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An Effectual Strategy to Augment the Hello Interval of Each Node Using Enhanced Humpback Whales Optimization

An Effectual Strategy to Augment the Hello Interval of Each ...

引用

Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Richa Purohit T. Saravanan Rashmi Dharwadkar Y Vahidhabanu S.P. Sasirekha N. Krishnaveni School of Computer Studies Sri Balaji University Pune India Dept. of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Dept. of Computer Science Sri Balaji University Pune India Department of Computer Science and Engineering Kongunadu college of Engineering and Technology Trichy India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Wireless networks are highly utilized, especially Mobile Ad-hoc Networks (MANETs) for real-time data transmission. However, current routing protocols for MANETs face challenges that limit their effectiveness. Design issues include minimizing energy usage and ensuring node security. A commonly used routing protocol, AODV, involves periodic broadcasting of hello messages to monitor link connectivity and maintain routing tables. When link failure occurs due to node mobility, hello messages are broadcasted more frequently, which consumes more node energy and increases network overhead. To address this issue, this research proposes an Enhanced Humpback Whales Optimized Routing Approach (EHWORA) that calculates an optimal hello interval based on a node's energy and mobility using an adaptive fuzzy inference system. This approach involves optimizing the hello interval of AODV routing through the use of an adaptive fuzzy inference system. Each node can dynamically adjust the transmission of hello messages based on the fuzzy-based hello interval. Simulation results demonstrate that ORA reduces network overhead, end-to-end delay, hello message fraction, average energy consumption, packet delivery ratio, and improves throughput compared to a recent method. The proposed protocol is evaluated using MATLAB software. To assess the validity of the research, we utilized MATLAB software to compare the proposed method with a recent approach. During the simulation, the proposed protocol depleted the energy of 3 nodes after 10 seconds, 5 nodes after 20 seconds, 9 nodes after 30 seconds, 11 nodes after 40 seconds, and 13 nodes after 50 seconds, respectively.

关键词： Fuzzy logic Adaptive systems Routing Whale optimization algorithms Ad hoc networks Software Routing protocols MATLAB Artificial intelligence Mobile computing

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A No-Reference Image Quality Assessment Method Based on Random Forest for Power Scenario 3

A No-Reference Image Quality Assessment Method Based on Rand...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Li, Junwei Zeng, Jijun Kong, Tianwen Su, Huaquan Wu, Yang Huo, Yejing Wu, Shiting Shi, Hanyu Information Center Guangdong Power Grid Co. Ltd. Guangzhou China Guangdong University of Technology Dept. School of Computer Science Guangzhou China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Guangdong University of Finance and Economics Dept. School of Statistics and Mathematics Guangzhou China

ISBN: (纸本)9798350365443

No-reference image quality assessment (NR-IQA) is a crucial task in computer vision, aiming to evaluate the visual quality of images solely through distorted images without the need for original (reference) images. This evaluation method is particularly useful in practical applications where obtaining the original image is not feasible, such as in the task scenario discussed in this article: assessing whether a camera is blurred, overexposed, etc., in power grid scenarios. Traditional no-reference image quality assessment methods usually face issues such as over-reliance on manually designed features, which may not comprehensively capture all aspects of image quality, and good performance on specific datasets but significant performance degradation across different datasets or types of image distortion, resulting in poor consistency with actual subjective scores. Although with the continuous development of deep learning, many models are able to extract the features of an image with relatively high efficiency for effective evaluation, many of the models require high computational resources, which is not conducive to the needs of real-world scenarios where low-computing power is deployed to achieve timely image quality assessment. In order to solve these problems, we propose a random forest-based approach for reference-free image quality assessment in power scenarios. First, we collect original power scene images and perform image expansion, including abnormal and normal images, to increase the diversity of the dataset. The expanded images were merged with the original images to form a balanced dataset. Next, we take the merged dataset as input and perform data processing, including random cropping and simulated image damage, to enhance the model's adaptability to different observation viewpoints and damage scenarios. The processed images are used for feature extraction, and these features are subsequently used to train the random forest model. The experimental result

关键词： Image quality

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations 18

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Majeed, Omar Kashif Ul Abideen, Zain Arshad, Usama Ali, Raja Hashim Habib, Abdullah Mustafa, Rafay BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty. © 2023 IEEE.

