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Parallel Approaches in Deep learning: Use Parallel computing 7

Parallel Approaches in Deep Learning: Use Parallel Computing

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7th international conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Rakhimov, Mekhriddin Javliev, Shakhzod Nasimov, Rashid Department Of Computer Systems Tashkent University Of Information Technologies Named After Muhammad Al-Khwarizmi Uzbekistan Department Of Artificial Intelligence Tashkent University Of Information Technologies Named After Muhammad Al-Khwarizmi Uzbekistan Department Of Artificial Intelligence Tashkent State University Of Economics Uzbekistan

ISBN: (纸本)9798400709036

In the present context, the rise of artificial intelligence (AI) has brought to light the importance of expediting processes due to the advancement in AI. this issue holds significance across various domains of machine learning. Consequently, all sectors linked to deep learning, a crucial facet of artificial intelligence, are experiencing continuous advancements. To illustrate, tasks associated with training such as the multiplication of extensive matrices or the manipulation of images to extricate vital features can lead to an increase in time consumption. It is common knowledge that dealing with substantial data quantities demands a considerable amount of time. the primary focus of this research revolves around significantly enhancing the time efficiency of deep learning procedures. While it is a recognized fact that graphics processing units (GPUs) deliver notably quicker outcomes for specific data handling tasks in comparison to a computer's central processing unit (CPU), this study delves into heterogeneous computing systems in cases where GPUs are inaccessible. Herein, we investigate strategies for attaining elevated processing speed through the utilization of advanced technologies. Ultimately, this study culminates by presenting comparative findings derived from di-verse approaches, accompanied by crucial recommendations for future endeavors. © 2023 ACM.

关键词： Parallel processing systems

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An intricate spectrogram processing in speech enhancement using machine learning with cloud computing 7

An intricate spectrogram processing in speech enhancement us...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Reddy, Yenna Geetha Mahendran, G. Al-Khaykan, Ameer Alkawak, Omar A. Kalaiselvan, S.A. Priya, A. Department of Computer Science And Engineering Kompally Telangana Secunderabad India R.M.K Engineering College Department of Mechancal Engineering Chennai Tamil Nadu Kavaraipettai India University of Kufa Department of Electronic And Communications Engineering Najaf Iraq Al-Mustaqbal University College Department of Air Conditioning And Refrigeration Technical Engineering Babil Hilla Iraq Department of Computer Science And Engineering Tamil Nadu Chennai India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India

ISBN: (纸本)9798350371406

the spectrogram is defined as the visual representation of the signal strength with a particular interval of time regarding frequencies in the appropriate waveform. It is demonstrated using the Fourier transform. It is illustrated in a three-dimensional plot of signal composed with amplitude versus frequency and time. they are classified as long signal sections and short signal sections with filters. It demonstrates the consecutive spectacle of the sound signal at a particular interval of time. the time-frequency representation of audio signals is obtained through spectrograms. this helps in the understanding of the speech data. machine learning plays an important role in the identification of the relevant features in the spectrogram. the intricate patterns in the time and frequency domains are represented through CNN and RNN. these models are trained and tested through a large dataset which includes both clean and noisy speech samples. this helps to learn complex interrelationships and patterns associated with varied environmental conditions and interference systems. the computational demands of these systems are handled through the cloud computing techniques. they provide a platform for effective solutions for performing real-time interference. these cloud services provide various collaborations to numerous models to achieve desired outcomes. the proposed system provides higher adaptability in varied environmental and noisy profiles. thus the integration of cloud computing with machine learning provides real-time processing with a natural seamless user experience. © 2023 IEEE.

