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Artificial Intelligence, applications, Innovation and Ethics (AI2E), International Conference for

作者： Zarina Ramlia Sri Herwiningsih Mohd Saiful Asmal Mohd Amiruddin Zaharudin Haron Muhammad Khalis Abdul Karim Department of Physics Universiti Putra Malaysia Selangor Malaysia Department of Physics Universitas Brawijaya Malang Indonesia Department of Radiology Institut Kanser Negara Putrajaya Malaysia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Contemporary technology has made it possible to diagnose diseases with greater precision, enabling doctors to determine tumor stages by measuring and analyzing features in medical images. This study aimed evaluate radiomic features of DWI-MRI cervical and to classify tumors based on their stages using several machine learning models. The study involved 60 patients from the National Cancer Institute in Putrajaya using which 852 radiomic were derived from each patient. The patients were divided into two groups based on the stage of cervical cancer: Group one is defined by stages I and ii, while group two is labelled by stages iiI and IV. A semi-automatic process was used to segment the image data and extract various features such as shape, wavelength, and both first and second-order statistics (GLSZM) from the segmented images. In our study, we used three different model algorithms: Logistic Regression (LR), Support Vector machine (SVM) and Decision Tree (DT). We conducted a detailed analysis to compare their performance. The SVM algorithm stood out as the top performer, achieving the highest accuracy among the models tested. We evaluated its effectiveness using multiple performance metrics, which confirmed its superiority in processing features extracted from enhanced images. Notably, the SVM classifier demonstrated excellent results, with an accuracy of 0.77, and precision of 0.63. It also showed a robust area under the ROC curve of 96%, highlighting its effectiveness in classification tasks. Furthermore, this research emphasizes the significance of image enhancement as the useful preprocessing step which increases the classification accuracy.

关键词： Support vector machines image segmentation Technological innovation Accuracy Feature extraction Decision trees image enhancement Cervical cancer Radiomics Tumors

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DES to HSC: Detecting low-surface-brightness galaxies in the Abell 194 cluster using transfer learning

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ASTRONOMY & ASTROPHYSICS 2025年 695卷

作者： Thuruthipilly, H. Junais, J. Koda, J. Pollo, A. Yagi, M. Yamanoi, H. Komiyama, Y. Romano, M. Malek, K. Donevski, D. Natl Ctr Nucl Res Pasteura 7 Warsaw Poland Inst Astrofis Canarias Via Lactea S-N San Cristobal la Laguna 38205 Spain Univ La Laguna Dept Astrofis San Cristobal la Laguna 38206 Spain SUNY Stony Brook Dept Phys & Astron Stony Brook NY 11794 USA Jagiellonian Univ Astron Observ Orla 171 Krakow Poland Natl Astron Observ Japan Mitaka Tokyo 1818588 Japan Seikei Univ Fac Sci & Technol 3-3-1 Kichijoji Kitamachi Musashino Tokyo 1808633 Japan Hosei Univ Fac Sci & Engn Dept Adv Sci 3-7-2 Kajino Cho Koganei Tokyo 1848584 Japan Grad Univ Adv Studies SOKENDAI Mitaka Tokyo 1818588 Japan Max Planck Inst Radioastron Auf dem Hugel 69 DE-53121 Bonn Germany INAF Osservatorio Astron Padova Vicolo Osservatorio 5 I-35122 Padua Italy Aix Marseille Univ CNRS CNES LAM Marseille France

Context. Low-surface-brightness galaxies (LSBGs) are important for understanding galaxy evolution and cosmological models. Nevertheless, the physical properties of these objects remain unknown, as even the detection of LSBGs can be challenging. Upcoming large-scale surveys are expected to uncover a large number of LSBGs, which will require accurate automated or machine learningbased methods for their detection. Aims. We study the scope of transfer learning for the identification of LSBGs. We used transformer models trained on Dark Energy Survey (DES) data to identify LSBGs from dedicated Hyper Suprime-Cam (HSC) observations of the Abell 194 cluster, which are two magnitudes deeper than DES. A new sample of LSBGs and ultra-diffuse galaxies (UDGs) around Abell 194 was compiled, and their properties were investigated. Methods. We used eight models, divided into two categories: LSBG Detection Transformer (LSBG DETR) and LSBG vision Transformer (LSBG ViT). The data from DES and HSC were standardised based on the pixel-level surface brightness. We used an ensemble of four LSBG DETR models and another ensemble of four LSBG ViT models to detect LSBGs. This was followed by a singlecomponent S & eacute;rsic model fit and a final visual inspection to filter out potential false positives and improve sample purity. Results. We present a sample of 171 LSBGs in the Abell 194 cluster using HSC data, including 87 new discoveries. Of these, 159 were identified using transformer models, and 12 additional LSBGs were found through visual inspection. The transformer model achieves a true positive rate of 93% in HSC data without any fine-tuning. Among the LSBGs, 28 were classified as UDGs. The number of UDGs and the radial UDG number density suggests a linear relationship between UDG numbers and cluster mass on a log scale. The UDGs share similar S & eacute;rsic parameters with dwarf galaxies and occupy the extended end of the R-eff - M-g plane, suggesting they might be an extended sub-po

关键词： methods: data analysis methods: observational techniques: image processing catalogs galaxies: dwarf galaxies: evolution

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An Innovative Decision Support Glucose Metabolism Disorder Prediction Approach Based on Norm-Learning and Pre-Trained Architecture Pattern

An Innovative Decision Support Glucose Metabolism Disorder P...

