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GCN-ICD: A Graph Convolutional Network for Icing Cover Detection 5th

GCN-ICD: A Graph Convolutional Network for Icing Cover Detec...

引用

5th international conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Yang, Qi Zeng, Huarong Xiao, Shuzhou Huang, Huan Xie, Quan Electric Power Research Institute of Guizhou Power Grid Co. Ltd Guiyang550002 China College of Big Data and Information Engineering College of Physics Guizhou University Guiyang550025 China

ISBN: (纸本)9789819751808

We propose a novel icing prediction model consisting of a heterogeneous information embedding layer, a direction-aware information aggregation layer, and a detection layer. In particular, we represent transmission lines as nodes in a graph and establish edges based on their connections. Taking visual images and meteorological data as the content of nodes, we design a large language model (LLM) based embedding layer to fuse the heterogeneous information. Then, the direction-aware information aggregation layer efficiently absorbs and aggregates information from neighbor nodes while considering the edge weights and directions. Finally, with the obtained representations of nodes, the detection layer is able to measure the icing probability of the transmission line. By conducting extensive experiments on a real-world dataset, we demonstrate the superiority of our proposed model compared to baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Visual languages

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Investigation of Air Effluence Using IoT and Machine Learning 6th

Investigation of Air Effluence Using IoT and Machine Learn...

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6th international conference on Emerging Technologies in computing, iCETiC 2023

作者： Shakil, Shalah Uddin Perbhez Kashem, Mohammod Abul Islam, Md. Monirul Nayan, Nasim Mahmud Uddin, Jia Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Birulia Savar Dhaka1216 Bangladesh Dhaka1212 Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031502149

Air pollution poses a significant issue in numerous cities worldwide, impacting public health and the environment. We study three significant cities under the Dhaka division, including Kuril Bishow Road, Uttara, and Tongi. Traditional air quality monitoring methods often need more coverage and accuracy. Leveraging Internet of Things (IoT) technology as well as machine learning (ML) algorithms, this study deploys an IoT-based sensor network using Arduino boards and various devices, including MQ135, DHT22, PM2.5, MQ9, and dust sensors to gather real-time values on air pollutants. The gathered data, including sulfur dioxide, ozone, particulate matter 2.5, nitrogen dioxide, particulate matter 10, as well as carbon monoxide, provides a comprehensive view of city pollution levels. ML models such as linear regression, decision trees, K-Nearest Neighbors (KNN), Naive Bayes (NB), Gradient Boosting (GB), and Random Forest classifiers are applied to predict pollution levels using environmental parameters. The Random Forest classifier achieves an impressive prediction accuracy of 97.2 %. Evaluation metrics, including precision, recall, F1 score, Kappa score, mean square error (MSE), root mean square error(RMSE), along mean absolute error (MAE), are used to assess the performance of the models. This study demonstrates the potential of IoT technology along with ML algorithms in accurately predicting air pollution levels, aiding in environmental management and public health efforts in urban areas. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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High-Capacity Reversible data Hiding in Encrypted Images Based on MSB Bit-Plane Serpentine Rearrangement and Compression

High-Capacity Reversible Data Hiding in Encrypted Images Bas...

引用

Communication, Image and Signal processing (CCISP), international conference on

作者： Tinghua Hu Sheng Qin Kaiyang Wang Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China International Joint Audit Institute Nanjing Audit University Nanjing China

Reversible data hiding techniques in encrypted images (RDH-EI) have been extensively researched to safeguard the privacy of images. To enhance the embedding capability of RDH-EI algorithms and security, this work leverages the topological coherence within the input plaintext image to create more additional space for embedding confidential information prior to image encryption. Initially, the prediction error of the input image is computed using the pixel prediction technique. Subsequently, the bit-plane serpentine rearrangement mechanism and the bit-stream compression method are employed to further enhance the process. This compression technique efficiently compresses the most significant bit-planes, resulting in a high-capacity space for embedding. Finally, the end-user successfully extracts the data and restores the input image using the possessed keys. The results clearly show that the proposed approach achieves not only attains a superior embedding capacity which achieves the maximum average embedding rate with a value of 3.624 (bpp), but also obtains a commendable reconstructed image quality that is PSNR=+∞ and SSIM=1.

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IoT-Based Power Metering Automation for Improving data Integrity and Reliability in Power Marketing Meters

IoT-Based Power Metering Automation for Improving data Integ...

