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data-Driven Unified Channel Modeling of WiFi and LiFi Using Conditional GAN 5

Data-Driven Unified Channel Modeling of WiFi and LiFi Using ...

引用

5th international conference on intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Saif, Amal Ibrahim, Rahmeh Almajali, Sufyan Salameh, Haythem Bany Princess Sumaya University for Technology Computer Science Department Amman11941 Jordan Princess Sumaya University for Technology Cyber Security Department Amman11941 Jordan College of Engineering Al Ain University Al Ain United Arab Emirates

ISBN: (纸本)9798350354690

Wireless communication systems, rooted in the propagation of electromagnetic waves, have traditionally operated within the framework of digital modules such as source coding, encryption, and modulation. However, the conventional 'modular' approach faces challenges in achieving 'global optimization,' relying on simulated environments that diverge from real-world channel conditions. This paper proposes a solution to integrate deep learning into wireless communication systems, envisioning intelligent communication systems that learn and adapt. Recent strides in wireless channel modeling have embraced Generative Adversarial Networks (GANs) to generate realistic communication channel models. GANs applied to optical and Radio Frequency (RF) channels bridge gaps where actual measurements are unavailable, providing synthetic models that mirror real-world statistics. This paper's contribution involves innovating channel modeling by replacing autoencoder latent spaces with a GAN model, generalizing the model for diverse channel types, implementing a data-driven approach using Conditional GANs for realistic channel effects, and constructing a unified model adept at simultaneously handling WiFi and LiFi channels. The envisioned outcome is a groundbreaking approach that transcends the limitations of traditional wireless communication, propelling intelligent communication networks into an era of adaptability and optimized performance. © 2024 ieee.

关键词： Wi-Fi

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An Analytical Comparison of Statistical and Machine Learning Approaches for Time Series data Analysis 14

An Analytical Comparison of Statistical and Machine Learning...

引用

14th international conference on Cloud computing, data Science and Engineering, Confluence 2024

作者： Kumar, Shashi Shekhar Tufani, Rojer Chandra, Ritesh Iiit Allahabad Dept. of Information Technology Prayagraj India

ISBN: (纸本)9798350344837

In recent times, intelligent system-based infrastructure has emerged exponentially and is growing rapidly. Smart building is one of the prominent applications of intelligent systems, which contain numerous sensors deployed at various locations. These sensors monitor the activity throughout and keep the occupants informed about any changes that occur inside smart buildings. One of the important operations inside smart buildings is temperature monitoring and prediction, which make occupants comfortable and more liveable. We compared different models that can be used to predict temperature in this paper. These models include machine learning ones like linear regression (LR), decision trees (DT), random forests (RF), and gradient-boosting trees (GBT) using PySpark and the Apache Spark library, as well as time-series and statistical ones like the auto-regressive integrated moving average (ARIMA). The results of all the models show that the random forest model is the least among the machine learning models, which is 0.7812 %. While the error of the ARIMA is 0.5065 %, which is also less than that of the random forest, while applying the rolling mean, the error was reduced significantly to 0.0479%. © 2024 ieee.

关键词： Decision trees

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intelligent Operation and Inspection Software Based on Edge computing Concept and Artificial Intelligence Technology 3

Intelligent Operation and Inspection Software Based on Edge ...

引用

3rd international conference on Energy Engineering and Power systems, EEPS 2023

作者： Ren, Jiaxuan Wang, Shaorong Yang, Youhang Zhang, Yanyan Wei, Zheng Zheng, Chao Li, Jingsong Chen, Yunan Huazhong University of Science and Technology School of Electrical and Electronic Engineering Wuhan China Dongguan Power Supply Bureau of Guangdong Power Grid Co. Ltd Dongguan China Wuhan Qiyi Electric Co. Ltd Wuhan China Yunnan Power Dispatch Control Center Kunming China

ISBN: (纸本)9798350313857

A new intelligent operation and inspection software based on the concept of edge computing and artificial intelligence technology is proposed for the physical and operational architecture of substation electrical equipment intelligent inspection, and a human-computer interaction interface is designed for the convenience of operation and maintenance personnel, which effectively avoids the problems of poor real-time and monitoring blind areas of off-line inspection and conventional energized inspection, and solves the defects of cloud computing and manual inspection. The key technologies included in the realization of intelligent operation and inspection software functions are: the design of physical and operational architecture, and the analysis of structured and unstructured charged inspection data. The intelligent operation and inspection software is stable and reliable, which can greatly improve the efficiency of inspection and the intelligence level of the substation. © 2023 ieee.

关键词： Edge computing

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A Novel Approach for Crowd Analysis and Density Estimation by Using Machine Learning Techniques

A Novel Approach for Crowd Analysis and Density Estimation b...

