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Expediting Prediction Accuracy with Exploration and Incorporation of Virtual Data

SN computer science 2024年第5期5卷 545页

作者： Nayak, Sarat Chandra Dehuri, Satchidananda Cho, Sung-Bae Department of Computer Science and Engineering School of Technology GITAM (Deemed to be University) Hyderabad India Department of Computer Science Fakir Mohan University Odisha Balasore 756019 India Department of Computer Science Yonsei University 50 Yonsei-ro Sudaemoon-gu Seoul 03722 South Korea

Achieving high accuracy in time series forecasting (TSF) is challenging;particularly for series with inadequate training data or insignificant correlation among data. Modelling such time series with ANN based systems which are data driven and necessitate abundant training samples, leads to poor generalization ability thus, inferior accuracy. In such circumstances, the time series may be enriched with few more virtual data points (VDPs) explored from existing training samples keeping the corelation factor in mind. Incorporation of such VDPs intensify the volume of training data and helps the model in extracting the inherent nonlinearity efficiently. This article proposes an extreme learning with Rao algorithm based VDP (ELRA-VDP) generation and incorporation approach to expediate TSF accuracy of ANN model. Extreme learning method (ELM) considers random hidden node parameters and apply a generalized inverse function on hidden node outputs to determines the output node parameters. Inclusion of random hidden node parameters may reduce the performance of ELM therefore, we used Rao algorithm (RA) to optimize the initial parameters and construct an efficient learning scheme, i.e., ELRA. Given a training sample, the ELRA estimates the VDPs as well as forecasts the next data in an alternative manner. The parameter-free optimization mechanism of RA and lower complexity with faster learning ability of ELM makes ELRA computationally efficient. TSF problems are then conducted with and without VDPs. Simulation studies show that ELRA-VDP method yields better predictions than others with a lowest MAPE value of 0.1746 and quite beneficial for datasets having a lesser amount of data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Extreme learning machine Interpolation Rao algorithm Time series forecasting Virtual data position

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Rule-based Classifiers for Suspect Detection from CCTV Footages

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Recent Advances in computer science and Communications 2022年第4期15卷 594-600页

作者： Pathak, Sunil Department Of Computer Science and Engineering Amity School of Engineering and Technology Amity University Rajasthan Jaipur India

Background: A significant work has been presented to identify suspects, gathering information and examining any videos from the CCTV Footage. This exploration work expects to recognize suspicious exercises, i.e., object trade, the passage of another individual, peeping into other's answer sheet and individual trade from the video caught by a reconnaissance camera amid examinations. This requires the procedure of face acknowledgment, hand acknowledgment and distinguishing the contact between the face and hands of a similar individual and that among various people. Methods: Segmented frames have been given as input to obtain a foreground image with the help of Gaussian filtering and background modeling method. Such foreground images have been given to Activity Recognition model to detect normal activity or suspicious activity. Result: Accuracy rate, Precision and Recall are calculated for activities detection, contact detection for Best Case, Average Case and Worst Case. Simulation results are compared with performance parameter such as Material Exchange, Position Exchange, and Introduction of a new person, Face and Hand Detection and Multi Person Scenario. Conclusion: In this paper, a framework is prepared for suspect detection. This framework will absolutely realize an unrest in the field of security observation in the training area. © 2022 Bentham science Publishers.

关键词： Face recognition

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A Bloom Filter-based Algorithm for Fast Detection of Common Variables

A Bloom Filter-based Algorithm for Fast Detection of Common ...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： Wang, Zhen Liu, Kang Xu, Chao Changsha University of Science & Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350301113

Satisfiability problem has significant applications in many fields, such as software verification and Robot path planning. Researchers have proposed many reduction rules for the conjunctive normal form formula, some of which need to detect the common variables in the formula. Even though these reduction rules only take polynomial time, when the time complexity of the rules exceeds O(n3), recursive inprocessing detections with in each branch are time-consuming. Due to this, the Bloom Common Variable Detection Algorithm (CVDA-Bloom) is proposed, which is based on the Bloom filter concept and hash clause detection technique. The hash method is utilized as a filter to detect common variables, while the Bloom filter concept is used to reduce the false positive rate in the hash process. The experiments used 85 SAT instances from 7 series of the SAT2021 Competition. The results show that compared to the pure hash clause detection technique, the clause pass rate of the CVDA-Bloom algorithm decreases by 21.01% on average, and the false positive rate decreases by 66.57% on average. Furthermore, disabling the Bloom hash clause detection technique will increase the time by 260.03%. When used with the actual reduction rules, the CVDA-Bloom algorithm effectively lowers the number of clauses that must be checked explicitly, which can lower the performance overhead and speed up the detection. © 2023 IEEE.

