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Personalized Walking Speed Prediction: Cloud-Enabled Long Short-Term Memory Networks for Health Monitoring 2

Personalized Walking Speed Prediction: Cloud-Enabled Long Sh...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Reddy, Chilukala Mahender Juliet, P. Sudha Ravinder, Bathini Ashfaqul Hasan, M.D. Ramesh, N. Sujatha, S. Gitam University Department of Computer Science and Engineering Telangana Hyderabad India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Mother Theresa Institute of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Palamaner India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Cloud-Enabled Long Short-Term Memory (LSTM) Networks for individualized walking speed prediction in health monitoring applications are discussed in the research paper. Walking speed is important for mobility and health assessment. Traditional approaches for forecasting walking speed are frequently generic. It uses Cloud-Enabled LSTM Networks to record individual walking patterns and change predictions using real-time data. The proposed approach uses cloud computing to train and update LSTM models. This allows the model to learn and improve over time, keeping it relevant and accurate for each user. Personalization takes into consideration age, fitness, and health to predict walking speed more accurately. Extensive tests on different datasets show that the proposed strategy outperforms current approaches in accuracy and flexibility. The cloud-enabled system integrates with wearable devices, making it suitable for real-world health monitoring. It provides an effective and scalable strategy for predicting walking pace, an important parameter in measuring and maintaining individual well-being, to develop customized health monitoring systems. © 2024 IEEE.

关键词： Long short-term memory

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VeriCombTest: Automated Test Case Generation Technique Using a Combination of Verification and Combinatorial Testing 18

VeriCombTest: Automated Test Case Generation Technique Using...

引用

18th International Conference on Evaluation of Novel Approaches to Software engineering, ENASE 2023

作者： Godboley, Sangharatna Department of Computer Science and Engineering National Institute of Technology Telangana Warangal India

ISBN: (纸本)9789897586477

We propose VeriCombTest which is the combination of Verification and Combinatorial Testing. We experimented with 38 C-Programs from The RERS challenge repository. Verification (CBMC) produced 940 test cases and Combinatorial Testing (PICT) populated a total of 42053 test cases. The good point is that for 40 programs, PICT consumed only 2.6 Minutes to populate the test inputs, however, CBMC which is a Static Symbolic Executor consumed 546.99 Minutes to generate the test inputs. We performed mutation analysis for this work. VeriCombTest has 355 extra killed mutants as compared to the baseline. VeriCombTest is a fully automated tool. Copyright © 2023 by SCITEPRESS - science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： C (programming language)

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IoT-enabled Smart Waste Management: Leveraging the Power of IoT for Sustainable Solutions 3

IoT-enabled Smart Waste Management: Leveraging the Power of ...

引用

3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Tandon, Righa Bansal, Abhishek Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350303360

Waste management is now a critical issue due to the rapid urbanisation and population growth. The Internet of Things (IoT) technology is a possible threat and a practical and efficient trash management solution. The collection, transportation, and disposal of garbage were just a few of the IoT-based waste management concerns covered in this article. Additionally, IoT sensors have the ability to detect overflowing trash bins and alert the proper authorities, enabling them to take immediate corrective action. In addition, IoT-based waste management systems can improve the efficiency of garbage transportation by optimising collection routes and consuming less fuel. Waste may be converted into electricity or fertiliser using IoT-based waste disposal solutions. These methods not only deal with the issue of waste disposal but also offer fossil fuel alternatives that are sustainable. The advantages of IoT technology in waste control, however, outweigh these difficulties. IoT-based waste management systems can increase garbage collection efficiency. © 2023 IEEE.