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Evolutionary algorithms and Soft computing Techniques (EASCT), international conference on

作者： Sumaiya Samreen Yerrolla Chanti P Mounika Manjusha Mandava Kruthika C G IT Department Malla Reddy Engineering College for Women Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE (DS) CMR Technical Campus Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

The goal of this paper is to provide an internet of things (IoT)-based approach for automated electricity metering. When measuring power using standard methods, inaccuracies and erroneous readings are possible, putting the data's integrity and authenticity at risk. The suggested method employs sensors linked to the internet of things (IoT) and machine learning algorithms to detect abnormalities and inconsistencies in real-time energy use statistics. This was evident during the testing phase. The proposed method offers several benefits over the present standard of power metering systems in use. The testing findings demonstrated that the proposed system is more reliable and effective in terms of 0.957 success rate, 0.943 accuracy, 0.967 recall, 0.956 F1 score and 0.982 AUC.

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Lightweight Approximate Multiplier with Improved Accuracy in FPGA for Error Resilient Application

Lightweight Approximate Multiplier with Improved Accuracy in...

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international conference on VLSI Design

作者： Apurba Prasad Padhy Bishnu Prasad Das Electronics and Communication Engineering Indian Institute of Technology Roorkee Roorkee India

Approximate computing finds its application in image processing, machine learning, data mining and multimedia data processing. The paper proposes a scalable and lightweight $4\times 4$ approximate multiplier, which is designed to efficiently utilize the FPGA resources. We have divided the partial product matrix and selectively chosen the input variables for the effective usage of the LUTs and carry-chain. The bit error position due to the approximation is reduced by assigning the possible lower bits to ‘1’ which reduces the maximum error magnitude from 16 to 4. The proposed $4\times 4$ approximate multiplier achieves a reduction in area, power and latency of 26.7%, 57.6% and 60.1% respectively compared to the Xilinx multiplier IP. The proposed multiplier is used as a building block to implement $8\times 8$ approximate multipliers for image sharpening applications. We achieve a high PSNR value of 69.4 dB using the $8\times 8$ approximate multiplier.

关键词： Embedded systems Input variables Multimedia computing Machine learning Very large scale integration data processing Distance measurement

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A Review of Multi-Agent Path Finding algorithms

A Review of Multi-Agent Path Finding Algorithms

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Information Systems and computing Technology (ISCTech), international conference on

作者： Mengdie Wu Wenyao Yan Haslin Hasan Robiatul A’Dawiah Jamaluddin School of Data Science and Engineering Xi'an Innovation College of Yan'an University Xi’an Chian Faculty of Creative Media And Innovative Technology Infrastructure University Kuala Lumpur De Centrum City Malaysia Faculty of Business Information and Human Sciences Infrastructure University Kuala Lumpur De Centrum City Malaysia

In the context of Industry 4.0, Autonomous Guided Vehicles (AGVs) and other types of Multi-Agent System (MAS) have been widely used in large-scale warehouses and unmanned factories. Coordination of AGVs in shared space is drawing increasing attention of researchers, especially in the field of Multi-Agent Path Finding (MAPF). The MAPF problem has been studied a lot, and there are many kinds of solutions. Not all kinds of MAPF solvers can be applied in the field of AGV. Few literature focused on which MAPF solutions are suitable for AGV navigation. This review categorized existing MAPF algorithms into five branches, Conflict-Based Search (CBS) algorithms, Rule-Based algorithms, Priority-Based algorithms, Numerical Optimization-based algorithms and Learning-based algorithms. Furthermore, we evaluate these algorithms in terms of their optimality, lifelong property, scalability and computational cost. Finally, we analyzed which MAPF solvers are suitable for AGV navigation.

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Default Prediction on Commercial Credit Big data Using Graph-based Variable Clustering

Default Prediction on Commercial Credit Big Data Using Graph...

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international conference on Semantic computing

作者： Mallika Boyapati Ramazan Aygun School of Data Science and Analytics Kennesaw State University Kennesaw GA USA Department of Computer Science Kennesaw State University Kennesaw GA USA

Credit-lending organizations have resorted to the use of machine learning (ML) algorithms in the recent past to predict the probability of the default of a business. Explainability of the decisions made by the traditional statistical algorithms like Logit models brings transparency to every stakeholder involved in the process. On the other hand, machine learning models like XGBoost and Neural Nets have achieved better accuracy scores, but their decisions are not easily comprehensible. In this paper, we propose a graph based variable clustering (GVC) method as a filter based approach to select prominent features while retaining as much variance as possible. Our experiments show that our GVC approach is not only almost 40 times faster than the existing variable clustering methods but retains retains 5% more variance than the existing *** feature set from GVC approach has performed better with an increase of 6% accuracy on an average. The predictions on the feature set from GVC were 98% accurate using XGBoost algorithm.

