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共 36226 条记录，以下是791-800 订阅

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Predicting Collaboration Requests in Dynamic Construction Environments for Smooth Worker-Robot Collaboration

Predicting Collaboration Requests in Dynamic Construction En...

引用

2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Pan, Zaolin Yu, Yantao Dept. of Civil and Environmental Engineering Hong Kong Univ. of Science and Technology Hong Kong

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Worker-Robot Collaboration (WRC) holds great potential in boosting productivity and safety in construction by leveraging the strengths of humans and robots. One crucial aspect of WRC is the ability of assistive robots to anticipate collaboration requests, enabling them to proactively support their human counterparts. However, dynamic construction environments present unique challenges due to constantly changing workplaces, worker interactions, and intentions. To address this challenge, our study focuses on determining when workers require assistance in such dynamic environments. We propose a vision-based approach, employing YOWOv2-based action detection for real-time prediction of workers' locations and their assistance intentions. We evaluate the effectiveness of our method through experiments conducted on a scaffold assembly task, showcasing its efficiency (52 FPS) and accuracy (0.92 frame mAP). These findings contribute to the development of robot assistive policies tailored to dynamic workplaces, paving the way for smooth WRC in construction. © ASCE.

关键词： Scaffolds

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Deep Learning Model for Emotion Prediction from Speech, Facial Expression and Videos 5

Deep Learning Model for Emotion Prediction from Speech, Faci...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Rajyalakshmi, Chepuri Lakshminadh, K. Reddy, M. Sathyam Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

The rapid development of computer vision and machine learning in recent years has led to fruitful accomplishments in a variety of tasks, including the classification of objects, the identification of actions, and the recognition of faces, among other things. Nevertheless, identifying human emotions remains one of the most difficult tasks to do. To find a solution to this issue, a significant amount of work has been put in. In order to achieve higher accuracy in this reactivity towards a variety of speeches and vocal -based methods, computer intelligence, natural language modelling systems, and other similar technologies have been used. The examination of the emotions has the potential to be useful in a number of different settings. Cooperation with human computers is one example of such a field. computers can help customers recognize emotions, make wiser decisions, and create more lifelike human-robot interactions. In recent times, there has been a lot of focus placed on the ability to forecast dynamic facial emotion expressions in videos. Therefore, this work proposes a deep convolutional neural networks (CNNs) model for emotion prediction from speech samples, facial expression images, and videos with enhanced prediction accuracy and reduced loss. In addition, the speech CNN model also utilizes mel-frequency Cepstrum coefficients (MFCC) as feature extraction from given speech samples. The proposed MFCC-CNN model resulted in superior performance than traditional models. © 2023 IEEE.

关键词： Neural network models

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Deepfake Detection Using Multi-Modal Fusion Combined with Attention Mechanism 4

Deepfake Detection Using Multi-Modal Fusion Combined with At...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Abisha, M.B. Venkatesan, R. Yashvanth Ram, R.V. Elango, Elakkiya Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Government Arts College for Woman Dept. of computer Science Sivaganga India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

The proliferation of deepfake technology poses a significant challenge to the authenticity of digital content. This research explores the application of multimodal fusion techniques to enhance deepfake detection accuracy. By combining visual and audio features, the proposed method leverages the complementary nature of different data types to detect discrepancies introduced by deepfake manipulation. An attention mechanism is incorporated to focus on salient regions within each modality, further improving detection accuracy. Convolutional Neural Networks (CNNs) and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction, followed by feature fusion for deepfake detection. This approach demonstrates the effectiveness of multimodal fusion in combating the evolving threat of deepfake technology. By advancing deepfake detection techniques, this research contributes to safeguarding the integrity of digital content and preserving trust in media. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Framework for Real-Time Fire Detection in CCTV Videos Using a Hybrid Approach of Motion-Flicker Detection, Colour Detection and YOLOv7

A Novel Framework for Real-Time Fire Detection in CCTV Video...

