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Optimized Deep Learning Model for Early Parkinson’s Disease Detection Using Speech Spectrograms

Optimized Deep Learning Model for Early Parkinson’s Disease...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Oumaima Majdoubi Asma Ouabd Abdelilah Jilbab Achraf Benba Ahmed Hammouch E2SN Research Team ENSAM-R Mohammed V University in Rabat Rabat Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, and early detection is crucial for timely intervention and management. this study proposes an innovative approach for non-invasive PD detection using deep learning techniques applied to speech analysis. Speech recordings of Parkinson’s patients and healthy individuals were converted into spectrograms using the Short-Time Fourier Transform (STFT), which were then processed using Principal Component Analysis (PCA) for dimensionality reduction. We employed several convolutional neural network (CNN) architectures, including CNN, MobileNet, ResNet50, and Inception, to classify the data. A 5-fold cross-validation was applied. Among the models, CNN demonstrated superior performance, achieving 99.19% accuracy, 100% precision, and 99.21% F1 score without cross-validation, which improved to perfect scores across all metrics when cross-validation was applied. the results indicate that deep learning, coupled with speech signal processing and cross-validation, provides a powerful tool for the early, non-invasive detection of Parkinson’s disease. this approach holds significant potential for improving diagnosis accuracy and enabling timely medical intervention.

关键词： Deep learning Accuracy Speech analysis Convolutional neural networks Artificial intelligence Speech processing Spectrogram Diseases Principal component analysis Residual neural networks

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Pyramidal Connected Component Labeling by Irregular Graph Pyramid 5

Pyramidal Connected Component Labeling by Irregular Graph Py...

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5th international conference on Pattern Recognition and Image Analysis, IPRIA 2021

作者： Banaeyan, Majid Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group 193/03 Vienna Austria

ISBN: (纸本)9781665426596

this paper presents a new logarithmic-time algorithm which simultaneously assigns labels to all connected components of a binary image in parallel. the irregular graph pyramid of an input binary image is constructed based on the optimized combinatorial structure. the novel small built pyramid has only three levels and is created at worst case with O(log(N2)) complexity for a N× N-sized (2D) binary image. To assign a label to each connected component, instead of the common linear-time raster scan techniques (with complexity O(N2)), only two important movements, namely bottom-up and top-down traversing, are needed. First, in the bottom-up traversing, the redundant connections are removed and the contraction kernels are contracted. this results in a simpler graph at top of the pyramid each of its vertices has a unique label identifying corresponding connected component. Such reduced graph preserves all connecting relations including inclusions. Second, in the top-down traversing, each unique label propagates down into each individual corresponding pixel at the base level. the complexity of the labeling propagation procedure in worst cases is O (log(image - size)). the GPU implementation of the algorithm has high performance and the bottleneck is the bandwidth of the memory or equivalently the number of available independent processing elements. Finally, the experimental results show the proposed algorithm outperforms the other state-of-the-art methods. © 2021 IEEE.

关键词： Binary images

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Multiple Transform Selection concept modeling and implementation using Interface Based SDF graphs 21

Multiple Transform Selection concept modeling and implementa...

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14th Workshop on Design and architectures for Signal and Image processing, DASIP 2021, held jointly with the 16th HiPEAC conference

作者： Haggui, Naouel Belghith, Fatma Hamidouche, Wassim Masmoudi, Nouri Nezan, Jean-François Ietr Laboratory Insa Rennes France National School of Engineering of Sfax Tunisia

ISBN: (纸本)9781450389013

Recent studies predict that video data accounts for 82% of Internet traffic by 2022. this fact has motivated MPEG to define a new Video Coding Standard called Versatile Video Coding (VVC), which will be released by the end of 2020. VVC will offer the possibility to handle new video formats and to improve significantly video compression over its predecessor HEVC. Indeed, the objective is to reduce the necessary bit rate by half, at equivalent quality. these advances require the use of more complex algorithms, although the increase in complexity has been limited throughout the standardization process. In order to decrease the complexity of VVC and consequently the coding execution time, several methods have been introduced at different stages of the encoder. the aim of this paper is to explore the available parallelism of VVC to accelerate the coding and the decoding processes. this paper focuses on the transformation block and more specifically the new concept of Multiple Transform Selection (MTS) introduced by VVC. Moreover, a study of several granularity levels of Interface-Based Synchronous Dataflow (IBSDF) models and their impact on the performances obtained on x86 architectures is presented. IBSDF dataflow graph has been developed to reveal the available parallelism of MTS. the PREESM fast prototyping tool is then used for the mapping and the scheduling of MTS on virtual and real parallel architectures and for generating efficient parallel implementations on real architectures. PREESM has been used in this work to explore the potential parallelism offered by MTS and to prove the efficiency of MTS on multicore x86 architectures. Experimental results show a speed-up close to the optimum. © 2021 ACM.

