AI-driven precision agriculture is revolutionising the agricultural business by using advanced techniques to monitor crop health and optimize output. By integrating data from sensors, drones, and satellite photos with...
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ISBN:
(数字)9798350387490
ISBN:
(纸本)9798350387506
AI-driven precision agriculture is revolutionising the agricultural business by using advanced techniques to monitor crop health and optimize output. By integrating data from sensors, drones, and satellite photos with artificial intelligence, farmers may get up-to-date information on crop condition, soil health, and environmental factors. Through meticulous analysis of vast amounts of data, these artificial intelligence systems are capable of detecting the first signs of pests, diseases, and nutritional deficiencies. This enables timely interventions to enhance the overall health of crops. AI-driven predictive analytics manage the timing of irrigation, fertilisation, and harvesting to enhance productivity and guarantee optimum resource use. This approach addresses growing global need in case of food while also improving agricultural sustain ability and productivity. The Python script demonstrates integration of AI and IoT technologies into a Precision Agriculture System to optimize agricultural yields and monitor crop health. It generates a synthetic dataset with real-world parameters and evaluates the Random Forest Regressor model on training along with testing data. The simulation provides visual insights and performance metrics, highlighting the potential of AI and IoT in precision agriculture in case of enhanced decision-making along with productivity.
Students must be attentive in the classroom to improve on their learning. Further, it is important for the faculty to know whether the students pay complete attention in the classroom. If not, faculty can slow down an...
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ISBN:
(纸本)9781665454315
Students must be attentive in the classroom to improve on their learning. Further, it is important for the faculty to know whether the students pay complete attention in the classroom. If not, faculty can slow down and modify their mode of delivery by making the classes more interesting to gain students attention. The aim of this work is to monitor the student’s attention in the classroom based on their physical state which is the result of their bio chemical and electrical signals in the brain. The approaches like Electro dermal activity (EDA), Electroencephalography (EEG), Electrocardiogram (ECG) are measured by using physiological signals but is hard to extract and analyze. Head bands extracting EEG signals may also distract a student’s attention in classes. The proposed work aims to make use of Deep Convolution Neural Network (DCNN) with Histogram of Gradient (HoG) for face detection and facenet algorithm for face recognition. To evaluate the attentiveness of the students, A Convolution Neural Network model is proposed. The trained model is tested by deploying in Jetson Nano. To identify the attentiveness of the student, the physical state of the face is considered, such as head movement, and the state of eyes and mouth, whether it is opened or closed. Furthermore, the open eye region is analysed if it is partially opened or fully opened. By using CNN to train the model instead of Machine learning algorithms, the accuracy of the system is found to increase.
Learning to generate motions of thin structures such as plant leaves in dynamic view synthesis is challenging. This is because thin structures usually undergo small but fast, non-rigid motions as they interact with ai...
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ISBN:
(数字)9798350379037
ISBN:
(纸本)9798350379044
Learning to generate motions of thin structures such as plant leaves in dynamic view synthesis is challenging. This is because thin structures usually undergo small but fast, non-rigid motions as they interact with air and wind. When given a set of RGB images or videos of a scene with moving thin structures as input, existing methods that map the scene to its corresponding canonical space for rendering novel views fail as the object movements are too subtle compared to the background. Disentangling the objects with thin parts from the background scene is also challenging when the parts show fast and rapid motions. To address these issues, we propose a Neural Radiance Field (NeRF)-based framework that accurately reconstructs thin structures such as leaves and captures their subtle, fast motions. The framework learns the geometry of a scene by mapping the dynamic images to a canonical scene in which the scene remains static. We propose a ray masking network to further decompose the canonical scene into foreground and background, thus enabling the network to focus more on foreground movements. We conducted experiments using a dataset containing thin structures such as leaves and petals, which include image sequences collected by us and one public image sequence. Experiments show superior results compared to existing methods. Video outputs are available at https://***/.
In this work, we demonstrate the impact of an unconventional convolutional photonic accelerator, based on an optical spectrum slicing (OSS) [1], on the classification accuracy of objects, generated through a high-fram...
