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Performance Comparison for Different Neural Network architectures for chest X-Ray Image Classification 7

Performance Comparison for Different Neural Network Architec...

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7th international conference on Engineering, Applied Sciences and Technology, ICEAST 2021

作者： Rajadanuraks, Pinyada Suranuntchai, Sarapom Pechprasam, Suejit Treebupachatsakul, Treesukon King Mongkut's Institute of Technology School of Engineering Department of Biomedical Engineering Bangkok Thailand Rangsit University College of Biomedical Engineering Pathum Thani Thailand

ISBN: (纸本)9781665441223

Nowadays, the diagnose of diseases or abnormally clinical conditions of the chest can be done by reading chest X-Ray images by radiologists. the diagnosis from radiologist is considered qualitative analysis, which collects and analyzes the non-numerical data, which relates to the individual's experience. Consequently, errors may occur during the diagnosis, such as misdiagnosis. therefore, this research has a purpose for developing the software program which gathers Medical Image processing and Artificial Intelligence focuses on Deep Learning Neural Network, implementing on MATLAB program by training and classifying on labeled and non-labeled chest X-Ray images between normal condition and 13 abnormal cases. It is also stated that the trained model results are considered quantitative analysis, which can be confirmed or rejected by testing the causal relationship between variables. In this research, we apply several pre-trained architectures to our network, which, finally, the ResNet-50 architecture gave the highest percentage of accuracy compared with other architectures. Moreover, it is the network that appropriates for our image dataset. © 2021 IEEE.

关键词： Convolutional neural networks

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GastroNet FusionAI: Artificial Intelligence Diagnosis of Clinical Gastric Cancer Tumors

GastroNet FusionAI: Artificial Intelligence Diagnosis of Cli...

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IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Jialun Xu Xiayang Wu Zhenyuan Xu School of Statistics Nanjing Audit University Nanjing China School of Statistics Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

the significant advancements in natural image processing have sparked widespread interest in applying similar deep learning techniques to medical imaging. Unlike natural images, detecting details in medical images requires capturing finer details, necessitating the use of specially designed network architectures rather than standard ones developed for large-scale, downscaled natural images. Additionally, medical imaging often requires the fusion of images from multiple perspectives to form an accurate diagnosis. therefore, we propose a novel AI algorithm called GastroNet FusionAI, an advanced algorithm specifically designed for gastric cancer diagnosis. It employs an innovative image fusion strategy that effectively integrates images from four different imaging perspectives in gastric cancer detection. this approach not only provides a more comprehensive view but also significantly enhances the accuracy of disease detection by enriching the image content. the development of GastroNet FusionAI aims to improve the early detection rate of gastric cancer, which is crucial for increasing patient survival rates. Gastric cancer is one of the most common types of cancer globally and has a high incidence in certain regions. Despite the enormous potential of AI technology in gastric cancer detection, research on its effectiveness and reliability remains relatively scarce. Existing studies are often limited by small sample sizes, homogeneous datasets, or a lack of rare gastric cancer subtypes. To overcome these challenges, we employed Real-Time Test-Time Augmentation (R-TTA) to expand the original dataset (consisting of 54,706 images) to 273,530 images. this method not only improved the model's learning efficiency but also effectively reduced the risk of overfitting. Our model was trained on a preprocessed dataset from three different hospitals and four different imaging perspectives, and its performance was evaluated using cross-validation to ensure the broad applicability of

关键词： Accuracy Signal processing algorithms Cancer detection Real-time systems Artificial intelligence Medical diagnostic imaging Image fusion Cancer Overfitting Tumors

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Contrasting parallelized with Sequential Sorting

Contrasting Parallelized with Sequential Sorting

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Anurag Dutta Manjari Saha Department of Computer Science Government College of Engineering and Textile Technology Serampore Calcutta India

Sorting is one of the important Data Management Techniques that is quite commonly operated on Data Structures. It has been proved that best, any sorting algorithm can do, in asymptotic versions is φlog 2 φ, when the iterations are made to run in sequential manner. But what if we parallelize them? Architectural advancements, like Amdahl’s Law have made use of the concept of parallelism, and have successfully revolutionized the computing power of the microprocessors. In this paper, we will try to push the limit of computing, by making use of Graphical processing unit and will briefly compare the efficiency of the algorithms acquainted with both the techniques, sequential as well and parallel in terms of running time. In the first section, we have included some of the preexisting works on this topic, and have mentioned about scopes of further improvement. From the next section onwards, we have slowly put together all the pillars required to support our work.

关键词： Technological innovation Microprocessors Graphics processing units parallel processing Data structures Sorting

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Development of a Mobile Application for the Early Detection of Skin Cancer using Image processing algorithms, Transfer Learning, and AutoKeras 5

Development of a Mobile Application for the Early Detection ...

