In this paper, different classes of the whorl fingerprint are discussed. A general dynamical system with a parameter θ is created using differential equations to simulate these classes by varying the value of θ. The global dynamics is studied, and the existence and stability of equilibria are analyzed. The Maple is used to visualize fingerprint’s orientation image as a smooth deformation of the phase portrait of a planar dynamical system. In general, the databases of fingerprint are not categorized to retained by artificial intelligence tools such Convolutional Neural Networks (CNNs) architectures, so finding a dynamical system to categorize fingerprint database of fingerprints images allows CNNs architectures to retrained with more accuracy. NIST Special Database (SD) 302d fingerprint dataset is retrained over VGG16 as CNN architecture.

Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. This eliminates identity recognition manual work and enables automated processing. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively.

Purpose Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions. Design/methodology/approach Cloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance. Findings The performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively. Originality/value To the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

As COVID-19 pandemic emerged, quick decisions in response to sudden emergence and rapid spread around the world were required. Strict actions deployed to tackle the COVID-19 pandemic are likely to have prevented millions of additional infections and reduce the number of fatalities during the pandemic. The actions varied from one country or territory to another based on the threat control management (TCM) strategy such as preventing, avoiding, mitigating, and accepting. The TCM involved emergency measures such as isolation, restriction on traveling, closing of nonessential businesses, physical distancing, lockdown and quarantine. Thus, this study introduces a generic dynamic framework that a country can follow to reduce the effects of COVID-19 on the number of infected people and fatalities. Samples of information and data about countries were reviewed, collected, and analyzed. The countries were classified based on the collected data and the curve representing the numbers of infections into countries; with green (flattened the curve and winning), orange (need more actions), and red (failing) colors. The analyses indicate that most of the countries deployed strict actions and applied TCM with preventing and avoiding strategies were winning countries, apparently, avoiding is the best TCM based on the values of the performance indicators. In conclusion, the generic dynamic framework can be implemented to study the effects of the TCM on the number of infected patients and fatalities caused by the COVID-19. Also, strict actions in response to the pandemic are promising to prevent millions of additional infections and reducing the number of fatalities during the pandemic.

In the end of the year 2019 and the beginning of the year 2020, the world was overwhelmed by a medical pandemic that was not previously seen which is known Covid-19 (Coronavirus). Coronavirus (CoV) is a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). This paper aims to improve the accuracy of detection for CT-Coronavirus images using deep learning for Convolutional Neural Networks (CNNs) that helps medical staffs for classification chest CT- Coronavirus medical image in early stage. Deep learning is successfully used as a tool for machine learning, where the CNNs are capable of automatically extracting and learning features medical image dataset. This research retrains GoogleNet CNN architecture over the COVIDCT-Dataset for classification CT- Coronavirus image. In this research, COVIDCT-Dataset contains 349 CT images containing clinical findings of COVID-19. The validation accuracy of retraining GoogleNet is 82.14% where elapsed time is 74 min and 37 sec.

Different classes of the whorl fingerprint are discussed. A general dynamical system with a parameter theta is created using differential equations to simulate these classes by varying the value of theta. The global dynamics is studied, and the existence and stability of equilibria are analyzed. The Maple is used to visualize fingerprint orientation image as a smooth deformation of the phase portrait of a planar dynamical system.

Due to the growth of population traffic congestions are increasing and reaching to critical limits, so it is considered as a severe challenge, that facing cities and metropolitans to solve traffic congestion. To achieve this there are many approaches and one of them, is developing an adaptive traffic light signal in order to tackle this problem. Therefore, before designing traffic signal, it is necessary to study all the factors that affect the design of traffic signal. Traffic light management system is an important factor for everyone within the city as it controls the traffic flow. The main reasons behind poor traffic light management system occur due to poor road management, rapid growth in number of cars, legacy traffic light system, and poor practices on behalf of drivers. Traffic light management system aims to reduce traffic congestion, safety, and delay. This paper utilizes the new technology of artificial intelligence approaches to generate an automated traffic light management in order to improve vehicles flow and minimize intersection delay in Jordan as a case study. The proposed approach starts with extracting rules from the data set using Weka. According to the extracted rules and some exception constraints, Answer Set Programming (ASP) is used to generate the solution for the extracting rules to return an optimal slot time for the traffic light phases dynamically.

In this paper, different categories of the arch fingerprint are set up in a general dynamical system model using ordinary differential equations. We study its global dynamics and analyze the existence and stability of equilibria. Numerical simulations using Maple show the matching between real images of categories of arch fingerprint and phase portraits of the considered dynamical system.

This article describes a new method for generating extractive summaries directly via unigram and bigram extraction techniques. The methodology uses the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences. Extracted unigrams and bigrams along with other features are used to build a final summary. A new selective rule-based part of speech tagging system is developed that concentrates on the most important parts of speech for summarizations: noun, verb, and adjective. Other parts of speech such as prepositions, articles, adverbs, etc., play a lesser role in determining the meaning of sentences; therefore, they are not considered when choosing significant unigrams and bigrams. The proposed method is tested on two problem domains: citations and opinosis data sets. Results show that the proposed method performs better than Text-Rank, LexRank, and Edmundson summarization methods. The proposed method is general enough to summarize texts from any domain.

Internet of Things (IOT) system often consists of thousands of constrained connected devices. Resource-constrained devices one of critical issues in a low- power and lossy network LLNs. RPL is IPv6 routing protocol. It’s designed by IETF to be simple and inter-operable networking protocol to overcome these resource limitations. The RPL carries out Objective Functions (OFs) in the aim of finding the best path. The OFs chooses the best parent nodes aiming to build the route and optimize it. The metrics used to build the OF must be selected in an effective and accurate manner for finding the optimal path and meets all constraints. A survey about node metrics which can be utilized in OFs of RPL is presented, and node metrics calculations are explained then discussed thoroughly. The researcher displays the most relevant research efforts regarding the RPL OFs existing in literature.

The paper describes a new method for generating extractive summaries. The methodology is an unsupervised method and it employs a new linguistic method. It is based on using selective part of speech (PoS) tagging for significant unigrams and bigrams extraction. A new selective rule-based part of speech tagging system is developed that concentrates on the most important parts of speech for summarizations, such as noun, verb, adjective. Other parts of speech such as prepositions, articles, adverbs, etc., play a lesser role in determining the meaning of sentences; therefore, they are not considered when choosing significant unigrams and bigrams. The most significant unigrams and bigrams along with other features of a text are used to build a final summary. The proposed method is tested on Citations and Opinosis data sets (user reviews on selected topics). Results show that the proposed method performs better than Text-Rank, LexRank, and Edmundson summarization methods.