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Image Denoising using Multiscale Transforms 15

Image Denoising using Multiscale Transforms

引用

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

作者： Reddy, Itreddy Shyam Sundar Prakash, Kakarla Bhanu Reddy, Panyam Hemanth Singh, Tripty Mandal, Adirath Chatterjee, Suman Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Graphic Era Uttarakhand Dehradun India Kongju National University Dept of Mechanical Engineering Cheonan Korea Republic of

ISBN: (纸本)9798350370249

A crucial step in multimedia processing is image and video denoising, which aims to eliminate undesirable noise while keeping significant image properties. This research investigates the use of four distinct multiscale transforms for picture denoising: ridgelets, shearlets, curvelets, and wavelets. To capturing and displaying visual structures at different scales and orientations, each transform offers special features. Through studies on artificial and real-world noisy images, examine each method's denoising performance and compare its capacity to reduce noise while maintaining image information. The outcomes demonstrate the advantages and disadvantages of each strategy and shed light on how well they work with various kinds of noise and image content. This study investigates the effects of four different multiscale transforms (ridgelets, shearlets, curvelets, and wavelets) on the Signal-to-Noise Ratio (SNR) in addition to examining their denoising capabilities. SNR values are important measurements for assessing how well denoising works since they offer measurable information on how to strike a balance between noise reduction and detail preservation in the image. Through extensive experiments on synthetic and real-world noisy photos, this study evaluates the effectiveness of every denoising technique over a range of noise kinds and image contents. The abstract also emphasizes the distinct qualities that every multiscale transform offers in terms of capturing and expressing visual structures at different scales and orientations. By means of a thorough examination, the research reveals the benefits and drawbacks of every technique, providing significant understanding regarding their appropriateness for various denoising situations. © 2024 IEEE.

关键词： and image denoising curvelets image enhancement Matlab Multiscale transforms noise reduction ridgelets shearlets signal processing wavelets

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Early Cancer Detection and Diagnosis using Deep Learning Models in Medical Imaging 4

Early Cancer Detection and Diagnosis using Deep Learning Mod...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sikarwar, Shailendra Singh Rathore, Rachna Kumar, Sharma Sonu Monalisha, Mitali Satapathy, Lopamudra Nisha, M. Jiwaji University Dept. of Computer Science Pgv College Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Paramedical Science Ranchi India Usha Martin University Dept. of Agriculture Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India

ISBN: (纸本)9798350364729

Breast cancer is a leading cause of mortality among women globally. While its exact cause remains uncertain, no definitive methods exist for its prevention or treatment. However, early detection through mammography plays a crucial role in improving management strategies and increasing the likelihood of recovery. Mammography aids not only in early cancer detection but also in identifying various conditions and distinguishing between cancer types - benign, malignant, or normal. This study explores an advanced machine learning and image processing framework for detecting and classifying breast cancer. The approach incorporates machine learning techniques alongside feature extraction, selection, and image preprocessing to classify breast cancer and related skin diseases effectively. Key advancements include the use of a geometric mean filter to enhance image quality and AlexNet for feature extraction. The relief algorithm is employed for feature selection, and classification is performed using machine learning models such as random forest, K-nearest neighbors (KNN), least square support vector machine (LS-SVM), and Naïve Bayes. This innovative methodology provides an accurate and efficient solution for breast cancer detection through advanced image analysis techniques. © 2024 IEEE.

关键词： Lung cancer

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EDITH: Enhanced Disease Identification and Tracking Through Hybridization 2

EDITH: Enhanced Disease Identification and Tracking Through ...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Navya, Soleti Sarika, Tuniki Parvathi, S. Chinnasamy, P. Nabi, Shaik Abdul Kiran, Ajmeera Mlr Institute of Technology Department of Computer Science and Information Technology India Cmr Engineering College Department of Information Technology Hyderabad India Avniet Jntuh Cse Dept India

ISBN: (纸本)9798350370188

The EDITH project represents a significant advance in the application of machine learning in healthcare. Harnessing the power of massive medical data and sophisticated algorithms, EDITH aims to transform disease detection and help design more accurate and personalized healthcare interventions. Ongoing research and development of the EDITH model will further enhance its capabilities and cement its position as an invaluable tool for medical diagnosis. © 2024 IEEE.

