Machine learning-based improvements in anomaly detection, visualization, and segmentation are made possible by the growing digitization of medical imaging, which reduces the workload for medical specialists. Neverthel...
详细信息
Background Cumulus clouds are important elements in creating virtual outdoor *** cumulus clouds that have a specific shape is difficult owing to the fluid nature of the ***-based modeling is an efficient method to sol...
详细信息
Background Cumulus clouds are important elements in creating virtual outdoor *** cumulus clouds that have a specific shape is difficult owing to the fluid nature of the ***-based modeling is an efficient method to solve this *** of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development *** In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single *** method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial ***,a 3D cloud shape is mapped into a unique hidden space using the proposed ***,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered *** train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus *** cumulus clouds were rendered under different lighting *** The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing ***,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction *** The proposed autoencoder network learns the latent space of 3D cumulus cloud *** presented reconstruction architecture models a cloud from a single *** demonstrated the effectiveness of the two models.
This paper would provide a comprehensive review of the synergistic integration of measurement science and artificial intelligence (AI) in the realm of smart agriculture. As agriculture undergoes a transformative phase...
This paper would provide a comprehensive review of the synergistic integration of measurement science and artificial intelligence (AI) in the realm of smart agriculture. As agriculture undergoes a transformative phase toward precision and data-driven practices, the paper explores the critical role of measurement science in providing accurate data, while AI processes and analyzes this data to facilitate intelligent decision-making in smart agricultural systems.
Watershed partitioning, following a maximal vertex-cut constraint, ensures each basin is strictly isolated from its neighboring basins. This partition is composed of watershed arcs positioned between pairs of adjacent...
详细信息
ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
Watershed partitioning, following a maximal vertex-cut constraint, ensures each basin is strictly isolated from its neighboring basins. This partition is composed of watershed arcs positioned between pairs of adjacent basins. When these arcs create a connected partition, applying watershed partition on an arc-graph ensures that the corresponding arcs of watershed points in the arc-graph still maintain this connected partition. This crucial property is leveraged in the proposed iterative watershed partition method for extracting hierarchical partition lines in an image. The method effectively extracts river basins from SRTM DEM data of the Indian state Uttarakhand, revealing a well-structured hierarchy of catchment basins.
All human endeavors revolve around opinions, which also significantly affect human behavior. Sentimental analysis with opinion mining research focuses attitudes & feelings. There are several difficulties in opinio...
All human endeavors revolve around opinions, which also significantly affect human behavior. Sentimental analysis with opinion mining research focuses attitudes & feelings. There are several difficulties in opinion mining. The fact that a term is either good or negative based on the situation is the first difficulty. People’s differences in viewpoint expression present a second obstacle. The usage of online social media facilitates decision-making with feedback. Consequently, social media communications may be used for sentiment analysis. Dealing with public health concerns is the goal of the suggested endeavor. Traditional surveillance methods are costly, have a narrow field of view, and take a long time to collect data on public health issues. Social media communications, which are available for free and are created globally, are employed to solve this issue. A two-step word alignment technique can be used to gauge public concern in this. Personal and news reviews make up the first two categories of raw reviews. Reviews are further separated into personal negative and non-negative categories at second stage. In both phases, the dataset trained may assessed using a ML model like Naive Bayes, and training data is generated automatically using emotion-focused, clue-based method. The suggested approach has increased the field of epidemics’ accuracy.
The exponential growth of the Internet of Things (IoT) alongside the increasing significance of cryptocurrencies has unveiled critical security challenges in digital transactions. This study uses a comprehensive metho...
详细信息
ISBN:
(数字)9798350386813
ISBN:
(纸本)9798350386820
The exponential growth of the Internet of Things (IoT) alongside the increasing significance of cryptocurrencies has unveiled critical security challenges in digital transactions. This study uses a comprehensive method to design, create, and assess secure and efficient cryptocurrency wallets specifically designed for the Internet of Things (IoT) environment. The study presents the Enhanced Elliptic Curve Digital Signature Algorithm (EECDSA), which integrates sophisticated ECDSA, blockchain technology, Golang programming language, and JSON data exchange. The design methodically emphasizes scalability, interoperability, and strict adherence to security protocols for various IoT devices and networks. The study thoroughly analyzes ECDSA and introduces EECDSA, highlighting the double-and-add algorithm to enhance efficiency. Comparative analyses show that EECDSA outperforms RSA, ECDSA, and multi-signature algorithms in terms of execution time and memory usage on different CPUs, particularly on ARM-based architectures. The results highlight EECDSA’s capacity for efficient cryptographic operations, making it suitable for IoT devices with constrained resources.
The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of ...
详细信息
ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO; therefore, it is able to provide an effective solution for the optimization of features within medical image analysis.
In black-box optimization, noise in the objective function is inevitable. Noise disrupts the ranking of candidate solutions in comparison-based optimization, possibly deteriorating the search performance compared with...
详细信息
Few shot in-context learning (ICL) typically assumes access to large annotated training sets. However, in many real world scenarios, such as domain adaptation, there is only a limited budget to annotate a small number...
详细信息
The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previ...
详细信息
暂无评论