引用

2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Jamal, Sangeetha Jackson, Henok Benny, Evin Koshy, Jeroen Dani Joseph, Gloria School of Engineering and Technology Dept. of Computer Science and Engineering Rajagiri Kochi India

ISBN: (纸本)9798350322972

This paper provides a practical view of implementing a real-time vision-based fire detection system that can accurately detect and alert the presence of fires in live CCTV footage. The proposed system combines motion-flicker detection, which uses a background subtraction algorithm to extract the moving objects in a video and a flicker detection algorithm to verify flickering in suspected regions. Then using a YCbCr colour model, the colour of the flickering region is verified to match the characteristics of fire. Further, a YOLOv7 model trained on a large dataset of fire images is employed to verify the results and raises the alarm to alert the presence of fire. Several optimizations have been done to reduce false alarm rate and GPU memory usage times. The accuracy is 98.4%, and false alarm rate is as low as 2.1%. © 2023 IEEE.

关键词： Fires

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Diabetic Retinopathy Prediction and Analysis Using Ensemble Classifier in Deep Learning Technique

Diabetic Retinopathy Prediction and Analysis Using Ensemble ...

引用

2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Hemanth, S.V. Saravanan, A. Dept. of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9781665492942

The primary cause of vision impairment is diabetic retinopathy (DR). Diabetes can cause diabetic retinopathy, a disorder that damages the retinal blood vessels and affects the eyes. According to the analysis, 90% of sight-threatening cases may be cured if it is discovered early. We proposed an Ensemble algorithm to predict diabetes from retinal images. The primary goal of this work is to enhance the accuracy and predict the result with minimum time consumption. The proposed system is a two-phase work in which, the initial bootstrap resampling method is employed for extracting the subsamples. Next, the classified decision tree establishes a voting system for finalizing the prediction result. The performance accuracy achieved by the proposed Ensemble algorithm is compared with the logistic regression and Deep neural network. The observation shows proposed enhanced Ensemble mechanism attained 96% accuracy, which is more prominent than the other models. © 2023 IEEE.

关键词： Decision trees

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Machine Learning Framework for Women Safety Prediction using Decision Tree 5

Machine Learning Framework for Women Safety Prediction using...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sowmika, P. Sareena Rao, S.Siva Nageswara Rafi, Shaik Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

In every city, harassment and violence becomes one of the major problems for women. Further, women's personal life is suffered by the bullying and abusive content presented in Online Social Networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. When it came to predicting the maximum safety analysis, however, traditional methodologies came up short. This study, then, employs a decision tree (WSP-DT) classifier to make predictions about women's safety. After considering the Twitter dataset for system implementation, it is pre-processed to get rid of the blanks and the unknowns. The tweets were then processed by a natural language toolkit (NLTK) that handled tasks including tokenization, case-conversion, stop-word detection, stemming, and lemmatization. Next, we create a text blob protocol to determine the positive, negative, and neutral polarity of pre-processed tweets. To further extract the data characteristics based on word and character frequency, term frequency-inverse document frequency (TF-IDF) is used. At last, a decision tree classifier was used, based on several rounds of training, to determine if a tweet was phoney or real. Testing on the Twitter dataset demonstrates that the proposed WSP-DT classifier outperforms the competition in simulations. © 2023 IEEE.

关键词： Decision trees

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Enhancing WSNs Lifetime Through GOA based Clustering and Routing 1

Enhancing WSNs Lifetime Through GOA based Clustering and Rou...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Neeraj Singhal, Amit Pandey, Ashish Monad University Dept. of Computer Science and Engineering UP Hapur India GCET Dept. of Electronics and Communication Engineering UP Greater Noida India

ISBN: (纸本)9798350356816

WSNs are regularly battery-powered and it is not continually feasible to recharge or replace them due to a variation of applications. Hence, in this work, a Grasshopper Optimization Algorithm based on routing and power restriction optimization has been proposed for improving the battery lifetime in wireless sensor network. The proposed approach aims to enhance the effective longevity of individual sensor nodes within the network by accomplishing energy-efficient clustering along with energy-aware routing. The outcomes of the suggested method are compared with the several existing optimization methods in WSNs. It is observed that the proposed Grasshopper Optimization Algorithm (GOA) based technique outperforms all the other existing methods in terms of lifetime achieved under various power constraints. For checking robustness of approach, two different locations of base station and various populations of sensing nodes and gateways are also considered. © 2024 IEEE.

