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Spatially Attentive Scale Invariant Feature Modelling for Alzheimer's Disease Detection 9

Spatially Attentive Scale Invariant Feature Modelling for Al...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Tripathy, Santosh Kumar Behera, Chandan Kumar Pandey, Pranjal Mohan Pandey, Vedant Verma, Pratishtha Vit Bhopal University School of Computing Science And Engineering Kothri-Kalan Sehore MP Bhopal India Nit Kurukshetra Department of Computer Science And Engineering Haryana India

ISBN: (纸本)9798350394474

The prevalence of Alzheimer's Disease (AD) patients has significantly increased recently, and early diagnosis is crucial. AI-aided solutions for diagnosing AD have been rising for decades. The majority of these solutions show the application of deep learning methods, both with and without transfer learning. However, a few issues have been noted that require attention in order to detect AD: a. there is a lack of scale-invariant feature modelling, and b. there is a lack of spatial attention paid to scale-invariant features. The suggested model addresses these issues in two ways: first, an efficient multiscale feature modelling module (EMFMM) is designed to exploit scale invariant features through multilayers each having multicolumn of dilated convolution layers. At each layer the multicolumn features are concatenated and forwarded to its subsequent layer. Secondly, the spatial convolution block attention (SCBA) is plugged in different layers of concatenated features to provide spatial attention over the multiscale features. The spatially attentive feature maps from several SCBAs are further concatenated and given to a multilayer neural network to employ disease forecasting. Two open-source datasets, OASIS and AD-Dataset, are used to demonstrate extensive experiment. The suggested model performs better than current best practices, with accuracy values on OASIS and AD-Dataset of 99.75% and 96.20%, respectively. © 2024 IEEE.

关键词： Multilayer neural networks

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Physiological Variables Interface Design for MIOT Ward 3

Physiological Variables Interface Design for MIOT Ward

引用

3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Mishra, Saket Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350399264

A key component of all IOT applications used in the medical sector is the Internet of Things (IOT) interface. An IOT interface for physiological variables application is created to address the requirements for wireless transmission and administration of physical measurements in hospitals. The gateway is created using an adaptation of carrier sensors, automated network, & multi-thread coding technologies. To address the various requirements of the sensor nodes in the Medical IOT (MIoT) Wards, a hardware gate as well as a physiological characteristics gateway server with functionalities of protocol interpretation, route management, & data protection management are built. The measured parameters gate, the primary IOT gadget used in the healthcare profession, is intended to facilitate the smart gathering and thorough convergence of continuous physiological data. The MIoT ward streamlines the previously manual process of collecting physiological signals & offers trustworthy full security for the sufferers' caretaking. It will be highly valuable in healthcare situations. © 2023 IEEE.

关键词： Internet of things

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Integrated Whale Swarm and Neuro-Evolutionary computing for Large-Scale Sparse Optimization Problems 2nd

Integrated Whale Swarm and Neuro-Evolutionary Computing for ...

引用

2nd international conference on Advances in Data-driven computing and Intelligent Systems, ADCIS 2023

作者： Balakrishnan, Biju Shanthini, B. Amudha, R. St. Peter’s Institute of Higher Education and Research Avadi Tamilnadu Chennai India Hindusthan College of Engineering and Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789819995233

Sparse optimization problems at a large scale present considerable difficulties in diverse fields, such as machine learning, data mining, and signal processing. The aim is to identify the most efficient solutions within expansive search spaces, while constraining the number of non-zero variables. The present study aims to tackle this obstacle by introducing an innovative methodology that integrates whale swarm optimization (WSO) with neuro-evolutionary computing (NEC). The WSO algorithm draws inspiration from the social behavior of humpback whales and presents a proficient approach for investigating and utilizing the search space. The integration of evolutionary algorithms with neural networks by NEC has demonstrated potential in effectively addressing intricate problems. Through the simultaneous evolution of neural networks and optimization processes, the algorithm is capable of adapting and enhancing its search strategy progressively. The study aims to improve the efficiency of WSO in addressing optimization problems characterized by sparsity at a significant scale by incorporating techniques from neuro-evolutionary computing. The method under consideration entails the integration of neural networks into the WSO algorithm with the aim of directing and augmenting the exploration procedure. The study evaluates the efficacy of the hybrid algorithm in relation to established methodologies based on criteria such as the quality of solutions, rate of convergence, and computational efficiency. The results obtained from the experiment provide evidence to support the efficacy of the proposed approach. The hybrid algorithm effectively utilizes the sparsity structure inherent in the problems, resulting in expedited convergence and enhanced precision of the solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Evolutionary algorithms

