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arXiv 2024年

作者： Truica, Ciprian-Octavian Apostol, Elena-Simona Marogel, Marius Paschke, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Splaiul Independentei 313 Bucharest060042 Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Fraunhofer Institute for Open Communication Systems Kaiserin-Augusta-Allee 31 Berlin10589 Germany

In today’s digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Graph embeddings

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A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

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arXiv 2023年

作者： Dhinakaran, D. Udhaya Sankar, S.M. Elumalai, G. Jagadish kumar, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India R.M.K College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors. © 2023, CC BY.

关键词： Energy utilization

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Harmonizing Energy Harvesting and Backscattering communications Through Agile Power Allocation

Harmonizing Energy Harvesting and Backscattering Communicati...

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European Conference on Microwave

作者： Kai Xu Yishan Wang Wei Gong Jayakrishnan Methapettyparambu Purushothama Chaoyun Song George Goussetis Yuan Ding The Institute of Sensors Signals and Systems Heriot-Watt University U.K Institute for Digital Communication University of Edinburgh U.K. School of Computer Science and Technology and the School of Data Science University of Science and Technology of China China Department of Engineering Strand Campus King’s College London U.K.

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

Backscatter communication (BackCom) emerges as a promising solution for the Internet of Things (IoT) ecosystem due to its low-power operation, which provides a potential for power autonomy through radio frequency (RF) energy harvesting (EH) technology. However, the dynamic power allocation tuning between communication and powering is still a challenge. To overcome it, in this paper we propose an agile power allocation strategy by integrating the EH circuit into backscatter modulator, i.e. employing it as one of the load elements in the backscatter tag. The power allocation can thus be dynamically adjusted in the time domain, utilizing a square wave with variable duty cycles to fine-tune the power ratio digitally. Our approach balances the backscatter system in communication and powering, providing a scalable solution adaptable to most existing BackCom systems.

关键词： Radio frequency Power dividers Modulation Microwave communication Resource management Internet of Things Energy harvesting Time-domain analysis Tuning Backscatter

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A Novel Spatial–Temporal Analysis Approach to Pedestrian Groups Detection

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Procedia computer science 2022年 207卷 2364-2373页

作者： Claudia Cavallaro Giuseppe Vizzari Department of Mathematics and Computer Science University of Catania Viale Andrea Doria 5 Catania 95125 Italy Department of Informatics Systems and Communication University of Milano-Bicocca Viale Sarca 336/14 Milano 20126 Italy

The growing availability of geo–referred data describing human behaviour, at different scales and levels of granularity, represents an opportunity for the development and application of data analysis algorithms, whose usage can range from security, to traffic, to architectural design and planning, and even marketing. Focusing on pedestrian generated trajectories, the presence of groups within an analyzed population can influence overall dynamics, from microscopic perspective, and it can provide significant indications. Several approaches for video footage analyses are available, but they generally focus on microscopic features of videos and trajectories and they are generally not suited to scale to the analysis of relatively large datasets of trajectories. The present work proposes a novel approach to spatial–temporal analysis of pedestrian trajectories aimed at detecting groups of pedestrians within large datasets and having minimal assumptions on the nature of these groups.

关键词： Groups Analysis of pedestrian trajectories Clustering

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Map Building of Indoor Environment with Sensors using Neural Network

Map Building of Indoor Environment with Sensors using Neural...

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Smart Computing and Networks (WiSSCoN), Winter Summit on

作者： S Angel Latha Mary K. Ulagapriya A Poonguzhali R. Menaha Beaulah David T.R. Priyadharshini Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Vels Institute of Science Technology and advanced studies Chennai India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bangalore India Department of Information Technology Sri Eshwar College of Engineering Coimbatore India Department of Information Technology Hindusthan College of Engineering and Technology Coimbatore India

The necessity of a blueprint of a building structure is a mandatory requirement for any reconnaissance or rescue operations. In our project, we build a modular system combining sensors related to sonar, laser, micro-wave to read sensory values and generate a 2D path of any building. The data is fetched and stored to feed to anOptimal Neural Network (ONN)-based computing system to create a 2D route with minimal discrepancies of error. Here the NN architecture is fine-tuned using Modified Dolphin Partner Optimization (MDPO) Exploration of unknown environments and space using autonomous vehicles has recently gained good attention in the field of Robotic Mapping. The recent advancements in the Internet of Things have enabled us to establish an ideal frame of reference for sonar and lidar-based systems. New effects are displayed by the sensors according to the physical characteristics of a room. The range data from sensors in various surroundings are interpreted by NNs. The distorted errors due to the material medium, particles, and moving objects present in the environment pose a threat to building a high-quality path map. The sensor fusion technique is applied to the rotatable modular array sensor to minimalize discrepancies caused by cloth materials during sonar readings, particle noise in an environment for Lidar reading, and moving human bodies present in the environment for path building and obstacle detection.