关键词： Sensor nodes

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Novel Hybrid One-Class RNN Model Using Adversarial Data Augmentation for Anomaly Detection in Systems Operations 10

Novel Hybrid One-Class RNN Model Using Adversarial Data Augm...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Jaisharma, K. Devi, T. Deepa, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384369

The industries are expanding their infrastructure landscape with advanced processors and supercomputers. It is also important to maintain the systems operations robustly and efficiently to handle the service with zero interrupts. But the illegal contractors will be tampering the system's ability by using augmented data dynamically. This research is to detect the anomaly presence through system operations data. The proposed Novel Hybrid One Class RNN model utilizes the advertiser augmented data to compare with the actual system operations data such as processor utilization, data read/write, network utilizations, request/response count, systems temperature and latency measure over the time period. The result shows the Novel Hybrid One Class RNN model achieves the accuracy of 98.4%, precision of 98.1%, recall of 97.8% and F1-score of 97.3% and performed better than the existing models such as K-mean, Gaussian, Markov Chain, Isolation Forest and One Class SVM models for the considered real world AWS EC2 operations dataset collected under various work load conditions. © 2024 IEEE.

关键词： Markov chains

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Monitoring AI-Based Processing for Predicting Poisonous Gas Emissions in Smart Cities Using Novel Temporal Dynamics Prediction Model 2

Monitoring AI-Based Processing for Predicting Poisonous Gas ...

引用

2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Jaisharma, K. Deepa, N. Devi, T. Saveetha Institute Of Medical And Technical Sciences Saveetha School Of Engineering Dept. Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350381665

Smart cities, driven by technological advancements, face challenges related to environmental pollution, including poisonous gas emissions. Existing systems often struggle to efficiently monitor and predict these emissions, leading to limitations in accurately assessing and mitigating air quality issues. This study proposes a groundbreaking solution, the Novel Temporal Dynamics Prediction (NTDP) model, designed to overcome the limitations of current systems. By harnessing the NTDP model's innovative approach, smart cities can enhance their capability to analyze and forecast poisonous gas emissions, thereby improving the effectiveness of environmental management. The NTDP model offers a promising avenue for the future, revolutionizing the way smart cities address and mitigate the impact of toxic pollutants on air quality. The NTDP model achieved an accuracy of 99.1%, sensitivity of 98.9% and RMSE training 1.6 and testing 1.54 ug/m3. The results affirm the robustness and effectiveness of our optimized implementation, positioning it as a standout solution in disease prediction compared to commonly used machine learning techniques. © 2024 IEEE.

关键词： Forecasting

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Empowering Android Malware Detection: A Deep Learning Ensemble with Optimal Features 26

Empowering Android Malware Detection: A Deep Learning Ensemb...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Shakil, Mehedi Hasan Hasan, Md. Mynul Chittagong University of Engineering & Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798350359015

Because of the rapid expansion of mobile devices and the growing reliance on mobile applications, there has been an increase in Android malware threats, necessitating the development of sophisticated detection systems. This research proposes a technique for detecting Android malware that makes use of several deep learning models. The goal is to provide an accurate and dependable method for classifying mobile applications as malicious or benign. To assure data integrity, the Drebin dataset is preprocessed. RFECV was employed to obtain the optimal features for deep learning algorithms such as Bi-LSTM, Bi-GRU, and 1D CNN. The dataset was trained using the selected features for each deep learning algorithm based on the feature selection method. The ensemble technique, employing a weighted average method, was utilised to combine the predictions of the individual models. Extensive testing revealed that combining Bi-LSTM and CNN in the ensemble technique achieves the greatest accuracy of 98.99%. This combination highlights the efficiency of combining various models strengths for Android malware detection. © 2023 IEEE.

关键词： Bi-Directional Gated Recurrent Unit (Bi-GRU) Bi-Directional Long Short Term Memory (Bi-LSTM) Convolutional Neural Network (CNN) Recursive Feature Elimination with Cross-Validation (RFECV) weighted average ensemble

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