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Early Brain Tumour Detection in MRI Images Through Hybrid Deep Learning Model

Early Brain Tumour Detection in MRI Images Through Hybrid De...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE international conference on-

作者： Samrat Aditya Keshari Parida S.B Ashirbad Mangal Maharana Debasis Kumar Ghadai Sumane De Dept. of Computer Science & Engineering C.V Raman Global University Bhubaneswar Odisha

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Brain tumours are among the emerging significant health challenges that require early detection for complete management. Though the mainstay of diagnosis remains MRI, it can be applied in its interpretation with some challenges. This study investigates the potential of leveraging Convolutional Neural Networks, a form of artificial intelligence, to classify different brain tumours from MRI images with enhanced metrics. CNNs are said to provide the best currently available solution in achieving minimal preprocessing followed by feature engineering especially with respect to image analysis. Accuracy and efficiency in brain tumour diagnosis can be drastically improved by leveraging the power of AI.

关键词： Deep learning measurement Accuracy Magnetic resonance imaging Feature extraction Brain modeling Hybrid power systems Convolutional neural networks Artificial intelligence Tumors

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Plausible and Effective Gesture-Based Interactions for the Virtual Runway

Plausible and Effective Gesture-Based Interactions for the V...

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Data, Decision and systems (ICDDS), IEEE international conference on

作者： Anonymous Author(s)

ISBN: (数字)9798350363890

ISBN: (纸本)9798350363906

As Virtual Reality and other virtual environment platforms evolve, the need for and research into diverse new input methods as alternatives to traditional keyboard inputs have been highlighted. This study developed a motion recognition-based input method using IMU sensors to track user movements, thereby designing a virtual runway where users can control avatars within a virtual environment. Additionally, we defined flight movements for human avatars in virtual environments and trained a motion generation network using a preprocessed dataset to enable the generation of these flight movements. User motions were recognized through the measurement of joint angles and permissible ranges. To assess the usability and user experience of this innovative approach, a usability test was conducted that compared the gesture-based input method to traditional keyboard input under various experimental conditions. The results demonstrated that the gesture-based input method had a lower error rate than keyboard input. Furthermore, the gesture-based input method outperformed keyboard input in the hedonic quality of the User Experience Questionnaire: attractiveness, simulation, and novelty.

关键词： Human computer interaction Training Translation Avatars Keyboards Virtual environments Gesture recognition Real-time systems User experience Usability

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An Effective TF-IDF Model to Improve the Text Classification Performance

An Effective TF-IDF Model to Improve the Text Classification...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Shitanshu Jain Sapan Kumar Jain Somil Vasal Department of CSE-DSE School of Information Technology Manipal University Jaipur India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Term weight is utilized as a baseline classifier with text classification and other text mining techniques used for a significant increase in efficiency. The words, documents, and datasets are taken into consideration while applying various term weighting techniques. In this paper, various TF-IDF models are evaluated for text classification performance, and a proposed TF-IDF is suggested. The TF-IDF model's mathematical definition that we put forward was based on the computation of term frequency, term occurrence, TF-IDF Variants, and typical TF-IDF. Our aim is to test various phrase weighting techniques on the Amazon review dataset. With respect to accuracy performance parameters employing various classifiers, the suggested method outperforms the other weighting system. This A research indicates that modifications to the original TF-IDF weighting techniques offer considerable gains in

关键词： Training Text mining measurement Accuracy Reviews Communication systems Computational modeling

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Data-Driven Urban Mobility - Comprehensive Predictive modeling for Traffic Congestion

Data-Driven Urban Mobility - Comprehensive Predictive Modeli...

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Intelligent systems, Smart and Green Technologies (ICISSGT), IEEE international conference on

作者： Chinimilli Venkata Rama Padmaja Sudha Tushara Sadasivuni Department of Computer Science & Engineering Institute of Aeronautical Engineering Hyderabad India Department of Computer Science & Engineering Georgial State University Atlanta USA

ISBN: (数字)9798350304329

ISBN: (纸本)9798350304336

In the rapidly changing landscape of urbanization, this study delves into traffic congestion management within smart cities. Leveraging a diverse dataset from Kaggle, which includes details such as timestamps, weather conditions, road types, and vehicle specifics, the research examines the temporal dynamics of congestion, identifying patterns across various times of the day, week, and year. By applying machine learning models like Linear Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, SVR, K-Neighbors Regressor, and MLP Regressor, the study evaluates their predictive performance, with a focus on distinctions in effectiveness using Mean Squared Error as a metric. The findings highlight the critical role of historical data and weather factors in accurate congestion forecasting, offering valuable insights for the development of smart cities. This research enhances the understanding of urban mobility challenges and provides a practical framework for implementing proactive traffic management strategies in increasingly complex urban environments.

