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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741205

the proceedings contain 157 papers. the special focus in this conference is on Innovative Computing. the topics include: Proactive Auto Scaling Based on Marginal Request Change Analysis for Reducing Tail Latency in Kubernetes Cluster;Integration of Generative Adversarial Networks (GAN) and AI Drawing in Criminal Sketches—Applied in Crime Scene Investigation by Law Enforcement;implementation of a High Quality Cardiopulmonary Resuscitation Teaching Intelligence Training and Assessment System;Predicting Twitter Posts from Fake Accounts Using XGBoost Model;the Interplay Between Self-regulated learning and Programming learning Achievement;a Study on the Continued Viewing Intention of YouTuber Videos: Experiential Marketing, Social Capital, and Habit Perspectives;an Exploratory Study on the Design of Human-Computer Interaction Interfaces for Augmented Reality Integrated Museum Exhibitions;communication-Aware Optimization of Microservice Scheduling on Edge Computing;Design of Computer Model for Raw Material Supplier Selection Based on Entropy Weight TOPSIS Method;exploring How Cybersecurity Competitions Can Help Improve Students’ Practical Abilities in Cybersecurity;Adversarially Residual U2Net for COVID-19 Lung Infection Segmentation from CT Images;partnerships Analysis Based on Measurements of Common Research Interest;System Design of Operating Costs of intelligent Healthcare Services for the AHP Algorithm;a Multi-party Private Set Union Protocol Against Malicious Adversary;analysis of the Current Status of Research on Unmanned Collaborative Equipment Above and Below Water;data Mining Analysis of New Energy Vehicles Based on Cluster Analysis Technology;Two-Layer Minimum Variance FIR Filter-Based DPLL Design;fuzzy Proximity Two-Dimension Inductance Gesture Recognition System Analysis and Implement;proximity Capacitive Gesture Recognition for Recursive Neighbor Memory Neural Network;application of Genetic Algorithms in automated Mechanical Design.

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Deep learning Transforming the Manufacturing Industry: A Case Study 23

Deep Learning Transforming the Manufacturing Industry: A Cas...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ahmad, Hafiz Mughees Rahimi, Afshin Hayat, Khizer University of Windsor Mechanical Automotive and Materials Engineering WindsorON Canada Ifiveo Canada INC. WindsorON Canada

ISBN: (纸本)9781665494571

the traditional manufacturing industry uses decades-old technology and manual processes that are expensive, laborious, redundant, and a slight human error can cost millions of dollars. With the industrial revolution 4.0, industries are adopting intelligent additions and modifications into their systems involving robotics and internet of things (IoT) based technology, which can help in their operations. Artificial intelligence and machine learning have revolutionized every field, and the manufacturing industry has also started to reap its benefits. In this case study, we have explained the applications of deep learning-based approaches contributing to the automation of surveillance of production lines. Several cameras are installed to collect live feeds on the floor lines where the manually operated cranes and different tasks it performs are detected and tracked by state-of-the-art Convolutional Neural Network-based YOLOv3, an object detection model, and DeepSORT tracking algorithm. the post-processing is done on the matrices generated. It varies based on the use case and helps the manufacturers with automated inspection, quality control, and labor productivity. this process will save millions of dollars per year just in the crane maintenance operation for our industry partner's client. the developed technology can be replicated in almost every manufacturing industry using traditional methods and provide them better key insights into their production lines. © 2021 IEEE.

关键词： Manufacturing industries Deep learning Cranes Tracking Surveillance Object detection Maintenance engineering

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automated sleep staging based on multi-module neural network using simpler signal: Respiratory signal 7

Automated sleep staging based on multi-module neural network...

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7th international conference on Mechatronics and intelligent Robotics, ICMIR 2023

作者： Que, Yinqing Jiang, Pengyi Zhang, Tianyi Cheng, Yunzhang School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

Sleeping is a vital biological state which help maintaining the homeostasis of organisms of all biological lives. A full sleep can be divided into different repeating stages, rapid eye movement sleep (REM) stage, non-rapid eye movement sleep (NREM) stage 1-4. An effective sleep staging system can help patients improving their sleep quality. In the past, patients are required to wear polysomnogram (PSG) for the whole night to collect signals like electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG) for diagnosis. And the traditional sleep staging system use one or more signals above to predict a sleep stage. In this paper, we introduce a new sleep staging algorithm based on machine learning. Our model has two main inputs: patients respiratory signal and their physical data, like age, gender, and weight. the strategy is to use two CNNs to extract features from raw respiratory signal in time domain and frequency domain, several Word2vec layers are built to extract features from patients meta data and a transformer encoder to collect all the features. Using the MIT-BIH Polysomnographic database, our model achieves a result of 81.96% accuracy. this shows that it is completely feasible to classify patients sleep stage with their respiratory signal and meta information. © 2023 SPIE. All rights reserved.