关键词： Diagnosis

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Predictive Analysis of Diabetes Mellitus Using Decision Tree Approach 2

Predictive Analysis of Diabetes Mellitus Using Decision Tree...

引用

2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Thotad, Puneeth Bharamagoudar, Geeta R. Anami, Basavaraj S. Kle Institute of Technology Dept. of Master of Computer Applications Hubballi India Kle Institute of Technology Dept. of Computer Science & Engineering Hubballi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Diabetes Mellitus is one of the Non-Communicable Disease. It is characterized by hyperglycemia. It may be reason for numerous health difficulties. According to the statistics in coming years, in 2030, worldwide it reaches up to 643 million counts of diabetic patients. The various factors such as Bad Diet, High Blood Pressure, Obesity, Age, Lack of Exercise etc. can led to Diabetes Mellitus. The various diseases like nerve damage, heart problem, eye problem, kidney disease, stroke in many people caused by high risk of Diabetes Mellitus. Machine Learning plays vital role in healthcare sectors. Using various Machine Learning techniques, a study can be done on large datasets and discover unseen facts, unseen patterns to realize awareness from the data and predict results consequently. In this work, the PIMA Indian Diabetes dataset is considered for Predictive Analysis. The Decision Tree algorithm, namely ID3 (Iterative Dichotomize 3) is used for a predictive analysis. The major phases include preprocessing, feature extraction, classification, and prediction. The classification accuracies of 79.76% and 76.3% are obtained for balanced and unbalanced datasets respectively. The maximum specificity and sensitivity obtained are 79.75% and 79.78% respectively. The ROC curve gives 79.57% overall classification accuracy. The work assists physicians as a second opinion. It can help various health industries to predict the diabetes disease in a better way. © 2022 IEEE.

关键词： Decision trees

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Electric Vehicle Charging System Using RFID and IoT for Enhanced User Experience 5

Electric Vehicle Charging System Using RFID and IoT for Enha...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Hassain, Md Mehedi Haque Jamil, Injamul Hoque, Md Mahmudul Hasan, Md. Jahid International Islamic University Chittagong Dept. of Electrical and Electronics Engineering Chittagong Bangladesh University of Greenwich Dept. of Computing and Mathematical Sciences London United Kingdom CCN University of Science and Technology Dept. of Computer Science and Engineering Cumilla Bangladesh Comilla University Dept. of Information and Communication Technology Cumilla Bangladesh

ISBN: (纸本)9798331519957

Electric vehicles (EVs) are surging in popularity globally, offering a greener and more energy-efficient alternative to traditional cars. Our research aims to develop an RFID based system designed to automate EV charging with minimal user input. This system integrates RFID readers, tags, a microcontroller, and a relay module to manage and monitor charging sessions efficiently. An IoT platform supports real time monitoring and remote control, while a secure payment mechanism handles transactions seamlessly. RFID tags identify vehicles for automatic charging initiation, and the relay module prevents overcharging by disconnecting power after a set time. The system uses MQTT for data transmission and offers real-Time insights into energy use and account balances. All information is updated and displayed in real-Time on the server. MySQL ensures scalability, enabling effective handling of large volumes of data. The mobile application dashboard provides a view of the charging and discharging processes for user monitoring, and an email notification is sent after the recharge. EV battery charging status is monitored through visual graphs. Key benefits include a streamlined user experience, improved charging management, and support for EV adoption. This innovation advances sustainable mobility with a user friendly, efficient charging solution. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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Design and Development of Cognitive Training Systems Based on Extended Reality and BCI Technology

Design and Development of Cognitive Training Systems Based o...

