Smartphones and their mobile applications have become an inseparable part of modern daily life. One of them is a public transportation app that helps people commute with public transportation. Many public transportati...
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The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computersystems. Lately, there have been a lot of reported issues of software vulnerabilit...
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Offline signature verification(OfSV)is essential in preventing the falsification of *** learning(DL)based OfSVs require a high number of signature images to attain acceptable ***,a limited number of signature samples ...
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Offline signature verification(OfSV)is essential in preventing the falsification of *** learning(DL)based OfSVs require a high number of signature images to attain acceptable ***,a limited number of signature samples are available to train these models in a real-world *** researchers have proposed models to augment new signature images by applying various ***,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature ***,augmenting a sufficient number of signatures with variations is still a challenging *** study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature *** proposed model is capable of augmenting better quality signatures with diversity from a single signature image *** is empirically evaluated on widely used public datasets;*** quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy *** results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation *** improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.
In this paper, a model was built to compare the performance of the following machine learning (ML) models: DT, RF, SVM, and MLP, using two types of classification: binary classification and multi classification. The r...
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Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief N...
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Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.
Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos ...
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Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.
Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects ...
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This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in...
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Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous systems(AS).BGP nodes,com...
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Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.
In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distanc...
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In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the ver-tices in B.A resolving set B of G is connected if the subgraph B induced by B is a nontrivial connected subgraph of *** cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of *** problem is solved heuristically by a binary version of an enhanced Harris Hawk Optimization(BEHHO)*** is thefirst attempt to determine the connected resolving set *** combines classical HHO with opposition-based learning,chaotic local search and is equipped with an S-shaped transfer function to convert the contin-uous variable into a binary *** hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving *** feasibility is enforced by repairing hawks such that an addi-tional node selected from V\B is added to B up to obtain the connected resolving *** proposed BEHHO algorithm is compared to binary Harris Hawk Optimi-zation(BHHO),binary opposition-based learning Harris Hawk Optimization(BOHHO),binary chaotic local search Harris Hawk Optimization(BCHHO)*** results confirm the superiority of the BEHHO for determining connected metric dimension.
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