This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to expl...
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Breast cancer is a malignant tumor with a high mortality rate among women. Therefore, it is necessary to develop novel therapies to effectively treat this disease. In this study, iron selenide nanorods (FeSe2 NRs) wer...
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Despite Indonesia's leading position in palm oil processing, the inaccurate assessment of oil palm fruit maturity poses challenges in determining the optimal harvest timing and maintaining product quality. With ad...
Despite Indonesia's leading position in palm oil processing, the inaccurate assessment of oil palm fruit maturity poses challenges in determining the optimal harvest timing and maintaining product quality. With advancements in information technology, including image processing and object detection techniques like YOLO and EfficientDet, there is potential for automated and precise identification of palm fruit maturity. To explore this, the authors conducted research to identify the most suitable algorithm for detecting the ripeness of palm oil fruit. The study utilized a dataset consisting of 8299 images with six maturity levels, employing augmentation techniques to increase image variability. Results show that YOLO V8s outperformed EfficientDet Lite 4 in terms of object detection precision, achieving higher mean average precision (mAP) scores. YOLO V8s showed a 4% improvement in mAP@50 and a 16% improvement in mAP@50-95 compared to EfficientDet Lite 4. YOLO V8s achieved a mAP of 0.978 for mAP@50 and 0.765 for mAP@50-95, surpassing EfficientDet Lite 4. However, EfficientDet Lite 4 had an advantage in terms of class accuracy and bounding box placement, as indicated by smaller box loss and cIs_ loss values. YOLO V8s also demonstrated better frames per second (FPS) performance, ranging from 10 to 15 FPS, compared to EfficientDet Lite 4.
This paper presents an offline path planning strategy for unmanned ground vehicles (UGVs) using Q-learning. The proposed method addresses path optimization in warehouse-like environments, where tasks involve item pick...
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ISBN:
(数字)9798331508807
ISBN:
(纸本)9798331508814
This paper presents an offline path planning strategy for unmanned ground vehicles (UGVs) using Q-learning. The proposed method addresses path optimization in warehouse-like environments, where tasks involve item pickup and delivery to specific locations. The Q-learning algorithm trains an agent to determine the most efficient routes, with validation conducted in an $8 \times 5$ meter workspace equipped with an Optitrack motion capture system. The workspace was discretized into a $16 \times 10$ grid, allowing the Q-learning to effectively navigate through complex obstacle-laden scenarios. Experimental results indicate that the Q-learning approach outperforms traditional methods such as Dijkstra, A-star, and Breadth-First Search in terms of path length, number of turns, planning time, and overall success rate; being up to 7 times faster to plan a path and reducing the number of bends by up to 41%. The Q-learning based paths feature more linear segments, which contribute to energy savings and improved navigational efficiency. Future work will explore applications in heterogeneous multi-agent systems and enhancements in training time and agent collaboration.
Environmental hazards place certain individuals at disproportionately higher risks. As these hazards increasingly endanger human health, precise identification of the most vulnerable population subgroups is critical f...
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The implementation of Gated Recurrent Neural Networks (GRU) to generate background music (BGM) combines deep learning technology with music that is used for the visual content of a commercial or educational. Indeed, t...
The implementation of Gated Recurrent Neural Networks (GRU) to generate background music (BGM) combines deep learning technology with music that is used for the visual content of a commercial or educational. Indeed, this BGM is necessary to enhance the intended message expressed to the other audience. This work aimed to provide the model network of GRU which is based on RNN to generate multi-label genres of music by using the open source of GTZAN to evaluate the new BGM. Our GRU networks can solve the vanishing gradient problem by utilizing both the reset gate and the update gate on the network. In the results, we achieved a new BGM that synchronized with the human mood which made more variety of sounds.
We developed a dual optical/x-ray ultrafast photodetector based on in-house grown Cdo * Mg0.03Te single crystals. The detector is characterized by ~200 ps full-width-at-half-maximum, readout-electronics limited photor...
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Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and ...
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Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computerscience. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.
Oil spills represent a growing environmental challenge that poses a significant threat to living organisms. Moreover, the treatment of oil spills, especially in severe cases, has serious economic repercussions and req...
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Oil spills represent a growing environmental challenge that poses a significant threat to living organisms. Moreover, the treatment of oil spills, especially in severe cases, has serious economic repercussions and req...
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ISBN:
(数字)9798331516963
ISBN:
(纸本)9798331516970
Oil spills represent a growing environmental challenge that poses a significant threat to living organisms. Moreover, the treatment of oil spills, especially in severe cases, has serious economic repercussions and requires substantial labor and time. Therefore, the effective detection of oil spills has become an important research problem. Traditional methods for detecting oil spills, such as manual patrolling and dynamic sensors, are often limited in accuracy and coverage. As a result, the automation of oil spills detection has emerged as a critical global imperative in scientific research. The aim of this paper is to employ deep learning technology to achieve effective detection of oil spills based on aerial images. Our approach is composed of two phases. In the first phase, a Deep Convolutional Neural Network (DCNN), namely ResNet50, is trained on a large dataset containing images showing oil spills at a seaport. The trained DCNN is used to classify the input image as "Oil Spill" or "No Oil Spill". In the second phase, the images classified as "Oil Spill" are analyzed using a deep learning detection model, namely You-Only-Look-Once (YOLOv4), to localize the oil spills. The results indicate the capability of the proposed method to achieve effective oil spill detection. In particular, the classification accuracy obtained by the ResNet50 model is equal to 98%. Moreover, the YOLOv4 model was able to obtain effective localization of the oil spills with mean-average precision of 62%.
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