关键词： Machine learning

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Strategic-cum-Domestic vehicular movement detection through Deep learning approach using designed Fiber-Optic distributed vibration sensor 7

Strategic-cum-Domestic vehicular movement detection through ...

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7th IEEE international conference for Convergence in Technology, I2CT 2022

作者： Khan, Mohd Mansoor Jaiswal, Prateek Mishra, Mohit Sonkar, Ramesh Kumar Indian Institute of Information Technology Guwahati Department of Electronics and Communication Engineering Assam Guwahati781015 India Indian Institute of Technology Guwahati Department of Electrical and Electronics Engineering Assam Guwahati781039 India

ISBN: (纸本)9781665421683

this paper presents the design and proposal of vehicle identification and classification sensing mechanism using fiber-optic distributed acoustic sensing (DAS). the seismic signatures of strategic-cum-domestic vehicles were made to interact with the embedded optical fibers and the acquired backscattered optical data was processed for feature extraction. the vehicle type was identified through the signal processing using a deep learning approach resulting in a novel and intelligent vehicle identification platform. data denoising was performed using various time-series mining algorithms with at least 500 epochs to test the model accuracy. this fully automated and end-to-end simulated sensing system used an asymmetric generative adversarial network (GAN) for denoising the time series data resulting in the training and testing accuracy of 73.63% and 69.5% respectively. the model can also be employed to measure the real-time speed and position of the vehicle. © 2022 IEEE.

关键词： Fiber optics

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Enhancing Big data Management for Elderly Care through a Blockchain-Empowered Deep Reinforcement learning Model 7th

Enhancing Big Data Management for Elderly Care Through a Blo...

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7th international conference on intelligent Computing and Optimization, ICO 2023

作者： Shixiao, Xiao Goyal, S.B. Rajawat, Anand Singh Solanki, Ram Kumar Chengyi University College Jimei University Xiamen361021 China Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computers Science and Engineering Sandip University Nashik422213 India

ISBN: (纸本)9783031733239

the surging demand for elderly care services, propelled by the global population’s demographic shift, necessitates innovative approaches for efficient data management and decision-making. this paper introduces a novel framework that synergizes blockchain technology with a deep reinforcement learning (DRL) model to elevate the integrity, security, and effectiveness of big data management in elderly care. Our proposed model offers dual benefits: the decentralized nature of blockchain ensures the secure storage and exchange of patient data, countering potential breaches and unauthorized accesses. this is particularly crucial given the sensitive nature of elderly patients’ information. Additionally, using smart contracts, automated and transparent caregiving decisions can be executed with-out intermediaries, thus ensuring trust among stakeholders. On the other hand, the DRL model dynamically learns and adapts to individual elderly care requirements by processing vast amounts of data, offering personalized care recommendations. By integrating it with blockchain, we not only ensure data integrity but also optimize decision-making in real-time, accommodating the evolving needs of the elderly population. A series of experiments were conducted using real-world data to validate the effectiveness of our proposed model. Results highlighted significant improvements in data security, reduction in fraudulent activities, and efficient, tailored caregiving decisions. the holistic integration of blockchain and DRL presents a revolutionary stride in elderly care, offering an optimized, secure, and personalized caregiving paradigm. this paper also discusses the potential challenges of implementing this model and offers directions for future research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep reinforcement learning

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Efficient Net-based Expert System for Personalized Facial Skincare Recommendations 7

Efficient Net-based Expert System for Personalized Facial Sk...

