Attributed to the widespread use of general artificial intelligence (AI), large-scale datasets have become a critical component for the success of AI-powered applications. While collecting a larger dataset is desirabl...
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With the development of intelligent technology, the intelligent processing of remote sensing images has become a research hotspot and the object detection for remote sensing images is becoming more and more extensive....
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Fortran compilers that provide support for Fortran’s native parallel features often do so with a runtime library that depends on details of both the compiler implementation and the communication library, while others...
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ISBN:
(数字)9798350355543
ISBN:
(纸本)9798350355550
Fortran compilers that provide support for Fortran’s native parallel features often do so with a runtime library that depends on details of both the compiler implementation and the communication library, while others provide limited or no support at all. This paper introduces a new generalized interface that is both compiler- and runtime-library-agnostic, providing flexibility while fully supporting all of Fortran’s parallel features. The parallel Runtime Interface for Fortran (PRIF) was developed to be portable across shared- and distributed-memory systems, with varying operating systems, toolchains and architectures. It achieves this by defining a set of Fortran procedures corresponding to each of the parallel features defined in the Fortran standard that may be invoked by a Fortran compiler and implemented by a runtime library. PRIF aims to be used as the solution for LLVM Flang to provide parallel Fortran support. This paper also briefly describes our PRIF prototype implementation: Caffeine.
Plant disease diagnosis is critical for reducing crop losses, and increasing agricultural growth. This research paper discussed methods used to diagnose plant diseases using leaf pictures and discussed specific classi...
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ISBN:
(纸本)9789811922817;9789811922800
Plant disease diagnosis is critical for reducing crop losses, and increasing agricultural growth. This research paper discussed methods used to diagnose plant diseases using leaf pictures and discussed specific classifications and the algorithms to extract a factor used in the diagnosis of plant disease. Then, plant specific diseases plays an essential role in the agricultural sectors, because plant-borne diseases are quite nature. If proper care is not taken in plant disease, it causes adverse effects on the plants and consequently affects the quality of product, quantity or product. Plant diseases cause's outbreaks that results in production loss, and this problem need to be addressed in the first phase, saving lives and money. Detection of diseases in plants is critical research area, which shows benefits in monitoring large plant fields. Farm owners, and caregivers of plants (say, kindergarten) can benefits greatly from early detection of diseases, to prevent the bad from reaching their plants and to inform the person what needs to be done in advance for the similar to work properly, to prevent the worse.
The proceedings contain 168 papers. The topics discussed include: design for ergonomics: application of single- passenger electric car via design of experiments;detection of breast cancer using machine learning and de...
ISBN:
(纸本)9781665467568
The proceedings contain 168 papers. The topics discussed include: design for ergonomics: application of single- passenger electric car via design of experiments;detection of breast cancer using machine learning and deep learning methods;automobile insurance fraud detection: an overview;a survey on detection of lung cancer using different imageprocessing techniques;deep learning methods for automatic number plate recognition system: a review;skin cancer detection using machine learning: a survey;transforming education system through artificial intelligence and machine learning;model for retailers with price dependent demand rates;neuromarketing: an emerging domain in the formal education system;design of dual slot microstrip antenna for wireless application;analysis of ancillary services provided by distributed energy resources in smart distribution grid;and grey scale image skeletonization of vein pattern using Hausdroff image binarization technique in biometric security system.
In recent times, there has been notable progress in deep learning-based image compression, showcasing enhanced coding efficiency and subjective quality. However, comparatively less attention has been devoted to video ...
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ISBN:
(数字)9798350367171
ISBN:
(纸本)9798350367188
In recent times, there has been notable progress in deep learning-based image compression, showcasing enhanced coding efficiency and subjective quality. However, comparatively less attention has been devoted to video compression employing deep neural networks. This paper introduces DeepLVCnet is a deep predictive video compression network that operates from end to end. With multiple framework theories, multiple scale arrangements, and a temporal context-adaptive entropy model, the method makes use of mode-selective uni-and bi-directional predictions. DeepLVCnet jointly compresses motion information and residual data through feature transformation layers within the multi-scale structure. A mode-selective architecture with uni-directional and bidirectional predictive modes is incorporated into DeepLVCnet, in contrast to earlier deep learning-based video compression techniques limited to P-frame or B-frame usage. Furthermore, utilizing temporal context information from reference frames during current frame coding, a unique temporal-context-adaptive entropy model is developed. This model is designed to enable parallelprocessing, offering computational and architectural advantages over autoregressive entropy models used in some CNN-based video compression methods. The overall objective is to enhance coding efficiency and subjective video enlargement quality through these innovations.
With few exceptions, the echo is a common phenomenon that occurs more or less during our talks. We then hear our own voice as a sound wave that bounced off the floor, walls, or other objects and returned to our ears. ...