关键词： Video signal processing

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An Empirical Study for the GPT-Based LLM in Paper Similarity Measurement

An Empirical Study for the GPT-Based LLM in Paper Similarity...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Jinglin Yang College of Information Science and Engineering/ College of Artificial Intelligence China University of Petroleum (Beijing) Beijing China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

the Transformer model, with its core self-attention mechanism and encoder-decoder architecture, offers enhanced parallel computing capabilities and extended contextual memory. It has led to groundbreaking advancements in tasks such as machine translation and text generation. the development of Transformer models has also spurred the emergence of large language models like GPT. In theory, GPT models should have superior processing capabilities and flexibility in NLP tasks compared to conventional Transformer. However, there is a lack of research comparing the actual performance differences between them.. In this work, we bridge this gap by a systematical study of text similarity evaluation between ChatGPT and conventional Transformer. To this end, we propose an innovative paper similarity measurement to experimentally and comparatively analyze the performance differences. In this study, we collected paper data from public paper dataset and categorized it according to topic-based standards. Using the categorized dataset as a benchmark, we analyzed the similarity assessment results of comparison paper data. Additionally, we introduced a custom GPT scoring standard to evaluate paper similarity, further comparing the accuracy, interpretability, and stability of Transformerand GPT models.

关键词： Training Accuracy Computational modeling Stability criteria Transformer cores parallel processing Transformers

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Optimizing IoT Capabilities: Leveraging FPGA for Superior Performance, Efficiency and Security

Optimizing IoT Capabilities: Leveraging FPGA for Superior Pe...

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Emerging Technology (INCET), international conference for

作者： Gurdeep Singh Amanpreet Kaur Vaneeta Bhardwaj Shishir Shrivastava Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Nowadays IoT (Internet of things) is used everywhere, like homes, industry, automation, health sector, hospitality, smart cities, farming, tourism, self-driving cars etc. IoT is implemented successfully on many available hardware devices such as Microcontrollers, Arduino, Raspberry Pi, ASIC (Application-Specific Integrated Chip), FPGA (Field Programmable Gate Array) etc. Out of all these hardware platforms, FPGA is found to be more energy-efficient and the least power-consumable device. the use of FPGA with IoT is more reliable and effective in terms of power and energy saving. In this work, the integration of different technologies using IoT and FPGA is represented in the comparison table. the number of research works and publications on IoT using different hardware devices have been shown from year 2015 to 2023.

关键词： Performance evaluation Microcontrollers Smart cities Energy conservation parallel processing Hardware Internet of things

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HMA: An Efficient Training Method for NLP Models 2021

HMA: An Efficient Training Method for NLP Models

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5th international conference on Innovation in Artificial Intelligence (ICIAI)

作者： Yang, Yuetong Lai, Zhiquan Cai, Lei Li, Dongsheng Natl Univ Def Technol Sch Comp Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (纸本)9781450388634

Nowadays, deep learning has been widely used for solving natural language processing (NLP) problems. Embedding matrices are common-used in the NLP deep models for automatical feature learning. However, the sparsity of embedding matrices makes it challenging to efficiently train the NLP models in data parallelism. When training with synchronous optimization methods, the aggregation on sparse gradients brings high communication cost and low scalability for distributed training. In this paper, we combine Model Average (MA) and synchronous optimization methods together, and propose HMA, a hybrid training method for NLP deep models. Furthermore, we implement HMA method in Horovod+TensorFlow training framework and conduct experimental evaluation with representative NLP models. For NLP models with a large number of sparse parameters, HMA saves over 30% wall-clock time compared with the state-of-the-art distributed training framework, while maintaining the same final training loss.

关键词： distributed training deep learning model average local sgd

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parallel Frequent Itemset Mining Algorithm and Optimization Based on Spark

Parallel Frequent Itemset Mining Algorithm and Optimization ...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Chunran Liu Tianjin University of Technology Tianjin China

Frequent item set mining is an important task in data mining, which can help us find the variable combinations that occur frequently in the data set. In order to solve the problem of frequent itemset mining in large-scale high-dimensional data, we propose a parallel frequent itemset mining algorithm based on Spark, OSFPG. the algorithm is optimized on the basis of the traditional FP-growth algorithm, and the number of candidate item sets is reduced by constructing a special data structure called FP tree, thus improving the efficiency of frequent item set mining. the algorithm mainly uses the idea of load balancing to optimize the grouping strategy. It also considers two factors of partition computation and FP-Tree size to ensure that the total load between each group is approximately equal. On the basis of Spark based SFPG algorithm, we implement this optimized algorithm. Based on the SFPG algorithm, it is improved to dynamically allocate computing resources and carry out distributed computing, so that the intermediate results generated in the operation process are saved in memory, thus effectively reducing the data consumption. In order to verify the performance of OSFPG algorithm, we apply it to a real data set for experiments. Experimental results show that compared with the traditional FP-Growth algorithm, the proposed algorithm has higher efficiency and lower time complexity when processing large-scale high-dimensional data. In addition, the algorithm can deal with dynamically updated data effectively and has strong practicability.

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Deep Learning and Classical Machine Learning for code mapping in Heterogeneous Platforms 5

Deep Learning and Classical Machine Learning for code mappin...

