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Öğe A way forward towards the improvement of tensor force in pf-shell(Elsevier, 2020) Jha, Kanhaiya; Kumar, Pawan; Sarkar, Shahariar; Raina, P. K.; Aydin, SezginIn many shell model interactions, the tensor force monopole matrix elements are observed to retain sys-tematic trends originating in the bare tensor force. In this work, however, we note for GX-interactions of pf-shell that the seven out of ten T = 1 tensor force monopole matrix elements do not share these systematic. We ameliorate this disparity making use of Yukawa-type tensor force and spin-tensor decomposition. Furthermore, we have modified the single-particle energy of 1(p3/2) orbit and two TBMEs of 0f-orbit, and the revised interaction has been tested from Ca to Ge isotopes with various physics viewpoints. The results are found to be satisfactory with respect to the experimental data. (c) 2020 Elsevier B.V. All rights reserved.Öğe Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image(Mdpi, 2022) Jameel, Samer Kais; Aydin, Sezgin; Ghaeb, Nebras H.; Majidpour, Jafar; Rashid, Tarik A.; Salih, Sinan Q.; JosephNg, Poh SoonCorneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.Öğe Four pseudo-mirror nuclei in the left-lower part of the nuclear chart(Elsevier, 2022) Kumar, Pawan; Gautam, Ratindra; Aydin, Sezgin; Ozfidan, AyselIn the present work, we have reported four new pseudo-mirror nuclei Fe-64-Zn-70 and Cr-60-Ge-74. We have discussed the symmetry in their ground state energy band and kinematic moment of inertia. Further, we have discussed the pseudo-spin coupling in the view of pseudo-mirror nuclei. (C) 2022 Elsevier B.V. All rights reserved.Öğe IoT-Based Intrusion Detection Systems: A Review(Taylor & Francis Ltd, 2022) Mohamed, Tamara Saad; Aydin, SezginInternet of Things (IoT) is a modern prototype that merges physical entities affiliated with a variety of fields, like industrial tasks, firm automation, mortal fitness, and habitat observance with the internet. It intensifies the occupancy of Internet-linked objects in everyday tasks, resulting in issues linked to security besides usefulness. For many years, Intrusion Detection Systems (IDS) have proven to be advantageous for guarding information systems and networks. Conversely, enacting old IDS procedures on IoT is unrealistic owing to some specific elements. For instance, specific protocol stacks, strained-asset gadgets, and measures. This study furnishes inspection of IDS inquiry achievements for IoT. The objective entails the establishment of key biases, general drawbacks, and inquiry guidelines. Grouping of the suggested IDSs was undertaken in the state-of-art regarding the subsequent qualities: discernment technique, IDS placement procedure, security peril, and confirmation procedure. The paper further considered every attribute shortcoming, exploring views of material with suggestions on certain IDS procedures for IoT or inaugurating attack distinguishing procedures for IoT menaces with the possibility of being fixed in IDSs. [GRAPHICS] .Öğe Kalman and Cauchy clustering for anomaly detection based authentication of IoMTs using extreme learning machine(Inst Engineering Technology-Iet, 2022) Mohamed, Tamara Saad; Aydin, Sezgin; Alkhayyat, Ahmed; Malik, Rami Q.The vulnerabilities of the Internet of Things (IoTs) in general and the Internet of Mobile Things (IoMTs) in particular motivate researchers to equip them with security systems against intruders and attacks. The integration of anomaly detection with intrusion detection for IoMTs has not been addressed adequately. This paper tackles this issue through building a Kalman filter and Cauchy clustering algorithm for anomaly detection and using them for authentication nodes within IoMTs using the Extreme Learning Machine classifier. The algorithm of this proposed work is composed of various components; first, the Kalman filter-based model for estimating the trajectory of pedestrians within an indoor environment based on fusing WiFi with IMU data. Second, trustworthiness assessment for detecting anomaly behaviour in IoMT based on the estimated trajectory using the Kalman filter. Third, the trust IDS model for IoMT systems by integrating anomaly detection with online learning for attacks identification using an online sequential extreme learning machine. The algorithm has been implemented and evaluated using TamperU dataset for WiFi fingerprinting and KDD99 for intrusion detection. Furthermore, a comparison with benchmarks (the algorithms which used in other studies) for intrusion and anomaly detection proves the superiority of this proposed approach in terms of all the considered classification metrics.Öğe Local information pattern descriptor for corneal diseases diagnosis(Institute of Advanced Engineering and Science, 2021) Jameel, Samer Kais; Aydin, Sezgin; Ghaeb, Nebras H.Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting. © 2021 Institute of Advanced Engineering and Science. All rights reserved.Öğe Machine Learning Techniques for Corneal Diseases Diagnosis: A Survey(World Scientific Publ Co Pte Ltd, 2021) Jameel, Samer Kais; Aydin, Sezgin; Ghaeb, Nebras H.Machine learning techniques become more related to medical researches by using medical images as a dataset. It is categorized and analyzed for ultimate effectiveness in diagnosis or decision-making for diseases. Machine learning techniques have been exploited in numerous researches related to corneal diseases, contribution to ophthalmologists for diagnosing the diseases and comprehending the way automated learning techniques act. Nevertheless, confusion still exists in the type of data used, whether it is images, data extracted from images or clinical data, the course reliant on the type of device for obtaining them. In this study, the researches that used machine learning were reviewed and classified in terms of the kind of utilized machine for capturing data, along with the latest updates in sophisticated approaches for corneal disease diagnostic techniques.Öğe Pseudo-mirror nuclei in A-80 mass region(Elsevier, 2021) Kumar, Pawan; Aydin, SezginIn the present work, an investigation of pseudo-mirror nuclei is extended to A-80 mass region. Within the limits of N-p N-n approximation, nuclei Ge-72-Sr-84, Se-76-Kr-80, Se-74-Sr-82, and Kr-76-Sr-80 are identified as pseudo-mirror nuclei. A systematic comparison of their ground-state energy spectra, kinematic moment of inertia, and reduced transition probability B(E2) is presented. Furthermore, we have discussed the purview of the permitted range of N-p N-n approximation for pseudo-mirror nuclei. (C) 2021 Elsevier B.V. All rights reserved.Öğe SWFT: Subbands wavelet for local features transform descriptor for corneal diseases diagnosis(Tubitak Scientific & Technological Research Council Turkey, 2021) Al-Salihi, Samer K.; Aydin, Sezgin; Ghaeb, Nebras H.Human cornea is the front see-through shield of the eye. It refracts light onto the retina to induce vision. Therefore, any defect in the cornea may lead to vision disturbance. This deficiency is estimated by sets of topographical images measured, and assessed by an ophthalmologist. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Images produced by a Pentacam device can be subjected to rotation or some distortion during acquisition; therefore, accurate diagnosis requires the use of local features in the image. Accordingly, a new algorithm called subbands wavelet for local features transform (SWFT) which is mainly based on the algorithm of a scale-invariant feature transform (SIFT) has been developed. This algorithm uses wavelets as a multiresolution analysis to produce images with different scales instead of using the difference of Gaussians as in the SIFT algorithm. The experimental results on the corneal topography dataset indicate that the proposed SWFT outperforms the baseline SIFT algorithm.