关键词： Application programs

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Detection of Tile Surface Based on SSM-R-CNN Algorithm 5

Detection of Tile Surface Based on SSM-R-CNN Algorithm

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5th International Conference on Frontiers technology of Information and computer, ICFTIC 2023

作者： Wang, Jiahua Li, Lin Kong, Siman Zhou, Chenyang Sun, Jianzhi Beijing Technology and Business University School of Computer Science and Engineering Beijing China

ISBN: (纸本)9798350309034

A computer vision algorithm, SSM-R-CNN, based on feature extraction and adaptive weighting, is proposed to address the challenges of diverse tile types, complex surface areas, and varied defect morphologies, as well as low image contrast in tile surface defect detection. The algorithm extracts templates and defect images from the original images, aligns them using the SURF algorithm, and enhances the features through gray image differencing, mean, and variance normalization. By incorporating edge-preserving filters and bilateral filters, the algorithm achieves a flexible balance between image smoothing and detail preservation to meet the requirements of different application scenarios. Furthermore, the algorithm improves the Mask R-CNN model by integrating the SENet channel attention mechanism, Explore the interaction and importance of feature information between learning channels, thereby enhancing the network's expressive power and accuracy. Experimental results demonstrate the outstanding performance of the proposed algorithm in tile surface defect detection, achieving excellent accuracy, recall rate, and F1-score of 98.72%, 95.65%, and 97.18%, respectively. The algorithm exhibits stable and reliable performance in handling complex textures, non-uniform lighting, and low contrast. With its promising prospects in the field of tile surface defect detection, this method provides a new approach to adaptive weighting. © 2023 IEEE.

关键词： Adaptive weighting Bilateral filter computer vision Feature extraction SENet channel attention mechanism

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Sustainable Clustering Neural Networks

Sustainable Clustering Neural Networks

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Huashun Li Jingzhao Li Wei Chen Zechao Liu School of Electrical and Information Engineering Anhui University of Science and Technology Hunan China School of Computer Science Anhui University of Science and Technology Hunan China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

In the modern field of power and renewable energy, advancements in data analysis technology are key to driving innovation and sustainable development. However, existing clustering algorithms and neural networks are inefficient and inaccurate when processing large-scale, high-dimensional data, and they consume significant computational resources. To address these issues, this paper proposes a Sustainable Clustering Neural Network (SCNN). By introducing Gaussian units based on joint Gaussian distribution and combining them with a max-pooling layer, we propose a universal clustering module with a competitive update mechanism. This module includes two sets of trainable parameters, allowing for flexible adjustment of the shape and position of the Gaussian distribution to fit different data distribution characteristics. During clustering, feature selection and dimensionality reduction are achieved through the max-pooling layer, effectively enhancing the model's clustering performance. The competitive update mechanism further promotes competition among Gaussian units, enabling each unit to focus more on specific clusters, thereby improving the accuracy and stability of the clustering results. Experiments on the MNIST and Fashion-MNIST datasets achieved clustering accuracies of 93.38% and 72.83%, respectively, demonstrating strong competitiveness compared to existing methods. The code is available at: https://***/ZS520L/HGL-CAE.

关键词： Dimensionality reduction Renewable energy sources Technological innovation Accuracy Shape Neural networks Gaussian distribution Feature extraction Stability analysis Sustainable development

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Plant Disease Diagnosis and Image Classification Using Deep Learning

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computers, Materials & Continua 2022年第5期71卷 2125-2140页

作者： Rahul Sharma Amar Singh Kavita N.Z.Jhanjhi Mehedi Masud Emad Sami Jaha Sahil Verma School of Computer Applications Lovely Professional University Jalandhar144411India Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computer and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the *** farming employs technology to improve *** and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop *** disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s *** learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant *** this paper,the CNN model is proposed for the classification of rice and potato plant leaf *** leaves are diagnosed with bacterial blight,blast,brown spot and tungro *** leaf images are classified into three classes:healthy leaves,early blight and late blight *** leaf dataset with 5932 images and 1500 potato leaf images are used in the *** proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%*** results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.