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5th IEEE international conference on Networking and Advanced Systems, ICNAS 2021

作者： Hakimi, Yacine Baghdadi, Riyad Challal, Yacine Algiers Algeria de Massachusetts United States

ISBN: (纸本)9781665428217

Programming modern heterogeneous systems is becoming more and more challenging due to their complexity. To simplify software development for such architectures, more advanced compilers are being designed. Such compilers automatically optimize code and hide the complexity of the target heterogeneous architecture from the developer. An example of problems that these compilers need to solve is to decide whether to map (run) a piece of code on CPU or GPU (Graphics processing Unit). State-of-the-art compilers use accurate optimization heuristics to solve such problems and decide about how to optimize a code automatically. Traditional machine learning and Deep Learning approaches have both been used to build such heuristics. While traditional machine learning is well suited for training on small datasets, it is not well suited for extracting a set of high-quality features, on the other hand, deep learning can automatically extract features, but it needs a large amount of data to get satisfactory *** this paper, we propose a new machine-learning-based model that allows a compiler to automatically decide whether to map a piece of code to CPU or GPU. Our model uses traditional machine learning and deep learning by exploiting the advantages of each of them. We show that our proposed model, which requires a small amount of data and training time, matches and outperforms state-of-the-art pre-trained deep learning models that require large amounts of data. © 2021 IEEE.

关键词： Graphics processing unit

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sTiles: An Accelerated Computational Framework for Sparse Factorizations of Structured Matrices

sTiles: An Accelerated Computational Framework for Sparse Fa...

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ISC High Performance 2025 Research Paper Proceedings (40th international conference)

作者： Esmail Abdul Fattah Hatem Ltaief Håvard Rue David Keyes CEMSE Division King Abdullah University of Science and Technology Makkah Saudi Arabia

ISBN: (数字)9783982633619

this paper introduces sTiles, a GPU-accelerated software framework that computes factorization of sparse structured symmetric matrices. By leveraging tile algorithms to achieve fine-grained computations, sTiles employs a structure-aware flow of task executions for the Cholesky factorization to tackle challenging arrowhead sparse matrices with variable bandwidths. these matrices, common in various scientific and engineering fields, necessitate an adaptive factorization strategy that operates only on nonzero tiles. Our approach relies first on a collection of permutation techniques to minimize fill-in during factorization. then, sTiles proceeds with a customized static scheduler to orchestrate computational tasks on shared-memory systems equipped with GPU hardware accelerators. sTiles strikes the required balance between the tile size and the degree of parallelism. the former has a direct impact on the algorithmic intensity, although monitoring the additional floating-point operations and memory footprint becomes crucial for parallel performance. the latter may be limited due to the inherent arrowhead sparse matrix structure. To further expose parallelism, we must adopt a left-looking variant of the Cholesky factorization to break the sequential dependencies during the accumulation operations on the trailing submatrix and operate on them instead using tree reductions. Extensive evaluations demonstrate a boost in performance for the sparse Cholesky factorization on various arrowhead structured matrices against the state-of-the-art sparse libraries, with sTiles achieving up to 8.41X/9.34X/5.07X/11.08X speedups compared to CHOLMOD/SymPACK/MUMPS/PARDISO, respectively. On GPUs, sTiles further exploits the computational throughput by adjusting the two key ingredients aforementioned to attain a 5X speedup compared to a 32-core AMD EPYC CPU when executed on an NVIDIA A100 GPU. Our generic software framework 1 1 the software is available at https://***/esmail-abdulfatta

关键词： Symmetric matrices Software algorithms Graphics processing units Bandwidth parallel processing throughput Software Libraries Computational efficiency Sparse matrices

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Research on Trust Evaluation Model for Vehicle-to-Grid Interaction Based on Zero Trust Architecture

Research on Trust Evaluation Model for Vehicle-to-Grid Inter...

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Information Science, parallel and Distributed Systems (ISPDS), international conference on

作者： Nankai Zhou Shenglong Ji Qingmei Mao North China University of Technology Beijing China Qianxin Technology Group Co. LTD

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

During vehicle-to-grid (V2G) interactions, information is transmitted across multiple platforms, posing significant security risks to both information transmission and network boundaries. Traditional evaluation techniques, which rely on fixed permission assignments and predefined rules, struggle to adapt to scenarios characterized by a large number of vehicles, frequent changes in network topology, and diverse service demands. this paper proposes a trust evaluation model based on zero trust architecture. By introducing multidimensional user attribute sets, platform attribute feature sets, and user recommendation attributes, the model dynamically evaluates the trustworthiness of users and platforms. It uses trust values to dynamically authorize access subjects and assess platform reliability. this approach enhances the system's ability to identify malicious behavior and improves the security of data resources and transmission in V2G interactions. Experimental results indicate that the proposed zero trust-based trust evaluation model significantly enhances the security and dynamic responsiveness of V2G interactions.

关键词： Vehicle-to-grid Technological innovation Network topology Data security Evaluation models Information processing Zero Trust

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