关键词： Smart city

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Metric-Oriented Comparison of Selective Forwarding Attack Detection Techniques in IoT-Based Systems 1

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4th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2023

作者： Sinha, Nidhi Kumar Mishra, Alekha Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur India

ISBN: (数字)9789819912032

ISBN: (纸本)9789819912025

The Internet of Things (IoT) is an innovative technology nowadays due to its broad variety of applications in industrial and consumer markets. Secure and timely packet transmission in IoT is the basic requirement of the network. The selective forwarding attack possesses a higher level of threat to the process of packet transmission. Here, attacker compromises its trustworthiness and decide to discard packets selectively instead of dropping all of them like a blackhole node. This results in packet loss, latency, link failures, data inconsistency, and collision. In this paper, we provide a characteristic level analysis of the selective forwarding attack in an IoT environment. We have enlisted and analyzed the approaches and metrics commonly used to detect a selective forwarding attack. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Privacy-Preserving of Edge Intelligence using Homomorphic Encryption 3

Privacy-Preserving of Edge Intelligence using Homomorphic En...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Jerang, Ronaldo Nayak, Sumitra Mahato, Ganesh Kumar Kumar Chakraborty, Swarnendu National Institute of Technology Department of Computer Science & Engineering Arunachal Pradesh India

ISBN: (纸本)9798350338607

Edge intelligence has paved the way for a stronger and more secure system by combining security and artificial intelligence. A lot of research have been undertaken, revealing remarkable development and a variety of viewpoints on this novel phenomenon. We suggested a way enabling edge intelligence to move via homomorphic encryption-enabled gadgets and automobiles. This encrypted data will be delivered to an edge node for the building of a machine learning model in order to gather any potential information hidden in the data as well as any potential obstacles that may need to be addressed before any event happens. Several machine learning models, such as KNN, K-means, SVM, and others, are used to gain the best possible data analysis. Once secure, decisions will be taken, and the outcome will be visible. All machine learning training will be done in encrypted data, which will be encrypted to ensure that no one's privacy is abused. This may be set up for a variety of machine learning modules. © 2023 IEEE.

关键词： Deep learning

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An Overview of SQL Optimization Techniques for Enhanced Query Performance 2

An Overview of SQL Optimization Techniques for Enhanced Quer...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Hayath, T.M. Usman, K. Shafiulla, Mohammed Dadapeer Ballari Institute of Technology and Management Department of Computer Science and Engineering Ballari India

ISBN: (纸本)9798350347456

According to recent trends, the size of databases is increasing rapidly, which calls for the need for larger databases that can be scaled to support future upgrades without compromising performance. Additionally, the way SQL queries are structured can have a significant impact on their execution performance. This paper proposes a set of rules for formatting SQL queries that must be followed to optimize them. The approach includes identifying any need for indexing on specific columns if the query includes filtration. By avoiding the unnecessary use of data and columns, our proposed model aims to convert user SQL queries into optimized ones, reducing query execution time which provides an in-depth analysis of SQL optimization techniques to enhance the performance of database queries. The research discusses the significance of query optimization and outlines some of the commonly used techniques in SQL optimization. © 2023 IEEE.

关键词： Query languages

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Pulmonary Nodule Detection in Computed Tomography Images Using Deep Learning 1

Pulmonary Nodule Detection in Computed Tomography Images Usi...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Longkumer, Akumlong Walling, Supongmen Kalita, Shobhanjana Icfai University Department of Computer Science Dimapur India National Institute of Technology Department of Computer Science and Engineering Dimapur India Tezpur University Department of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350348057

the well-being of individuals is seriously threatened by lung cancer, which has one of the highest fatality rates of all cancers. Therefore, to increase patient life expectancies, early diagnosis of lung nodules is essential. For many years, the science of machine vision and medical diagnostics has used convolution neural networks. Early diagnosis can significantly lower the patient's mortality, and computer-aided diagnosis has emerged as a vital tool for medical professionals. computer-aided diagnosis depends on the ability to identify pulmonary nodules in computed tomography images. In the Computed Tomography scans, it is difficult to find the lump inside the lungs. This project uses U-Net to implement automated lung nodule identification. Furthermore, the data sets used for pulmonary nodule recognition from CT scan images are from the freely accessible LIDC/IDRI database. © 2023 IEEE.

