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AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation 4

AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentat...

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

作者： Kolekar, Samruddhi Maheshkumar Yadav, Agnesh Chandra Yadav, Suchita Dash, Daba Prasad Thaper Institue of Engineering and Technology Dept. Electrical Engineering Patiala India India Instite of Technology Patna Dept. Electrical Engineering Patna India Bennett University Dept. of Computer Science and Engineering Gr. Noida India

ISBN: (纸本)9798350364729

Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Image encryption using XOR-based continuous tone MSS and Cellular Automata 6

Image encryption using XOR-based continuous tone MSS and Cel...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Akanksha Garg, Hitendra Shivani, Shivendra GLA University Dept. of Computer Engineering and Applications Mathura India Thapar Institute of Engineering and Technology Dept. of Computer Science Patiala India

ISBN: (纸本)9798350346961

Online communication has become quicker and simpler as a result of the growth of the internet and the advancement in technology. People are using multimedia data in almost all spheres of life. The amount of multimedia data transmitted has been increasing at a massive rate. Multimedia data delivered over open and unreliable networks are susceptible to unauthorized access. As a result, a cryptographic standard is needed to securely and efficiently transmit multimedia data such as images over the networks. This study uses cellular automata and an XOR-based continuous tone MSS approach to create an image encryption method. The suggested approach has eliminated VC's drawbacks, including secret/share quantity constraints, unpredictable share patterns, codebook requirements, and pixel extension in shares and recovered secret pictures. The recommended approach uses an (n,n) multi-secret sharing system that can send n secret photos simultaneously. At the time of encoding, the master share overcomes been made using the secret key, and it may be successfully retrieved when decoding using a comparable secret key. The exploratory results and security examination shows the proficiency of the given approach. The complexity of the proposed approach can be adjusted by using the different combinations of cellular automata rules.. © 2023 IEEE.

关键词： Cellular automata

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DMDA: A Computational Resource Allocation Approach for IoT Devices in Fog Computing 4

DMDA: A Computational Resource Allocation Approach for IoT D...

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4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Pramono, Luthfan Hadi Shen, Shan-Hsiang National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506490

Fog Computing enables the efficient offloading of computational tasks from IoT Devices to Fog nodes, enhancing processing efficiency and reducing latency. This paper introduces the Dynamic Matching with Deferred Acceptance (DMDA) algorithm, designed to optimize the allocation of GPU and CPU resources within Fog Computing environments, specifically for smart city applications. The DMDA algorithm allocates resources to IoT Devices based on parameters such as data type, computational load, priority, and responsiveness. Devices processing image or video data are given priority for GPU allocation, while other devices are assigned to CPUs based on their cumulative performance scores. Devices with lower priority are reassigned to CPU resources or excluded when resource availability is exhausted, significantly enhancing performance under high-demand scenarios. The algorithm employs a Mixed-Integer Linear Programming (MILP) model to maximize the aggregate score of allocated devices while adhering to resource constraints. Experiments conducted with 50 to 1,000 devices demonstrate that both the total Device Score rate and Allocated Device rate reach 100%, indicating optimal allocation. Empirical findings show that the DMDA algorithm improves resource utilization, reduces processing times, and ensures effective workload distribution across large-scale IoT deployments. © 2024 IEEE.

关键词： Fog computing

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Enhancing Blind Communication With Voice-Assisted Email 4

Enhancing Blind Communication With Voice-Assisted Email

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Sasikala, C. Rupalakshmi, M. Sameer Ahammad, S. Meghana, U. Harshad Ali, M. Srinivasa Ramanujan Institute of Technology Dept. Computer Science and Engineering Anantapur India Anantapur India

ISBN: (纸本)9798350385793

Considering the key role of the internet in daily life, accessibility becomes crucial, especially for visually impaired individuals who heavily rely on it for accessing information. However, a significant portion of text materials on the internet remains inaccessible to blind users, limiting their opportunities for development and inclusion in various communities worldwide. Leveraging audio virtual environments, predominantly through screen reader technology, offers a notable advantage for blind individuals in accessing online applications like emails. This study presents a new design aimed at facilitating the email retrieval for blind users with the utilization of speech-to-text and text-to-speech technologies. This approach is particularly suitable for individuals with average human capabilities and a limited level of education, as it relies solely on interactive voice response for input, ensuring effective functionality for the proposed system. © 2024 IEEE.

