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Publication Metadata only Impact of predation in the spread of an infectious disease with time fractional derivative and social behavior(WORLD SCIENTIFIC PUBL CO PTE LTD, 2021) Bentout, Soufiane; Ghanbari, Behzad; Djilali, Salih; Guin, Lakshmi Narayan; Universite Abou Bekr Belkaid; Kermanshah University of Technology; Bahcesehir University; Universite Hassiba Ben Bouali de Chlef; Visva Bharati UniversityThe main purpose of this paper is to explore the influence of predation on the spread of a disease developed in the prey population where we assume that the prey has a social behavior. The memory of the prey and the predator measured by the time fractional derivative plays a crucial role in modeling the dynamical response in a predator-prey interaction. This memory can be modeled to articulate the involvement of interacting species by the presence of the time fractional derivative in the considered models. For the purpose of studying the complex dynamics generated by the presence of infection and the time-fractional-derivative we split our study into two cases. The first one is devoted to study the effect of a non-selective hunting on the spread of the disease, where the local stability of the equilibria is investigated. Further the backward bifurcation is obtained concerning basic reproduction rate of the infection. The second case is for explaining the impact of selecting the weakest infected prey on the edge of the herd by a predator on the prevalence of the infection, where the local behavior is scrutinized. Moreover, for the graphical representation part, a numerical simulation scheme has been achieved using the Caputo fractional derivative operator.Publication Metadata only SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(IEEE, 2022) Hmitti, Zakaria Ait; Ben Ammar, Hamza; Soyak, Ece Gelal; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Sabanci University; Concordia University - Canada; Bahcesehir UniversityThe unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.Publication Metadata only Throughput-maximizing OFDMA Scheduler for IEEE 802.11ax Networks(IEEE, 2020) Kuran, Mehmet Sukru; Dilmac, A.; Topal, Omer; Yamansavascilar, Baris; Avallone, Stefano; Tugcu, Tuna; Bahcesehir University; Bogazici University; University of Naples Federico IIIn this paper, we develop a novel throughput-maximizing OFDMA scheduler for the multi-user MAC framework for the IEEE 802.11ax networks. The scheduler works both in the downlink and uplink directions and assigns resource units to stations using a linear programming technique considering load of each client, possible resource unit configurations, modulation-coding scheme of each client, and ageing factor of each client's load. The performance of the proposed scheduler has been evaluated using the NS3 simulator and compared against the legacy MAC layer mechanism of IEEE 802.11 protocol (i.e., DCF/EDCA). Simulation results show that our proposed throughput-maximizing scheduler increases the total throughput in the network as well as decrease the average end-to-end delay regardless of the number of stations connected to the access point by prioritizing the traffic of clients connected via high modulation-coding schemes.Publication Metadata only SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes(IEEE, 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Daffodil International University; Khulna University of Engineering & Technology (KUET); Bahcesehir University; Daffodil International UniversitySkin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy.Publication Metadata only Age of Information in G/G/1/1 Systems: Age Expressions, Bounds, Special Cases, and Optimization(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Soysal, Alkan; Ulukus, Sennur; Bahcesehir University; Virginia Polytechnic Institute & State University; University System of Maryland; University of Maryland College ParkWe consider the average age of information in G/G/1/1 systems under two service discipline models. In the first model, if a new update arrives when the service is busy, it is blocked, in the second model, a new update preempts the current update in service. For the blocking model, we first derive an exact age expression for G/G/1/1 systems. Then, using the age expression for G/G/1/1 systems, we calculate average age expressions for special cases, i.e., M/G/1/1 and G/M/1/1 systems. We observe that deterministic interarrivals minimize the average age of G/M/1/1 systems for a given mean interarrival time. Next, for the preemption in service model, we first derive an exact average age expression for G/G/1/1 systems. Then, similar to blocking discipline, using the age expression for G/G/1/1 systems, we calculate average age expressions for special cases, i.e., M/G/1/1 and G/M/1/1 systems. Average age for G/M/1/1 can be written as a summation of two terms, the first of which depends only on the first and second moments of interarrival times and the second of which depends only on the service rate. In other words, interarrival and service times are decoupled. We prove that deterministic interarrivals are optimum for G/M/1/1 systems for a given mean interarrival time. On the other hand, we observe for non-exponential service times that the optimal distribution of interarrival times depends on the relative values of the mean interarrival time and the mean service time. Finally, we propose a simple to calculate upper bound to the average age for the preemption in service discipline.Publication Metadata only Fuzzy fractional differential equations with the generalized Atangana-Baleanu fractional derivative(ELSEVIER, 2022) Vu, Ho; Ghanbari, Behzad; Ngo Van Hoa; Duy Tan University; Duy Tan University; Kermanshah University of Technology; Bahcesehir University; Ton Duc Thang University; Ton Duc Thang UniversityIn this paper, we introduce a generalization of Atangana-Baleanu type fractional calculus with respect to the generalized Mittag-Leffler kernel which has been named as the generalized Atangana-Baleanu (GAB) type fractional calculus. Existence and uniqueness results for the initial value problems of fuzzy differential equations involving a