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Carcinikos - Presentiment of Cancer in Males using Multimodal Techniques 9

Carcinikos - Presentiment of Cancer in Males using Multimoda...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Arora, Tanya Joshi, Soumye Sahoo, Soubhagya Ranjan Anand, Shruthika Pes University Dept. of Computer Science Bangalore India

ISBN: (纸本)9798350394474

Lung and prostate cancer don't exhibit observable symptoms until the disease is well along its course, and even then, they are frequently disregarded. In the initial stages of the disease, these cancer nodules are very small and cannot be seen with the naked eye thereby resulting in a delayed diagnosis of the illness. The proposed methodology makes use of a modified CNN with multiple layers comprising a convolutional layer, a custom pooling layer where randomness is introduced into training by selecting max pooling or average pooling according to the distribution of values in the pooling region and a softmax layer along with an updated LReLU with leakage correction, for detecting the presence of lung cancer. This procedure has been applied on images used for medical purposes, including MRIs scans for lung cancer. By analyzing these images the proposed methodology aims to enhance accuracy and reduce false positives and false negatives. Random Forest model using Decision Tree Classifier is proposed for detecting the presence of prostate cancer by utilizing various clinical parameters. The proposed work has the potential to revolutionize cancer diagnosis, leading to improved treatment outcomes and reduced disease burden in male patients. © 2024 IEEE.

关键词： Diseases

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RGB-D Scene Classification: A Unified Framework with Vision Transformers and Contextual Models 3

RGB-D Scene Classification: A Unified Framework with Vision ...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Ahmed, Muhammad Waqas Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Indoor scene classification poses a significant challenge, crucial for advancements in robotics, augmented reality, and smart environments. This paper presents an innovative model designed for classifying indoor scenes using RGB-D images. The model seamlessly integrates unsupervised segmentation, Vision Transformers (VT), convolutional neural networks, and conditional random fields. The workflow starts with segmenting the RGB-D data through the DBSCAN algorithm, followed by feeding the segmented images into a Vision Transformer for feature extraction. The VT features two branches: one dedicated to capturing fine-grained local patch representations and another focused on modeling long-range dependencies through self-attention mechanisms. The resulting features are then processed by a ResNet-18 architecture for multi-object classification, enhancing the understanding of the scene's content. Ultimately, the classified objects and contextual information from the VT's relation modeling branch are merged within a Conditional Random Field framework for comprehensive scene classification. Testing on the NYUDv2 and SUN RGB-D datasets reveals that our approach significantly outperforms existing techniques in accurately classifying diverse indoor environments. This methodology sets the stage for enhanced scene understanding, with promising applications in robotics, augmented reality, and smart environments. ©2024 IEEE.

关键词： Convolutional neural networks

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Revolutionizing Leaf Classification for Agricultural Sustainability and Plant Species Analysis

Revolutionizing Leaf Classification for Agricultural Sustain...

引用

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Mumtaz, Sara Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504465

Agricultural production is critical for economic stability and food security, consequently early disease identification is essential for preserving plant health. Our study solves this need by incorporating Random Forest into a complete framework for leaf categorization. We recommend applying Particle Swarm Optimization (PSO) to construct an AI-driven, four-step procedure for this type of segregation. Crop images may be processed by preprocessing, segmenting, extracting features, and classifying them with Random Forest. During pre-processing, a Median Filter was applied, proceeded by thresholding for segmentation, eventually Kaze and HOG (Histogram of Oriented Gradient) were used to extract relevant features. A Random Forest classifier that has been optimized with PSO was used for classification. The random forest-based technique outperformed alternative methods such as naive-bayes and adaboost with an accuracy of 92.63. © 2024 IEEE.

关键词： Median filters

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Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey

Recent Advancements in Microscopy Image Enhancement using De...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Dutta, Debasish Sonowal, Neeharika Barauh, Risheraj Chetia, Deepjyoti Kalita, Sanjib Kr Gauhati University Dept. of Computer Science Assam India

ISBN: (纸本)9798350376876

Microscopy image enhancement plays a pivotal role in understanding the details of biological cells and materials at microscopic scales. In recent years, there has been a significant rise in the advancement of microscopy image enhancement, specifically with the help of deep learning methods. This survey paper aims to provide a snapshot of this rapidly growing state-of-the-art method, focusing on its evolution, applications, challenges, and future directions. The core discussions take place around the key domains of microscopy image enhancement of super-resolution, reconstruction, and denoising, with each domain explored in terms of its current trends and their practical utility of deep learning. © 2024 IEEE.