关键词： Application programming interfaces (API)

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Recognition of Human Anomalies Using Convolution Neural Network

Recognition of Human Anomalies Using Convolution Neural Netw...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Parella, Teja Ponnam, Balakrishna Santhiya, P. Praveen, A. Sujihelen, L. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

By instantly identifying suspicious occurrences, people, and things, smart CCTV systems greatly improve monitoring capacities. In this research, we investigate how to enhance CCTV video stream anomaly detection using state-of-the-art machine-vision and machine-learning techniques, namely UNET and ALEXNET. In contrast to more conventional methods that depend on supervised learning, this research makes use of uncontrolled and semi-supervised learning techniques to tackle the variety and complexity of anomalies. Using algorithms like UNET and ALEXNET, the system can learn to spot outliers on its own, with little to no help from humans. Because it can tell the difference between normal and abnormal situations, this method improves monitoring efficiency and decreases false alarms. In addition, the system maintains original quality for unusual occurrences while keeping typical video at a lower level, optimizing storage economy. By combining UNET and ALEXNET algorithms, smart CCTV systems may detect suspicious events before they happen, which increases security and allows for faster reactions to any dangers. © 2024 IEEE.

关键词： Anomaly detection

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Unmasking Frame Duplication: Comprehensive Approach to Detection in Digital Video Using Machine Learning Algorithms and Signal Processing Techniques

Unmasking Frame Duplication: Comprehensive Approach to Detec...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Praddipraj, N.N. Praveen Kumar, B. Sreeji, S. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction. Next, feature extraction captures statistical and structural information from each frame. Deep neural networks and other machine learning methods are used to classify frames as original or duplicated using these features. To enhance detection accuracy, signal processing methods like block-based and key point-based approaches are applied. Block-based methods divide frames into segments for region comparison, while key point-based methods identify and match key points between frames. Our method's effectiveness and robustness are demonstrated through experiments on various digital videos. This combination of machine learning and signal processing offers a comprehensive solution to uncover frame duplication, ensuring video content's integrity and authenticity. © 2024 IEEE.

关键词： Learning algorithms

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Sensitivity Analysis for Normal and Canonical Internal Descriptions of A Notch Digital Filter

Sensitivity Analysis for Normal and Canonical Internal Descr...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Hinamoto, Yoichi Doi, Akimitsu Dept. of Electrical and Computer Engineering National Institute of Technology Kagawa College Takamatsu761-8058 Japan Dept. of Computer Science Hiroshima Institute of Technology Hiroshima731-5193 Japan

ISBN: (纸本)9798350353464

This paper analyzes coefficient sensitivity for the internal descriptions (state-space models) of a 2nd-order recursive notch digital filter. To this end, a 2nd-order recursive notch digital filter is realized by a normal-form or canonical-form internal description. The coefficient sensitivity for each internal description is then analyzed to derive the improved l2-sensitivity measure. Moreover, the coefficient sensitivity of the normal-form internal description is compared with that of the canonical-form counterpart by means of numerical experiments. The simulation results clarify that the normal-form internal description has low coefficient sensitivity as a whole comparing to the canonical-form counterpart. ©2024 IEEE.