关键词： Cloud computing

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Alzheimer's Disease Detection Using Transfer learning: Performance Analysis of InceptionResNetV2 and ResNet50 Models 7

Alzheimer's Disease Detection Using Transfer Learning: Perfo...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Sanjeev Kumar, K. Reddy, B. Saketh Ravichandran, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Chennai India

ISBN: (纸本)9798350371406

Alzheimer's disease (AD), a well-recognized form of dementia, is characterized by cognitive decline and memory impairment, significantly impacting an individual's daily functioning. Recent investigations have explored the potential of innovative imaging techniques, such as machine learning and deep learning algorithms in conjunction with magnetic resonance imaging (MRI), for diagnosing AD. this study utilizes convolutional neural network (CNN) models to automatically classify brain MRI images, capturing relevant features associated with Alzheimer's disease. the effectiveness of CNN models in distinguishing between the four classes of AD (Mild Demented, Very Mild Demented, Non-Demented, and Moderate Demented) is compared against traditional techniques. the CNN architectures employed include InceptionResNetV2 and ResNet50. the performance of each CNN model is rigorously evaluated, leading to the selection of the model with the highest accuracy. this research contributes to advancing diagnostic methodologies for Alzheimer's disease, holding promise for improving early detection and intervention strategies. © 2023 IEEE.

关键词： Neural network models

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Analyzing the Performance of Novel Activation Functions on Deep learning Architectures 7th

Analyzing the Performance of Novel Activation Functions on D...

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Chaturvedi, Animesh Apoorva, N. Awasthi, Mayank Sharan Jyoti, Shubhra Akarsha, D.P. Brunda, S. Soumya, C.S. Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9789811954818

Deep learning is a cutting-edge technology that functions similarly to the human nervous system. Neural networks are at the heart of Deep learning. Neural networks are made up of numerous layers, including the input layer, which accepts raw data as input, hidden layers, which process the input data, and a final layer, the output layer, which provides the result. Its workflow pattern is comparable to machine learning (Basak et al. in Eur. Phys. J. Spec. Top. 230:2221–2251, 2021;Makhija, Simran et al. "Separating stars from quasars: machine learning investigation using photometric data. Astron. Comput. 29:100,313, 2019) [2,, 11], allowing us to gain hands-on expertise with this technology, speed up our work, and allow us to make several efforts without having to develop a basic machine learning algorithm from scratch. In the case of deep learning, there are several neural networks to choose from. the majority of Deep learning architectures are built on neural networks such as CNN, RNN, and others. Deep neural network activation function development is often guided by set goals and gradual steps toward tackling specific challenges. the primary goal of this study is to examine the performance of innovative activation functions (SBAF parabola (Saha et al. DiffAct: A Unifying Framework for Activation Functions. international Joint conference on Neural Networks (IJCNN) (2021);1–8;Saha et al. A new activation function for artificial neural net based habitability classification. 2018 international conference on Advances in computing, Communications and Informatics (ICACCI) (2018): 1781–1786) [6,, 16], AReLU (Mediratta et al. LipAReLU: AReLU Networks aided by Lipschitz Acceleration. 2021 international Joint conference on Neural Networks (IJCNN) (2021);1–8.) [,7], Leaky ReLU, SWISH) on deep learning architectures such as CNN, DenseNet. On deep learning architectures, our study will compare the classification performance of the aforementioned activation functions. Our study all

关键词： Convolutional neural networks

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A Model-Based machine learning Approach for Assessing the Performance of Blockchain Applications 7

A Model-Based Machine Learning Approach for Assessing the Pe...

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7th IEEE international conference on Smart Internet of things, SmartIoT 2023

作者： Albshri, Adel Alzubaidi, Ali Solaiman, Ellis Newcastle University School of Computing United Kingdom University of Jeddah College of Computer Science and Engineering Saudi Arabia Umm Al-Qura University College of Computing Saudi Arabia

ISBN: (纸本)9798350316575

the recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's complexity and distributed nature. therefore, a reliable modelling approach is needed to boost Blockchain-based applications' development and evaluation. While simulation-based solutions have been researched, machine learning (ML) model-based techniques are rarely discussed in conjunction with evaluating blockchain application performance. Our novel research makes use of two ML model-based methods. Firstly, we train a k nearest neighbour (k\text{NN}) and support vector machine (SVM) to predict blockchain performance using predetermined configuration parameters. Secondly, we employ the salp swarm optimization (SO) ML model which enables the investigation of optimal blockchain configurations for achieving the required performance level. We use rough set theory to enhance SO, hereafter called ISO, which we demonstrate to prove achieving an accurate recommendation of optimal parameter configurations;despite uncertainty. Finally, statistical comparisons indicate that our models have a competitive edge. the k\text{NN} model outperforms SVM by 5% and the ISO also demonstrates a reduction of 4% inaccuracy deviation compared to regular SO. © 2023 IEEE.