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2024 international conference on intelligent systems for Cybersecurity, ISCS 2024

作者： Kamra, Vikas Vaishnav, Aditya Verma, Anilansh Khan, Rehan Singh, Shubham Amity University Cse Department Uttar Pradesh Noida India

ISBN: (纸本)9798350375237

In an era characterized by urbanization and densely populated events, the ability to accurately analyze and estimate crowd density is paramount. From public safety measures in cities and event venues to strategies for retail optimization, understanding the flow and behavior of crowds can significantly improve decision-making processes and resource allocation. This research report delves into the realm of crowd analysis, emphasizing its significance across various sectors and highlighting contemporary techniques. At its core, the report focuses on three cutting-edge machine learning algorithms: Mobilenet SSD, Yolov4, and Mask RCNN, dissecting their capabilities and potential applications. It also acknowledges the inherent challenges, from occlusions in densely packed areas to diverse environmental influences, and offers a perspective on how modern tools, combined with data from surveillance cameras, lidar, and thermal sensors, can overcome them. The integration of these technologies into a coherent, real-time system, driven by big data analytics and cloud computing, further underscores the potential of this research. While this study is grounded in current methodologies, it also looks forward, contemplating the ethical implications and considering the integration of multimodal data fusion and privacy preservation techniques as avenues for future exploration. © 2024 ieee.

关键词： Real time systems

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Applying Machine Learning & Knowledge Discovery to intelligent Agent-Based Recommendation for Online Learning systems

Applying Machine Learning & Knowledge Discovery to Intellige...

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2023 ieee international conference on Device Intelligence, computing and Communication Technologies, DICCT 2023

作者： Murugesan, S. Bharathiraja, N. Pradeepa, K. Ravindhar, N.V. Kumar, M. Vinoth Marappan, Raja Tagore Engineering College Department of It Chennai India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India St. Michael College of Engineering and Technology Department of Computer Science and Engineering TN Sivaganga India Saveetha Engineering College Department of Cse Tamil Nadu India Dmi-St.Eugene University Dept. of Cse Zambia Sastra Deemed University School of Computing Thanjavur India

ISBN: (纸本)9781665474924

E-learning is one of the favorite jobs where all the learners can learn through different online sites to update their knowledge acquisition. Though online learning supports several benefits, there are many challenges to be considered, for example, the information resources, quality of the search, accessing the correct information, getting the search results within a reasonable computing time, etc. This research develops the new natural language processing (NLP) intelligent and online recommender using the learner's profile information and semantic analysis of the search history at different times. The intelligent clustering strategy is applied, so the system learns automatically, and the learner's characteristics are analyzed periodically. The simulation results of the proposed research are compared with the state-of-the-art techniques and provide the minimized error rates to the selection of courses and recommendations with the recommendation accuracy in (98.2%, 98.9%). The MAE lies between (0.12, 0.35) for the considered clusters. Compared to the other methods, the method works well for clusters of sizes 10, 20, 30, 40, and 50. © 2023 ieee.

关键词： Knowledge acquisition

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Enhancing Employee Motivation Through AI: The Role of Corporate Culture in the Workplace

Enhancing Employee Motivation Through AI: The Role of Corpor...

引用

2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Prakash, S. Pavithrashree, S. Gokul, S. Karthick Raja, K. Dr.SNS Rajalakshmi College of Arts and Science Department of Commerce Tamil Nadu India

ISBN: (纸本)9798331543617

Motivating the employees is a vital factor for the success of an organization but measuring motivation is challenging, especially with the conventional tools and techniques that do not capture current situations and flexible working organizational environments. Presumably, this paper came up with an AI model that incorporates machine learning algorithms with corporate culture measures to estimate employees' motivation levels. The model relies on the IBM HR Analytics dataset that focuses on employee behavior and feedback data, for performance records. Approaches used herein are Principal Component Analysis for feature extraction and XGBoost and Logistic Regression for modeling. The proposed model obtained an accuracy of 90.5%, 89.2% precision, and an AUC of 0.92, surpassing typical models. Also, the system analyzed different employees and labeled them as high-risk employees with an accuracy level of 87.5%. The findings also show how AI can boost employee motivation by offering precise information on their requirements. Future research will involve applying the model with more substantial datasets and making the model more suitable for complex working contexts. © 2024 ieee.

关键词： data accuracy

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A Biomimetic Multi-Segment Foot Design Based on Joint Motion Synergy 9

A Biomimetic Multi-Segment Foot Design Based on Joint Motion...