关键词： Machine learning algorithms Runtime Semantics Neural networks Clustering algorithms Machine learning Organizations

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An Automatic Segmentation Fitting Method for Freehand Sketches Based on Greedy Strategy 7th

An Automatic Segmentation Fitting Method for Freehand Sketch...

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7th international conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Zhang, Xiuli Chen, Lei Wan, Zhong College of Information and Intelligence Engineering Zhejiang Wanli University Ningbo China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9789819927296

In this paper, we propose an automatic segmentation and fitting method for hand-drawn sketches based on greedy strategy. This method first resamples and smoothly filter the input sketch stroke sequence, and then automatically segments the hand-drawn sketches based on the greedy strategy. The maximum Euclidean distance is found as the segmentation point in the sketch strokes before each iteration starts, the proximity error of sub-strokes between adjacent segmentation points is calculated, the parameters of sub-strokes whose proximity error less than the threshold are stored, the sub-strokes whose proximity error great than the threshold are recorded to be repartitioned in next iteration processing until proximity error of all the sub-strokes less than the threshold. The least squares method is used to fit the sub-strokes one by one, and removes the redundant segmentation points by relying on error control to maximize the retention of the original information of the hand-drawn sketches while obtaining the canonical geometric This provides the basis for recognition, semantic understanding and retrieval from sketches to normative design drawings. The experiments show that the method has good segmentation fitting effect for all kinds of figures, and the fitting residuals are controllable. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Errors

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Detection and Discrimination of Arabic Phonemes Using Long Short-Term Memory (LSTM) Model

Detection and Discrimination of Arabic Phonemes Using Long S...

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IEEE international conference on Intelligent computing and Information Systems (ICICIS)

作者： Rahma M. Tolba Zaki T. Fayed Hamzah A. Alsayadi NourEldin O. Saad Taha Elarif Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Science Department Faculty of Sciences Ibb University Yemen Data Science Department School of Computer Science Canadian International Collage New Cairo Egypt

Phonetics is a crucial branch of linguistics that studies human speech sounds and is essential for language learning, speech therapy, and speech technology development. However, current Arabic speech systems cannot instantly analyze and detect mispronunciation of Arabic phonemes, only offering a recording feature for self-evaluation. So, this paper presents a state-of-the-art approach using a Long Short-Term Memory (LSTM) model for detecting and discriminating between Arabic phonemes. The model was tested on a collected dataset for the Arabic phonemes /s/ 'sīn' and /sˤ/ 'ṣād and demonstrated excellent performance in discriminating between them despite their close articulation. The model achieved an accuracy of 97.33% and outperformed other techniques proposed for small datasets. The model can be utilized either independently or integrated into a computer-assisted language learning (CALL) system. Furthermore, our findings suggest that there is significant room for improvement in this area, particularly in collecting large audio datasets for all the Arabic phonemes, refining the algorithms, and optimizing the data processing pipeline.

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Research on Information System Cloud computing Scheduling Method Based on Swarm Intelligence Algorithm

Research on Information System Cloud Computing Scheduling Me...

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Signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Wang Xiang Wang Yancheng School of Computing and Data Science Xiamen University Malaysia Jalan Sunsuria Bandar Sunria Sepang Selangor Malaysia

With the advancement of cloud computing technology, research on cloud computing task scheduling has become a hot topic. This paper mainly studies the cloud computing scheduling method of information systems based on swarm intelligence algorithm, including cloud computing scheduling model based on particle swarm optimization(PSO), improved Invasive Tumor Growth Optimization(ITGO) model structure, and multi-objective cloud computing task scheduling based on improved artificial bee colony algorithm. This paper concludes that by introducing multi-objective optimization terms, replacing multi-objective optimization terms with fitness functions, initializing tumor cell set functions, and improving search strategies and selection strategies, the efficiency and quality of cloud computing task scheduling can be improved. In order to realize the optimal utilization of cloud computing resources and meet the needs of users.

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