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ISBN:
(纸本)9798350345995
In this work, we demonstrate the impact of an unconventional convolutional photonic accelerator, based on an optical spectrum slicing (OSS) [1], on the classification accuracy of objects, generated through a high-frame rate neuromorphic event-based camera [2]. The experimental setup is depicted in Fig. 1a. It consists of a 5 mW-632 nm-LED source, two objective lenses with NA=0.65 that concentrate the light beam into a $100\ \mu \mathrm{m}\times 100\ \mu \mathrm{m}$ channel. In our experiments, the targeted objects consist of test spheres, of different diameters (12, 16, and $20 \mu{m}$ ), used in aqua solutions. A pump regulated the sphere's speed to 0.8 m/s. The objects were recorded by a 10 kframe/s capable neuromorphic camera, with a temporal resolution of $1\ \mu \mathrm{s}$ . The camera detects pixel's contrast changes (events), similar to biological systems [2]. The recorded events were exported into 1 kframes/sec videos through a synthetic frame generator software, resulting to 2988 images per particle [2]. Images were post-processed using only noise reduction by frame subtraction and image cropping so as to reduce data volume to $100\times 100$ pixels.
Parallel or multithreaded sorting algorithms have been proposed and researched for multicore and manycore CPU and GPU systems. Some of them are based on divide and conquer concept to exploit data parallelism as well a...
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In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning...
In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning models in this setting. Experiment 1 used a Random Forest model and achieved 95% accuracy, 0.92 precision, 0.96 recall, and 0.94 F1 Score. This shows that the model can accurately categorize security threats with a good balance of true and false positives. A Deep Neural Network (DNN) improved accuracy to 97% in Experiment 2. Precision, recall, and F1 Score values of 0.94, 0.98, and 0.96 demonstrate the DNN’s ability to discriminate threats from normal activity. The model captures complicated patterns well, making it a powerful cloud security tool. Security analysis using reinforcement learning, specifically Q-learning, was introduced in Experiment 3. The model’s 88% detection rate showed its capacity to identify threats, but its 0.05 false positive rate created a tradeoff between true and false positives. With the 0.12 false negative rate, one can infer improvements in threat detection accuracy. These results indicate that state-of-the-art machine learning is capable of protecting cloud data. The Random Forest and Deep Neural Network models have very high accuracy balanced with reasonable precision-recall trade-offs, whereas reinforcement learning through Qlearning shows promise but needs modification to improve the model’s performance in terms of both accuracy and false positive rates. While rising threats need constant adaptation and learning, the model should also fulfill security needs. This study helps formulate a secure cloud computing infrastructure that can stand threats of change.
The protective angle method based on IEC standard 62305 is an important concept that is popularly utilized to design the air termination for the external lightning protection system because this method can be easily i...
The protective angle method based on IEC standard 62305 is an important concept that is popularly utilized to design the air termination for the external lightning protection system because this method can be easily implemented based on trigonometric calculation. However, the protective angle method demonstrates the weakness and limitation in a factor of the penetration distance which is rarely mentioned in publications. Especially, the penetration distance of the protective angle method is difficult to evaluate. The penetration distance is always neglected implementation while the penetration distance directly influences the lightning flash into the structure’s building. Thus, the effect of a penetration distance cannot be definitely *** mentioned above, this article presents the simulation results and analysis of the penetration distance for the protective angle method based on IEC standard 62305, as well as, this article illustrates the concerned mathematical correlation and the approximate correlation for implementation respectively.
The vast array of cloud providers present in today’s market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and co...
The vast array of cloud providers present in today’s market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module’s primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase.
With the rapid advancement and increased use of deep learning models in image identification, security becomes a major concern to their deployment in safety-critical systems. Since the accuracy and robustness of deep ...
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Virtual Reality (VR) technology provides a novel approach for therapy, offering controlled exposure to sensory stimuli, personalized learning experiences, and social skills practice in a safe and engaging virtual envi...
Virtual Reality (VR) technology provides a novel approach for therapy, offering controlled exposure to sensory stimuli, personalized learning experiences, and social skills practice in a safe and engaging virtual environment. The use of VR has the potential to improve social and cognitive skills, reduce anxiety, and increase engagement, ultimately enhancing the quality of life for children with Autism Spectrum Disorder (ASD) and their families, while also reducing the stigma associated with autism and fostering ongoing research and innovation in the field.
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