引用

Educational Innovation (ECEI), IEEE Eurasian Conference on

作者： Pei-Jung Lin Hsiu-Chien Ku Li-Lun Lin Dept. of Information Engineering and Computer Science Feng Chia University Taichung Taiwan Dept. of Computer Science and Information Engineering Hungkuang University Taichung Taiwan

Dementia is considered a Mild Cognitive Impairment (MCI), representing a blurred transition from normal cognitive function to dementia. MCI might be a precursor to dementia being caused by various neurodegenerative diseases, including Alzheimer's disease, stroke, metabolic disorders, and mental health conditions. According to the World Health Organization (WHO), over 55 million people worldwide are suffering from dementia, with nearly 10 million new cases each year. This number is expected to rise to 78 million by 2030. Numerous studies have proposed various training methods targeting specific cognitive domains in dementia. For instance, reminiscence therapy encourages individuals to recall buried memories, while reality orientation therapy focuses on strengthening orientation cues to help patients with dementia establish a stronger sense of control. With aging, changes occur in cognitive and physical functions. Research suggests that memory functions can be improved through training. Therefore, the primary goal of this study was to develop an Extended Reality (XR) cognitive system to assist MCI patients in enhancing logical thinking, increasing independence in daily life, and improving response accuracy. The application of extended reality technology in the medical field is expanding, covering areas such as surgical assistance, medical consultation, disease treatment, and medical training. We integrated mixed reality devices with brain-computer interface (BCI) equipment. By detecting brainwave signals in the frontal region of the brain through sensors, neural activity was quantified, and digital brainwave data was transmitted to the system. This data was then visualized on screen in real-time to analyze changes in brainwave data during training. Through multifaceted task training, the system enhanced cognitive response and sensory perception, stimulating brain activity with the goal of strengthening cognitive functions.

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DViTPolyp: Small Polyp Detection in Colonoscopy Videos based on Deep Vision Transformer 4

DViTPolyp: Small Polyp Detection in Colonoscopy Videos based...

引用

4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Ma, Ming Xia, Likun Seo, Hyunseok Katz School of Science and Health Yeshiva University Dept. of Graduate Computer Science and Engineering New York United States College of Information Engineering Capital Normal University Beijing China Biomedical Research Institute Korea Institute of Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798350395914

Early detection of colorectal polyps and removal play a crucial role in the prevention of colon cancer. computer-aided detection (CAD) methods can be employed to help endoscopists reduce the polyp miss-rates. Numerous automatic polyp detection methods have been explored. However, it is still challenging to automatically detect colon polyps in small size in colonoscopy videos. This paper presents a method termed DViTPolyp for detecting small colorectal polyp in colonoscopy videos. The proposed method leverages the power of the deep vision transformer as the detection head in the network to aggregate rich global features, and uses the re-attention mechanism to regenerate attention maps to capture the features from the backbone model and DViT block. Also, we propose to use a power regularized Intersection Over Union (IoU) as the loss function to further enhance the accuracy of detection. Experiments are conducted on a mix of several public polyp datasets. Comparative results show that our proposed method has a better performance than other methods. © 2024 IEEE.

关键词： colonoscopy videos loss function polyp detection vision transformer

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Efficient Semantic Detection and Analysis of Misinformation in CBD-Related Tweets Using FAISS and Mistral NeMo Instruct

Efficient Semantic Detection and Analysis of Misinformation ...

引用

International Conference on Semantic Computing

作者： Jason Turner Mehmet A. Gulum Mehmed Kantardzic Dept. Computer Science & Engineering University of Louisville Louisville KY United States Dept. Of Computer Science DePauw University Greencastle IN United States

ISBN: (数字)9798331524265

ISBN: (纸本)9798331524272

The growing popularity of cannabidiol (CBD) has led to a surge in misinformation, particularly on social media platforms like Twitter, posing risks to public health. This paper presents a scalable method for detecting CBD-related misinformation in a large corpus of tweets. Using approximately 3.7 million tweets collected from 2011 to 2021, we implement a two-step process: first, FAISS (Facebook AI Similarity Search) efficiently identifies tweets semantically similar to false claims extracted from FDA warning letters. Second, Mistral NeMo Instruct, a zero-shot model, classifies tweets as ‘Misinformation’ or ‘Non-Misinformation’, providing justifications for transparency. This approach minimizes computational costs while maintaining accuracy, making it a practical tool for large-scale misinformation detection. The framework is scalable and adaptable, evolving with new FDA data or emerging cannabis research.