关键词： Blockchain

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Forecasting Stock Exchange Trends for Discrete and Non-discrete Inputs Using machine learning and Deep learning Techniques 5th

Forecasting Stock Exchange Trends for Discrete and Non-discr...

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Dhondi, Teja Ravi, G. Vazralu, M. Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789811986680

the trend of stock exchange prices is always uncertain for the stockholders, and venture capitalists is there governed by multiple factors that can influence them. In this project, we aim to predict the stock market trends using deep learning and machine learning techniques. We had chosen four distinct stock market groups from current stock exchange to be our dataset for experimentation and evaluation. they are metals, petroleum, non-metallic mineral and financials. In this paper, we try to build machine learning models for stock market correction using 9 different machine learning algorithms like SVM, KNN, ANN, RNN, LSTM, logistic regression, XGBoost, AdaBoost, etc. Ten different technical indicators from the past 10 years are used to train our machine learning models to predict the stock exchange movement in two ways, i.e., using non-discrete and discrete data. Our experimentation results reveal that for non-discrete data, the models built using RNN and LSTM give better performance bi for discrete data deep learning techniques outperform other mechanisms. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Financial markets

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Towards Democratizing AI: A Comparative Analysis of AI as a Service Platforms and the Open Space for machine learning Approach 23

Towards Democratizing AI: A Comparative Analysis of AI as a ...

引用

2023 7th international conference on Cloud and Big Data computing, ICCBDC 2023

作者： Rall, Dennis Bauer, Bernhard Fraunholz, thomas Wogra Ag Germany Software Methodologies for Distributed Systems University of Augsburg Germany

ISBN: (纸本)9798400707339

Recent AI research has significantly reduced the barriers to apply AI, but the process of setting up the necessary tools and frameworks can still be a challenge. While AI-as-a-Service platforms have emerged to simplify the training and deployment of AI models, they still fall short of achieving true democratization of AI. In this paper, we aim to address this gap by comparing several popular AI-as-a-Service platforms and identifying the key requirements for a platform that can achieve true democratization of AI. Our analysis highlights the need for self-hosting options, high scalability, and openness. To address these requirements, we propose our approach: the "Open Space for machine learning"platform. Our platform is built on cutting-edge technologies such as Kubernetes, Kubeflow Pipelines, and Ludwig, enabling us to overcome the challenges of democratizing AI. We argue that our approach is more comprehensive and effective in meeting the requirements of democratizing AI than existing AI-as-a-Service platforms. © 2023 ACM.

关键词： Space platforms

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Online Prediction of Positive and Negative Emotionology Applying machine learning Technique 7

Online Prediction of Positive and Negative Emotionology Appl...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Vijayaragavan, Pichiyan Suresh, Chalumuru Surya Narayana Reddy, V. Sathar, G. Vijila, T. Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Dept.of CSE-DS Telangana Hyderabad India Dept.of CSE Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Dept.of CSE-DS Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Tamilnadu Chennai India Bharath Institute of Higher Education and Research University Dept.of CSE-DS Telangana Hyderabad India

ISBN: (纸本)9798350397253

Owing to the meteoric rise of the Web, social media platforms have evolved into an essential part of the manner in which people express their thoughts and emotions through writing. Written words, visuals, sound, and moving pictures are just some of the many ways in which people share their ideas and opinions with the world. In the blink of an eye, a myriad of unstructured social media structures unexpectedly generat vast amounts of unstructured online data that can be used to better comprehend human psychology. Sentiment analysis, which determines the tone of a piece of writing, can be used for this purpose. Analyze the reviews that the individual has read and decide whether the author's point of view is positive, negative, or neutral. Some critics argue that emotion recognition is necessary to select a person's emotional/mental kingdom because emotion analysis is inadequate,- and that judgment of a person's emotional/mental kingdom at the level of emotion evaluation, numerous emotional models, emotion studies, and feeling intensity is unreliable. Sentences that recognize your mannerisms. Evaluations of emotions and perspectives are difficult because of the difficulties encountered. © 2023 IEEE.

