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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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AI-Driven Optimization for Urban and Vertical Agriculture Planning: A Multi-Model Approach 5

AI-Driven Optimization for Urban and Vertical Agriculture Pl...

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5th international conference on Electrical, Communication and Computer Engineering, ICECCE 2024

作者： Sayad, Ismail El Sidhu, Harjobanpreet Singh Kundan, Montek Glaros, Alesandros Pizzirani, Stefania School of Computing University of the Fraser Valley AbbotsfordBC Canada Food and Agriculture Institute University of the Fraser Valley AbbotsfordBC Canada

ISBN: (纸本)9798331529437

Urban agriculture has emerged as a critical strategy for enhancing food security, mitigating urban heat islands, and promoting community well-being in densely populated areas. However, the complexity of urban environments poses significant challenges for effective planning and implementation. This paper presents an AI-driven framework to optimize urban and vertical agriculture planning by leveraging advanced machine learning models, including Artificial Neural Networks (ANN), Spiking Neural Networks (SNN), and Large Language Models (LLM). The proposed framework integrates diverse dataset that includes as socioeconomic data, geographic information, and urban zoning regulations to provide actionable insights for decision-makers. The Spiking Neural Network model demon-strated superior predictive accuracy in identifying optimal sites for urban agriculture by effectively handling complex patterns and temporal dynamics in the data. Additionally, the integration of an LLM-powered chatbot into a user-friendly web application enhances interactivity and supports real-time decision-making, guiding users through the prediction process with context-specific recommendations. Experimental results validate the robustness and scalability of the framework across various urban settings, demonstrating its potential to transform urban agriculture practices by providing precise, data-driven recommendations. The findings of this study highlight the transformative potential of AI in urban planning and agriculture, offering a novel approach to fostering sustainable urban development and food security. Future research will focus on expanding the dataset, refining model performance, and enhancing the application's capabilities to support more complex user queries. © 2024 ieee.

关键词： Agricultural Technology Artificial Intelligence (AI) Artificial Neural Networks (ANN) data Mining Machine Learning Spiking Neural Networks (SNN) Sustainability Urban Agriculture Urban Planning Vertical Farming

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The Electrical Control System for The Automatic Plug-seedling Feeding Device of Pepper Transplanting Machine Based on Arduino

The Electrical Control System for The Automatic Plug-seedlin...

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cognitive computing and complex data (ICCD), international conference on the

作者： Xueqi Wei Zhengmin Li Guang Liao Fujun Zhou College of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

To address the issue of manually placing plug-seedlings into the seedling feeding mechanism in current pepper transplanting machines, and considering the high cost of electromechanical systems, this paper presents a design for an automatic plug-seedling feeding and plug-seedling cell feeding system based on Arduino. The system employs three stepper motors and thirteen cylinders working in coordination to achieve automatic plug-seedling transportation and synchronized plug-seedling cell feeding. Using the Arduino IDE, a control program was developed, and an electrical control system was constructed, followed by plug-seedling delivery experiments. The experimental results indicate that when the plug-seedling carrier is inclined at 35°, the delivery success rate reaches 93.74%. The system is reliable and stable, offering new ideas for future automation control.

关键词： Costs Automation Transportation Control systems Motors Synchronization Reliability Electromechanical systems cognitive systems

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Supervised Radio Frequency Interference Detection with SNNs

Supervised Radio Frequency Interference Detection with SNNs

引用

2024 international conference on Neuromorphic Systems, ICONS 2024

作者： Pritchard, Nicholas J. Wicenec, Andreas Bennamoun, Mohammed Dodson, Richard International Centre for Radio Astronomy Research University of Western Australia Perth Australia School of Physics Mathematics and Computing University of Western Australia Perth Australia