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Role of RPA to Seize Opportunities in 5G Edge Computing in Telecommunications

Role of RPA to Seize Opportunities in 5G Edge Computing in T...

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Advances in Computing Research on science Engineering and Technology (ACROSET), International Conference on

作者： Dileep Kumar Yendluri Reshmi Tatikonda Ramya Tatikonda Jainath Ponnala M. Kempanna A. Bhuvanesh Department of Computer Science University of the Cumberlands Williamsburg USA Department of Computer Information Systems University of the Central Missouri Warrensburg USA Department of Information Technology University of the Cumberlands Williamsburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderabad India Department of AI and ML Bangalore Institute of Technology Bangalore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This paper explores the role of Robotic Process Automation (RPA) in leveraging opportunities presented by 5G edge computing in the telecommunications industry. Integrating RPA and 5G edge computing offers numerous benefits, including enhanced operational efficiency, real-time decision-making, improved service delivery, and cost savings. However, it also poses challenges such as complex integration, security concerns, and the need for effective change management. The paper comprehensively analyzes the potential benefits, challenges, and recommendations for implementing RPA in 5G edge computing. It explores use cases, future advancements, emerging technologies, and anticipated opportunities and challenges. The findings highlight the need for clear objectives, process optimization, collaboration between IT and business teams, and continuous monitoring to leverage RPA in 5G edge computing effectively.

关键词： Intelligent automation Industries 5G mobile communication Decision making Real-time systems Telecommunications Security Optimization Monitoring Edge computing

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Enhancement of Agriculture Productivity and Automation of Agriculture using IoT

Enhancement of Agriculture Productivity and Automation of Ag...

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International Conference on Inventive Computation Technologies (ICICT)

作者： J. Seetha Senthil Kumar S Nruthya K Nynalasetti Kondala Kameswara Rao D. Leela Rani Department of Computer science and Business Systems Panimalar Engineering College Chennai India Department of Mechanical Engineering R.M.K College of Engineering and Technology Thiruvallur India Department of Civil Engineering Indian Institute of Science Bengaluru India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram India Department of Electronics and Communication Sree Vidyanikethan Engineering College Tirupati India

The utilisation of sensor networks in agriculture monitoring is the invention that benefits Indians the most. Given that agriculture is the primary source of revenue, it is essential to boost production while decreasing expenses, inputs of time, and labour. Information gathered from agricultural areas is stored in a database, which may be monitored via the Internet of Things (IoT). Connecting Raspberry Pi with IoT and sensors to increase agricultural activity productivity is the challenging task in the Farm Management System for secure agriculture. Temperature, humidity, soil moisture content, and any field fires are tracked as a challenge's outcome. Specifications like temperature, moisture content, and fire are currently detected manually in farm systems. This technique automates the completion of watering. It used to lighten the workload for farmers. Any type of crop productivity is feasible. This method's drawbacks include an increase in labour costs, increased time requirements, and the inability to do continuous monitoring.

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Automated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation

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Human-Centric Intelligent systems 2024年第2期4卷 299-315页

作者： Ahamed, Md. Faysal Islam, Md. Rabiul Nahiduzzaman, Md. Chowdhury, Muhammad E. H. Alqahtani, Abdulrahman Murugappan, M. Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Biomedical Technology College of Applied Medical Sciences Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Medical Equipment Technology College of Applied Medical Science Majmaah University Majmaah City Saudi Arabia Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Doha Kuwait Department of Electronics and Communication Engineering Vels Institute of Sciences Technology and Advanced Studies Chennai India DCenter for Unmanned Autonomous Systems (CoEUAS) Universiti Malaysia Perlis Perlis Malaysia