关键词： Emotion Recognition

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BharatFakeNewsKosh: A Data Repository for Fake News Research in India 7th

BharatFakeNewsKosh: A Data Repository for Fake News Researc...

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Singh, Manish Kumar Ahmed, Jawed Raghuvanshi, Kamlesh Kumar Alam, M. Afshar Department of Computer Science Jamia Hamdard Delhi India Department of Computer Science Ramanujan College UoD Delhi India

ISBN: (纸本)9789819908370

the fake news-related events are on the high rise all across the world, particularly in India. there has been a lot of multidisciplinary research taking place in this area. Nevertheless, Artificial Intelligence-based research is considered to provide better solutions to detect fake news on an automated basis. However, a large-sized benchmark dataset is pertinent for running machine learning algorithms. Further, the dataset used for training and testing machines to spot fake news is limited to the geo-political events and the linguistic aspects of a particular state or nation. there has been a good amount of work done in the area of building fake news-related datasets in different parts of the world. However, a benchmark dataset covering fake news events in India for carrying out fake news-related research is still lacking. this paper introduces BharatFakeNewsKosh, a benchmark data repository of fake news events in India, for training and testing machine learning models to detect fake news. Further, the key machine learning techniques are used to detect fake news using BharatFakeNewsKosh dataset and the resulting performance is compared with that of the existing benchmark datasets that are so far widely utilized for studying fake news all across the globe. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Cloud and IoT based Framework for Air and Noise Pollution Monitoring System via Extreme learning machine 7

Cloud and IoT based Framework for Air and Noise Pollution Mo...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Tinnaluri, V S Narayana Sahukar, Latha Chaudhari, Ankur Malathy, G. Kolavennu, Subbarao Leonard, L. Koneru Lakshmaiah Education Foundation Department of Computer Science Green fields Vaddeswaram Andhra Pradesh Guntur India Sreenidhi Institute of Science and Technology Yamnampet ECE Department Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Paavai Engineering College Pachal Department of Computer Science & Engineering Tamilnadu Namakkal India Sphoorthy Engineering College CSE Cyber Security Department Telangana Hyderabad India ISBR Business School Department of Analytics Karnataka Bangalore India

ISBN: (纸本)9798350397253

Nowadays, air and noise pollution pose the greatest threat to the environment and to public health. Air and noise pollution are rising rapidly. In order to get it under control, close observation of it is strongly advised. this research addresses this problem by unveiling a method of detecting both ambient noise and the presence of potentially dangerous chemicals. Pollution is increasing at an alarming rate, and it's starting to cause problems for living things. Pollutants like loud noises and hazardous gases have a negative impact on people's health and want special attention. In this research, the proposed approach suggests the development of a network of sensors to monitor the environmental noise and air quality. A NodeMCU equipped with an ESP8266 WLAN adaptor is part of an Internet of things (IoT) based Air and noise pollution monitoring system, together with a DHT11, a MQ9 gas sensor, and a Lm393 sound sensor. this monitoring system was developed with the help of neural network-based algorithms like CNN, SVM, and ELM to accurately forecast the level of pollution in a given area. Root-Mean-Square-Error (RMSE), Mean Square Error (MSE), and Mean-Absolute-Error (MAE) were used as performance indicators. Based on the performance, the Extreme learning machine (ELM) based training algorithm for single hidden layer feed forward neural networks (SLFNs) has revolutionized SLFN development. the results showed ELM as the most effective solution. the proposed model can now be used in high-pollution and noisy locations. © 2023 IEEE.

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