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Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

Intelligent Automation & Soft computing 2023年第7期37卷 1145-1161页

作者： H.Shalma P.Selvaraj Department of Computing Technologies Faculty of Engineering and TechnologySchool of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203Tamil NaduIndia

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity *** work uses the consistency check method to find an accurate depth map for identifying occluded *** prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for *** improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction *** experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and *** observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain *** this gain,we have created our dataset with occlu-sion using the structured lighting *** proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing *** experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

关键词： Depth maps occlusion detection reconstruction refined disparity map

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Solar Power Output Prediction with AI: Leveraging Meta Learner Ensemble Stacked Regressor

Solar Power Output Prediction with AI: Leveraging Meta Learn...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Shreeja, G. Kannan, Shruthi Sivakumar, Soubraylu SRM Institute of Science and Technology Computing Technologies School of Computing Chennai India

ISBN: (纸本)9798331541170

As the world shifts from fossil fuels to renewable energy, solar power has become essential in meeting global energy demands. The efficiency of solar energy depends on effectively capturing the environmental and physical factors that impact electricity production by solar panels. This study aims to leverage available data on power output and environmental conditions to build advanced machine learning models for predicting solar power. Models such as KNN, DNN, LGBM, RF, and a Meta Learner will be rigorously trained and fine-tuned using various data subsets and hyperparameter optimization. By employing the stacking regressor module from Scikit-learn, a Python machine-learning library, we successfully combined four distinct models, achieving an R-squared value of 0.68 in predicting solar power. This method not only improves the accuracy of solar power predictions but also enhances our ability to capture the complex data relationships critical for forecasting renewable energy. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9057-9068页

作者： Qu, Ying Wang, Xuming Data Science and Intelligent Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang China School of Economics and Management Hebei University of Science and Technology Hebei Shijiazhuang China

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention. © 2024 – IOS Press.

关键词： Association rules

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Advanced Pedestrian Detection in Low Visibility Scenarios Using UltraLytics YOLOv8 and Kalman Filtering 5

Advanced Pedestrian Detection in Low Visibility Scenarios Us...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pattem, Sanjay Kumar Reddy Rajagopal, Manjula Nirujogi, Nandan SRM Institute of Science and Technology Computing Technologies Chennai India SRM Institute of Science and Technology Computing Technologies Faculty of Engineering and Technology Chennai India

ISBN: (纸本)9798331530013

Nighttime detection of pedestrians brings important problems such as low light conditions, limitations of sensors, and environmental noise. To tackle this problem, in this study, we propose a novel visual-radar data fusion to enhance a pedestrian detection system using the UltraLytics YOLOv8 deep learning model together with Kalman Filtering technique. The system is based on a Low-Light Vision Infrared Paired (LLVIP) dataset that includes infrared vision data to attenuate the performance decrement of a pedestrian detector in the dark. The combination of infrared cameras and millimeter-wave radar achieves a detection rate of 97.6%, in which UltraLytics YOLOv8 exhibits excellent object recognition performance by generating accurate spatial & category information from the infrared image;complementary distance and velocity data are obtained by millimeter-wave radar. It brings Kalman Filtering to track the radar-detected pedestrian targets, making the system more robust by predicting target and noise reduction. It was capable of fusing infrared and radar data at decision-level to make the system provide very precise position accurately in even nighttime situations. The experimental results show that the proposed model has improved capability of detection accuracy, robustness, and response time in comparison with the single-sensor system, which is suitable for practical autonomous driving scenarios. © 2024 IEEE.

关键词： Kalman filters

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Image Caption Generation with Python: A Deep Learning-Based Approach 5

Image Caption Generation with Python: A Deep Learning-Based ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Krishna, Siram Chaitanya Reddy, Painti Nagi Kirubanantham, P. School of Computing SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331518097