关键词： Notch filters

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Fake/Counterfeit Medical Product Identification Using Blockchain & QR Code 2

Fake/Counterfeit Medical Product Identification Using Blockc...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Devanshu Singhal, Aryan Singh, Khushi Singh, Pushpa Gl Bajaj Institute of Technology and Management Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350371055

Counterfeiting in the field of medicine has raised substantial concerns as a global issue, with its prevalence noticeably expanding in recent years. Counterfeit medicines pose a particularly severe threat to patient's life or safety and cause severe implications. It is difficult to trace a medical product as well as to identify its authenticity. This paper aims to combat the widespread issue of counterfeit goods in industries like Pharmaceuticals and Healthcare by creating a Blockchain based product identification system. Combining the potential of Blockchain technology along with a secure QR code mechanism will offer robust security, immutability of data and have transparency in all the transactions. Thus, this system will ensure product traceability in the supply chain whereas the QR code will make it feasible for consumers to access product information instantly. © 2024 IEEE.

关键词： blockchain counterfeiting identification medicines QR code

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Advancing Medical Imaging: A Comprehensive Synthetic Dataset for Infant Brain MRI Analysis 2

Advancing Medical Imaging: A Comprehensive Synthetic Dataset...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Yashasvi, B.N. Kavya, N.P. R N S Institute of Technology VTU Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350371314

Accurate diagnosis and treatment planning are consistently challenging tasks in the medical field, especially when reliant on image diagnosis. The availability of sufficient and high-quality datasets becomes crucial in addressing this challenge. To tackle this issue, synthetic datasets can play a vital role in augmenting the number of samples while maintaining good quality. This study focuses on utilizing Deep Convolutional GANs (DCGANs) and Wasserstein GANs (WGANs) to generate synthetic datasets, specifically for Infant Brain MRI imaging. The Human Connectome Project (dHCP) dataset, comprising 800 samples categorized into four classes, was used for this study. Several key metrics, including the Similarity Index (SSI), Inception Score (IS), and Fréchet Inception Distance (FID), were employed to evaluate the performance of the generated synthetic datasets. The Inception Score was calculated for each class individually as well as for the overall synthetic dataset. For the normal class (first class), DCGAN achieved an Inception Score of lS.45, while WGAN scored 15.13. In the second class, DCGAN scored 19.70 compared to WGAN's 16.21. Similarly, in the third class, DCGAN scored lS.65 and WGAN 15.65, and in the abnormal class (fourth class), DCGAN scored 17.58 against WGAN's 16.00. The overall synthetic dataset scored 19.29 for DCGAN and 16.47 for WGAN. As the higher Inception Scores across different classes and for the overall dataset denoted that DCGAN performed well compare to WGAN in generating synthetic images for the Infant Brain MRI dataset. © 2024 IEEE.

关键词： Generative adversarial networks

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Classification and Prediction of Stroke Disease Using Machine Learning Algorithms 7

Classification and Prediction of Stroke Disease Using Machin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Lahari, Podutur Kumar, Akarsh Nandini, D. Usha Praveena, M. D. Anto Selvan, Mercy Paul Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Using text mining tools and machine learning algorithms, the paper presents a prototype for classifying strokes. The significance of machine learning extends across various domains, including surveillance, medicine, and data management, where appropriately trained algorithms prove invaluable. The study incorporates data mining techniques, offering a comprehensive understanding of information tracking from both semantic and syntactic perspectives. By analyzing medical case sheets, the proposed approach extracts symptoms and trains the system based on that information. A total of 507 case sheets are collected at "Sugam Multispecialty Hospital"in "Kumbakonam", Tamil Nadu, India, during the data collection phase. A tagging and maximum entropy approach was used to mine these case sheets. Stroke classification relies heavily on the suggested stemmer for distinguishing between common and special features. To handle the data that has been processed, support vector machines, XG Boost algorithms, SGD algorithms, decision trees, and random forests were used as machine learning algorithms. Random Forest demonstrated the highest accuracy among stroke classification algorithms evaluated. This integration of text mining and machine learning offers a promising framework for enhancing stroke classification based on patients' symptoms extracted from medical case sheets. The synergy between these technologies provides a robust methodology that can contribute significantly to improving the efficiency and accuracy of stroke diagnosis and classification in a medical. © 2024 IEEE.

关键词： Semantics

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