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convergence of Machine Learning with Blockchain technology 13

Convergence of Machine Learning with Blockchain Technology

引用

13th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2022

作者： Yojana Chaba, Yogesh Guru Jambheshwar University of Science and Technology Hisar India

ISBN: (纸本)9781713857938

Recently, ML and BCT convergence has become the most disruptive and trending technology. ML uses a large database, which emphasizes the synchronization and security of data. To handle the former issues, use the decentralization structure of BCT for the ML data, which is also immutable and uses distributed ledger, which makes the framework more secure and data reliable. ML offers intelligent machines which work similarly to hu- mans. In this paper, both technologies summarized, how they work, and their types. Also, discuss some research objectives of how ML with BCT integrated and how this technology use for analyzing traffic. © Grenze Scientific Society, 2022.

关键词： Distributed ledger

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Quantitative Analysis of Biofield Energy Imbalance Through Color Segmentation and Classification Using Machine Learning Techniques 9

Quantitative Analysis of Biofield Energy Imbalance Through C...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Gornale, Shivanand S. Patil, Supriya S. Deo, Anup Kumar, Sathish Rani Channamma University School of Mathematics And Computing Sciences Department of Computer Science Karnataka Belagavi India Institute of Biofield Evaluation & Research Centre Thane West Maharashtra India

ISBN: (纸本)9798350394474

Human biofield imaging is a non-invasive, radiation free, preventive, live imaging system capable of diagnosing human ailments at physical, mental, and emotional level and complements current practices of diagnosis. Biofield image shows energy field existing around and within the body. Imbalance in this energy field indicates disharmony in an individual, which appears in biofield image in different shades of red color. As part of the research work conducted, we tried to create a systematic approach to train a machine learning model to detect the severity of imbalance in the biofield energy and classify the biofield images as in three buckets namely less, moderate, and high imbalance of energy. In this experimentation we selected only the images capturing the knee section of person and performed the analysis. The method designed to train and classify the images has shown positive results and is matching with the clinical analysis done by Biofield experts on the same set of data with more than 80% accuracy. This helps to claim to manipulate individual energy fields enhance healing and wellbeing. © 2024 IEEE.

关键词： Color

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Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC 23rd

Long Short-Term Deterministic Policy Gradient for Joint Opti...

引用

23rd international conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Lei, Xiang Li, Qiang Bo, Peng Zhou, Yu Zhu Chen, Cheng Peng, Si Ling Southwest Univ Sci & Technol Sch Informat Engn Mianyang Sichuan Peoples R China

ISBN: (纸本)9789819708109;9789819708116

Mobile Edge computing (MEC) is regarded as a promising paradigm for reducing service latency in Mobile users data processing by providing computing resources at the network edge. Existing deep reinforcement learning (DRL) algorithms struggle to effectively handle the joint optimization of computational offloading and resource allocation (JCORA). To overcome this challenge, we propose a Long Short-Term Deterministic Policy Gradient (LSTDPG) approach to tackle JCORA. Building upon the Deep Deterministic Policy Gradients (DDPG) algorithm, LSTDPG incorporates two key features. Firstly, it utilizes a Temporal Attention Network composed of Long Short-Term Memory (LSTM) networks, which facilitates high-quality state representation and function approximation. Secondly, an Episode-Based Prioritized Experience Replay (ePER) method is introduced to expedite and stabilize the convergence of model training. Experimental results demonstrate that the proposed LSTDPG outperforms several state-of-the-art DRL agents in terms of task completion time and energy consumption.

关键词： Mobile Edge computing Computational offloading Deep reinforcement learning Deep deterministic policy gradient Resource allocation

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Eight-bit Quantization for Super-Resolution Networks and Its Hardware Implementation

Eight-bit Quantization for Super-Resolution Networks and Its...