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7th international conference on intelligent Computing and Control Systems, ICICCS 2023

作者： Akshya, J. Mehra, Vinit Sundarrajan, M. Sri, Pattapu Teja Choudhry, Mani Deepak SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India KGiSL Institute of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350397253

In recent years, recommender systems have gained a lot of traction in both the commercial world and the academic research community. the term 'recommendation system for facial skin care' refers to an automated system that gives consumers advice on which facial skin care products to use based on their specific skin type, skin issues, and preferences. Product recommendation systems analyze user data using a variety of methods, including collaborative filtering, content-based filtering, and machine learning algorithms. Better skincare results may be achieved through the usage of a suggestion system in which consumers receive advice related to their own skin issues. the problem with current models is that they don't adequately address users' issues and concerns while being based on collaborative or content-based filtering. In order to create an effective face skincare model, the authors of this study suggest a hybrid approach that integrates several existing methods (including KNN, CNN, Transfer learning of Efficient Net BO, and a content-based filtering recommendation engine). the user's skin tone, skin type, and acne severity are all inputs into the algorithm, which then outputs a recommendation for the most suitable product. the results of a comparison between the suggested model and other models prove its superiority. Using a hybrid model that takes into account a number of different methodologies and user inputs, the article aspires to deliver a more all-encompassing and individualized solution for facial skincare recommendations. the proposed model has the potential to enhance the precision and efficiency of current skincare recommendation systems. the implementation of EfficientNet B0 improves the model's accuracy, which now has a validation accuracy of 80% and a training accuracy of 87.10%. © 2023 IEEE.

关键词： Recommender systems

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Machine learning-Driven Optimization of Livestock Management: Classification of Cattle Behaviors for Enhanced Monitoring Efficiency 24

Machine Learning-Driven Optimization of Livestock Management...

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7th international conference on Machine learning and Machine Intelligence, MLMI 2024

作者： Zhao, Zhuqing Shehada, Halah Ha, Dong Dos Reis, Barbara White, Robin Shin, Sook Electrical and Computer Engineering Virginia Tech Blacksburg United States Department of CREC-White Sand Unit Mississippi State University Starkville United States School of Animal Sciences Virginia Tech Blacksburg United States

ISBN: (纸本)9798400717833

Monitoring cattle health in remote and expansive pastures poses significant challenges that necessitate automated, continuous, and real-time behavior monitoring. this paper investigates the effectiveness and reliability sensor-based cattle behavior classification for such monitoring, emphasizing the impact of intelligent feature selection in enhancing classification performance. To achieve this, we developed Wireless Sensor Nodes (WSN) affixed to individual cattle, enabling the capture of 3-axis acceleration data from five cows across varying seasons, spanning from summer to winter. Initially, we extracted a comprehensive set of 52 features, representing a broad spectrum of cow behaviors alongside statistical attributes. To enhance computational efficiency, we employed the Recursive Feature Elimination (RFE) method to distill 30 critical features by discarding redundant or less significant ones. Subsequently, these optimized features were utilized to train four machine learning (ML) models: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF), and Histogram-based Gradient Boosted Decision Trees (HGBDT). Notably, the HGBDT model demonstrated superior performance, achieving remarkable F1-scores of 99.01% for'grazing', 98.74% for'ruminating', 89.62% for'lying', 84.06% for'standing', and 91.87% for'walking'. these findings underscore the potential of our approach to serve as a robust framework for precision livestock farming, offering valuable insights into enhancing cattle health monitoring in remote environments. © 2024 Copyright held by the owner/author(s).

关键词： Support vector machines

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Review on Research of automated Machine learning 23

Review on Research of Automated Machine Learning

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Proceedings of the 2023 7th international conference on Computer Science and Artificial Intelligence

作者： Yuyanzhen Zhong Chuan Yang Xinyi Su Biao Li Xiaoping Huang Yong Shuai Chongqing CEPREI Industrial Technology Research Institute Co. Ltd China

ISBN: (纸本)9798400708688

automated Machine learning (AutoML) can automatically discover high-performance models to build deep learning systems without human assistance, with the ultimate goal of reducing the complexity and entry barriers of building deep learning systems. Although AutoML has achieved a certain degree of automation through four important steps: data preparation, feature engineering, model generation, and model evaluation, there is still a significant gap compared to the ultimate ideal of achieving truly intelligent lifelong learning. therefore, a deep understanding of AutoML can help drive the development of artificial intelligence. Firstly, we comprehensively reviewed the latest technologies and achievements involved in these four steps, then we introduced their shortcomings and challenges. Secnodly, a detailed introduction was given to the existing AutoML libraries and the theoretical and practical applications of AutoML. Finally, we summarized AutoML models and Proposed an outlook.