关键词： Image denoising

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Advanced Gait Event Recognition and Pose Estimation Model through Deep Learning

Advanced Gait Event Recognition and Pose Estimation Model th...

引用

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Tayyab, Muhammad Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504465

Analyzing activities of humans, events, Gait, human-to-human interaction, and sports analysis are some prominent subjects, and key areas of interest in machine learning, artificial intelligence, and deep learning fields. Nevertheless, beginning with intelligent computing in a somewhat less complex context for enhanced scene perception remains a topic that still waits for scholars’ exploration. In this system, we have made a model to allow machines to understand the behaviors in the external environment by proposing a novel methodology to discern sports and events. This research aims to incorporate AI functions in information technologies to innovative future trajectories of intelligent media. Our proposed system recognizes complex human activities from two well-known Kinetics and YouTube datasets. After preprocessing and human detection, we have used numerous feature algorithms, such as DOF and 3D cartesian plane for joint points while distance transform and optical flow for full body points. These feature point values are then fed to joint feature descriptors and PSO for enhancement before passing to the CNN for recognition. However, the obtained data prove that the suggested approach is effective in realistic scenarios with a high level of uncertainty with an accuracy of 91.2% and 88.2%, on Kinetics and YouTube datasets respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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Discussion on the Connection of Different Games Sharing Goal Awareness and Physical Actions 13

Discussion on the Connection of Different Games Sharing Goal...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Maruyama, Ayaka Kurihara, Kazutaka Tsuda Univercity Dept. Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

Connecting two different games can lead to an increase in communication opportunities among players through shared gameplay. Furthermore, the shared goal awareness and physical actions in these games allow for the sharing of highly common experiences, which contributes to the realization of higher quality communication. In this paper, we propose a game design that connects games while preserving this commonality. Specifically, we propose a game, which we refer to as "Hyakunin Isshu Karuta vs Whack-a-Mole,"where one player engages in the traditional Japanese card game Hyakunin Isshu Karuta, and the other player plays Whack-a-Mole. Both games are competitive games, and they share the commonality that the player who touches the designated target the fastest earns points. By connecting games that preserve this commonality, players can utilize experience of their respective games while still engaging in a competitive game that shares goal awareness and physical actions. © 2024 IEEE.

关键词： Game design

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Intelligent Multimodal Human Behavior Recognition using Inertial and Video Sensors

Intelligent Multimodal Human Behavior Recognition using Iner...

引用

2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Abro, Iqra Aijaz Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331510503

Tracking human actions, especially in the field of medicine, has encouraged interest in computer vision, specifically for detecting gait anomalies to aid in rehabilitating patients with irregular patterns. In this paper, the proposed system employs vision-based and inertial-based modalities that enable the recording of human actions and encourage human-machine collaboration. The inertial sensors were preprocessed using a Butterworth filter, as its magnitude response is often monotonic and maximally flat in the passband, providing smoothness. Multiple key features, such as Mel Frequency Cepstral Coefficients (MFCC) and auto-regression coefficients have been extracted. On the other hand, human detection has been performed for vision-based data, and main features such as angles and velocities have been calculated. The final results have been fused using multimodal survey fusion. The fused data was then forwarded for optimization using yeo-johnson power optimizer. Artificial Neural Networks (ANN) have been trained to perform classification. Evaluation using the confusion matrix on the Heriot-Watt University of Sao Paulo (HWU-USP) dataset proficient of identifying nine complex actions showed an accuracy rate of 83.33%. © 2024 IEEE.

关键词： Butterworth filters

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Analysis of Scanline and Minimum Entropy Selection Heuristics in Model Synthesis and Wave Function Collapse 5

Analysis of Scanline and Minimum Entropy Selection Heuristic...