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5th IEEE international conference on Computer and Informatics Engineering, IC2IE 2022

作者： Shrimali, Samyak Jesuit High School PortlandOR United States

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

Skin cancer is one of the most common and dangerous types of cancer. With global ozone levels depleting and more ultraviolet radiation reaching the Earth's surface, rates of skin cancer are predicted increase rapidly. As per WHO, around 3 million cases of skin cancer are diagnosed every year which lead to thousands of deaths. the most important step in skin cancer treatment is early and accurate diagnosis when the survival rate is high, and successful medical treatment is possible. But with current tools, the skin cancer diagnosis process is subject to errors and results to be inaccurate, inefficient, and not globally scalable for developing and underdeveloped countries. this research proposes, SkinScan, a novel mobile application that uses deep learning to efficiently and accurately diagnose the 7 main types of skin cancer. this application utilizes a fine-tuned EfficientNetB7 CNN model that was found to be the most optimal after a comparative analysis of ten different CNN architectures. this chosen model had the highest validation accuracy of 95% and F1 score of 0.94. SkinScan's supplemental features include self-assessment tests for skin cancer risk, protective guidelines for exposure to UV radiation, and thorough information about each of the types of skin cancers, and their symptoms and treatments. SkinScan is an all-in-one that can significantly mitigate skin-cancer rates around the world by providing early skin cancer diagnosis. © 2022 IEEE.

关键词： Diseases

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Detection of Unstructured Sensitive Data Based on a Pre-Trained Model and Lattice Transformer

Detection of Unstructured Sensitive Data Based on a Pre-Trai...

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international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Feng Jin Shaozhi Wu Xingang Liu Han Su Miao Tian Yangtze River Delta Research Institute of University of Electronic Science and Technology of China (Quzhou) University of Electronic Science and Technology of China Quzhou China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

Sensitive data often contains a significant amount of unstructured information, posing great challenges to its processing. When dealing with unstructured data, effectively utilizing the structure of this sensitive data without a fixed format can greatly enhance the accuracy of sensitive data detection. therefore, in this paper, we have developed and evaluated a Chinese sensitive data detection method for unstructured data using a pre-trained model and a Lattice-Transformer structure. this method leverages the lattice structure to fully utilize the structural information of sensitive data, overcomes the complexity and uncertainty of unstructured information through the powerful parallel computing capability of the transformer, and benefits from the strong language representation ability of the pre-trained model, enabling the method to possess strong detection capabilities for unstructured sensitive data. the outcomes of the experiment demonstrate that the model is capable of adeptly managing sensitive data in Chinese, regardless of its unstructured nature, type, or provenance.

关键词： Training Uncertainty Computational modeling Lattices parallel processing Big Data Transformers

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PPBT: A High Performance parallel Search Tree 28

PPBT: A High Performance Parallel Search Tree

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28th Annual IEEE international conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Guan, Jiawen Fan, Rui ShanghaiTech Univ Shanghai Peoples R China

ISBN: (纸本)9781665410168

Search trees are one of the most important and widely used data structures, and parallelization is an effective method to improve their performance. However, many existing parallel search trees incur high synchronization costs and low memory I/O efficiency, which limits their performance. We propose PPBT, a batched parallel search tree which minimizes synchronization by partitioning the tree using novel algorithms and minimizing I/O cost using buffering. We give a new sequential algorithm for batch processing on search trees with optimal I/O efficiency for insert and delete operations, and also present a fast parallel algorithm for joining disjoint search trees. We show experimentally that PPBT is over 6x faster than the state-of-the-art parallel tree in [1] and over 40x faster than the concurrent search tree in [7], and achieves 21x speedup using 32 threads. PPBT's throughput on searches is lower due to reduced opportunities for buffering, but is still 1.3x that of [1]. In addition, PPBT has good response times for searches, for example completing 100K searches in under 1 ms in a tree with 10M elements.

关键词： parallel algorithms data structures search tree cache efficiency

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mpiPython: Extensions of Collective Operations

mpiPython: Extensions of Collective Operations

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international conference on Information and Computer Technologies (ICICT)

作者： Judah Nava Jaden Jinu Lee Hanku Lee Computer Science and Information Systems Minnesota State University Moorhead Moorhead MN

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

Despite performance limitations due to its interpreted nature, Python remains a dominant language among scientists and engineers. Enhancing its capabilities for parallel programming unlocks significant potential within parallel and cloud computing environments. mpiPython, a Python binding for message-passing interfaces, empowers Python for Single Program Multiple Data (SPMD) execution, enabling efficient parallel computations. Additionally, Python's inherent accessibility and versatility foster a growing demand for scaling and parallelizing it on distributed cloud environments. this paper extends mpiPython, bridging the gap in collective operations for parallel computing. the extension builds upon the original mpiPython's class-based structure, emphasizing two core principles: supporting vanilla Python with MPI and focusing on a C-based CPU-focused implementation. Unlike existing implementations like mpi4py, mpiPython directly interacts with the Python C API, offering greater control. Two new functions, MPI Gather and MPI Reduce, significantly improve efficiency and streamline collective operations between working nodes. the results demonstrate mpiPython's ability to perform at the level of other libraries while prioritizing a simple implementation accessible to a broad range of users.