关键词： Reinforcement learning

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Machine Learning-Based Detection of Cardiovascular Disease using Classification and Feature Selection 12

Machine Learning-Based Detection of Cardiovascular Disease u...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Jain, Anuj Kumar Kumar, Kamal Tiwari, Raj Gaurang Jain, Nitin Gautam, Vinay Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India National Institute of Technology Uttarakhand Dept. of Computer Science and Engineering Uttarakhand Srinagar India Manav Rachna International Institute of Research and Studies Career Development Centre Haryana Faridabad India

ISBN: (纸本)9781665462617

The application of ameliorative datasets to foretell chronic diseases has grasped considerable attention amongst data scientists all around the globe. The study is based on the Cardiovascular disease dataset. Cardiovascular disease is a broad term concerning disorders of the heart and blood vessels and is ranked to be the topmost cause of demise worldwide. Various feature selection techniques have been applied to each of the given classification algorithms and the features which give the best accuracy are then utilized for that particular algorithm. © 2023 IEEE.

关键词： Neural networks

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques 18

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ishaq, Muhammad Haseeb Mustafa, Rafay Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim Habib, Abdullah Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies. © 2023 IEEE.

关键词： Emotion Recognition

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Survey on Use of AI/ML Algorithms in Enhancing Filesystem Performance and Efficiency

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Pe...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Vijay Olekar Yash Patil Vishvam Pawar Shubham Pitale V R. Jaiswal Vishal Naranje Dept. of Information Technology Pune Institute of Computer Technology Pune Pune India School of Engineering Architecture and Interior Design Amity University Dubai

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The processing power of CPUs is increasing day by day, but systems often struggle to fully utilize this power due to the bottleneck of file access time from the disk. Over the years, various techniques have been devised to reduce I/O latency while considering various file parameters. Our survey paper aims to shed light on these techniques, efficiently comparing them and highlighting the benefits and drawbacks of each approach. For our evaluation, we have considered various file parameters, such as access time, file size, directory structure, and kernel execution flow. We have combined these parameters with a range of supervised and unsupervised machine learning algorithms, including Decision Tree, ABLE, RNN, GNN, CART, XGBoost, CNN, and more. This survey aims to highlight the use of ML/AI algorithms in the intelligent detection of file access and I/O patterns to optimize the performance of modern applications.

关键词： Surveys Machine learning algorithms Adaptive systems File systems System performance Neural networks Predictive models

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Heart Attack Detection And Alerting System

Heart Attack Detection And Alerting System

引用

Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Aarthi Elaveini Pragadeesh E Bhavesh Aglecha Metha G P Dept of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Dept. of Electronics and Computer Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This research presents a cutting-edge IOT-based heart attack detection and alerting system. It combines a number of sensors to continuously track vital indicators in the present. The device identifies physiological parameter irregularities that may be signs of a probable heart attack through a carefully crafted algorithm. Instant notifications are sent via IOT-enabled systems to the patient's family and designated healthcare professionals upon identification. The goal of the technology is to drastically shorten response times during critical cardiac episodes, allowing for timely medical intervention. This technology attempts to improve early detection by utilizing sensor data and IoT connectivity, thereby reducing the severity of heart attacks and raising the chance of successful intervention and patient survival.

关键词： Cardiac arrest Medical services Sensor phenomena and characterization Sensor systems Real-time systems Sensors Telecommunication computing Time factors Biomedical monitoring Monitoring

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Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms. © 2023 IEEE.

关键词： autoencoders feature extraction Network anomaly detection particle swarm optimization XGBoost

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Investigating the Role of Block Chain to Secure Identity in IoT for Industrial Automation 2

Investigating the Role of Block Chain to Secure Identity in ...

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Veeraiah, Vivek Preetham, Anusha Karthikeyan, P. Kumari, P. Lalitha Mehta, Kamakshi Kumbhkar, Makhan Adichunchanagiri University Dept. of R & D Computer Science Karnataka Mandya India Dayananda Sagar Academy of Technology and Management Dept. of Ise Karnataka India School of Management Studies Kongu Engineering College Perundurai TN Erode India Visakha Institute of Engineering & Technology Dept. of CSE AP Narava Visakhapatnam India Amity College of Commerce Amity University Haryana India Christian Eminent College Dept. of Computer Science Madhya Pradesh Indore India

ISBN: (纸本)9781665437899

Everyone who has an app that can access the block chain can get their hands on the data. There are blocks for each new piece of data that come in. A chronological chain of data is formed when a block is filled with data and chained to the previous block. Smart things are protected from cyber-attacks thanks to the IoT's technology's transparency and virtual incorruptibility. The block chain may be used to enable secure connectivity between IoT devices by storing data in transactions and confirming those transactions with nodes. The Internet of Things (IOT) has helped industrial automation reach new heights. We can improve the security and privacy by adding block chain to it. © 2022 IEEE.

关键词： Internet of things

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Joint Optimization of Cache-Assisted Offloading and Resource Allocation in Mobile Edge Computing

Joint Optimization of Cache-Assisted Offloading and Resource...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Sun, Zhe Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations. © 2023 IEEE.

关键词： cloud computing Computation offloading particle swarm optimization vehicular edge computing

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