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ISBN:
(纸本)9783031219665;9783031219672
With few exceptions, the echo is a common phenomenon that occurs more or less during our talks. We then hear our own voice as a sound wave that bounced off the floor, walls, or other objects and returned to our ears. If the reflected sound comes back after a very short period of time, then it is perceived not as an echo, but as a kind of spectral distortion or reverberation, consisting of extending the sound. Depending on the situation, the occurrence of reverberation may or may not be desirable. When the time of arrival of reflections exceeds a certain value, it occurs as a separate sound that interferes with the reception of sound information. This paper will present methods of acoustic echo cancellation in telecommunication networks, in which parallel and distributed computing environments will be applied using multi-pass parallel algorithms based on non-recursive adaptive filtering.
Large Language Models (LLMs) are widely used in natural language processing tasks due to their powerful semantic understanding and knowledge integration capabilities. Numerous existing recommendation studies consider ...
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ISBN:
(数字)9798350391954
ISBN:
(纸本)9798350391961
Large Language Models (LLMs) are widely used in natural language processing tasks due to their powerful semantic understanding and knowledge integration capabilities. Numerous existing recommendation studies consider recommendation tasks as a type of natural language processing, and thus LLMs have consequently brought new changes to the recommendation system paradigm. Existing research on recommendations using LLMs partly utilizes their rich data information, fine-grained user profiling, and expanded recommendation content to improve recommendation effectiveness. Additionally, some and partly studies directly uses LLMs to implement a generative recommendation paradigm. This paper adopts the literature review method to systematically sort out the current research status of news recommendation based on LLMs and classifies and summarizes the relevant research. To comprehensively understand the research in the field of news recommendation using LLMs, this paper introduces the current major work in the field of news recommendation from the two categories of generative LLM-assisted recommendation and direct generative recommendation and summarizes the current work as well as the potential future research directions and challenges.
Neural Networks’ performance on a sequence of incremental class tasks drops over time for Class Incremental Learning (IL). The gradient-based IL methods can simultaneously adapt to both new and previous tasks by prom...
Neural Networks’ performance on a sequence of incremental class tasks drops over time for Class Incremental Learning (IL). The gradient-based IL methods can simultaneously adapt to both new and previous tasks by promoting the update of model in the correct direction. However, existing methods simply consider the previous/new task gradients separately. In this paper, we propose parallel Gradient Blend (PGB) paradigm. On the one hand, PGB uses the gradients generated by mixing previous and new samples in equal proportions with Batch-Normal layers to adjust a reasonable model update direction. By comparing gradient similarities, the model selects either the previous task gradient or mixed gradient to update. On the other hand, PGB uses the sample feature gradient distribution difference to construct a regularized gradient. Finally, we experimentally demonstrate that PGB outperforms state-of-the-art methods on class-IL benchmarks.
The proceedings contain 47 papers. The special focus in this conference is on Emerging Trends and Applications in Artificial Intelligence. The topics include: Artificial Neural Network Model of Nonlinear Behavior of M...
ISBN:
(纸本)9783031567278
The proceedings contain 47 papers. The special focus in this conference is on Emerging Trends and Applications in Artificial Intelligence. The topics include: Artificial Neural Network Model of Nonlinear Behavior of Micro-ring Gyroscopes;a Framework for Knowledge Representation Integrated with Dynamic Network Analysis;time Series Forecasting Using parallel Randomized Fuzzy Cognitive Maps and Reservoir Computing;review of Offensive Language Detection on Social Media: Current Trends and Opportunities;Text Mining and Sentimental Analysis to Distinguish Systems Thinkers at Various Levels: A Case Study of COVID-19;ADHD Prediction in Children Through Machine Learning Algorithms;commonsense Validation and Explanation for Arabic Sentences;predicting Students Answers Using Data Science: An Experimental Study with Machine Learning;arabic News Articles Classification Using Different Word Embeddings;tree Fruit Load Calculation with imageprocessing Techniques;prediction and Analysis of Water Quality Using Machine Learning Techniques;comparative Analysis of Feature Selection Techniques with Metaheuristic Grasshopper Optimization Algorithm;supermarket Shopping with the Help of Deep Learning;A Decision Support System for Detecting FIP Disease in Cats Based on Machine Learning methods;a Numerical Simulation for the Ankle Foot Orthosis Using the Finite Element Technique with the Aid of an Experimental Program;numerical and Experimental Simulations of Damage Identification in Carbon/Kevlar Hybrid Fiber-Reinforced Polymer Plates Using the Free Vibration Measurements;computer Modelling of the Gait Cycle Patterns for a Drop Foot Patient for the Composite a Polypropylene Ankle-Foot Orthoses;arabic Sign Language Alphabet Classification via Transfer Learning;evaluation of Chemical Data by Clustering Techniques;novel Quantum Key Distribution Method Based on Blockchain Technology;smart Parking System Based on Dynamic and Optimal Resource Allocation;spatio-Angular Resolution Trade-Off in Face
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