Today, the day-to-day generation of such a large amount of content on social media and other websites makes it impossible to maintain each image manually, so the need for automatically identifying sensitive images and filtering them out becomes inevitable. This project proposes a sensitive image classification application using automatic captions in identifying and filtering inappropriate content. Thus, the information extracted from images by ResNet50 in the caption generation process is allowed to integrate such features with a standard model through Senti-Long Short-Term Memory networks (SLSTM) for better captioning. Deep learning models bring about drastically improved quality of captions along with contextual understanding. The captions are tested rigorously with training loss metrics, BLEU scores, and sentiment analysis through the VADER Sentiment Analyzer. This system efficiently categorizes images involving violence, negativity, or dangerous situations into the class of negative. A Flask-based web application was developed for implementing a scalable and user-friendly interface, ensuring efficient content moderation. This project has illustrated the possibility of integrating deep learning techniques with natural language processing to yield beneficial content moderation, further ensuring safe and controlled online environments. © 2024 IEEE.

关键词： Long short-term memory

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IoT-CDS:Internet of Things Cyberattack Detecting System Based on Deep Learning Models

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Computers, Materials & Continua 2024年第12期81卷 4265-4283页

作者： Monir Abdullah Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT *** learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT *** rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT *** DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT *** BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal *** experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy *** are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack *** method,without feature selection,demonstrates advantages in training time and detection ***,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.

关键词： Cyberattacks intrusion detection system deep learning internet of things

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Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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Computer Systems science & Engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia Department of Information Technology MLR Institute of TechnologyHyderabad500043TelanganaIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various *** same can be adapted to the problem of brain tumor ***,several deep learning models are presented earlier,but they need better classification *** efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this *** method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple *** noise-removed image has been equalized for its quality by using histogram ***,the features like white mass,grey mass,texture,and shape are extracted from the *** features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality *** texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution *** neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of *** on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor

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Knowledge Discovery of the Internet of Things (IoT) Using Large Language Model

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International Journal of Advanced Computer science and Applications 2025年第4期16卷 993-998页

作者： Alsulami, Bassma Saleh Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Internet of Things (IoT) technology quickly transformed traditional management and engagement techniques in several sectors. This work explores the trends and applications of the Internet of Things in industries, including agriculture, education, transportation, water management, air quality monitoring, underground mining, smart retail, smart home systems, and weather forecasting. The methodology involves a comprehensive review of the literature, followed by data extraction and analysis using BERT to identify key insights and patterns in IoT applications. The findings show that IoT significantly impacts the improvement of real-time monitoring, increasing efficiency, and encouraging innovative solutions in various sectors. Despite its transformative potential, cybersecurity threats, data privacy concerns, and the need for strong policy frameworks persist. The study emphasizes the necessity of multidisciplinary approaches to address these difficulties and optimize IoT implementation. Future research should focus on establishing secure IoT systems, maintaining data integrity, and encouraging collaboration between disciplines to realise the benefits of IoT technology. © (2025), (science and Information Organization). All rights reserved.

关键词： Data integrity

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Managing the surge:A comprehensive review of the entire disposal framework for retired lithium-ion batteries from electric vehicles

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Journal of Energy Chemistry 2024年第5期92卷 648-680页

作者： Ruohan Guo Feng Wang M.Akbar Rhamdhani Yiming Xu Weixiang Shen Department of Engineering Technologies School of ScienceComputing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia Department of Chemistry and Biotechnology School of ScienceComputing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia Department Mechanical and Product Design Engineering School of EngineeringSwinburne University of TechnologyMelbourne VIC 3122Australia

Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has *** paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and ***,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening ***,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic *** the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery ***,we introduce several global leading recyclers to illustrate their industrial processes and technical ***,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal *** hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.

关键词： Lithium-ion battery Battery reproposing and recycling Miaieiials recovery technologies Techno-economic issues End-of-life management Disposal framework

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CALTM:A Context-Aware Long-Term Time-Series Forecasting Model

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Computer Modeling in Engineering & sciences 2024年第4期139卷 873-891页

作者： Canghong Jin Jiapeng Chen Shuyu Wu Hao Wu Shuoping Wang Jing Ying School of Computer and Computing Science Hangzhou City UniversityHangzhou310015China School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China College of Computer Science and Technology Zhejiang UniversityHangzhou310058China

Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series ***,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term ***,the effectiveness of existing methods diminishes in such ***,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic *** model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final *** results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.

关键词： Traffic volume forecasting scene matching multi module fusion

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