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2023 international conference on Electronics, Information, and Communication, ICEIC 2023

作者： Nguyen, Xuan Truong Ko, Younghoon Nguyen, Tuan Nghia Lee, Hyuk-Jae Lee, Kyujoong Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Sungshin Women's University School of Ai Convergence Seoul Korea Republic of

ISBN: (纸本)9798350320213

This work presents an 8-bit quantization scheme for super-resolution (SR) networks and its hardware implementation. Quantizing SR models to run in 8-bit is a non-trial task, frequently leading to non-negligible restoration performance degradation. To address the problem, we first present a quantization scheme that mitigates accuracy drop by effectively capturing the distributions of both weights and activations. Second, the proposed scheme is implemented using Verilog HDL and the design is synthesized on Synopsys Design Compiler using TSMC 65 nm library. Lastly, experimental results illustrate the effectiveness and efficiency of the proposed method. © 2023 IEEE.

关键词： 8-bit quantization SR accelerator SR networks

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OCIT 2023 - 21st international conference on Information technology, Proceedings

OCIT 2023 - 21st International Conference on Information Tec...

引用

21st OITS international conference on Information technology, OCIT 2023

ISBN: (纸本)9798350358230

The proceedings contain 170 papers. The topics discussed include: a short survey on fog computing and its convergence with Internet of Things (IoT) network;high frequency architecture of lightweight authenticated cipher ELEPHANT -160 for resource-constrained IoT applications;unlocking the potential of unlabeled data: semi-supervised learning for stratification of hyperspectral images;detection of ground holes using deep learning for surveillance;secure beamforming assisted NOMA-SM based multiuser MISO downlink transmission with artificial noise;from pixels to insights: image datasets for AI/ML in software-defined networking;multiwavelet-based distilled transformer network for remote sensing image super resolution;and exploring the significance of feature analysis in AI/ML modeling.

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Research on Intrusion Detection Based on Markov Chain Monte Carlo Method 2

Research on Intrusion Detection Based on Markov Chain Monte ...

引用

2nd international conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2023

作者： Zhou, Dan Gao, Shan Long, Fei People's Daily People's Daily Online Beijing China Chinaso Inc Xinhua News Agency Beijing China Xinhua News Agency State Key Laboratory of Media Convergence Production Technology and Systems Beijing China

ISBN: (纸本)9798350331448

He rapid development of the Internet has brought about exponential growth in the amount of data in the network. With the growth of data volume in the network and the increasing dependence of humans on the Internet, network intrusion has become increasingly frequent, posing a serious threat to network security. In view of this, network intrusion detection has gradually become an important research topic in the field of network security. Due to the increasing maturity of deep learning technology, a large amount of intrusion detection research is currently based on deep learning technology. Deep learning technology can certainly improve the accuracy of intrusion detection, but it requires a large amount of data and computing resources and can easily cause overfitting when the data volume is insufficient, thereby reducing the recognition rate of unknown intrusions. This paper proposes an intrusion detection algorithm based on Markov chain Monte Carlo method, which can complete network intrusion detection tasks with low resource consumption. Compared with the intrusion detection algorithm based on basic Extreme learning machine, this method has better detection performance. © 2023 IEEE.

关键词： Intrusion detection

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Exploration of Automatic Spam/Ham Message Classifier Using NLP 9

Exploration of Automatic Spam/Ham Message Classifier Using N...

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9th IEEE international conference for convergence in technology, I2CT 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394474

Text classification holds great significance for analyzing textual information in one or more predetermined categories. Text classification tools allows to arrange all different kinds of texts, emails, court records, advertisements, databases, and other materials. This saves time in making informed decisions based on accurate information. A text classifier is software functionality designed to segment unresolved text into a number of preordained groups within a given text. Such software is frequently incorporated into various applications, including sentiment analysis, online content moderation, spam detection in emails, and language detection. Classifying a text is an essential but complex undertaking, as it requires a level of language comprehension akin to that of humans. Consequently, text classification has emerged as a subject of ongoing research interest. This study's primary goal is to investigate how cutting-edge natural language processing techniques are used to automatically detect spam messages. The proposed model will examine two distinct datasets to construct and categorize the disaster tweets. One dataset was obtained from the NUS SMS Corpus (NSC), while the other was specifically generated for this study using Grumbletext. When a spam email turns up, the system will inevitably identify the category using procedures like GloVe and BERT. The methodology, Naive Bayes DTM and XGBoost functionalities are used. The system's performance will be assessed through metrics such as precision, recall, f-score, FNR, FPR. © 2024 IEEE.

关键词： Sentiment analysis

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