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Performance Analysis of Denoising Filters for Ultrasound Fetal Head Images

SN computer Science 2025年第2期6卷 1-13页

作者： Prasad, Kauleshwar Patnaik, Pawan Kumar Agrawal, Anupam Department of Computer Science and Engineering Bhilai Institute of Technology Chhattisgarh Durg India Department of Electrical and Electronics Engineering Bhilai Institute of Technology Chhattisgarh Durg India

Deep learning has significantly advanced image processing in the medical domain, including the analysis of ultrasound (US) fetal images for prediction of fetal growth retardation. Before analysis of fetal images there is need of correction since they are usually adulterated by noise during acquisition of image. In the recent years, there are various deep learning based denoising methods in which U-Net have attracted the attention of many researchers. This paper describes the comparative study of different filters such as mean filter, gaussian filter, anisotropic diffusion, non-local means (NLM) filter and U-Net based on their performance on denoising fetal images prior to the prediction of fetal growth retardation (FGR). In this article peak signal to noise ratio (PSNR), mean square error (MSE), similarity index measure (SSIM), peak signal to interference ratio (PSIM) and multi scale structural similarity index measure (MSIM) values are analyzed for the performance of these filters. This paper reveals that U-Net performs better in image quality evaluation when compared to other filtering algorithms, based on testing and analytical findings. We have also discussed the significance of MSE, PSNR, SSIM, PSIM, MSIM and U-Net in this article. This article also covers the advantages, disadvantages, and characteristics of different filtering methods, including U-Net. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Denoising techniques Fetal growth retardation U-Net

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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Development of Blockchain-Enabled E-Healthcare Appointment System 5th

Development of Blockchain-Enabled E-Healthcare Appointment S...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Rawat, Abhishek Suryavanshi, Raghuraj Singh Nagar, Vishal Yadav, Divakar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Institute of Engineering and Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819719228

The adoption of the technology of smart contracts to construct an e-appointment system has an array of advantages over conventional appointment systems. E-appointment system commitments can be instantly conducted and reported using blockchain, removing the requirement for mediators and the possibility of fraud. In this paper, a framework is implemented in Remix IDE by using the smart contract that manages agreements between the user and the healthcare service provider. The E-appointment system will be able to privately and openly manage end-users using Ethereum, with terms and conditions enforced automatically via the smart contract. This work has the potential to significantly disrupt the centralized appointment system because it provides a safer and more effective alternative to conventional appointment scheduling methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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Science China(Technological Sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213159China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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Sentiment and Statistical Analysis on Custom Twitter Dataset for 2022 Russo-Ukrainian Conflict

Sentiment and Statistical Analysis on Custom Twitter Dataset...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Thakkar, Harsh Patil, Aryan Saudagar, Onkar Yenkikar, Anuradha Pune Institute of Computer Technology Department of Information Technology Pune India Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665462631

For a better understanding of the situation during a war or crisis, it is helpful to comprehend the public opinion. There are various social media networks which provide a platform for people across the world to express their opinions on globally significant issues. Recently, Russo-Ukrainian war began and people have shared their opinions on the same on various social media platforms including Twitter. In this paper, we have extracted tweets about the war using some of the popular hashtags, spanning over 7 months. The dataset will be open sourced so that people can contribute to it and use it for their own research. The study consisting of tweets extraction, preprocessing, assigning sentiments using polarity and subjectivity, fine tuning BERT based model on English language tweets, using M-BERT on 13 different languages supported by Text Blob and performing sentiment analysis. The study accumulated 1.5 million tweets out of which 7.5 lakh tweets, in English language, were used to train Naive Bayes Classifier and Random Forest classifier to get a F1 score of 0.79 and 0.911 respectively and fine-tuned BERT to get a F1 Score of 0.99. M-BERT was fine-tuned on a corpus of 2.5 lakh tweets having other 13 different languages to get a F1 Score of 0.98. The scope of this dataset can further be extended to various NLP downstream applications including hate speech detection, fake tweet detection and many more. © 2023 IEEE.

关键词： Sentiment analysis

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Smartphone based human activity recognition irrespective of usage behavior using deep learning technique

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International Journal of Information technology (Singapore) 2025年第1期17卷 69-85页

作者： Kundu, Soumya Mallik, Manjarini Saha, Jayita Chowdhury, Chandreyee Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Human activity recognition (HAR) from sensory data is a crucial task for a wide variety of applications. The in-built inertial sensor facilities of commercial smartphones have made the data collection process easier. However, different smartphone configurations exhibit variations in sensor readings for the same activities. Different smartphone holding positions, like in hand, shirt, or trouser pockets, also lead to variations in signal patterns for the same activity. Some recent works have shown that automated feature extraction using deep learning methods can significantly improve activity recognition, although there is a lack of experimentation considering device heterogeneity and different smartphone holding positions. The proposed work addresses this research gap with a two-fold contribution. First, a CNN-based HAR framework is proposed that forms 2-D frequency domain images to capture temporal patterns in the data along with inter-axis spatial features. Second, an ensemble of conditional classifiers has been designed based on CNN that exhibits generality in terms of device configurations and usage behavior. Real life data have been collected for different activities using different devices for experimentation. The proposed ensemble model is found to recognize activities with 94% accuracy even when the training and test devices are different for real datasets. © The Author(s) 2024.

