The use of surveillance cameras (CCTV) is very limited and has many weaknesses. Surveillance cameras can only record events that are happening without being detected, so supervisors still need protection. From these l...
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This study evaluates the performance of the REAL-ESRGAN [1] model on images with varying levels of degradation using the DIV2K dataset [2], such as the Wild, the Mild, the Difficult, and the x8 subsets. REAL-ESRGAN wa...
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The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...
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The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based *** researchers used data preprocessing techniques such as feature selection and normalization to overcome such *** most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider ***,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis ***,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,*** IDS models were implemented using the full and feature-selected copies of the datasets with and without *** models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art *** forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 *** RF models also achieved an excellent performance compared to recent *** results show that normalization and feature selection positively affect IDS ***,while feature sel
The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection m...
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The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was significant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classifi*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).
In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given ***,finding the best estimation results in softwar...
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In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given ***,finding the best estimation results in software devel-opment is ***,accurate estimation of software development efforts is always a concern for many *** this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model *** state-of-the-art publicly available datasets are used for *** backpropagation feed-forward procedure used a training set by iteratively processing and training a neural *** proposed model is tested on the test *** estimated effort is compared with the actual effort *** results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.
Deep ensemble learning models that combine multiple independent deep learning models with multi-layer processing architectures have proven to be effective techniques for improving the accuracy and robustness of deep l...
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There are numerous agricultural products in the world, and chili is among the most important ones due to its role in adding spice to food, particularly in Indonesia. Consequently, diseases affecting chili plants can l...
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Recommender systems are playing a significant role in modern society to alleviate the information/choice overload problem, since Internet users may feel hard to identify the most favorite items or products from millio...
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When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models t...
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When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.
Business Email Compromise (BEC) is a sophisticated and increasingly prevalent form of cyber fraud that targets businesses and individuals to gain financial benefits and access sensitive data. BEC fraud involves variou...
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