引用

5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Harvey, Lucas Srivastava, Gautam Brandon University Dept. of Math and Computer Science Canada

ISBN: (纸本)9798350354690

In this paper, we analyze two versions of a texture synthesis algorithm, study the cases in which they fail to produce a successful result and present modifications that could be made to lessen their rates of failure. This algorithm, Model Synthesis, and its variation Wave Function Collapse are designed to take in a small sample input image, or set of image constraints, and produce a larger pseudorandom output image in which every region of the output image is locally similar to an element of the input image. Both versions of the algorithm accomplish this task by considering their output image as a grid of cells with each cell initially in a superposition of all possibilities for itself and resolving cells one by one until all cells have been resolved from their superposition to a fixed value. One of the key differences between the two versions of the algorithm is the order in which cells are selected to be resolved, one simply selects in a scanline order, while the other resolves first those cells that have the minimum entropy, and thus which we can be most certain of their eventual state. For many inputs, the minimum entropy model reaches a state in which its output is not consistent with the input and thus fails, while the scanline model does not. This paper looks at the cases in which this occurs and concludes that this is often caused by the minimum entropy model creating regions of elevated constraints in its solution. Finally, it presents a possible alteration to the algorithm which allows a minimum entropy model to avoid this manner of failure among a subset of test cases. © 2024 IEEE.

关键词： Image segmentation

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Gender Classification from Thermal Images using CNN

Gender Classification from Thermal Images using CNN

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Gurram, Harika Kethineni, Keerthi Kommineni, Deva Harshini Soumya, Kakani VRSEC Dept. Computer Science & Engineering Vijayawada India

ISBN: (纸本)9798350361537

In this study, two deep learning architectures - AlexNet and InceptionV3 - are used to evaluate gender categorization using thermal imagery. One of the most promising techniques for classifying gender is thermal imaging, which offers distinct insights into physiological traits. Divided into separate training and testing subsets, the dataset is made up of carefully preprocessed thermal pictures. InceptionV3, known for its complex design combining inception modules, and AlexNet, a pioneering convolutional neural network (CNN), are refined on the training data to adjust to the complexities of gender categorization. Then, in order to measure the performance of the trained models, they are rigorously evaluated on the testing set. Specifically, their accuracy is evaluated. Experimental findings show that InceptionV3 performs better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This significant disparity highlights how much better InceptionV3 is at identifying subtle thermal patterns and characteristics, which improves gender categorization accuracy. The work makes a substantial contribution to the rapidly developing area of thermal imagingbased gender categorization by highlighting how crucial it is to use advanced deep learning architectures in order to improve performance and accuracy. Subsequent investigations might delve into novel methodologies, including multi-modal fusion or sophisticated methods, to enhance precision and resilience in tasks using thermal-based gender categorization. © 2024 IEEE.

关键词： AlexNet Convolutional Neural networks Deep learning Gender classification Inception V3 Thermal images

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Secure Messaging in Post Quantum Cryptography using NTRUEncryption 15

Secure Messaging in Post Quantum Cryptography using NTRUEncr...

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

作者： Kaveri, S.P. Radhakrishnan, Indu Honnavalli, Prasad B. Pes University Dept. Computer Science Bangalore India

ISBN: (纸本)9798350370249

QuantumChat is a real-time messaging application designed to address the emerging threat of quantum computing to traditional encryption. By utilizing post-quantum encryption methods, the software guarantees confidential and safe communication. Hybrid cryptography facilitates a smooth transition for consumers by enabling backward compatibility with traditional encryption techniques. Combining state-of-the-art security with intuitive features, QuantumChat provides a next-generation solution for safe communication in a rapidly changing digital environment. The hybrid cryptography approach strikes a compromise between compatibility and security by combining both classical and post-quantum encryption. This makes it possible for users to use QuantumChat without compromising their ability to safely interact with others using conventional cryptographic techniques. With regard to secure communications, the QuantumChat software is a major advancement that tackles the impending risks associated with quantum computing. In the rapidly changing world of digital security, QuantumChat seeks to provide consumers a private and secure communication platform by fusing cutting-edge encryption technology with an intuitive user interface. © 2024 IEEE.

关键词： Quantum computers

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