GAB fractional derivative in the Caputo sense are established by employing the method of successive approximation and by means of fixed point theorems. To visualize the theoretical results, some examples and numerical simulations are given. (c) 2020 Elsevier B.V. All rights reserved.Publication Metadata only A Holistic Methodology for Digital Governance Transition and Measuring Its Effect on Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansDigital transformation has become one of the most emphasized issues for organizations today. Organizations have a long way to go in this difficult road. First of all, they should develop and measure their governance skills, and then they need to create the necessary studies in both technological and managerial terms for IT systems to become a value. While doing all these, they should also create a risk management structure with a correct approach by performing consultancy, supervision, and evaluation services completely. In line with this whole process, there are different standards and practices in every field they need to follow. This paper presents a methodology proposition in a general perspective that can be applied end-to-end in the field of digitalization, starting from the governance structure to the IT value-adding strategy. Thanks to this new methodology, it is possible to see many processes such as governance, consultancy, digital transformation, auditing, internal control, which are disconnected from each other, in the big picture and arrange all activities accordingly. With long-term follow-up, the strategic and tactical perspectives of institutions can be created and their progress over time can be followed by this new methodology. This paper performs both qualitative and quantitative measurements of the applications of the new methodology to compare them with existing ones. This methodology introduces a holistic methodology lacking in the market.Publication Metadata only A Research for Measuring the Effects of COVID-19 on Digital Transformation within Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansPandemics are not only medical phenomenon, but they also influence people and society in many respects. It has an effect on almost all markets all over the world. COVID-19 pandemic, expressed as change, empowerment, or post-traumatic growth, with several negative consequences as well as positive consequences. It also has the potential for opportunities. Years of change in the way companies do business have resulted from the COVID 19 crisis across all industries and regions. The aim of this research is to examine the relationship between different demographic variables, COVID-19's impact on digital transformation and post-traumatic effects. The article reflects in practical terms on whether and how the COVID-19 emergence in organizations accelerates digital transformation. This study is a descriptive quantitative approach research based on general survey model.Publication Metadata only Contrastive learning based facial action unit detection in children with hearing impairment for a socially assistive robot platform?(ELSEVIER, 2022) Gurpinar, Cemal; Takir, Seyma; Bicer, Erhan; Uluer, Pinar; Arica, Nafiz; Kose, Hatice; Istanbul Technical University; Galatasaray University; Bahcesehir University; Piri Reis UniversityThis paper presents a contrastive learning-based facial action unit detection system for children with hearing im-pairments to be used on a socially assistive humanoid robot platform. The spontaneous facial data of children with hearing impairments was collected during an interaction study with Pepper humanoid robot, and tablet -based game. Since the collected dataset is composed of limited number of instances, a novel domain adaptation extension is applied to improve facial action unit detection performance, using some well-known labelled datasets of adults and children. Furthermore, since facial action unit detection is a multi-label classification prob-lem, a new smoothing parameter, beta, is introduced to adjust the contribution of similar samples to the loss func-tion of the contrastive learning. The results show that the domain adaptation approach using children's data (CAFE) performs better than using adult's data (DISFA). In addition, using the smoothing parameter beta leads to a significant improvement on the recognition performance. (c) 2022 Elsevier B.V. All rights reserved.Publication Metadata only Machine Vision-Based Expert System for Automated Skin Cancer Detection(SPRINGER INTERNATIONAL PUBLISHING AG, 2022) Junayed, Masum Shah; Jeny, Afsana Ahsan; Rada, Lavdie; Islam, Md Baharul; BritoLoeza, C; MartinGonzalez, A; CastanedaZeman, V; Safi, A; Bahcesehir University; Daffodil International UniversitySkin cancer is the most frequently occurring kind of cancer, accounting for about one-third of all cases. Automatic early detection without expert intervention for a visual inspection would be of great help for society. The image processing and machine learning methods have significantly contributed to medical and biomedical research, resulting in fast and exact inspection in different problems. One of such problems is accurate cancer detection and classification. In this study, we introduce an expert system based on image processing and machine learning for skin cancer detection and classification. The proposed approach consists of three significant steps: pre-processing, feature extraction, and classification. The pre-processing step uses the grayscale conversion, Gaussian filter, segmentation, and morphological operation to represent skin lesion images better. We employ two feature extractors, i.e., the ABCD scoring method (asymmetry, border, color, diameter) and gray level co-occurrence matrix (GLCM), to extract cancer-affected areas. Finally, five different machine learning classifiers such as logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) used to detect and classify skin cancer. Experimental results show that random forest exceeds all other classifiers achieving an accuracy of 97.62% and 0.97 Area Under Curve (AUC), which is state-of-the-art on the experimented open-source dataset PH2.
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