ISBN: (纸本)9798350368659

Radio Frequency Interference (RFI) poses a significant challenge in radio astronomy, arising from terrestrial and celestial sources, disrupting observations conducted by radio telescopes. Addressing RFI involves intricate heuristic algorithms, manual examination, and, increasingly, machine learning methods. Given the dynamic and temporal nature of radio astronomy observations, Spiking Neural Networks (SNNs) emerge as a promising approach. In this study, we cast RFI detection as a supervised multi-variate time-series segmentation problem. Notably, our investigation explores the encoding of radio astronomy visibility data for SNN inference, considering six encoding schemes: rate, latency, delta-modulation, and three variations of the step-forward algorithm. We train a small twolayer fully connected SNN on simulated data derived from the Hydrogen Epoch of Reionization Array (HERA) telescope and perform extensive hyper-parameter optimization. Results reveal that latency encoding exhibits superior performance, achieving a per-pixel accuracy of 98.8% and an f1-score of 0.761. Remarkably, these metrics approach those of contemporary RFI detection algorithms, notwithstanding the simplicity and compactness of our proposed network architecture. This study underscores the potential of RFI detection as a benchmark problem for SNN researchers, emphasizing the efficacy of SNNs in addressing complex time-series segmentation tasks in radio astronomy. © 2024 ieee.

关键词： Radio interference

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Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT

Improving VTE Identification through Language Models from Ra...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Deng, Jamie Wu, Yusen Yesha, Yelena Nguyen, Phuong University of Miami Department of Computer Science MiamiFL United States University of Miami Frost Institute for Data Science and Computing MiamiFL United States University of Miami Department of Radiology MiamiFL United States

ISBN: (纸本)9798350386226

Venous thromboembolism (VTE) is a critical cardio-vascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE is essential for effective medical care. This study builds upon our previous work, which addressed VTE detection using deep learning methods for DVT and a hybrid approach combining deep learning and rule-based classification for PE. Our earlier approaches, while effective, had two major limitations: they were complex and required expert involvement for feature engineering of the rule set. To overcome these challenges, we utilize the Mamba architecture-based classifier. This model achieves remarkable results, with a 97% accuracy and F1 score on the DVT dataset and a 98% accuracy and F1 score on the PE dataset. In contrast to the previous hybrid method on PE identification, the Mamba classifier eliminates the need for hand-engineered rules, significantly reducing model complexity while maintaining comparable performance. Additionally, we evaluated a lightweight Large Language Model (LLM), Phi-3 Mini, in detecting VTE. While this model delivers competitive results, outperforming the baseline BERT models, it proves to be computationally intensive due to its larger parameter set. Our evaluation shows that the Mamba-based model demonstrates superior performance and efficiency in VTE identification, offering an effective solution to the limitations of previous approaches. © 2024 ieee.

关键词： Deep learning

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Investigating Physiological Responses During Driving as Potential Biomarkers of cognitive Decline in Seniors Using Decision Tree Ensemble Modeling

Investigating Physiological Responses During Driving as Pote...

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2024 ieee-EMBS international conference on Biomedical and Health Informatics, BHI 2024

作者： Kumar, Anu M. Murphey, Yi Lu Maher, Amanda Cook Persad, Carol Koeppe, Robert Giordani, Bruno Engineering University of Michigan Dept. of Electrical and Computer MI United States University of Michigan Medical School Dept. of Psychiatry MI United States University of Michigan Medical School Dept. of Radiology MI United States

ISBN: (纸本)9798350351552

Early identification of individuals at risk for Alzheimer's disease is essential to improve treatment effectiveness. Cerebrospinal fluid analyses and positron emission tomography (PET) scans are commonly used to detect the presence of beta-amyloid and tau, which are associated with an increased risk of conversion to Alzheimer's disease. However, these biomarker tests are expensive and involve invasive procedures. Researchers are working towards discovering easily measurable biomarkers to detect individuals at risk, but only a few have been identified thus far. There is a need to discover biomarkers that are cost-efficient and non-invasive to test. We propose a machine learning approach for discovering potential risk biomarkers of Alzheimer's disease through the analysis of physiological responses to the cognitively complex task of driving by using decision tree ensemble techniques. Though driving patterns in early Alzheimer's have been previously studied, physiological responses of cognitively normal seniors during driving remain unexplored. As a first step, we measure heart rate, electrodermal activity, and temperature responses to several driving events, such as right turns and roundabouts, of seniors with and without elevated PET beta-amyloid levels to explore the relationship between these physiological responses and amyloid level. data were collected from 26 participants with elevated beta-amyloid and 28 without. We used four machine learning algorithms for classification: Random Forest, Extra Trees, AdaBoost, and XGBoost, and developed a novel methodology to extract significant features from these models. By doing so, we successfully identified five risk biomarkers most influential in differentiating the two groups with and without elevated beta-amyloid. © 2024 ieee.

