We propose a novel strategy of using the bosonic Gottesman-Kitaev-Preskill (GKP) code in a repeater architecture with multiplexing. We also quantify the number of GKP qubits needed for the implementation of our scheme...
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Smart city energy generation must be efficient and dependable. As a result of studies conducted in this field, reliable control schemes for microgrid management have been developed, which seamlessly integrate with sma...
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ISBN:
(数字)9798331534967
ISBN:
(纸本)9798331534974
Smart city energy generation must be efficient and dependable. As a result of studies conducted in this field, reliable control schemes for microgrid management have been developed, which seamlessly integrate with smart building management systems. In order for a building microgrid's solar energy system to recover from problems, this article suggests reliable controllers and the hardware to install them. Training models and Internet of Things (IoT) sensors provide the backbone of this proposed approach. When it comes to organizational and industrial contexts, sensors have evolved significantly with the introduction of the IoT. Pressure, optical, temperature, chemical sensors, and proximity are just a few examples of the many types of data that IoT devices may collect and send through sensor networks, allowing for greater efficiency. The model achieves a 91.37% accuracy rate in solar energy harvesting prediction after being trained using the Extended DSOM.
In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of g...
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In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of glioma. Adult-type diffuse gliomas, a type of brain tumor, are classified into astrocytoma, oligodendroglioma, and glioblastoma. Astrocytoma and oligodendroglioma are also called low grade glioma (LGG), and glioblastoma is also called glioblastoma multiforme (GBM). LGG patients frequently have isocitrate dehydrogenase (IDH) mutations. Patients with IDH mutations have been reported to have a better prognosis than patients without IDH mutations. Therefore, IDH mutations are an essential indicator for the classification of glioma. That is why we focused on the IDH1 mutation. In this paper, we aimed to classify the presence or absence of the IDH1 mutation using WSIs and clinical data of glioma patients. Ensemble learning between the WSIs model and the clinical data model is used to classify the presence or absence of IDH1 mutation. By using slide level labels, we combined patch-based imaging information from hematoxylin and eosin (H & E) stained WSIs, along with clinical data using deep image feature extraction and machine learning classifier for predicting IDH1 gene mutation prediction versus wild-type across cohort of 546 patients. We experimented with different deep learning (DL) models including attention-based multiple instance learning (ABMIL) models on imaging data along with gradient boosting machine (LightGBM) for the clinical variables. Further, we used hyperparameter optimization to find the best overall model in terms of classification accuracy. We obtained the highest area under the curve (AUC) of 0.823 for WSIs, 0.782 for clinical data, and 0.852 for ensemble results using MaxViT and LightGBM combination, respectively. Our experimental results indicate that the overall accuracy of the AI models can be improved by using both clinical data and im
Recent applications of decorrelation methods to the multi-head attention layers and output embeddings of transformer-based models have resulted in improvements in efficiency and accuracy. Despite these advancements, t...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Recent applications of decorrelation methods to the multi-head attention layers and output embeddings of transformer-based models have resulted in improvements in efficiency and accuracy. Despite these advancements, there is a lack of research focused on the influence of decorrelation on transformer interpretation techniques. This study investigated the impact of two decorrelation methods on interpreting the decision-making logic of a Bidirectional Encoder Representations from Transformers (BERT) model. Two metrics, namely Comprehensiveness and Sufficiency, were used to quantify the interpretation quality, while the changes in correlation within each multi-head self-attention layer was statistically analyzed. Results indicate that decorrelating BERT embeddings leads to a sparser distribution of weights in the middle attention layers and a significantly improved interpretation quality. Conversely, decorrelating the attention maps of specific attention layers increases the correlation in the corresponding attention weight matrices, yielding a less marked improvement in interpretation quality and, in some instances, degraded model performance.
One of the negative effects of urbanization and population increase in developing nations is air pollution. As a result, the Air Quality Index (AQI) is assessed in a descriptive system that serves as a medium for info...
One of the negative effects of urbanization and population increase in developing nations is air pollution. As a result, the Air Quality Index (AQI) is assessed in a descriptive system that serves as a medium for informing the public about the danger of pollution. Thus, a deep learning system automates the assessment of air contaminants using time series data. Over the years, one of the most often used linear models in time series forecasting has been the Autoregressive Integrated Moving Average (ARIMA). Artificial neural networks (ANNs) have been the subject of recent study into forecasting, and these studies indicate that ANNs may prove to be a viable substitute for more conventional linear methods. In order to take advantage on the distinct advantages of ARIMA and ANN models in linear and nonlinear modelling, a hybrid methodology combining both models are presented in this study. This study made use of the data from the UCI Machine Learning Repository 2017. The primary objective of the Long Short-Term Model (LSTM), which is used for classification, is to improve accuracy in the vicinity of the test point. Based on the findings, the suggested ARIMA-ANN model outperforms the current model in terms of accuracy. The combined model can be an efficient means of enhancing the predicting accuracy attained by either of the models employed separately, according to the study results with the given data sets.
