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Article Citation - WoS: 4Citation - Scopus: 6A Conceptual Design of Smart Management System for Flooding Disaster(Mdpi, 2021) Ibrahim, Thaer; Mishra, AlokDisasters pose a real threat to the lives and property of citizens; therefore, it is necessary to reduce their impact to the minimum possible. In order to achieve this goal, a framework for enhancing the current disaster management system was proposed, called the smart disaster management system. The smart aspect of this system is due to the application of the principles of information and communication technology, especially the Internet of Things. All participants and activities of the proposed system were clarified by preparing a conceptual design by using The Unified Modeling Language diagrams. This effort was made to overcome the lack of citizens' readiness towards the use of information and communication technology as well as increase their readiness towards disasters. This study aims to develop conceptual design that can facilitate in development of smart management system for flooding disaster. This will assist in the design process of the Internet of Things systems in this regard.Article Citation - WoS: 1Citation - Scopus: 3Gamified Text Testing for Sustainable Fairness(Mdpi, 2023) Takan, Savas; Ergun, Duygu; Katipoglu, GoekmenAI fairness is an essential topic as regards its topical and social-societal implications. However, there are many challenges posed by automating AI fairness. Based on the challenges around automating fairness in texts, our study aims to create a new fairness testing paradigm that can gather disparate proposals on fairness on a single platform, test them, and develop the most effective method, thereby contributing to the general orientation on fairness. To ensure and sustain mass participation in solving the fairness problem, gamification elements are used to mobilize individuals' motivation. In this framework, gamification in the design allows participants to see their progress and compare it with other players. It uses extrinsic motivation elements, i.e., rewarding participants by publicizing their achievements to the masses. The validity of the design is demonstrated through the example scenario. Our design represents a platform for the development of practices on fairness and can be instrumental in making contributions to this issue sustainable. We plan to further realize a plot application of this structure designed with the gamification method in future studies.Article Citation - WoS: 36Citation - Scopus: 66Cybersecurity Enterprises Policies: a Comparative Study(Mdpi, 2022) Mishra, Alok; Alzoubi, Yehia Ibrahim; Gill, Asif Qumer; Anwar, Memoona JaveriaCybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization's cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises' security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.Review Citation - WoS: 3Citation - Scopus: 3Spatial Effectiveness in High-Rise Timber Towers: a Global Perspective(Mdpi, 2024) Ilgin, Huseyin Emre; Aslantamer, Ozlem NurHigh-rise timber structures signify a rising trend, thanks to their significant environmental and economic advantages that occur over their complete lifespan. Enhancing spatial effectiveness in these structures is a critical design consideration for project feasibility. Currently, there has been no comprehensive study on the space efficiency of such towers. This article analyzed 79 cases all over the world to deepen the knowledge of design features shaping spatial efficiency. The critical findings are as follows: (1) the most common architectural preferences include residential function, a centrally located service core, and prismatic arrangements; (2) the preferred structural material is composite, while a shear walled frame system is the favored structural system; (3) the average spatial efficiency and percentage of core area to GFA were recorded at 84% and 10%, ranging from the lowest values of 70% and 4% to the highest values of 95% and 21%, respectively; and (4) no significant differences were detected in the effect of core design approaches on spatial effectiveness if appropriately planned, with similar inferences drawn concerning form and the structural material used. This article will assist in developing design directions for different interested parties, including architectural designers taking part in the advancement of high-rise timber towers.Article Citation - WoS: 13Citation - Scopus: 13A Systematic Approach To Optimizing Energy-Efficient Automated Systems With Learning Models for Thermal Comfort Control in Indoor Spaces(Mdpi, 2023) Erisen, SerdarEnergy-efficient automated systems for thermal comfort control in buildings is an emerging research area that has the potential to be considered through a combination of smart solutions. This research aims to explore and optimize energy-efficient automated systems with regard to thermal comfort parameters, energy use, workloads, and their operation for thermal comfort control in indoor spaces. In this research, a systematic approach is deployed, and building information modeling (BIM) software and energy optimization algorithms are applied at first to thermal comfort parameters, such as natural ventilation, to