关键词： computer aided diagnosis

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GWO-Based Workflow Scheduling in Cloud-Fog Environments

GWO-Based Workflow Scheduling in Cloud-Fog Environments

引用

2023 IEEE International Conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Soni, Dinesh Kumar, Neetesh Indian Institute of Technology Department of Computer Science and Engineering Roorkee247667 India

ISBN: (纸本)9798350305111

The scientific workflows are a set of predefined tasks aiming to achieve a target goal state. Cloud computing provides an appropriate environment for orchestrating such scientific workflows. However, scheduling these workflows in a cloud environment is a big challenge due to the high latency and connectivity on low bandwidth network infrastructure. Nowadays, fog and edge computing devices offer an alternative to cloud computing. As a result, these devices can provide cloud computing capabilities for processing scientific workflows near the source. This paper explores the possibilities of scheduling workflows on both cloud and fog computing devices. In order to schedule workflows on cloud-fog integrated devices, we implemented the Grey Wolf Optimizer (GWO) based algorithm using dynamic weights for updating the grey wolf position on the FogWorkflowSim simulator. The proposed scheme avoids getting into a local optimum and improves the performance of the QoS parameters. We also compare the outcomes of different scheduling algorithms in cloud and hybrid cloud-fog architectures. The simulation results of the proposed approach using well-known scientific workflows suggest that cloud-fog integration for workflow scheduling improves processing performance while reducing energy consumption and cost. © 2023 IEEE.

关键词： Fog

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Exploring the Self-Organizing Mechanisms for Maintaining Stability in Multi-Agent Systems within Dynamic Environments 3

Exploring the Self-Organizing Mechanisms for Maintaining Sta...

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3rd International Conference on computer science, Electronic Information engineering and Intelligent Control technology, CEI 2023

作者： Gao, Sheng Liu, Wei Mu, Yongan Wang, Kai Wuhan Institute of Technology Dcse Department of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350372052

In the absence of external interference, independent multi-agent systems evolve over time, leading to a transition from an ordered to a chaotic state within the internal dynamics of the scene. To assist in maintaining the stability of dynamic systems, this paper explores the design and composition of the scenario. A category of altruistic agents with selfless attributes is introduced into the multi-agent environment, and the decentralized algorithm I-DDPG is employed to train these altruistic agents to perform tasks with altruistic tendencies. Weakly connected self-organization is used to enable cooperation among altruistic agents in addressing internal issues. The study demonstrates a positive correlation between the overall system stability and individual information entropy, as well as the method of obtaining individual altruistic rewards. To investigate the connection between overall scene stability and self-organization among internal individuals, a traffic scenario is constructed with both human-driven and autonomous vehicles. Autonomous vehicles (AVs) represent a type of altruistic agent, and experiments are conducted to explore the relationship between scene stability and internal self-organization among individuals. The study concludes that using I-DDPG improves the learning efficiency of agents compared to DDPG and demonstrates a higher safety level in stress tests. Post-training, agents utilizing weakly connected self-organization in scenarios involving merging and exiting obtain higher altruistic rewards. This approach aids human-driven vehicles in achieving their intended goals, contributing to an overall improvement in the stability of dynamic systems. © 2023 IEEE.

关键词： Multi agent systems

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Knowledge engineering-Based Analysis of Convolutional Neural Network Architectures’ Performance on Luna16 and GAN Generated Pulmonary Nodule Clipped Patches to Diagnose Lung Cancer 3rd

Knowledge Engineering-Based Analysis of Convolutional Neural...

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3rd International Conference on Sustainable Expert Systems, ICSES 2022

作者： Sheshadri, Ramasubramanya Mysore Chopra, Yash Aryan Anand, Yashas Sumukh, G. Geetha, S. Department of Computer Science and Engineering BNM Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9789811978739

Lung cancer patients constitute a relatively smaller number than the overall population of non-cancerous individuals. This causes an imbalance between benign and malignant nodule image class data, which impacts the Convolutional Neural Network model that classifies whether the nodule image is benign or malignant. The Deep Convectional Generative Adversarial Network (DCGAN) is used to produce nodule samples in order to balance classes. A larger dataset of nodule images generated by using DCGAN can solve the problem of overfitting. The DCGAN-generated nodule samples and original nodule clipped image patches data are fed into various CNN architectures, and their performance is examined to find the best CNN architecture that can classify benign and malignant nodules effectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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