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9th ieee international conference on advanced Robotics and Mechatronics (ICARM)

作者： Zhang, Xinyue Sun, Ronglei Huazhong Univ Sci & Technol Sch Mech Sci & Engn Inst Med Equipment Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9798350385731;9798350385724

The intricate multi-segment structure of human foot, which enables versatile and adaptable movement, has attracted attention to the development of biomimetic feet. However, directly imitating biological multi-segment foot structures can pose challenges in both mechanical and control systems due to their inherent complexity. This study aims to investigate the synergy and workload distribution of ankle-foot complex joints during walking to simplify the design of biomimetic multi-segment feet. We collected motion data of ankle-foot complex joints across various ground slopes (-10 degrees, -5 degrees, 0 degrees, 5 degrees, 10 degrees). We conducted hierarchical clustering analysis, focusing on seven joints: ankle, subtalar, talonavicular, calcaneocuboid, medial and lateral tarsometatarsal, and metatarsophalangeal joints, to elucidate their synergistic patterns. Based on these joint synergies, we propose an underactuated design concept for biomimetic multi-segment feet. This approach utilizes the ankle joint as the actuation source, with other joints interconnected to it. Furthermore, by analyzing the distribution of workload among joints throughout the gait cycle, we adjusted the configurations of certain joints. Specifically, we considered the calcaneocuboid, medial, and lateral tarsometatarsal joints, which exhibit roughly equivalent positive and negative work, as elastic joints, and the metatarsophalangeal joints, primarily involved in negative work, as damping joints. Simulation results confirm that our multi-segment foot design enables multidimensional humanoid motion using a single actuation source.

关键词： Joints (anatomy)

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IMU data Based HAR Using Hybrid Model of CNN & Stacked LSTM

IMU Data Based HAR Using Hybrid Model of CNN & Stacked LSTM

引用

2024 international conference on Advancements in Power, Communication and intelligent systems, APCI 2024

作者： Dubey, Adarsh Zacharias, Joseph College of Engineering Trivandrum Dept. of Ece Thiruvananthapuram India

ISBN: (纸本)9798350363289

Human activity recognition (HAR) keeps the track of various activities in different domains including health, security, sports and robotics. Inertial sensors - specifically, gyroscopes and accelerometers - can be used for these purposes and can now also be found in the accelerometer chip or inertial measurement unit of common smartphones and smartwatches. The widespread use of these smart devices necessitates the acquisition of activity data for HAR systems. Unlike previous studies, we propose the hybrid model of CNN & Stacked-LSTM which is an integration of convolutional neural networks (CNNs) and long-short term memory networks (LSTMs) to complete the task. Behind our design, lies the idea that the integrating architecture is capable of capturing the spatial features as well as temporal features of sequential data which are equally important for the prediction of the future values. We evaluate the model in benchmark dataset UCI-HAR to demonstrate its comparative performance with the existing work. Our proposed model achieves an accuracy of 93.4%, displaying its effectiveness. We trust that the architecture can be used in various social sectors attached with HAR. © 2024 ieee.

关键词： Convolutional neural networks

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Algorithm Implementation of Digital Protection System for Traditional Cultural Industry Based on Grey Clustering

Algorithm Implementation of Digital Protection System for Tr...

引用

2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Chen, Hongxia Hou, Lanbao School of Literature and Media Jingchu University of Technology Jingmen448000 China School of Mathematics and Physics Jingchu University of Technology Jingmen448000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The protection mode of digitalization effectively transforms various national traditional cultures into digital products, providing a way of thinking for the protection of national traditional cultures. This mode still lacks the mode of how to mine the data of national traditional culture and analyze the internal relationship structure after facing the massive data, which can guide the digital protection of national traditional culture under the complex social conditions in the future. This paper discusses the significance of digitalization for the protection of traditional national culture under the environment and ideology of big data development. The possibility of protecting traditional national culture based on big data was analyzed and discussed. This paper designs and establishes the digital perception analysis system of traditional culture. The system uses grey clustering algorithm to identify the characteristics of cultural products. The experimental results show that this method can improve the efficiency of traditional cultural product classification. This method has certain reference value for the research of digital protection system of traditional cultural industry. © 2025 SPIE.

关键词： Augmented reality Visualization Telecommunications computing systems Cultural heritage data communications Image processing Video data storage Cameras

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Advancements in AI-Assisted Plant Disease Diagnosis 4

Advancements in AI-Assisted Plant Disease Diagnosis

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4th ieee international conference on computing, Communication, and intelligent systems, ICCCIS 2023

作者： Chhabra, Anmol Saroch, Tarun Gupta, Ruchika Shukla, Aditya Raghav Chandigarh University Centre of Excellence-Data Science and Cognitive Computing Computer Science & Engineering Department Mohali India

ISBN: (纸本)9798350306118

Plant disease detection is a crucial task in agriculture to ensure healthy crop production. It is vital to identify plant diseases early in order to avert economic and environmental damages. A Machine learning-based approach including Deep Learning techniques for identifying plant disease was developed using Grape leaf images in this work. Inception v3, Squeezenet, VGG-16, and VGG-19 are some embedders that are used in the suggested method to convert raw image data into a fixed-length vector representation, also known as a 'embedding' or 'feature vector.' Our research concludes that CNN stands out with exceptional accuracy above 99.70% compared to classifiers like Naive Bayes, Logistic Regression, and SVM. Additionally, Inception V3 proved to be an efficient feature extractor, enhancing feature representation and reducing processing time. © 2023 ieee.

关键词： Embeddings

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