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International Conference on Inventive Computation Technologies (ICICT)

作者： PreethaRajagopalan. M Anuratha. V Elamparithi. M Department of Computer Science Kamalam College of Arts and Science Anthiyur Bharathiar University Coimbatore Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Glucose metabolism disorder has the potential to adversely affect various organs, including the eyes, coronary arteries, and kidneys, through multiple mechanisms. Consequently, it ranks among the foremost contributors to disability and premature mortality globally. The number of deaths from glucose metabolism disorder is increasing year by year, so developing a system that can correctly detect people with glucose metabolism disorder is essential. This study presents an advanced medical decision-making system that uses L-norm Support Vector machine with pre-trained UNet (ι - SVM with UNet) to forecast glucose metabolism disorder. These state-of-the-art algorithms, well-known in computer vision, image analysis, and natural language processing, show promise in producing highly accurate outcomes when used for predictive and medical diagnostic applications. Furthermore, their integration with medical data can significantly improve the decision-making process's adaptability, transparency, and effectiveness. A comparative analysis is conducted between the ι - SVM with UNet algorithm's performance, various established methodologies, and several widely used machine learning (ML) techniques. The findings of the study show that the suggested approach, which is based on ι - SVM with UNet technology, performs admirably, attaining an F1-score of 96% and an accuracy of 96%. This enhancement can increase the correctness of the final judgment while saving effort, labor, and time in healthcare services.

关键词： Support vector machines Representation learning Accuracy Decision making Mortality Prediction algorithms Natural language processing Glucose Metabolism Medical diagnostic imaging

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Intelligence Pest Detection and Control in Agriculture using Computer vision and Deep Learning

Intelligence Pest Detection and Control in Agriculture using...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： S. Ashok Kumar Fahimuddin Shaik B. Yashwitha P. Vidyavathi A.Uma Maheswari P. Spurthi Department of AI&DS Annamacharya University Andhra Pradesh Department of ECE Annamacharya University Andhra Pradesh Department of AI&DS Annamacharya Institute of Technology and Sciences Andhra Pradesh

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

One of the most significant challenges in agriculture over the years has been the detection and also control of pests and while it continues to this day, the advent of technology in the agricultural sector is providing solutions that are incredibly promising. Pests cause significant worldwide crop loss and represent a threat to food security and farmer livelihoods. Conventional pest management approaches revolve around labour-intensive strategies, large applications of pesticides, and reactionary measures, resulting in environmental degradation and sub-optimal crop yields. This work utilizes the power of AI(Artificial Intelligence),computer vision and deep learning to create a smart pest detection system. It treats pest identification and damage detection as real-time visual data processing tasks with machine learning approaches, making it possible to swiftly and accurately locate pests and their damage in the photos taken through the camera, facilitating extra prompt preventive measures. Working under the hood, the algorithms adapt to changing environmental conditions and shifting pest behaviours, learning as they go. Most of the pest detection systems today are manual, requiring manual visual inspection and image recognition approaches, this solution not only provides the next level of system but it also paves the way for pest management in the most efficient, accurate & sustainable way.

关键词： Deep learning Computer vision Visualization Accuracy Pesticides Manuals Control systems Real-time systems Agriculture Farming

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Data Fusion Approach for Unmodified UAV Tracking with vision and mmWave Radar

Data Fusion Approach for Unmodified UAV Tracking with Vision...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Guilherme Amaral João J. Martins Pedro Martins André Dias José Almeida Eduardo Silva INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

The knowledge of the precise 3D position of a target in tracking applications is a fundamental requirement. The lack of a low-cost single sensor capable of providing the three-dimensional position (of a target) makes it necessary to use complementary sensors together. This research presents a Local Positioning System (LPS) for outdoor scenarios, based on a data fusion approach for unmodified UAV tracking, combining a vision sensor and mmWave radar. The proposed solution takes advantage of the radar's depth observation ability and the potential of a neural network for image processing. We have evaluated five data association approaches for radar data cluttered to get a reliable set of radar observations. The results demonstrated that the estimated target position is close to an exogenous ground truth obtained from a Visual Inertial Odometry (VIO) algorithm executed onboard the target UAV. Moreover, the developed system's architecture is prepared to be scalable, allowing the addition of other observation stations. It will increase the accuracy of the estimation and extend the actuation area. To the best of our knowledge, this is the first work that uses a mmWave radar combined with a camera and a machine learning algorithm to track a UAV in an outdoor scenario.