关键词： Cloud computing Technological innovation Reviews parallel programming parallel processing Performance gain Libraries

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Audio signal Enhancement based on amplitude and phase deep learning model

Audio signal Enhancement based on amplitude and phase deep l...

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international conference on Advanced Technologies for Signal and Image processing (ATSIP)

作者： Mahrez Benneila Salah Hadji Line Mouhamed Anouar Ben Messouad National Engineers School of Tunis Labo Signal Images et Technologies de l’Information (LR-SITI-ENIT) Tunis Tunisia CERTn Borj-Hamm-lif Tunis labo of nano-materials and Energy(lanser) Tunis Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

In this work, we apply an approach based on simultaneous enhancement of the amplitude and phase time-frequency representation of a composite audio signal with two datasets, namely the Voice Bank+Demand and the NTCD TIMIT datasets. the approach is based on an encoder and a decoder connected by transformers having a convolution module. the encoder uses both amplitude and phase spectra as input, while the decoder has two parts one of parallel amplitude mask and the other is a phase decoder. thus allowing direct recovery of clean amplitude spectra and also clean enveloped phase spectra, its activation function is a sigmoid learnable and it estimates the phase in parallel. Like all neural networks there are losses at several levels such as: amplitude, phase, and short-term complex, without forgetting the temporal waveforms. All these losses are used jointly in training the model implemented, the validation of this model is done on the two datasets and the experimental results gave a PESQ of 3.5 for the Voice Banktdemand and a PESQ of 3.22 for NTCD-TIMIT.

关键词： Training Deep learning Time-frequency analysis Convolution Image processing Neural networks Speech enhancement

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Intelligent Design and Verification of Aircraft Autonomous Obstacle Avoidance and Collision Avoidance System

Intelligent Design and Verification of Aircraft Autonomous O...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Kaiming Yuan ATC and GCI College Air Force Engineering University Xi'an Shaanxi China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

With the rapid development of aviation industry, the obstacle avoidance challenges faced by aircraft in flight missions have become more severe. In order to improve flight safety, autonomous obstacle avoidance and collision warning system has become one of the key technologies. In this paper, an intelligent design scheme of autonomous obstacle avoidance and collision warning system for aircraft is proposed and verified. the system design combines high-precision radar, lidar sensor and stereo vision camera, and adopts parallel processing architecture accelerated by Graphics processing Unit (GPU) to realize real-time processing of collected data and obstacle recognition. By using technologies such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Depth Q Network, the system can accurately predict the trajectory of obstacles and automatically learn the best obstacle avoidance strategy. the path planning algorithm ensures that the aircraft can effectively avoid obstacles and minimize the deviation from the predetermined route. the experimental results show that the response time of the system is less than 479 ms, the highest obstacle avoidance success rate is 100%, and the path deviation is controlled within 9 cm, which can play an important role in the autonomous obstacle avoidance of aircraft.

关键词： Recurrent neural networks Graphics processing units parallel processing Alarm systems Real-time systems Safety Convolutional neural networks Aircraft manufacture Collision avoidance Aircraft

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Automated Cuneiform Symbol Detection and Translation Using Deep Learning Techniques

Automated Cuneiform Symbol Detection and Translation Using D...

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international conference on Signal processing and Information Security (ICSPIS)

作者： Shahad Elshehaby Mina Al-Saad Alavikunhu Panthakkan Hussain Al-Ahmad College of Engineering and IT University of Dubai Dubai United Arab Emirates

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

this paper introduces an automated methodology for the detection and translation of cuneiform symbols using high-level deep learning techniques. A total of five deep learning models were trained on a comprehensive dataset comprising cuneiform characters, which were evaluated according to major metrics such as precision and accuracy. After a thorough evaluation, two models demonstrated superior results and were further tested on cuneiform symbols extracted from the Hammurabi law corpus. Eventually, each model identified the Akkadian representation of each cuneiform symbol and provided its English translation. To further optimize the performance, ensemble and stacking techniques will be used in future work, combining these top models by creating hybrid architectures that improve the overall accuracy at detection. In addition to model development, this paper also explores the linguistic relations shared by Akkadian, an ancient Mesopotamian language, with Arabic, hence identifying the historical linguistic influences. this research presents an interdisciplinary investigation that bridges computational linguistics and archaeology, thus showing an emerging capability of deep learning in deciphering ancient scripts, which will contribute significantly to historical and cultural studies.

关键词： Deep learning Measurement Translation Accuracy Computational modeling Stacking Symbols Linguistics Signal processing Cultural differences

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