关键词： automated Machine learning Automatic Processing Lifelong learning Neural Architecture Search

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Deep learning Framework-Based automated Multi-class Diagnosis for Neurological Disorders 7

Deep Learning Framework-Based Automated Multi-class Diagnosi...

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7th international conference on Automation, Control and Robots, ICACR 2023

作者： Ali, Syed Saad Azhar Memon, Khuhed Yahya, Norashikin Sattar, Karim Asif El Ferik, Sami King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Smart Mobility Logistics Aerospace Engineering Department Dhahran31261 Saudi Arabia Universiti Teknologi Petronas Centre for Intelligent Signal & Imaging Research Department of Electrical & Electronic Engineering Perak Seri Iskandar32610 Malaysia King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Smart Mobility & Logistics Dhahran31261 Saudi Arabia King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Smart Mobility & Logistics Control Instrumentation Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350302882

Magnetic Resonance Imaging, being a harmless, non-invasive and highly informative modality, has proved to be one of the most widely accepted and used neuroimaging modality for visualizing human brain. Brain MR images possess similar features specially in case of neurodegenerative disorders containing very subtle and intricate changes rendering the diagnosis process very challenging. Manual inspection by experts of such images results in diagnoses based on their expertise, with a probability of misdiagnosis due to subtle changes in such images depending on disease stage, overlapping features, among other factors. In addition, in case of unavailability of experts in remote areas, accurate and timely diagnosis can be a problem. the advent of humongous multi-modal data and Deep learning techniques has enabled researchers to develop intelligent classification methods with adequate performance accuracies. A review of the literature suggests that a lot of research has been carried out in the direction of automatic diagnosis of neurological disorders, but to date, no consolidated framework has been developed with the capabilities to classify multiple diseases and their sub-types with adequate accuracy from structural and functional MR images of varying types and planes of orientation. the contributions of this research include the design of a unified framework for multiple neurological disease diagnosis resulting in the development of a generic assistive tool for hospitals and neurologists to precisely and briskly diagnose disorders that might result in saving lives in addition to increasing the quality of life of patients suffering from neurodegenerative disorders. To materialize this idea, Deep learning has been deployed to train a three class model to classify Brain Tumors, Parkinson's disease and normal subjects. A test accuracy of 83.69% has been achieved even with limited dataset used for training, thereby encouraging the idea of a unified framework to diagnose neurod

关键词： Computer aided diagnosis

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Monitor the Effectiveness of Cardiovascular Disease Illness Diagnostics Utilizing AI and Supervised Machine learning Classifiers 7th

Monitor the Effectiveness of Cardiovascular Disease Illness ...

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7th international conference on Information and Communication Technology for intelligent Systems, ICTIS 2023

作者： Rathod, Dushyantsinh B. Patel, Yesha Jethava, Archana Gohel, Namrata Suthar, Dhruvi Varia, Dhaval Shah, Nirav Barot, Janki Computer Engineering Department Ahmedabad Institute of Technology Gujarat Ahmedabad India Department of CSE GLS Institute of Faculty of Engineering & Technology GLS University Gujarat Ahmedabad India Vishwakarma Government Engineering College Gujarat Ahmedabad India Lambton College Toronto Canada Silver Oak University Gujarat Ahmedabad India

ISBN: (纸本)9789819940394

the heart is a crucial component of all living organisms. Greater thoroughness and precision are required for heart disease diagnosis and prognosis since even a little error may result in serious consequences or even death;there are many heart-related fatalities and the number is growing daily (Kumar et al. in Cardiac disease prediction using data mining algorithm. Springer Nature Singapore, Singapore, 2020). An illness awareness prediction system is a crucial component needed to address this issue. Machine learning is an illustration of AI (artificial intelligence). It is quite helpful for predicting all kinds of occurrences related to natural catastrophes. In this work, utilizing benchmark data sets from Kaggle for training and testing, we assess the efficiency of machine learning algorithms for heart disease prediction (Gupta and Vinayaka in Cardiac arrest disease prediction system using classification algorithms. Springer Nature Singapore, Singapore, 2020), including k-proximal neighbors, decision trees, linear regression, and Random Forest Classifier. the Anaconda (Jupyter) notebook, which includes a range of libraries and header files that make the process quick and easy, is the ideal tool for utilizing Python programming. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heart

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