关键词： Plant diseases detection CNN image classification deep learning in agriculture

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Constructing Conditional Random Fields for Automated Land Cover Mapping from Hyper Spectral Images

Constructing Conditional Random Fields for Automated Land Co...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Prabha, S. Singh, Vaishali Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes an automatic land cover mapping technique using Conditional Random Fields (CRFs) from hyper-spectral snapshots. It introduces a hierarchical feature vector method to encode the labels' spatial relationships. The paper opinion associated paintings on land cover mappings and introduces the idea of CRFs inside the context of the land cowl category. A description of the facts, pre-processing techniques, and the parameters used for schooling and checking out the model is supplied. The paper develops and evaluates the CRF model, evaluating the results of other land cowl class strategies. Finally, the authors discuss the capacity packages of this technique in the context of other land cowl mapping obligations. This painting is innovative in its method to land cowl mapping and offers a successful implementation of the CRF model for this hassle. It explores using Conditional Random Fields (CRFs) to facilitate automatic land cover mapping from hyperspectral photos (HSI). The CRF method was implemented to estimate the land cover labels in an unsupervised gaining knowledge of style. To this stop, the pixel-level land cowl information for a given area changed into modeled as a graphical version, permitting place-degree marginal probability computations for a dense prediction. Experimental outcomes validated that the proposed technique ought to obtain a sizeable development in the accuracy of land cover mapping for the tested records set compared to its baseline. The proposed CRF-primarily based land cowl mapping method exhibited a higher convergence price and better nearby minima convergence. Moreover, the proposed approach was located to be sturdy in phrases of sensitivity to preliminary land cowl labels. © 2024 IEEE.

关键词： category Conditional evaluating hyper spectral schooling

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Broad Learning and Convolutional Neural Network aided Principal Component Analysis for Detecting Faults in High-speed Trains

Broad Learning and Convolutional Neural Network aided Princi...

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2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Zhou, Mingyue Zhao, Hongyang Cheng, Chao School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781510667662

Traction systems provide the traction power of high-speed trains. Because the complex operation mechanism of train under actual working conditions and the measured data are nonlinear and non-Gaussian, and the sampling frequency of the sensor is high in actual working conditions. Directly using a neural network or the multivariate statistical method is challenging to obtain the ideal fault detection (FD) result. Therefore, this paper proposes a data-driven method based on broad learning system (BLS) and convolutional neural network (CNN) assisted principal component analysis (PCA). Two neural networks are used to enhance the robustness of the algorithm, so that the proposed method has better fault detection ability in nonlinear and non-Gaussian systems. The advantage of this method is that it does not require the establishment of a complex high-speed train data model. Instead, by processing the collected data, the proposed algorithm can ensure good fault detection capabilities. Finally, the effectiveness and feasibility of the proposed method are verified on the simulation platform of traction drive control system (TDCS). © 2023 SPIE.

关键词： Principal component analysis

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Graph-Based Feature Learning for Cross-Project Software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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Security Trends in Internet-of-things for Ambient Assistive Living: A Review

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Recent Advances in computer science and Communications 2024年第7期17卷 18-46页

作者： Patel, Ankit D. Jhaveri, Rutvij H. Shah, Kaushal A. Patel, Ashish D. Rathore, Rajkumar S. Paliwal, Manish Abhishek, Kumar Thakker, Dhavalkumar CSE Department School of Technology Pandit Deendayal Energy University Gandhinagar India Department of Computer Engineering SVM Institute of Technology Bharuch India Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom Department of CSE National Institute of Technology Patna India School of Computer Science University of Hull Hull United Kingdom

The Internet of Things (IoT) has revolutionized our society and become indispensa-ble to modern existence. The IoT allows users to access their electronic gadgets from any loca-tion. The widespread adoption of IoT across sectors, from manufacturing to surveillance to el-der care, has contributed to its rising profile. New security risks and challenges arise with the growth of the IoT. With the development of IoT, the likelihood of an attack by hackers has in-creased. The burden of addressing these dangers falls on researchers and security professionals. This article looks into the challenges of IoT security in a real-world Ambient Assisted Living (AAL) environment. This work discusses the numerous security attacks employed by cyber-criminals in AAL IoT. In addition, this research investigates the varied responses to the risks. We discussed the state-of-the-art technologies available for protecting AAL IoT networks. This work analyses and compares the majority of the latest technologies available. In conclusion, we offer a few suggestions for where the field could go from the current scenario. © 2024 Bentham science Publishers.

关键词： Internet of things

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