关键词： Adaptation models Analytical models Social networking (online) Computational modeling Scalability Semantics Natural language processing Hardware Fake news Public healthcare

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An Efficient Machine Learning Model for Identification of Seagrasses through Morphometrics

An Efficient Machine Learning Model for Identification of Se...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Ashwani Kumar Dubey Jibi G Thanikkal E. Dilipan Puneet Sharma Manoj Kumar Shukla Dept.of Electronics and Communication Engineering ASET Amity University Uttar Pradesh Noida UP India Dept.of Computer Science and Engineering ASET Amity University Uttar Pradesh Noida UP India Center for Marine Omics Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India School of Computer Science & AI SR University Warangal Telangana India

Seagrasses are marine flowering plants capable of completing their life cycle underwater. Globally, 72 species of seagrasses have been identified, including 15 species from India. Artificial intelligence/ Machine learning techniques are gaining wide use in many fields, and their applications in marine sciences are still in the initial stage. Considering this, a study is attempted to identify seagrass species through Artificial intelligence/ Machine learning techniques. Since the characteristics of seagrass vary depending on location and other environmental factors, identifying seagrass species is regarded as a difficult task. In the present study, a known seagrass species is taken as a reference and its leaf morphological features are considered for shape description and deep learning-based identification. Leaf morphometrics of seagrass species are drawn to develop the databases. Morphological feature extraction algorithm, descriptor generation algorithm, and deep learning-based identification are applied for species identification. Existing artificial intelligence/ machine learning models use image pixels as input to the deep learning model. However, this is the first attempt to recognize seagrasses through their morphological properties. Here, the leaf shape property is utilized for seagrass identification. The proposed machine learning model received 60% accuracy during testing. Additional investigation was possible with the same algorithm for future seagrass species identification. Utilizing Artificial intelligence/ Machine learning algorithms may lead to identifying new seagrass species.

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Understanding the Strength of Self-Identified Gender, Country, and Labor Sector on the Perception of Robots in Industrial Environments

Understanding the Strength of Self-Identified Gender, Countr...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Andrew Liu Natasha Kholgade Banerjee Sean Banerjee Dept. of Computer Science Clarkson University Potsdam NY USA Dept. of Computer Science & Engineering Wright State University Dayton OH USA

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

As robots pervade industrial environments, such as construction, manufacturing, and transportation, the fear of job displacement, lack of independence, and safety are natural concerns among workers. Prior survey-based research on the perception of robots in industrial environments show that education level, country of work, labor sector, self-identified gender, age, and minority status play an important role in shaping how workers view robots. When exploring demographic factors, such as gender, labor sector, or country of work, prior work has studied their impact independently. In our work we explore the strength of self-identified gender, country of work, and labor sector when it comes to a worker's sense of safety and task independence when working with a robot, fears of job safety due to the presence of robots in the workplace, and the type of assistance expected from a collaborative robot for lifting actions. We analyze responses acquired using a Google Forms online survey from 530 workers in Australia, Canada, United Kingdom, and United States working in construction, contract work, manufacturing, retail, transportation & delivery, and warehousing. Our results show the self-identified gender, country of work, and the labor sector play distinct and significant roles in how workers expect collaborative workplace robots to provide assistance during lifting tasks. We find that country of work significantly influences perceptions of safety, independence, and job security.

关键词： Surveys Service robots Employment Warehousing Transportation Robot sensing systems Safety Manufacturing Security Robots

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