derive the contextual information and compute the building performance of an indoor environment with Internet of Things (IoT) technologies installed. The open-source dataset from the experiment environment is also applied in training and testing unique black box models, which are examined through the users' voting data acquired via the personal comfort systems (PCS), thus revealing the significance of Fanger's approach and the relationship between people and their surroundings in developing the learning models. The contextual information obtained via BIM simulations, the IoT-based data, and the building performance evaluations indicated the critical levels of energy use and the capacities of the thermal comfort control systems. Machine learning models were found to be significant in optimizing the operation of the automated systems, and deep learning models were momentous in understanding and predicting user activities and thermal comfort levels for well-being; this can optimize energy use in smart buildings.Article Citation - WoS: 5Citation - Scopus: 8Clinical Characteristics of Patients With Intraocular Lens Calcification After Pars Plana Vitrectomy(Mdpi, 2023) Bopp, Silvia; Ozdemir, Huseyin Baran; Aktas, Zeynep; Khoramnia, Ramin; Yildirim, Timur M.; Schickhardt, Sonja; Ozdek, SengulAim: To determine the clinical risk factors that may increase the occurrence of intraocular lens (IOL) calcification in patients who had undergone pars plana vitrectomy (PPV). Methods: The medical records of 14 patients who underwent IOL explantation due to clinically significant IOL opacification after PPV were reviewed. The date of primary cataract surgery, technique and implanted IOL characteristics; the time, cause and technique of PPV; tamponade used; additional surgeries; the time of IOL calcification and explantation; and IOL explantation technique were investigated. Results: PPV had been performed as a combined procedure with cataract surgery in eight eyes and solely in six pseudophakic eyes. The IOL material was hydrophilic in six eyes, hydrophilic with a hydrophobic surface in seven eyes and undetermined in one eye. The endotamponades used during primary PPV were C2F6 in eight eyes, C3F8 in one eye, air in two eyes and silicone oil in three eyes. Two of three eyes underwent subsequent silicone oil removal and gas tamponade exchange. Gas in the anterior chamber was detected in six eyes after PPV or silicone oil removal. The mean interval between PPV and IOL opacification was 20.5 +/- 18.6 months. The mean BCVA in logMAR was 0.43 +/- 0.42 after PPV, which significantly decreased to 0.67 +/- 0.68 before IOL explantation for IOL opacification (p = 0.007) and increased to 0.48 +/- 0.59 after the IOL exchange (p = 0.015). Conclusions: PPV with endotamponades in pseudophakic eyes, particularly gas, seems to increase the risk for secondary IOL calcification, especially in hydrophilic IOLs. IOL exchange seems to solve this problem when clinically significant vision loss occurs.Article Citation - WoS: 21Citation - Scopus: 36Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets(Mdpi, 2022) Kadhim, Yezi Ali; Khan, Muhammad Umer; Mishra, AlokComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.Article Citation - WoS: 176Citation - Scopus: 196Interpolative Reich-Rus Type Contractions on Partial Metric Spaces(Mdpi, 2018) Karapinar, Erdal; Agarwal, Ravi; Aydi, HassenBy giving a counter-example, we point out a gap in the paper by Karapinar (Adv. Theory Nonlinear Anal. Its Appl. 2018, 2, 85-87) where the given fixed point may be not unique and we present the corrected version. We also state the celebrated fixed point theorem of Reich-Rus-Ciric in the framework of complete partial metric spaces, by taking the interpolation theory into account. Some examples are provided where the main result in papers by Reich (Can. Math. Bull. 1971, 14, 121-124; Boll. Unione Mat. Ital. 1972, 4, 26-42 and Boll. Unione Mat. Ital. 1971, 4, 1-11.) is not applicable.Article Citation - WoS: 11Citation - Scopus: 13Challenges in Agile Software Maintenance for Local and Global Development: an Empirical Assessment(Mdpi, 2023) Almashhadani, Mohammed; Mishra, Alok; Yazici, Ali; Younas, MuhammadAgile methods have gained wide popularity recently due to their characteristics in software development. Despite the success of agile methods in the software maintenance process, several challenges have been reported. In this study, we investigate the challenges that measure the impact of agile methods in software maintenance in terms of quality factors. A survey was conducted to collect data from agile practitioners to establish their opinions about existing challenges. As a result of the statistical analysis of the data from the survey, it has been observed that there are moderately effective challenges in manageability, scalability, communication, collaboration, and transparency. Further research is required to validate software maintenance challenges in agile methods.Article Citation - WoS: 16Citation - Scopus: 20Forecasting Air Quality in Tripoli: an Evaluation of Deep Learning Models for Hourly Pm2.5 Surface Mass Concentrations(Mdpi, 2023) Esager, Marwa Winis Misbah; Unlu, Kamil DemirberkIn this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.