关键词： Positron emission tomography

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Feature Importance and Model Performance in Deep Learning for Speech Emotion Recognition 11

Feature Importance and Model Performance in Deep Learning fo...

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11th ieee international conference on Advances in computing and Communications, ICACC 2024

作者： Ramkrishnan, P. Dhaman, Reji K Poulose, Alwin University of Kerala Department of Optoelectronics Kerala Thiruvananthapuram695581 India School of Data Science Kerala Thiruvananthapuram695551 India

ISBN: (纸本)9798350379136

Speech emotion recognition (SER) is detecting a person's emotional state through their voice, a technique now widely applied across various fields. This paper aims to explore the significance of different acoustic features in SER using the TESS dataset, which comprises approximately 2800 audio files featuring 200 words spoken by two female actors, representing seven emotions: anger, disgust, fear, happiness, pleasant surprise, sadness, and neutrality. We extracted seven key acoustic features from these audio files: Chromas, Mel Frequency Cepstral Coefficients (MFCCs), Mel spectrogram, Zero crossing rate, RMS power, contrast, and pitch. Our study involved applying LSTM and CNN deep learning methods to assess the performance of each feature. Results indicate that the MFCC feature performs better, with accuracy exceeding 99% in LSTM and CNN models. Additionally, we employed traditional machine learning models, specifically random forest and decision tree classifiers, combined with a grid search feature selection method to generate feature importance graphs. These results highlight pitch as the most critical feature in both models. However, pitch alone yielded only 28% accuracy in LSTM and CNN models. This discrepancy underscores the complex interplay between feature selection and model architecture in optimizing SER performance. Our findings reveal critical insights into the interplay between feature relevance and model efficacy, highlighting the necessity of feature optimization for enhancing SER systems. © 2024 ieee.

关键词： Contrastive Learning

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Research on Ceramic Tile Defect Detection Based on Enhanced YOLOv5

Research on Ceramic Tile Defect Detection Based on Enhanced ...

引用

2023 international conference on cognitive computing and complex data, ICCD 2023

作者： Wang, Hongdong Jin, Peng Wang, Jinyong Wu, Jiacheng Li, Dan Cheng, Hengshuo Xuzhou University of Technology School of Information Engineering Big Data School XuZhou China

ISBN: (纸本)9798350306293

Traditional ceramic tile defect detection methods are often limited by the finite nature of feature representation and the influence of complex backgrounds, resulting in low model accuracy and generalization ability. This paper proposes a Shuffle Attention Mechanism-based YOLOv5 detection algorithm, aiming to enhance the accuracy and robustness of ceramic tile defect detection. It is compared and analyzed against the YOLOv5 algorithm, as well as improved YOLOv5 algorithms based on three different attention mechanisms: SimAM, GAM, and NAM. Experimental results demonstrate that this algorithm effectively captures crucial features related to defects in ceramic tile images, enabling more accurate detection of various types of ceramic tile defects and reducing false positives. In summary, this study demonstrates the effectiveness and potential of the Shuffle Attention YOLOv5 algorithm in ceramic tile defect detection. © 2023 ieee.

关键词： Object detection

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Empowering Cross-Device data Security Verification for IoT Sensor Nodes 7

Empowering Cross-Device Data Security Verification for IoT S...