关键词： measurement Accuracy Smart cities Linear regression Predictive models Traffic control Regression tree analysis Traffic congestion Random forests Meteorology

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Colorization of Automatic Hologram Generation from Photographs using Machine learning

Colorization of Automatic Hologram Generation from Photograp...

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international conference on Advanced Mechatronic systems (ICAMechS)

作者： Koya Makita Akira Taguchi Yue Bao Department of Informatics Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan

ISBN: (数字)9798350366204

ISBN: (纸本)9798350366211

Holography is the only imaging technology that can faithfully reproduce reflected light from objects. The hologram reproduced on a computer is called a computer-generated hologram (CGH), but the generation of CGH has previously been problematic due to the enormous amount of computation *** research that has solved this problem is TensorHolography. This research will use techniques such as machine learning to estimate CGH at high speed. However, this research required specialized equipment such as a Tof camera and LiDAR to acquire depth information. This problem was solved by research called "From picture to 3D hologram". This research used machine learning to obtain depth information to create a monochromatic CGH. However, color CGH was not *** this research, we proposed a method to automatically generate color holograms from photographs by using machine learning to obtain depth information from photographs using a monocular depth estimation model and then to use machine learning to create color CGHs. And, the effectiveness of the proposed method was confirmed by playing back the CGH actually generated.

关键词： Three-dimensional displays Mechatronics Laser radar Image color analysis Stereo image processing Depth measurement Computational modeling Machine learning Holography Cameras

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From Text to Canvas: Unleashing the Artistry of GAN-Driven Image Synthesis

From Text to Canvas: Unleashing the Artistry of GAN-Driven I...

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Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE international conference on

作者： Vijay Madaan Neha Sharma Rahul Chauhan Akanksha Kapruwan Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research investigates the accuracy and loss metrics of many text-to-image synthesis models, such as VQGAN+CLIP, BigGAN, StyleGAN, SR3, Imagen, and Glide. We aim to study these models to discover their ability to develop high-quality images from textual descriptions. Models with a 95% classification accuracy or greater exhibit significant semantic coherence between text and visuals, indicating their potential to effectively depict the intended information. Thoroughly analyzing these metrics reveals important information about the strengths, weaknesses, and ways to enhance models for text-to-image synthesis. Research and development efforts in the field of text-to-image synthesis models are advanced by these discoveries. They serve as a roadmap for future efforts to improve these models' performance in a variety of contexts, such as multimedia synthesis, content development, and creative design. Words such as SR3, Image, Accuracy, Glide, Model evaluation, Performance metrics, Computational efficiency, and Semantic coherence are used in the index.

关键词： measurement Training Visualization Analytical models Accuracy Computational modeling Semantics

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Comparison Research: YOLO (You Only Look Once) Model for Indonesian Sign Language Detection Reducing Communication Inequalities

Comparison Research: YOLO (You Only Look Once) Model for Ind...

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Technology Innovation and Its Applications (ICTIIA), international conference on

作者： Brandon Steven Calista Vannia Nathalie Christoper Gerry Johanes Puti Andam Suri Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

This research explores the effectiveness of two YOLO (You Only Look Once) models, which are YOLOv8 and YOLOv9 in detecting Indonesian Sign Language gestures. The research focuses on AI (Artificial Intelligence) systems to bridge communication and prevent isolation among the DHH (Deaf and Hard of Hearing) community. The YOLOv8n and YOLOv9c from Ultralytics were compared in terms of their performance from preprocessing the dataset into the model compatibility followed by evaluating them by using the standard AI metrics. This research is significant as it contributes to developing more solutions for the tenth SDGs (Sustainable Development Goals) that is to promote equality. The result of this research clearly indicates that YOLOv9 is much better than YOLOv8 as a model in case of detecting Indonesian Sign Language gestures. This research not only can greatly enhance AI accuracy and dependability, but also opening doors for future improvements in computer vision and social equality.