关键词： Visualization Target tracking Radar clutter Airborne radar Data integration Radar imaging vision sensors Radar tracking Autonomous aerial vehicles Millimeter wave communication

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Proceedings of SPIE-image processing: machine vision applications

Proceedings of SPIE-Image Processing: Machine Vision Applica...

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image processing: machine vision applications

ISBN: (纸本)9780819469854

The proceedings contain 34 papers. The topics discussed include: vision robot with rotational camera for searching ID tags;a unifying software architecture for model-based visual tracking;video object tracking using chamber matching and condensation particle filter;compact and thin multi-lens system for machine vision applications;video-rate or high-precision: a flexible range imaging camera;polarization imaging for industrial inspection;machine vision approach for improving accuracy of focus-based depth measurements;3D geometric modelling of hand-woven textile;a novel circle detection method using Randon transform;an algorithm for automated registration of maps and images based on feature detection and mutual information;automatic cell segmentation and classification using morphological features and Bayesian networks;multi-model geometrical fitting for wide baseline image matching;and constraint optimization method for line fitting.

关键词： image segmentation

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Automatic generation of input-aware approximate arithmetic circuits

Automatic generation of input-aware approximate arithmetic c...

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IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Mario Barbareschi Salvatore Barone Alberto Bosio Bastien Deveautour Ali Piri Marcello Traiola Department of Electrical Engineering and Information Technologies University of Naples Federico II Naples Italy Department of Computer Science and Technologies Pegaso Digital University Naples Italy CNRS UCBL CPE Lyon INL UMR5270 Univ Lyon ECL INSA Lyon Ecully France CNRS IETR-UMR 6164 Nantes Université Nantes France CNRS Inria IRISA - UMR 6074 Univ Rennes Rennes France

ISBN: (数字)9798331528010

ISBN: (纸本)9798331528027

Approximate Computing (AxC) is systematically applied across various abstraction levels to reduce overheads and enhance the performance of applications such as image processing and machine learning. However, AxC does not typically consider the specific workload (i.e., data input) of a given application. For instance, in signal processing applications like filters, some inputs are constants (filter coefficients), which allows for an additional level of approximation by considering the specific input distribution. This method is known as "Input-Aware Approximation" (IAA) and has shown potential advantages in previous studies. Unfortunately, existing input-aware design methodologies lack scalability as they mostly depend on ad-hoc, non-automatic design approaches, limiting their applicability. In this paper, we investigate how the input-aware approximate design approach can be integrated into a systematic, generic, and automatic design flow. We employ state-of-the-art approximation and multi-objective optimization techniques to achieve inputawareness. Our experimental results, focusing on classical signal processing applications like FIR filters, demonstrate that the input-aware approach can provide significant savings in both area and power consumption.

关键词： Systematics Filters Power demand Philosophical considerations Finite impulse response filters Scalability Approximate computing Hardware Optimization Arithmetic

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Deepfake image and Video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks Artificial intelligence Research and development

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A comprehensive market analysis on camera and illumination sensors for image processing and machine vision applications

A comprehensive market analysis on camera and illumination s...

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2010 International Conference on Computational Intelligence and Communication Networks, CICN 2010

作者： Chaki, Ayan Prashant, M. Sen, Pompy Innovation Lab. Tata Consultancy Services Kolkata India

ISBN: (纸本)9780769542546

In the present industrial application space, conventional inspection techniques using manual intervention are getting replaced by automated inspection using image sensors. image sensors have empowered machines with vision and that has led to increased levels of process automation which used to be manually exhaustive. This paper mainly focuses on the comparative study of machine vision hardware aspect specifically on cameras and lighting- the technologies in use and their application across different industry verticals. © 2010 IEEE.

关键词： Computer vision

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Python-Based Optical Character Recognition (OCR)

Python-Based Optical Character Recognition (OCR)

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

作者： Priyanka Kaushik Priyanka Rawat Devyansh Batra P. Vensheeba Delin Saurabh Pratap Singh Rathore S. Kaliappan Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science and Engineering Chandigarh University Chandigarh India Dept. of Civil Eng. Dhanalakshmi Srinivasan College of Eng. Coimbatore India Dept. of Management & Research ICAPSR New Delhi India Division of Research and Development Lovely Professional University Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Optical Character Acknowledgment (OCR) stands as a transformative innovation at the crossing point of computer vision and machine learning, encouraging the extraction of printed data from pictures or checked archives. Within the period of advanced change, OCR serves as a foundation for different applications, such as archive digitization, content mining, and cleverly data recovery. The first thing that pops into one’s mind in building vigorous and versatile OCR frameworks for different scenarios is the choice of a successful programming dialect. The Python programming dialect is discussed here, which is renowned for its simplicity, vast libraries, and dynamic community, as an impressive tool to execute OCR arrangements.

关键词： Technological innovation image processing Optical character recognition Pipelines Programming Libraries Data mining Reliability Python Testing

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