The early detection of colorectal polyps is crucial for the reduction of mortality rates. However, manually identifying polyps is time-consuming and expensive, increasing the risk of missing them. Our paper aims to address this issue by presenting an automated segmentation approach for colorectal polyps. This paper proposes a method that combines a skip connection with hybrid attention guidance (AG) using attention guidance (AG) and residual path frameworks to identify salient features. Furthermore, we augment test samples using original, horizontal flip, and vertical flip transformations to enhance model robustness through Test Time Augmentation (TTA). The model was trained with Kvasir-seg samples and evaluated on Kvasir-seg and CVC-ClinicDB datasets to gauge generalizability. A significant accuracy (0.9546), a Dice Similarity Coefficient (DSC) of 0.8557, a Cross-section over Union (IoU) of 0.8824, a Recall (0.8221), a Precision (0.8922), an area under Receiver Operating Characteristics (ROC-AUC) of 0.9454, and an area under Precision-Recall (AUC-PR) of 0.8717 were achieved without TTA. Through TTA integration, accuracy (0.9993), DSC (0.8663), IoU (0.8277), Recall (0.8060), Precision (0.9364), and ROC-AUC (0.9587) have been improved. A comparison of our framework with state-of-the-art models demonstrated its effectiveness and segmentation capabilities. Additionally, the proposed model contains only 0.47 million parameters and a weight size of 6.71 MB, illustrating its potential for clinical diagnostics. A computer-aided diagnosis (CAD) system improves patient outcomes by detecting colorectal polyps early and improving segmentation accuracy.

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Optimizing Controller Tuning using Bio-Inspired Algorithms for Robot Path Tracking 3

Optimizing Controller Tuning using Bio-Inspired Algorithms f...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Rani, Taneti Lilly Deepa, S. Joon, Rakesh Kumar Jayaseelan, P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Jawaharlal Nehru Technological University College of Engineering Kakinada University College of Engineering in JNTU Department of Civil Engineering Andhra Pradesh Kakinada533003 India RMD Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavarapettai601206 India Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana Kablana124104 India Achariya College of Engineering Technology Department of Mechanical Engineering Puducherry605110 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9798350396157

In the field of industrial and commercial robotics, there has been a recent boom in research towards the navigation of wheel-based mobile robots. There are numerous aspects of this complex problem that need fixing, including issues with sensory data gathering and processing, trajectory planning, and controlling motion. In the field of motion control, creating reliable trajectory controllers is the highest priority. This study improves the Proportional-Integral-Derivative (PID) controller using Ant Colony Optimization (ACO) and Cat Swarm Optimization (CSO) to get the best possible trajectory tracking. The kinematics of a two-wheeled mobile robot serve as the foundation for its mathematical model. The user-specified reference trajectory is given to the robot. The conventional PID, ACO-PID, and CSO-PID controller's trajectory tracking capacity are evaluated using the metrics. The performance metrics such as the Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE) are taken. According to the simulation results, the CSO-PID controller beats the conventional PID and ACO-PID controllers in terms of error metrics. This discovery highlights the effectiveness of using CSO-PID to improve the trajectory-Tracking performance of mobile robots. © 2024 IEEE.

关键词： Three term control systems

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Development of an Artificial Intelligence-based Prediction System for Short-term Solar Power Generation

Development of an Artificial Intelligence-based Prediction S...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Kang, Minseok Choi, Suhyeon Kim, Woohyun Kim, Jaehyeon Cho, Seokheon Department of Computer Science Yonsei University Seoul Korea Republic of Department of Management Information Systems Kyonggi University Suwon Korea Republic of Department of Information and Communication Chosun University Gwangju Korea Republic of Department of Business Chosun University Gwangju Korea Republic of Qualcomm Institute University of California La Jolla San Diego United States

ISBN: (纸本)9798331521165

The increased occurrence of extreme weather events is closely related to global warming caused by excessive fossil fuel consumption. This has resulted in significant global interest in renewable energy sources, especially solar power, which is showing rapid growth worldwide. However, the excessive and disorganized utilization of solar power systems can cause instability of the existing power system because of a continuous time varying output of solar power systems. Hence, it is crucial to accurately predict the solar power generation for a stable energy supply to the traditional electricity system. This study aims to provide a short-term solar power generation prediction model based on various Artificial Intelligence algorithms and improve its performance. The Multiple Linear Regression, Random Forest Regression, Multi-Layer Perceptron and Long Short-Term Memory algorithms were utilized for a solar power generation prediction model. Also, solar power-related data measured every 15 minutes and hourly meteorological data were used to train and evaluate our proposed prediction models. Several datasets generated by the feature selection process and the addition of the historical solar power-related data as well as the historical weather data were used to enhance a short-term solar power generation prediction model. The comparison of performance results showed that the Multi-Layer Perceptron algorithm-based prediction model using the solar power-related data and the weather data as well as their past data outperforms by achieving the highest value of R-squared (R2) but the lowest value of the Root Mean Squared Error. An architecture of an Artificial Intelligence-based solar power prediction system embedded with this optimal short-term solar power prediction model is proposed. © 2025 IEEE.

关键词： Solar fuels

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