This research introduces a novel AI-based mechanism for optimizing threat mitigation in IoT banking systems, addressing the growing vulnerabilities in this critical sector. The proposed mechanism, characterized by a p...
This research introduces a novel AI-based mechanism for optimizing threat mitigation in IoT banking systems, addressing the growing vulnerabilities in this critical sector. The proposed mechanism, characterized by a precision of 0.88 and a balanced recall of 0.79, offers a robust defense against cyber threats. Leveraging a Deep Neural Architecture known as Pointer Networks, the mechanism adapts dynamically, ensuring high accuracy in threat identification (precision) while comprehensively covering potential threats (recall), resulting in a harmonious F1 score of 0.83. Through extensive threat-specific evaluations, the mechanism proves versatile, exhibiting high performance in scenarios involving malware (precision: 0.89, recall: 0.82, F1 score: 0.85), denial of service (DoS) attacks (precision: 0.87, recall: 0.78, F1 score: 0.82), and unauthorized access attempts (precision: 0.90, recall: 0.81, F1 score: 0.85). Scalability testing further validates its practical applicability, maintaining precision and F1 score values across varying sizes of IoT ecosystems. This research establishes the proposed mechanism as a potent and adaptable cybersecurity tool, poised to fortify the resilience of IoT banking systems against the dynamic landscape of cyber threats.
Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate portrait drawings using paired traini...
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The Internet of Things (IoT) and horticulture lighting systems may improve plant growth and agricultural operations. The variation in the light spectrum, intensities, and durations affect plant physiological systems. ...
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ISBN:
(数字)9798350365269
ISBN:
(纸本)9798350365276
The Internet of Things (IoT) and horticulture lighting systems may improve plant growth and agricultural operations. The variation in the light spectrum, intensities, and durations affect plant physiological systems. The proposed system can dynamically adjust and control lighting settings using real-time sensor data by seamlessly integrating IoT capabilities. These sensors carefully track plant health, ambient conditions, and energy use. This dynamic feedback system allows operators to make informed choices and adjust lighting techniques for optimum growth, yields, and resource use. Plant growth and resource efficiency benefit from light parameter improvement. The connection between IoT and horticulture lighting leads to sustainable agriculture that maximizes agricultural yields and energy efficiency. Moving from static to adaptive lighting systems represents a paradigm change in agriculture, as data-driven decisions enable precision farming. Combining IoT’s real-time abilities with horticulture lighting systems promises unique yields, energy savings, and sustainable agriculture practices.
OpenPose is employed for accurate human pose estimation, while bidirectional LSTM is used to model temporal dependencies in pedestrian movement. Despite its potential, this research faces several challenges. Firstly, ...
OpenPose is employed for accurate human pose estimation, while bidirectional LSTM is used to model temporal dependencies in pedestrian movement. Despite its potential, this research faces several challenges. Firstly, the accurate detection of subtle anomalies in pedestrian gait requires robust feature extraction and representation. Secondly, handling occlusions, varying walking speeds, and complex environmental conditions can impact pose estimation accuracy and anomaly detection. Additionally, the scarcity of annotated anomaly data poses a challenge for model training and generalization. Temporal Convolutional Networks (TCN), Attention mechanisms, LSTM are recently used techniques. This work deals with the processing of camera recordings of pedestrians walking and the subsequent detection of abnormal events such as a person falling while walking. To achieve this functionality, the bidirectional LSTM is used that detect people in the image and extract the coordinates of their skeleton. The study will subsequently use the data obtained from the human skeleton to detect anomalies using Openpose.
NFC tag authentication is highly demanded to avoid tag abuse. Recent fingerprinting methods employ the physical-layer signal, which embeds the tag hardware imperfections for authentication. However, existing NFC finge...
NFC tag authentication is highly demanded to avoid tag abuse. Recent fingerprinting methods employ the physical-layer signal, which embeds the tag hardware imperfections for authentication. However, existing NFC fingerprinting methods suffer from either low scalability for a large number of tags or incompatibility with NFC protocols, impeding the practical application of NFC authentication systems. To fill this gap, we propose NFChain, a new NFC fingerprinting scheme that excavates the tag hardware uniqueness from the protocol-agnostic tag response signal. Specifically, we harness an agile and compatible frequency band of NFC to extract the tag fingerprint from a chain of tag responses over multiple frequencies, which significantly improves fingerprint scalability. However, extracting the desired fingerprint encounters two practical challenges: (1) fingerprint inconsistency under different NFC reader and tag configurations and (2) fingerprint variations across multiple measurements of the same tag due to the signal noise in generic readers. To tackle these challenges, we first design an effective nulling method to eliminate the effect of device configurations. Second, we employ contrastive learning to reduce fingerprint variations for accurate authentication. Extensive experiments show we can achieve as low as 3.7% FRR and 4.1% FAR for over 600 tags.
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