Computer Engineering & Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals' wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city. © 2023 IEEE.

关键词： Forecasting

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Detecting and Locating Image Forgeries with Deep Learning 2

Detecting and Locating Image Forgeries with Deep Learning

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Singh, Kamred Udham Rao, Ankur Kumar, Ankit Varshney, Neeraj Chundawat, Pushpendra Singh Singh, Teekam School of Computing Graphic Era Hill University Dehradun India Rajashan Udaipur India Guru Ghasidas Vishwavidyalaya Department of Information Technology Chhattisgarh Bilaspur India GLA University Department of Computer Engineering & Applications UP Mathura India SS engineering College Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383652

It is becoming more important to have reliable methods for identifying picture forgeries, particularly passive forging techniques like copy-move and splicing, since the proliferation of digital images across a variety of platforms has raised the urgency of the situation. The purpose of this research is to explicitly target passive forgery tactics by presenting a unique strategy that makes use of deep learning approaches for the purpose of accurately identifying modified areas inside photographs. The methodology that we have suggested makes use of Mobile Net and ResNet as feature extraction networks. It does this by extracting rich representations from picture data in order to identify complicated patterns that are suggestive of possible manipulations, such as copy-move and splicing manipulations. We make use of Mask R-CNN, which is a cutting-edge instance segmentation model, in order to precisely localize and quantify forged areas. This allows us to calculate the proportion of changed material that is included inside photos. © 2024 IEEE.

关键词： Deep learning

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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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Melanoma Boundaries Detection Techniques using Artificial Intelligence 3

Melanoma Boundaries Detection Techniques using Artificial In...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Velmurugan, K. Jayasakthi Srinivasan, P. Gayathri, A. Yuvarani, S. Jeppiaar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha School of Engineering Department of Cse Tamil Nadu Chennai India Department of Computer Applications Tamil Nadu Perambalur India

ISBN: (纸本)9781665462167

Melanoma is the 6th most successive disease in the United States, with more than 9000 individuals kicking the bucket every year. Fast acknowledgment of melanoma expands an individual's life expectancy, but further developed analytic advancements are as yet required. Skin injury limit anomaly, which addresses the "B"includes in the "ABCD rule", is viewed as a critical clinical component for the early discovery of melanoma. What's more, blue-white line structure evacuation additionally further develops the identification proficiency. In this paper, an AI based location method for distinguishing skin injury limit inconsistencies is presented. The technique involves extricating the skin injury from the dermoscopic images, recognizing the skin sore line, estimating line inconsistency, and prepared famous directed learning models called SVM, RF, DT, and gathering move learning calculations to distinguish line anomaly naturally, bringing about a true choice on whether the skin sore boundary is customary or unpredictable. The method produces excellent results, with 92.6 percent accuracy, 91.3 percent sensitivity, 92.5 percent specificity, and 95.1 percent F-Score, respectively. © 2023 IEEE.

关键词： Support vector machines

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An Automated Face Mask Detection System using Deep CNN on AWS Cloud Infrastructure 4

An Automated Face Mask Detection System using Deep CNN on AW...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Pandey, Ankur Chaturvedi, Ankur Gupta, Manish Mannepalli, Praveen Kumar Kumar, Santosh Chhabra, Gunjan Manipal University School of Computer Science and Engineering Jaipur India Galgotia University Greater Department of Computer Applications Noida India Gla University Department of Electronics and Communication Engineering Mathura India India Era University Department of Computer Science Lucknow India Graphic Era Hill University Deptt. of Cse Dehradun India

ISBN: (纸本)9798350300093

Face detection methods have usually been used mostly for faces that aren't covered up (non-masked faces). These include nose, lips, ears, chin, and eyes. Faces must be concealed by masks in various locations, including labs, hospitals, crime scenes, and areas with high levels of pollution. The global spread of coronavirus known as COVID-19 has disastrous effects on community wellbeing. This study aims to apply a model too complicated data, namely, image and real-time video image tasks for personal face identification with and without a mask. The healthcare industry is in a hazardous state. Numerous preventative measures, including the use of masks, are being set in place to restrict the spread of this sickness;WHO (World Health organization) highly encourages the use of a mask. This extremely large number of COVID-19 samples provides valuable information regarding the pandemic's progression, as well as a possible way to encircle it to prevent future transfers. To prevent the transmission of the virus, several preventative measures have been implemented, including the use of masks. using a face mask that prevents droplet transfer discharged into the atmosphere, on either hand, may help avoid the outbreak. As a solution, the purpose of this function will be to form an FMD (Face Mask Detection) model which can be used as part of an embedded imaging system. It is expected to enhance the detection rate and achieve an accuracy of more than 99%. © 2023 IEEE.

关键词： COVID-19

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Optimizing Regulatory Compliance in Supply Chains with Blockchain and Gaussian Process Regression 2

Optimizing Regulatory Compliance in Supply Chains with Block...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Tulasiram, D. Jebaseeli, S. Kanmani Adnan, Myasar M. Madhavi, Arelli Varshney, Neeraj Nandhini, T.J. CMR College of Engineering & Technology Department of CSE India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of AI&ML Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

Increased regulation of the supply chain is another important issue of concern to industries in distinct countries as they struggle to implement the regulation in a network setting. This paper aims to explore ways to improve regulatory compliance through the application of the blockchain and machine learning model, namely Gaussian process regression. Blockchain is an accurate and permanent record-keeping system that can be applied to track the flow of items and associated activities in the supply chain. Compliance can also be enforced, and through smart contracts, analyses of various parameters such as obligations can be monitored and adjusted automatically through GPR which involves the analysis of data from systems that use blockchain to model compliance risks and predict compliance issues. Thus, using the above-mentioned technologies, a company can prevent compliance failures, meet the necessary standards and set up a constant process of improvement in the supply chain area. This research specifically defines the theoretical foundations, application, and advantages of blockchain and GPR in enhancing the compliance of supply chains with the regulations in this work. In this approach, case-study and use of examples show how this approach works in handling compliance issues in several industries. © 2024 IEEE.

关键词： Smart contract

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