The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence...
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The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)*** transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and *** survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision *** providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural *** focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming *** paper first discusses various salient crop metrics used in digital *** this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and *** establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.
Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by...
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Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redunda...
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Cervical cancer is a disease that develops in the cervix’s *** cancer mortality is being reduced due to the growth of screening *** cancer screening is a big issue because the majority of cervical cancer screening tr...
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Cervical cancer is a disease that develops in the cervix’s *** cancer mortality is being reduced due to the growth of screening *** cancer screening is a big issue because the majority of cervical cancer screening treatments are ***,there is apprehension about standard screening procedures,as well as the time it takes to learn the *** are different methods for detecting problems in the cervix using Pap(Papanico-laou-stained)test,colposcopy,Computed Tomography(CT),Magnetic Reso-nance Image(MRI)and *** obtain a clear sketch of the infected regions,using a decision tree approach,the captured image has to be segmented and *** goal of creating a decision tree is to establish prediction model that anticipate the feature vector based on the input *** paper deals with investigating various techniques of segmentation for detecting the cervical *** proposes a novel method to develop an assistance system for the detection diag-nosis of cervical cancer,based on work of Martin,Byriel and *** analysis is focused on Pap smear pictures of single *** testing is a method of detecting abnormalities in the *** processing is an effective method for extracting *** is used to determine the size of cervical carcinoma and the length of the ***’s database,which is open source and utilised for analysis and valida-tion,is obtainable for research *** malignancy information utilizing three grouping strategies to anticipate the disease and afterward analyzed the out-comes showed that choice tree is the best classifier indicator with the test *** investigations ought to be led to improve execution.
The rapid advancement of digital media technologies has given rise to DeepFake videos, synthetic content generated using deep learning algorithms that can convincingly mimic real individuals' appearances and actio...
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作者：
Mazhr, AhmedMougy, Amr El
Computer Engineering Department Cairo Egypt
Department of Computer Science and Engineering Cairo Egypt
This paper investigates the integration of monocular thermal imaging cameras into autonomous vehicles to address challenges faced by conventional sensors in adverse conditions. This study explores the performance of t...
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The proposed work objective is to adapt Online social networking (OSN) is a type of interactive computer-mediated technology that allows people to share information through virtual networks. The microblogging feature ...
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The proposed work objective is to adapt Online social networking (OSN) is a type of interactive computer-mediated technology that allows people to share information through virtual networks. The microblogging feature of Twitter makes cyberspace prominent (usually accessed via the dark web). The work used the datasets and considered the Scrape Twitter Data (Tweets) in Python using the SN-Scrape module and Twitter 4j API in JAVA to extract social data based on hashtags, which is used to select and access tweets for dataset design from a profile on the Twitter platform based on locations, keywords, and hashtags. The experiments contain two datasets. The first dataset has over 1700 tweets with a focus on location as a keypoint (hacking-for-fun data, cyber-violence data, and vulnerability injector data), whereas the second dataset only comprises 370 tweets with a focus on reposting of tweet status as a keypoint. The method used is focused on a new system model for analysing Twitter data and detecting terrorist attacks. The weights of susceptible keywords are found using a ternary search by the Aho-Corasick algorithm (ACA) for conducting signature and pattern matching. The result represents the ACA used to perform signature matching for assigning weights to extracted words of tweet. ML is used to evaluate Twitter data for classifying patterns and determining the behaviour to identify if a person is a terrorist. SVM (Support Vector Machine) proved to be a more accurate classifier for predicting terrorist attacks compared to other classifiers (KNN- K-Nearest Neighbour and NB-Naïve Bayes). The 1st dataset shows the KNN-Acc. -98.38% and SVM Accuracy as 98.85%, whereas the 2nd dataset shows the KNN-Acc. -91.68% and SVM Accuracy as 93.97%. The proposed work concludes that the generated weights are classified (cyber-violence, vulnerability injector, and hacking-for-fun) for further feature classification. Machine learning (ML) [KNN and SVM] is used to predict the occurrence and
Design patterns offer reusable solutions for common software issues,enhancing *** advent of generative large language models(LLMs)marks progress in software development,but their efficacy in applying design patterns i...
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Design patterns offer reusable solutions for common software issues,enhancing *** advent of generative large language models(LLMs)marks progress in software development,but their efficacy in applying design patterns is not fully *** recent introduction of generative large language models(LLMs)like ChatGPT and CoPilot has demonstrated significant promise in software *** assist with a variety of tasks including code generation,modeling,bug fixing,and testing,leading to enhanced efficiency and *** initial uses of these LLMs have had a positive effect on software development,their potential influence on the application of design patterns remains *** study introduces a method to quantify LLMs’ability to implement design patterns,using Role-Based Metamodeling Language(RBML)for a rigorous specification of the pattern’s problem,solution,and transformation *** method evaluates the pattern applicability of a software application using the pattern’s problem *** deemed applicable,the application is input to the LLM for pattern *** resulting application is assessed for conformance to the pattern’s solution specification and for completeness against the pattern’s transformation *** the method with ChatGPT 4 across three applications reveals ChatGPT’s high proficiency,achieving averages of 98%in conformance and 87%in completeness,thereby demonstrating the effectiveness of the *** RBML,this study confirms that LLMs,specifically ChatGPT 4,have great potential in effective and efficient application of design patterns with high conformance and *** opens avenues for further integrating LLMs into complex software engineering processes.
This article discusses the importance of cloud-based multi-tenancy in private–public-private secure cloud environments, which is achieved through the isolation of end-user data and resources into tenants to ensure da...
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—Neural networks (NNs) have been driving machine learning progress in recent years, but their larger models present challenges in resource-limited environments. Weight pruning reduces the computational demand, often ...
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—Neural networks (NNs) have been driving machine learning progress in recent years, but their larger models present challenges in resource-limited environments. Weight pruning reduces the computational demand, often with performance degradation and long training procedures. This work introduces distilled gradual pruning with pruned fine-tuning (DG2PF), a comprehensive algorithm that iteratively prunes pretrained NNs using knowledge distillation. We employ a magnitude-based unstructured pruning function that selectively removes a specified proportion of unimportant weights from the network. This function also leads to an efficient compression of the model size while minimizing classification accuracy loss. Additionally, we introduce a simulated pruning strategy with the same effects of weight recovery but while maintaining stable convergence. Furthermore, we propose a multistep self-knowledge distillation strategy to effectively transfer the knowledge of the full, unpruned network to the pruned counterpart. We validate the performance of our algorithm through extensive experimentation on diverse benchmark datasets, including CIFAR-10 and ImageNet, as well as a set of model architectures. The results highlight how our algorithm prunes and optimizes pretrained NNs without substantially degrading their classification accuracy while delivering significantly faster and more compact models. Impact Statement—In recent times, NNs have demonstrated remarkable outcomes in various tasks. Some of the most advanced possess billions of trainable parameters, making their training and inference both energy intensive and costly. As a result, the focus on pruning is growing in response to the escalating demand for NNs. However, most current pruning techniques involve training a model from scratch or with a lengthy training process leading to a significant increase in carbon footprint, and some experience a notable drop in performance. In this article, we introduce DG2PF. This unstruct
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