关键词： Activity image CNN Deep learning Human activity recognition Smartphone

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On prevention of attribute disclosure and identity disclosure against insider attack in collaborative social network data publishing

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International Journal of Business Intelligence and Data Mining 2023年第1期23卷 14-49页

作者： Kadhiwala, Bintu Patel, Sankita J. Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India

In a collaborative social network data publishing setup, privacy preservation of individuals is a vital issue. Existing privacy-preserving techniques assume the existence of attackers from external data recipients and hence, are vulnerable to insider attack performed by colluding data providers. Additionally, these techniques protect data against identity disclosure but not against attribute disclosure. To overcome these limitations, in this paper, we address the problem of privacy-preserving data publishing for collaborative social network. Our motive is to prevent both attribute and identity disclosure of collaborative social network data against insider attack. For the purpose, we propose an approach that utilises p-sensitive k-anonymity and m-privacy techniques. Experimental outcomes affirm that our approach preserves privacy with a reasonable increase in information loss and maintains an adequate utility of collaborative social network data. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Privacy-preserving techniques

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Leveraging Large Language Models and RAPIDS AI to Detect Fraudulent AI-Generated Marketplace Reviews

Leveraging Large Language Models and RAPIDS AI to Detect Fra...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Dudeja, Nishit Pundir, Arun Singh Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350350821

Consumer confidence is, in the present time, a dilemma given the steadily rising number of deceptive and inaccurate AI-generated reviews on internet marketplaces. There is an urgent need for a thorough dataset, which is nowhere to be found, to address the pervasiveness of fraudulent reviews. In order to close this gap, in this work, we propose relevant detection techniques using GPU-accelerated models from the RAPIDS AI library and new age Large Language Models like GPT-2 and BERT. By carefully constructing a unique dataset of about 54,000 reviews, we have combined human-authored reviews gleaned from web scraping with AI-generated reviews produced with the Chat-GPT 3.5 model, which supports the representative spectrum across all review polarities and offers a solid basis for precise analysis. We show significant accuracy in differentiating between reviews written by humans and those produced by AI and provide an all-inclusive solution to this new issue by using sophisticated LLMs and RAPIDS AI models. Moreover, we also compare the approaches to find the most successful one. This approach not only tackles the problem of fraudulent reviews but also establishes a new benchmark for keeping online marketplaces morally sound. © 2024 IEEE.

关键词： Marketplaces

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Enhancing skin lesion diagnosis with data augmentation techniques: a review of the state-of-the-art

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Multimedia Tools and Applications 2024年 1-40页

作者： Patil, Aniket Mehto, Anjula Nalband, Saif Maharashtra Pune411048 India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Bhadson Road Punjab Patiala147004 India

Even if more and more high-quality public datasets are available, one of the biggest problems with deep learning for skin lesion diagnosis is the scarcity of training samples. Deep Convolutional Neural Networks (CNNs) have exhibited remarkable performance across multiple computer Vision tasks. Despite their tendency to surpass conventional machine learning methods, the models produced by these algorithms require larger datasets to be accessible during training. An increasingly popular method to deal with this problem is data augmentation, especially in domains like medical imaging where big datasets are hard to come by. Experiments on independent, unknown datasets have demonstrated improved performance using data augmentation, which creates more data to train the model. This survey devotes a lot of time to using augmentation techniques based on basic data augmentation algorithms and the pros and cons of Test Time Augmentation (TTA), Generative Adversarial Networks (GANs), Variational Auto-Encoders (VAEs), and Synthetic Minority Oversampling Techniques (SMOTE). The main focus of this survey is data augmentation, which addresses the problem of limited data by providing a solution within the data space. When these approaches are incorporated into clinical practice, our research attempts to clarify them and increase trust in the reliability of AI models created from augmented data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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An Intelligent Analysis of Mobile Evidence Using Sentimental Analysis

An Intelligent Analysis of Mobile Evidence Using Sentimental...

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International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Jones, G. Maria Santhiya, P. Winster, S. Godfrey Sundar, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology and Panimalar Engineering College Chennai India

ISBN: (纸本)9789819950904

Smartphones are compatible and easily accessible compared to computers irrespective of place and time. Smartphones merge with our routine which acts as a medium of communication in several ways such as messaging, voice and video calling, sharing of audio and video contact, and many multimedia contents. We express our emotions through words and these communications can be analyzed by a technique called Natural Language Processing (NLP). Many researchers in the field of text mining and NLP have analyzed the polarity of online product reviews, blogs, social media comments, tweets, and many more. Mobile forensics investigation will be more effective by providing the evidences based on sentimental analysis of the text where the suspected messages contain a negative content of having threats, harassments, text against the community guidelines, and some keywords related to the cases which may be the turning point during the forensics investigation. In this paper, a novel framework called Integrated Mobile Device Forensic Investigation Process (IMDFIP) with inclusion of sentimental analysis techniques in the text which is used to emotionally analyze the texts during the communication process. This framework provides additional information to forensic analysts which will trap the suspect during investigation. The proposed framework of IMDFIP identifies the important terms, emotions, and polarity of the text during the crime. IMDFIP capitulates results compared with the previous models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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