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7th international conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Shi, Xin He, Sheng Wang, Ting Zhang, Chong Wang, Yang Zhang, Xiao School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China School of Information Engineering Mianyang Teachers College Mianyang China

ISBN: (纸本)9798350354973

In the process of realizing the Internet of Everything through the Internet of Things (IoT), the massive deployment of sensor nodes poses significant security challenges. This paper introduces a novel approach to anomaly detection. The proposed method focuses on generating secure replicas of sensor data, which are compared with real-time data to identify anomalies. This process ensures robust detection across multiple devices and terminals, a key challenge in the realization of the Internet of Everything. The model combines Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAE), leveraging their strengths in capturing temporal dependencies and learning latent feature representations from sensor data. To enhance detection accuracy in complex environments, an adaptive threshold algorithm is employed, dynamically adjusting thresholds based on real-time data's statistical properties. A crossattention mechanism is also integrated, allowing the model to effectively capture spatio-temporal relationships between sensors. Additionally, we employed a weighted smoothing algorithm to reduce noise while preserving data integrity, ensuring accurate anomaly detection even in noisy environments. Finally, we evaluated our method using the real-world dataset of the Belgian railway bridge KW51. The results indicate that our system can generate sensor data replicas with 99.02% accuracy and detect abnormal data with 99.8% precision. Based on these findings, we believe that this novel approach can effectively perform anomaly detection and provide robust support for IoT security across multiple terminals and devices. © 2024 ieee.

关键词： Sensor nodes

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Detection of Anomalous Components in Spatial Surveys Based on a Multidimensional Model of Poisson Flows and their cognitive Visualization 25

Detection of Anomalous Components in Spatial Surveys Based o...

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25th international conference on Soft computing and Measurements, SCM 2022

作者： Gorokhov, Vladimir L. Brusakova, Irina A. Saint Petersburg Electrotechnical University LETI Department of Innovation Management St. Petersburg Russia

ISBN: (纸本)9781665496698

The paper proposes a technique for detecting anomalous components in spatial scans of multidimensional data in the tasks of multidimensional reviews in GIS technologies. Detection of anomalous components is carried out on the basis of unbiased algorithms under conditions of deep a priori uncertainty regarding the parameters of the distributions of survey data. The results of the detection are controlled by means of cognitive computer graphics. The methods are used to process multidimensional data of astronomical observations. These methods are very successfully applied in astrophysics and can be used for a wide range of tasks in BIG data. The methodology of such a combination can also be focused on the identification and forecasting of emergency situations in complex systems. © 2022 ieee.

关键词： Surveys

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Metaheuristic Optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks 13

Metaheuristic Optimization for Path Planning in UAV Networks...

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Akshya, J. Manochitra, M. Indu Poornima, R. Jeevitha, T. Saravanakumar, M. Sundarrajan, M. Choudhry, Mani Deepak Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Coimbatore India Institute of Technology Department of Information Technology KGiSL Coimbatore India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Deportment of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

With broad applicability in surveillance, delivery, and environmental monitoring, multi-drone path planning has received a great deal of attention recently. So far, wide applicability has been explored by various applications of Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, which have already established reliable solutions to navigation and coverage through drone path optimization. However, in dynamic, obstacle-dense environments, these algorithms fail to adapt in real-time with energy efficiency. Traditional GA and PSO approaches can be effective for generating an initial path but fail to respond to collision avoidance and efficient resource management. In complex and unpredictable conditions, ACO fails to converge rapidly, which leads to suboptimal paths and increased energy consumption. To overcome such barriers, we present here an HGARL for enhanced multi-drone path optimization. The proposed algorithm thus combines the population-based search capability of GA with RL's adaptive learning ability, therefore providing an algorithm that in addition to finding optimal paths adjusts the paths according to changing environmental situations. HGARL presents better performance compared to existing methods. In path convergence, it reaches up to 15%, and in energy consumption, it reduces up to 20% with safe collision-free navigation. The strong potential of HGARL for robust, efficient, and adaptive solutions of real-time multi-drone operation in complex and dynamic environments has set a new benchmark for UAV path planning. © 2024 ieee.

关键词： Genetic algorithms

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