关键词： YOLO measurement Sign language computer vision Technological innovation Computational modeling Auditory system Classification algorithms Artificial intelligence Tuning

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Lightweight Semantic Segmentation for RoboCup SPL: A U-Net Model with MobileNetV2 and Attention Mechanisms

Lightweight Semantic Segmentation for RoboCup SPL: A U-Net M...

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Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), international conference on

作者： Jinwei Xia Ruiyang Su School of Software Engineering Jinling Institute of Technology Nanjing China School of Electronic Engineering JiangSu Ocean University Lianyungang China

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

Real-time and precise semantic segmentation is essential for robotic vision systems, particularly in dynamic and complex environments such as RoboCup SPL. This paper introduces a lightweight semantic segmentation model that combines the U-Net architecture with a MobileNetV2 subnetwork and the Convolutional Block Attention Module (CBAM). The UNet's encoder-decoder structure facilitates effective multi-scale feature fusion, while MobileNetV2 reduces computational overhead. CBAM further enhances the model's ability to focus on critical features. Evaluations on the RoboCup SPL instance segmentation dataset achieved a mean accuracy of 0.9370 . Through comparative analysis with various backbone networks and ablation studies, we investigated the effects of CBAM, joint loss functions, and data augmentation on model performance. Results indicate that the CBAM mechanism significantly improves segmentation accuracy, and both joint loss functions and data augmentation offer additional benefits. The proposed model achieves a favorable balance between accuracy and computational efficiency, making it highly suitable for real-time applications.

关键词： Accuracy Computational modeling Semantic segmentation Semantics Refining computer architecture Feature extraction Data augmentation Real-time systems Robots

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Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach

Contactless Fingerprint Biometric Anti-Spoofing: An Unsuperv...

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IEEE international joint conference on Biometrics (IJCB)

作者： Banafsheh Adami MohammadReza Hosseinzadehketilateh Nima Karimian Computer Science and Electrical Engineering West Virginia University

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Contactless fingerprint recognition offers a higher level of user comfort and addresses hygiene concerns more effectively. However, it is also more vulnerable to presentation attacks, such as photo-paper, paper printout, and various display attacks, making it more challenging to implement in biometric systems compared to contact-based modalities. Limited research has been conducted on presentation attacks in contactless fingerprint systems, and these studies have encountered challenges in terms of generalization and scalability since both bonafide samples and presentation attacks are utilized during the training model. Although this approach appears promising, it lacks the ability to handle unseen attacks, which is a crucial factor for developing PAD methods that can generalize effectively. We introduced an innovative anti-spoofing approach that combines an unsupervised autoencoder with a convolutional block attention module to address the limitations of existing methods. Our model is trained exclusively on bonafide images without exposure to any spoofed samples during the training phase. It is then evaluated against various types of presentation attack images in the testing phase. The scheme we proposed has achieved an average BPCER of 0.96% with an APCER of 1.6% for presentation attacks involving various types of spoofed samples.

关键词： Training Deep learning Scalability Biological system modeling Supervised learning Fingerprint recognition Testing

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Relaxations for Non-Separable Cardinality/Rank Penalties 18

Relaxations for Non-Separable Cardinality/Rank Penalties

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IEEE/CVF international conference on computer Vision (ICCVW)

作者： Olsson, Carl Gerosa, Daniele Carlsson, Marcus Lund Univ Ctr Math Sci Lund Sweden Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden

ISBN: (纸本)9781665401913

Rank and cardinality penalties are hard to handle in optimization frameworks due to non-convexity and discontinuity. Strong approximations have been a subject of intense study and numerous formulations have been proposed. Most of these can be described as separable, meaning that they apply a penalty to each element (or singular value) based on size, without considering the joint distribution. In this paper we present a class of non-separable penalties and give a recipe for computing strong relaxations suitable for optimization. In our analysis of this formulation we first give conditions that ensure that the globally optimal solution of the relaxation is the same as that of the original (unrelaxed) objective. We then show how a stationary point can be guaranteed to be unique under the restricted isometry property (RIP) assumption. (1)

关键词： computer vision conferences Computational modeling Robustness Optimization Standards

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